diff --git a/docs/docs/targets/doris.md b/docs/docs/targets/doris.md new file mode 100644 index 000000000..4a9e5e195 --- /dev/null +++ b/docs/docs/targets/doris.md @@ -0,0 +1,305 @@ +--- +title: Apache Doris +description: CocoIndex Apache Doris Target +toc_max_heading_level: 4 +--- + +# Apache Doris + +Exports data to Apache Doris (or VeloDB Cloud) with support for vector indexes and full-text search. + +## Data Mapping + +Here's how CocoIndex data elements map to Doris elements during export: + +| CocoIndex Element | Doris Element | +|-------------------|---------------| +| an export target | a unique table | +| a collected row | a row | +| a field | a column | + +For example, if you have a data collector that collects rows with fields `id`, `title`, and `embedding`, it will be exported to a Doris table with corresponding columns. + +:::info Vector Type Mapping + +Vectors are mapped to `ARRAY` columns in Doris. Vector columns are automatically created as `NOT NULL` since Doris vector indexes require non-nullable columns. + +::: + +:::info Table Model + +Doris tables are created using the `DUPLICATE KEY` model, which is required for vector index support in Doris 4.0+. + +::: + +## Spec + +The spec takes the following fields: + +### Connection + +* `fe_host` (`str`, required): The Doris Frontend (FE) host address. + +* `database` (`str`, required): The database name. + +* `table` (`str`, required): The table name. + +* `fe_http_port` (`int`, default: `8080`): The HTTP port for Stream Load API. + +* `query_port` (`int`, default: `9030`): The MySQL protocol port for DDL operations. + +* `username` (`str`, default: `"root"`): The username for authentication. + +* `password` (`str`, default: `""`): The password for authentication. + +* `enable_https` (`bool`, default: `False`): Whether to use HTTPS for Stream Load. + +### Behavior + +* `batch_size` (`int`, default: `10000`): Maximum number of rows per Stream Load batch. + +* `stream_load_timeout` (`int`, default: `600`): Timeout in seconds for Stream Load operations. + +* `auto_create_table` (`bool`, default: `True`): Whether to automatically create the table if it doesn't exist. + +* `schema_evolution` (`"extend"` | `"strict"`, default: `"extend"`): Schema evolution strategy. + - `"extend"`: Allow extra columns in the database, only add missing columns, never drop columns. Indexes are created only if referenced columns exist and are compatible. + - `"strict"`: Require exact schema match. If the database table has extra columns or type mismatches, an error is raised. Tables are only recreated when primary keys change. + +### Retry Configuration + +Retry logic covers transient connection errors for both HTTP (Stream Load API) and MySQL protocol (DDL operations). Retryable errors include connection timeouts, server disconnections, and MySQL errors like "server has gone away" or "too many connections". + +* `max_retries` (`int`, default: `3`): Maximum number of retry attempts for transient errors. + +* `retry_base_delay` (`float`, default: `1.0`): Base delay in seconds between retries (uses exponential backoff). + +* `retry_max_delay` (`float`, default: `30.0`): Maximum delay in seconds between retries. + +### Table Properties + +* `replication_num` (`int`, default: `1`): Number of replicas for the table. + +* `buckets` (`int` | `"auto"`, default: `"auto"`): Number of hash buckets, or `"auto"` to let Doris determine automatically. + +## Index Support + +### Vector Index + +Doris supports ANN (Approximate Nearest Neighbor) vector indexes using HNSW or IVF algorithms. + +#### HNSW Index + +```python +flow_builder.export( + "embeddings", + cocoindex.targets.doris.DorisTarget( + fe_host="your-doris-host", + database="your_db", + table="embeddings", + ), + primary_key=["doc_id"], + index=cocoindex.VectorIndex( + field="embedding", + metric=cocoindex.VectorSimilarityMetric.L2_DISTANCE, + index_method=cocoindex.HnswVectorIndexMethod( + m=16, + ef_construction=200, + ), + ), +) +``` + +#### IVF Index + +```python +flow_builder.export( + "embeddings", + cocoindex.targets.doris.DorisTarget( + fe_host="your-doris-host", + database="your_db", + table="embeddings", + ), + primary_key=["doc_id"], + index=cocoindex.VectorIndex( + field="embedding", + metric=cocoindex.VectorSimilarityMetric.L2_DISTANCE, + index_method=cocoindex.IvfFlatVectorIndexMethod( + lists=128, # Number of clusters (nlist) + ), + ), +) +``` + +Supported metrics: +- `L2_DISTANCE`: Euclidean distance (recommended) +- `INNER_PRODUCT`: Inner product similarity +- `COSINE_SIMILARITY`: Cosine distance (note: does not support index acceleration in Doris 4.0) + +:::info IVF Index Requirements + +IVF (Inverted File) indexes require training data. The `lists` parameter (number of clusters) must be less than or equal to the number of vectors in the table. For best results: +- Load data first, then create the IVF index +- Use `lists` value appropriate for your data size (e.g., `lists=128` for 10K+ vectors) + +::: + +:::tip Vector Search Functions + +Doris 4.0 provides two ways to query vectors: + +1. **Approximate functions** (use index acceleration): + - `l2_distance_approximate(vector_col, query_vector)` - L2/Euclidean distance + - `inner_product_approximate(vector_col, query_vector)` - Inner product similarity + +2. **Exact functions** (also benefit from index via ORDER BY optimization): + - `l2_distance(vector_col, query_vector)` + - `inner_product(vector_col, query_vector)` + +The `build_vector_search_query` helper automatically uses the `_approximate` functions. + +::: + +### Full-Text Search Index (Inverted Index) + +Doris supports inverted indexes for full-text search on text columns. + +```python +flow_builder.export( + "documents", + cocoindex.targets.doris.DorisTarget( + fe_host="your-doris-host", + database="your_db", + table="documents", + ), + primary_key=["doc_id"], + fts_index=cocoindex.FtsIndex( + field="content", + parameters={"parser": "unicode"}, # or "english", "chinese" + ), +) +``` + +### Hybrid Search + +You can combine both vector and full-text indexes for hybrid search: + +```python +flow_builder.export( + "documents", + cocoindex.targets.doris.DorisTarget(...), + primary_key=["doc_id"], + index=cocoindex.VectorIndex( + field="embedding", + metric=cocoindex.VectorSimilarityMetric.L2_DISTANCE, + ), + fts_index=cocoindex.FtsIndex( + field="content", + parameters={"parser": "unicode"}, + ), +) +``` + +## Schema Evolution + +The `schema_evolution` parameter controls how CocoIndex handles schema differences between your flow definition and the existing database table. + +### Extend Mode (Default) + +In `"extend"` mode: +- Extra columns in the database that aren't in your schema are kept untouched +- Missing columns are added via `ALTER TABLE ADD COLUMN` +- Indexes are created only if the referenced column exists and has a compatible type +- Tables are never dropped except for primary key changes + +This mode is recommended for production use as it's safe and non-destructive. + +### Strict Mode + +In `"strict"` mode: +- Extra columns in the database that aren't in your schema will raise an error +- Type mismatches between schema and database will raise an error +- Missing columns are still added via `ALTER TABLE ADD COLUMN` +- Tables are only dropped and recreated when primary key columns change + +This mode is useful for development environments where you want to catch schema drift early. Note that if you need to remove columns, you must manually drop and recreate the table or use database migration tools. + +## Query Helpers + +The connector provides helper functions for querying data: + +### connect_async + +```python +from cocoindex.targets.doris import connect_async + +conn = await connect_async( + fe_host="your-doris-host", + query_port=9030, + username="root", + password="your-password", + database="your_db", +) +try: + async with conn.cursor() as cursor: + await cursor.execute("SELECT * FROM your_table LIMIT 10") + rows = await cursor.fetchall() +finally: + conn.close() + await conn.ensure_closed() +``` + +### build_vector_search_query + +```python +from cocoindex.targets.doris import build_vector_search_query + +sql = build_vector_search_query( + table="your_db.embeddings", + vector_field="embedding", + query_vector=[0.1, 0.2, ...], # Your query vector + metric="l2_distance", + limit=10, + select_columns=["doc_id", "title", "content"], +) +``` + +## Requirements + +- Apache Doris 4.0+ (for vector index support) or VeloDB Cloud +- Python packages: `aiohttp`, `aiomysql` + +Install dependencies: +```bash +pip install aiohttp aiomysql +``` + +## Example + +```python +import cocoindex +from cocoindex.targets.doris import DorisTarget + +@cocoindex.flow_def(name="doris_example") +def doris_flow(flow_builder: cocoindex.FlowBuilder): + # ... your data source and transformations ... + + flow_builder.export( + "document_embeddings", + DorisTarget( + fe_host="your-doris-host.example.com", + fe_http_port=8080, + query_port=9030, + username="admin", + password="your-password", + database="cocoindex_demo", + table="document_embeddings", + schema_evolution="extend", + ), + primary_key=["doc_id"], + index=cocoindex.VectorIndex( + field="embedding", + metric=cocoindex.VectorSimilarityMetric.L2_DISTANCE, + ), + ) +``` diff --git a/docs/docs/targets/index.md b/docs/docs/targets/index.md index f90a5c329..78aef15f0 100644 --- a/docs/docs/targets/index.md +++ b/docs/docs/targets/index.md @@ -17,6 +17,7 @@ The way to map data from a data collector to a target depends on data model of t | [Postgres](/docs/targets/postgres) | Relational Database, Vector Search (PGVector) | | [Qdrant](/docs/targets/qdrant) | Vector Database, Keyword Search | | [LanceDB](/docs/targets/lancedb) | Vector Database, Keyword Search | +| [Apache Doris](/docs/targets/doris) | Analytical Database, Vector Search, Full-Text Search | | [Neo4j](/docs/targets/neo4j) | [Property graph](#property-graph-targets) | If you are looking for targets beyond here, you can always use [custom targets](/docs/custom_ops/custom_targets) as building blocks. diff --git a/examples/text_embedding_doris/.env b/examples/text_embedding_doris/.env new file mode 100644 index 000000000..23ed7f6cc --- /dev/null +++ b/examples/text_embedding_doris/.env @@ -0,0 +1,17 @@ +# Postgres database address for cocoindex +COCOINDEX_DATABASE_URL=postgres://cocoindex:cocoindex@localhost/cocoindex + +# Fallback to CPU for operations not supported by MPS on Mac. +# It's no-op for other platforms. +PYTORCH_ENABLE_MPS_FALLBACK=1 + +# Doris/VeloDB connection configuration +# Required: +DORIS_FE_HOST=localhost +DORIS_PASSWORD= + +# Optional (defaults shown): +# DORIS_USERNAME=root +# DORIS_HTTP_PORT=8080 +# DORIS_QUERY_PORT=9030 +# DORIS_DATABASE=cocoindex_demo diff --git a/examples/text_embedding_doris/.gitignore b/examples/text_embedding_doris/.gitignore new file mode 100644 index 000000000..d27cedebe --- /dev/null +++ b/examples/text_embedding_doris/.gitignore @@ -0,0 +1 @@ +.cocoindex/ diff --git a/examples/text_embedding_doris/README.md b/examples/text_embedding_doris/README.md new file mode 100644 index 000000000..d4af55b29 --- /dev/null +++ b/examples/text_embedding_doris/README.md @@ -0,0 +1,72 @@ +# Build text embedding and semantic search with Apache Doris + +[![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex) + +CocoIndex supports Apache Doris natively. In this example, we will build index flow from text embedding from local markdown files, and query the index. We will use **Apache Doris** (or **VeloDB Cloud**) as the vector database. + +We appreciate a star at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful. + +## Steps + +### Indexing Flow + +1. We will ingest a list of local files. +2. For each file, perform chunking (recursively split) and then embedding. +3. We will save the embeddings and the metadata in Apache Doris with vector index support. + +### Query + +1. We have `search()` as a [query handler](https://cocoindex.io/docs/query#query-handler), to query the Doris table with vector similarity search. +2. We share the embedding operation `text_to_embedding()` between indexing and querying, + by wrapping it as a [transform flow](https://cocoindex.io/docs/query#transform-flow). + +## Pre-requisites + +1. [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one. Although the target store is Apache Doris, CocoIndex uses Postgres to track the data lineage for incremental processing. + +2. Install dependencies: + + ```sh + pip install -e . + ``` + +3. Set up Apache Doris or VeloDB Cloud: + - **Option A: Local Doris** - [Install Apache Doris 4.x](https://doris.apache.org/docs/4.x/gettingStarted/quick-start/) + - **Option B: VeloDB Cloud** - Sign up at [VeloDB Cloud](https://cloud.velodb.io/) for a managed service, please use [VeloDB Cloud 5.0 beta](https://docs.velodb.io/cloud/5.x-preview/getting-started/quick-start) + +4. Configure environment variables in `.env`: + ```sh + DORIS_FE_HOST=your-doris-host.example.com + DORIS_PASSWORD=your-password + # Optional: + DORIS_USERNAME=root + DORIS_HTTP_PORT=8080 + DORIS_QUERY_PORT=9030 + DORIS_DATABASE=cocoindex_demo + ``` + +## Run + +Update index, which will also setup Doris tables at the first time: + +```sh +cocoindex update main +``` + +You can also run the command with `-L`, which will watch for file changes and update the index automatically. + +```sh +cocoindex update -L main +``` + +## CocoInsight + +I used CocoInsight (Free beta now) to troubleshoot the index generation and understand the data lineage of the pipeline. +It just connects to your local CocoIndex server, with Zero pipeline data retention. Run following command to start CocoInsight: + +```sh +cocoindex server -ci main +``` + +Open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight). +You can run queries in the CocoInsight UI. diff --git a/examples/text_embedding_doris/main.py b/examples/text_embedding_doris/main.py new file mode 100644 index 000000000..fcb114950 --- /dev/null +++ b/examples/text_embedding_doris/main.py @@ -0,0 +1,152 @@ +import os +import datetime +import math +from typing import Any +import cocoindex +import cocoindex.targets.doris as coco_doris + +# Define Doris connection from environment +DORIS_FE_HOST = os.environ.get("DORIS_FE_HOST", "localhost") +DORIS_DATABASE = os.environ.get("DORIS_DATABASE", "cocoindex_demo") +DORIS_TABLE = "TextEmbedding" + + +def get_doris_config() -> dict[str, Any]: + """Get Doris configuration from environment variables.""" + return { + "fe_host": DORIS_FE_HOST, + "fe_http_port": int(os.environ.get("DORIS_HTTP_PORT", "8080")), + "query_port": int(os.environ.get("DORIS_QUERY_PORT", "9030")), + "username": os.environ.get("DORIS_USERNAME", "root"), + "password": os.environ.get("DORIS_PASSWORD", ""), + "database": DORIS_DATABASE, + } + + +@cocoindex.transform_flow() +def text_to_embedding( + text: cocoindex.DataSlice[str], +) -> cocoindex.DataSlice[list[float]]: + """ + Embed the text using a SentenceTransformer model. + This is a shared logic between indexing and querying, so extract it as a function. + """ + return text.transform( + cocoindex.functions.SentenceTransformerEmbed( + model="sentence-transformers/all-MiniLM-L6-v2" + ) + ) + + +@cocoindex.flow_def(name="TextEmbeddingWithDoris") +def text_embedding_flow( + flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope +) -> None: + """ + Define an example flow that embeds text into Apache Doris vector database. + """ + config = get_doris_config() + + data_scope["documents"] = flow_builder.add_source( + cocoindex.sources.LocalFile(path="markdown_files"), + refresh_interval=datetime.timedelta(seconds=5), + ) + + doc_embeddings = data_scope.add_collector() + + with data_scope["documents"].row() as doc: + doc["chunks"] = doc["content"].transform( + cocoindex.functions.SplitRecursively(), + language="markdown", + chunk_size=500, + chunk_overlap=100, + ) + + with doc["chunks"].row() as chunk: + chunk["embedding"] = text_to_embedding(chunk["text"]) + doc_embeddings.collect( + id=cocoindex.GeneratedField.UUID, + filename=doc["filename"], + location=chunk["location"], + text=chunk["text"], + text_embedding=chunk["embedding"], + ) + + doc_embeddings.export( + "doc_embeddings", + coco_doris.DorisTarget( + fe_host=config["fe_host"], + fe_http_port=config["fe_http_port"], + query_port=config["query_port"], + username=config["username"], + password=config["password"], + database=config["database"], + table=DORIS_TABLE, + ), + primary_key_fields=["id"], + vector_indexes=[ + cocoindex.VectorIndexDef( + "text_embedding", cocoindex.VectorSimilarityMetric.L2_DISTANCE + ) + ], + fts_indexes=[ + cocoindex.FtsIndexDef(field_name="text", parameters={"parser": "unicode"}) + ], + ) + + +@text_embedding_flow.query_handler( + result_fields=cocoindex.QueryHandlerResultFields( + embedding=["embedding"], + score="score", + ), +) +async def search(query: str) -> cocoindex.QueryOutput: + print("Searching...", query) + config = get_doris_config() + + # Get the embedding for the query + query_embedding = await text_to_embedding.eval_async(query) + + # Build search query using Doris helper + sql = coco_doris.build_vector_search_query( + table=f"{config['database']}.{DORIS_TABLE}", + vector_field="text_embedding", + query_vector=query_embedding, + metric="l2_distance", + limit=5, + select_columns=["id", "filename", "text"], + ) + + # Execute query + conn = await coco_doris.connect_async( + fe_host=config["fe_host"], + query_port=config["query_port"], + username=config["username"], + password=config["password"], + database=config["database"], + ) + + try: + async with conn.cursor() as cursor: + await cursor.execute(sql) + search_results = await cursor.fetchall() + finally: + conn.close() + await conn.ensure_closed() + + return cocoindex.QueryOutput( + results=[ + { + "filename": result[1], + "text": result[2], + "embedding": query_embedding, # Use query embedding for display + "score": math.sqrt(result[3]) if len(result) > 3 else 0.0, + } + for result in search_results + ], + query_info=cocoindex.QueryInfo( + embedding=query_embedding, + similarity_metric=cocoindex.VectorSimilarityMetric.L2_DISTANCE, + ), + ) diff --git a/examples/text_embedding_doris/markdown_files/rfc8259.md b/examples/text_embedding_doris/markdown_files/rfc8259.md new file mode 100644 index 000000000..bf6c29410 --- /dev/null +++ b/examples/text_embedding_doris/markdown_files/rfc8259.md @@ -0,0 +1,362 @@ +Internet Engineering Task Force (IETF) T. Bray, Ed. Request for Comments: 8259 Textuality Obsoletes: 7159 December 2017 Category: Standards Track ISSN: 2070-1721 + +The JavaScript Object Notation (JSON) Data Interchange Format + +Abstract + + JavaScript Object Notation (JSON) is a lightweight, text-based, language-independent data interchange format. It was derived from the ECMAScript Programming Language Standard. JSON defines a small set of formatting rules for the portable representation of structured data. + + This document removes inconsistencies with other specifications of JSON, repairs specification errors, and offers experience-based interoperability guidance. + +Status of This Memo + +This is an Internet Standards Track document. + + This document is a product of the Internet Engineering Task Force (IETF). It represents the consensus of the IETF community. It has received public review and has been approved for publication by the Internet Engineering Steering Group (IESG). Further information on Internet Standards is available in Section 2 of RFC 7841. + + Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at https://www.rfc-editor.org/info/rfc8259. + +Bray Standards Track [Page 1] + +Copyright Notice + + Copyright (c) 2017 IETF Trust and the persons identified as the document authors. All rights reserved. + + This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as + +described in the Simplified BSD License. + + This document may contain material from IETF Documents or IETF Contributions published or made publicly available before November 10, 2008. The person(s) controlling the copyright in some of this material may not have granted the IETF Trust the right to allow modifications of such material outside the IETF Standards Process. Without obtaining an adequate license from the person(s) controlling the copyright in such materials, this document may not be modified outside the IETF Standards Process, and derivative works of it may not be created outside the IETF Standards Process, except to format it for publication as an RFC or to translate it into languages other than English. + +Bray Standards Track [Page 2] + +Table of Contents + +| 1.1. Conventions Used in This Document
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1.2. Specifications of JSON
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1.3. Introduction to This Revision
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2. JSON Grammar
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3. Values
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4. Objects
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5. Arrays
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6. Numbers
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7. Strings
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8. String and Character Issues
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8.1. Character Encoding
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8.2. Unicode Characters 10
8.3. String Comparison 10
9. Parsers 10
10. Generators 10
11. IANA Considerations 11
12. Security Considerations 12
13. Examples 12
14. References 14
14.1. Normative References 14
14.2. Informative References 14
Appendix A. Changes from RFC 7159 16
Contributors 16 | 1. Introduction | | 3 | +|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|--|---| +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | Author's Address 16 | | | + +## 1. Introduction + + JavaScript Object Notation (JSON) is a text format for the serialization of structured data. It is derived from the object literals of JavaScript, as defined in the ECMAScript Programming Language Standard, Third Edition [ECMA-262]. + + JSON can represent four primitive types (strings, numbers, booleans, and null) and two structured types (objects and arrays). + + A string is a sequence of zero or more Unicode characters [UNICODE]. Note that this citation references the latest version of Unicode rather than a specific release. It is not expected that future changes in the Unicode specification will impact the syntax of JSON. + + An object is an unordered collection of zero or more name/value pairs, where a name is a string and a value is a string, number, boolean, null, object, or array. + +An array is an ordered sequence of zero or more values. + +Bray Standards Track [Page 3] + + The terms "object" and "array" come from the conventions of JavaScript. + + JSON's design goals were for it to be minimal, portable, textual, and a subset of JavaScript. + +1.1. Conventions Used in This Document + + The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here. + + The grammatical rules in this document are to be interpreted as described in [RFC5234]. + +1.2. Specifications of JSON + + This document replaces [RFC7159]. [RFC7159] obsoleted [RFC4627], which originally described JSON and registered the media type "application/json". + +JSON is also described in [ECMA-404]. + + The reference to ECMA-404 in the previous sentence is normative, not with the usual meaning that implementors need to consult it in order to understand this document, but to emphasize that there are no inconsistencies in the definition of the term "JSON text" in any of its specifications. Note, however, that ECMA-404 allows several practices that this specification recommends avoiding in the interests of maximal interoperability. + + The intent is that the grammar is the same between the two documents, although different descriptions are used. If there is a difference found between them, ECMA and the IETF will work together to update both documents. + + If an error is found with either document, the other should be examined to see if it has a similar error; if it does, it should be fixed, if possible. + + If either document is changed in the future, ECMA and the IETF will work together to ensure that the two documents stay aligned through the change. + +Bray Standards Track [Page 4] + +- 1.3. Introduction to This Revision + In the years since the publication of RFC 4627, JSON has found very wide use. This experience has revealed certain patterns that, while allowed by its specifications, have caused interoperability problems. + + Also, a small number of errata have been reported regarding RFC 4627 (see RFC Errata IDs 607 [Err607] and 3607 [Err3607]) and regarding RFC 7159 (see RFC Errata IDs 3915 [Err3915], 4264 [Err4264], 4336 [Err4336], and 4388 [Err4388]). + + This document's goal is to apply the errata, remove inconsistencies with other specifications of JSON, and highlight practices that can lead to interoperability problems. + +- 2. JSON Grammar + A JSON text is a sequence of tokens. The set of tokens includes six structural characters, strings, numbers, and three literal names. + + A JSON text is a serialized value. Note that certain previous specifications of JSON constrained a JSON text to be an object or an array. Implementations that generate only objects or arrays where a JSON text is called for will be interoperable in the sense that all implementations will accept these as conforming JSON texts. + +JSON-text = ws value ws + +These are the six structural characters: + +| begin-array | | | | = ws %x5B ws ; [ left square bracket | +|----------------------------------------|--|--|--|---------------------------------------| +| begin-object | | | | = ws %x7B ws ; { left curly bracket | +| end-array | | | | = ws %x5D ws ; ] right square bracket | +| end-object | | | | = ws %x7D ws ; } right curly bracket | +| name-separator = ws %x3A ws ; : colon | | | | | +| value-separator = ws %x2C ws ; , comma | | | | | + +Bray Standards Track [Page 5] + + Insignificant whitespace is allowed before or after any of the six structural characters. + + ws = *( %x20 / ; Space %x09 / ; Horizontal tab %x0A / ; Line feed or New line %x0D ) ; Carriage return + +## 3. Values + + A JSON value MUST be an object, array, number, or string, or one of the following three literal names: + + false null true + + The literal names MUST be lowercase. No other literal names are allowed. + + value = false / null / true / object / array / number / string false = %x66.61.6c.73.65 ; false null = %x6e.75.6c.6c ; null true = %x74.72.75.65 ; true + +## 4. Objects + + An object structure is represented as a pair of curly brackets surrounding zero or more name/value pairs (or members). A name is a string. A single colon comes after each name, separating the name from the value. A single comma separates a value from a following name. The names within an object SHOULD be unique. + + object = begin-object [ member *( value-separator member ) ] end-object + +member = string name-separator value + + An object whose names are all unique is interoperable in the sense that all software implementations receiving that object will agree on the name-value mappings. When the names within an object are not unique, the behavior of software that receives such an object is unpredictable. Many implementations report the last name/value pair only. Other implementations report an error or fail to parse the + +Bray Standards Track [Page 6] + + object, and some implementations report all of the name/value pairs, including duplicates. + + JSON parsing libraries have been observed to differ as to whether or not they make the ordering of object members visible to calling software. Implementations whose behavior does not depend on member ordering will be interoperable in the sense that they will not be affected by these differences. + +5. Arrays + + An array structure is represented as square brackets surrounding zero or more values (or elements). Elements are separated by commas. + +array = begin-array [ value *( value-separator value ) ] end-array + + There is no requirement that the values in an array be of the same type. + +6. Numbers + + The representation of numbers is similar to that used in most programming languages. A number is represented in base 10 using decimal digits. It contains an integer component that may be prefixed with an optional minus sign, which may be followed by a fraction part and/or an exponent part. Leading zeros are not allowed. + +A fraction part is a decimal point followed by one or more digits. + + An exponent part begins with the letter E in uppercase or lowercase, which may be followed by a plus or minus sign. The E and optional sign are followed by one or more digits. + + Numeric values that cannot be represented in the grammar below (such as Infinity and NaN) are not permitted. + + number = [ minus ] int [ frac ] [ exp ] decimal-point = %x2E ; . digit1-9 = %x31-39 ; 1-9 e = %x65 / %x45 ; e E exp = e [ minus / plus ] 1*DIGIT frac = decimal-point 1*DIGIT + +Bray Standards Track [Page 7] + + int = zero / ( digit1-9 *DIGIT ) minus = %x2D ; plus = %x2B ; + zero = %x30 ; 0 + + This specification allows implementations to set limits on the range and precision of numbers accepted. Since software that implements IEEE 754 binary64 (double precision) numbers [IEEE754] is generally available and widely used, good interoperability can be achieved by implementations that expect no more precision or range than these provide, in the sense that implementations will approximate JSON numbers within the expected precision. A JSON number such as 1E400 or 3.141592653589793238462643383279 may indicate potential interoperability problems, since it suggests that the software that created it expects receiving software to have greater capabilities for numeric magnitude and precision than is widely available. + + Note that when such software is used, numbers that are integers and are in the range [-(2**53)+1, (2**53)-1] are interoperable in the sense that implementations will agree exactly on their numeric values. + +## 7. Strings + + The representation of strings is similar to conventions used in the C family of programming languages. A string begins and ends with quotation marks. All Unicode characters may be placed within the quotation marks, except for the characters that MUST be escaped: quotation mark, reverse solidus, and the control characters (U+0000 through U+001F). + + Any character may be escaped. If the character is in the Basic Multilingual Plane (U+0000 through U+FFFF), then it may be represented as a six-character sequence: a reverse solidus, followed by the lowercase letter u, followed by four hexadecimal digits that encode the character's code point. The hexadecimal letters A through F can be uppercase or lowercase. So, for example, a string containing only a single reverse solidus character may be represented as "\u005C". + + Alternatively, there are two-character sequence escape representations of some popular characters. So, for example, a string containing only a single reverse solidus character may be represented more compactly as "\\". + +Bray Standards Track [Page 8] + + To escape an extended character that is not in the Basic Multilingual Plane, the character is represented as a 12-character sequence, encoding the UTF-16 surrogate pair. So, for example, a string containing only the G clef character (U+1D11E) may be represented as "\uD834\uDD1E". + + string = quotation-mark *char quotation-mark char = unescaped / escape ( %x22 / ; " quotation mark U+0022 %x5C / ; \ reverse solidus U+005C %x2F / ; / solidus U+002F %x62 / ; b backspace U+0008 %x66 / ; f form feed U+000C %x6E / ; n line feed U+000A %x72 / ; r carriage return U+000D %x74 / ; t tab U+0009 %x75 4HEXDIG ) ; uXXXX U+XXXX escape = %x5C ; \ quotation-mark = %x22 ; " unescaped = %x20-21 / %x23-5B / %x5D-10FFFF + +# 8. String and Character Issues + +- 8.1. Character Encoding + JSON text exchanged between systems that are not part of a closed ecosystem MUST be encoded using UTF-8 [RFC3629]. + + Previous specifications of JSON have not required the use of UTF-8 when transmitting JSON text. However, the vast majority of JSON based software implementations have chosen to use the UTF-8 encoding, to the extent that it is the only encoding that achieves interoperability. + + Implementations MUST NOT add a byte order mark (U+FEFF) to the beginning of a networked-transmitted JSON text. In the interests of interoperability, implementations that parse JSON texts MAY ignore the presence of a byte order mark rather than treating it as an error. + +Bray Standards Track [Page 9] + +## 8.2. Unicode Characters + + When all the strings represented in a JSON text are composed entirely of Unicode characters [UNICODE] (however escaped), then that JSON text is interoperable in the sense that all software implementations that parse it will agree on the contents of names and of string values in objects and arrays. + + However, the ABNF in this specification allows member names and string values to contain bit sequences that cannot encode Unicode characters; for example, "\uDEAD" (a single unpaired UTF-16 surrogate). Instances of this have been observed, for example, when a library truncates a UTF-16 string without checking whether the truncation split a surrogate pair. The behavior of software that receives JSON texts containing such values is unpredictable; for example, implementations might return different values for the length of a string value or even suffer fatal runtime exceptions. + +## 8.3. String Comparison + + Software implementations are typically required to test names of object members for equality. Implementations that transform the textual representation into sequences of Unicode code units and then perform the comparison numerically, code unit by code unit, are interoperable in the sense that implementations will agree in all cases on equality or inequality of two strings. For example, implementations that compare strings with escaped characters unconverted may incorrectly find that "a\\b" and "a\u005Cb" are not equal. + +## 9. Parsers + + A JSON parser transforms a JSON text into another representation. A JSON parser MUST accept all texts that conform to the JSON grammar. A JSON parser MAY accept non-JSON forms or extensions. + + An implementation may set limits on the size of texts that it accepts. An implementation may set limits on the maximum depth of nesting. An implementation may set limits on the range and precision of numbers. An implementation may set limits on the length and character contents of strings. + +## 10. Generators + + A JSON generator produces JSON text. The resulting text MUST strictly conform to the JSON grammar. + +Bray Standards Track [Page 10] + +11. IANA Considerations + +The media type for JSON text is application/json. + +Type name: application + +Subtype name: json + +Required parameters: n/a + +Optional parameters: n/a + +Encoding considerations: binary + +Security considerations: See RFC 8259, Section 12 + +Interoperability considerations: Described in RFC 8259 + +Published specification: RFC 8259 + + Applications that use this media type: JSON has been used to exchange data between applications written in all of these programming languages: ActionScript, C, C#, Clojure, ColdFusion, Common Lisp, E, Erlang, Go, Java, JavaScript, Lua, Objective CAML, Perl, PHP, Python, Rebol, Ruby, Scala, and Scheme. + + Additional information: Magic number(s): n/a File extension(s): .json Macintosh file type code(s): TEXT + + Person & email address to contact for further information: IESG + +Intended usage: COMMON + +Restrictions on usage: none + + Author: Douglas Crockford + + Change controller: IESG + +Bray Standards Track [Page 11] + + Note: No "charset" parameter is defined for this registration. Adding one really has no effect on compliant recipients. + +- 12. Security Considerations + Generally, there are security issues with scripting languages. JSON is a subset of JavaScript but excludes assignment and invocation. + + Since JSON's syntax is borrowed from JavaScript, it is possible to use that language's "eval()" function to parse most JSON texts (but not all; certain characters such as U+2028 LINE SEPARATOR and U+2029 PARAGRAPH SEPARATOR are legal in JSON but not JavaScript). This generally constitutes an unacceptable security risk, since the text could contain executable code along with data declarations. The same consideration applies to the use of eval()-like functions in any other programming language in which JSON texts conform to that language's syntax. + +## 13. Examples + + This is a JSON object: { "Image": { "Width": 800, "Height": 600, "Title": "View from 15th Floor", "Thumbnail": { "Url": "http://www.example.com/image/481989943", "Height": 125, "Width": 100 }, "Animated" : false, "IDs": [116, 943, 234, 38793] } } + + Its Image member is an object whose Thumbnail member is an object and whose IDs member is an array of numbers. + +Bray Standards Track [Page 12] + +``` + This is a JSON array containing two objects: +[ +{ +"precision": "zip", +"Latitude": 37.7668, +"Longitude": -122.3959, +"Address": "", +"City": "SAN FRANCISCO", +"State": "CA", +"Zip": "94107", +"Country": "US" +}, +{ +"precision": "zip", +"Latitude": 37.371991, +"Longitude": -122.026020, +"Address": "", +"City": "SUNNYVALE", +"State": "CA", +"Zip": "94085", +"Country": "US" +} +] +Here are three small JSON texts containing only values: +"Hello world!" +42 +true +``` +Bray Standards Track [Page 13] + +## 14. References + +- 14.1. Normative References +- [ECMA-404] Ecma International, "The JSON Data Interchange Format", Standard ECMA-404, . +- [IEEE754] IEEE, "IEEE Standard for Floating-Point Arithmetic", IEEE 754. +- [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997, . +- [RFC3629] Yergeau, F., "UTF-8, a transformation format of ISO 10646", STD 63, RFC 3629, DOI 10.17487/RFC3629, November 2003, . +- [RFC5234] Crocker, D., Ed. and P. Overell, "Augmented BNF for Syntax Specifications: ABNF", STD 68, RFC 5234, DOI 10.17487/RFC5234, January 2008, . +- [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017, . +- [UNICODE] The Unicode Consortium, "The Unicode Standard", . +- 14.2. Informative References +- [ECMA-262] Ecma International, "ECMAScript Language Specification", Standard ECMA-262, Third Edition, December 1999, . +- [Err3607] RFC Errata, Erratum ID 3607, RFC 4627, . +- [Err3915] RFC Errata, Erratum ID 3915, RFC 7159, . + +Bray Standards Track [Page 14] + +- [Err4264] RFC Errata, Erratum ID 4264, RFC 7159, . +- [Err4336] RFC Errata, Erratum ID 4336, RFC 7159, . +- [Err4388] RFC Errata, Erratum ID 4388, RFC 7159, . +- [Err607] RFC Errata, Erratum ID 607, RFC 4627, . +- [RFC4627] Crockford, D., "The application/json Media Type for JavaScript Object Notation (JSON)", RFC 4627, DOI 10.17487/RFC4627, July 2006, . +- [RFC7159] Bray, T., Ed., "The JavaScript Object Notation (JSON) Data Interchange Format", RFC 7159, DOI 10.17487/RFC7159, March 2014, . + +Bray Standards Track [Page 15] + +Appendix A. Changes from RFC 7159 + + This section lists changes between this document and the text in RFC 7159. + +- o Section 1.2 has been updated to reflect the removal of a JSON specification from ECMA-262, to make ECMA-404 a normative reference, and to explain the particular meaning of "normative". +- o Section 1.3 has been updated to reflect errata filed against RFC 7159, not RFC 4627. +- o Section 8.1 was changed to require the use of UTF-8 when transmitted over a network. +- o Section 12 has been updated to increase the precision of the description of the security risk that follows from using the ECMAScript "eval()" function. +- o Section 14.1 has been updated to include ECMA-404 as a normative reference. +- o Section 14.2 has been updated to remove ECMA-404, update the version of ECMA-262, and refresh the errata list. + +Contributors + + RFC 4627 was written by Douglas Crockford. This document was constructed by making a relatively small number of changes to that document; thus, the vast majority of the text here is his. + +Author's Address + + Tim Bray (editor) Textuality + +Email: tbray@textuality.com + +Bray Standards Track [Page 16] diff --git a/examples/text_embedding_doris/pyproject.toml b/examples/text_embedding_doris/pyproject.toml new file mode 100644 index 000000000..a16ba2e7c --- /dev/null +++ b/examples/text_embedding_doris/pyproject.toml @@ -0,0 +1,9 @@ +[project] +name = "text-embedding-doris" +version = "0.1.0" +dependencies = [ + "cocoindex", + "sentence-transformers", + "aiohttp", + "aiomysql", +] diff --git a/pyproject.toml b/pyproject.toml index 105d51c72..881bf71b4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,12 +70,16 @@ editable-profile = "dev" # local editable builds embeddings = ["sentence-transformers>=3.3.1"] colpali = ["colpali-engine"] lancedb = ["lancedb>=0.25.0", "pyarrow>=19.0.0"] +doris = ["aiohttp>=3.8.0", "aiomysql>=0.2.0", "pymysql>=1.0.0"] all = [ "sentence-transformers>=3.3.1", "colpali-engine", "lancedb>=0.25.0", "pyarrow>=19.0.0", + "aiohttp>=3.8.0", + "aiomysql>=0.2.0", + "pymysql>=1.0.0", ] [dependency-groups] @@ -124,7 +128,7 @@ disable_error_code = ["unused-ignore"] [[tool.mypy.overrides]] # Ignore missing imports for optional dependencies from cocoindex library -module = ["sentence_transformers", "torch", "colpali_engine", "PIL"] +module = ["sentence_transformers", "torch", "colpali_engine", "PIL", "aiohttp", "aiomysql", "pymysql"] ignore_missing_imports = true [[tool.mypy.overrides]] diff --git a/python/cocoindex/targets/__init__.py b/python/cocoindex/targets/__init__.py index 8474ef79f..539d3efd6 100644 --- a/python/cocoindex/targets/__init__.py +++ b/python/cocoindex/targets/__init__.py @@ -3,3 +3,4 @@ """ from ._engine_builtin_specs import * +from . import doris diff --git a/python/cocoindex/targets/doris.py b/python/cocoindex/targets/doris.py new file mode 100644 index 000000000..480a84956 --- /dev/null +++ b/python/cocoindex/targets/doris.py @@ -0,0 +1,2066 @@ +""" +Apache Doris 4.0 target connector for CocoIndex. + +Supports: +- Vector index (HNSW, IVF) with L2 distance and inner product metrics +- Inverted index for full-text search +- Stream Load for bulk data ingestion +- Incremental updates with upsert/delete operations + +Requirements: +- Doris 4.0+ with vector index support +- DUPLICATE KEY table model (required for vector indexes) +""" + +import asyncio +import dataclasses +import json +import logging +import math +import time +import uuid +import re +from typing import Any, Callable, Awaitable, Literal, TypeVar, TYPE_CHECKING + +if TYPE_CHECKING: + import aiohttp # type: ignore[import-not-found] + +from cocoindex import op +from cocoindex.engine_type import ( + FieldSchema, + EnrichedValueType, + BasicValueType, + StructType, + ValueType, + TableType, +) +from cocoindex.index import ( + IndexOptions, + VectorSimilarityMetric, + HnswVectorIndexMethod, + IvfFlatVectorIndexMethod, +) + +_logger = logging.getLogger(__name__) + +T = TypeVar("T") + + +def _get_aiohttp() -> Any: + """Lazily import aiohttp to avoid import errors when not installed.""" + try: + import aiohttp # type: ignore[import-not-found] + + return aiohttp + except ImportError: + raise ImportError( + "aiohttp is required for Doris connector. " + "Install it with: pip install aiohttp" + ) + + +# ============================================================ +# TYPE MAPPING: CocoIndex -> Doris SQL +# ============================================================ + +_DORIS_TYPE_MAPPING: dict[str, str] = { + "Bytes": "STRING", + "Str": "TEXT", + "Bool": "BOOLEAN", + "Int64": "BIGINT", + "Float32": "FLOAT", + "Float64": "DOUBLE", + "Uuid": "VARCHAR(36)", + "Date": "DATE", + "Time": "VARCHAR(20)", # HH:MM:SS.ffffff + "LocalDateTime": "DATETIME(6)", + "OffsetDateTime": "DATETIME(6)", + "TimeDelta": "BIGINT", # microseconds + "Json": "JSON", + "Range": "JSON", # {"start": x, "end": y} +} + +_DORIS_VECTOR_METRIC: dict[VectorSimilarityMetric, str] = { + VectorSimilarityMetric.L2_DISTANCE: "l2_distance", + VectorSimilarityMetric.INNER_PRODUCT: "inner_product", + VectorSimilarityMetric.COSINE_SIMILARITY: "cosine_distance", +} + + +# ============================================================ +# SPEC CLASSES +# ============================================================ + + +class DorisTarget(op.TargetSpec): + """Apache Doris target connector specification.""" + + # Connection + fe_host: str + database: str + table: str + fe_http_port: int = 8080 + query_port: int = 9030 + username: str = "root" + password: str = "" + enable_https: bool = False + + # Behavior + batch_size: int = 10000 + stream_load_timeout: int = 600 + auto_create_table: bool = True + + # Timeout configuration (seconds) + schema_change_timeout: int = 60 # Timeout for ALTER TABLE operations + index_build_timeout: int = 300 # Timeout for BUILD INDEX operations + + # Retry configuration + max_retries: int = 3 + retry_base_delay: float = 1.0 + retry_max_delay: float = 30.0 + + # Table properties + replication_num: int = 1 + buckets: int | str = "auto" # int for fixed count, or "auto" for automatic + + # Schema evolution strategy: + # - "extend": Allow extra columns in DB, only add missing columns, never drop. + # Indexes are created only if referenced columns exist and are compatible. + # - "strict": Require exact schema match; drop and recreate table if incompatible. + schema_evolution: Literal["extend", "strict"] = "extend" + + +@dataclasses.dataclass +class _ColumnInfo: + """Information about a column in the actual database table.""" + + name: str + doris_type: str # "BIGINT", "TEXT", "ARRAY", etc. + nullable: bool + is_key: bool + dimension: int | None = None # For vector columns (ARRAY) + + +@dataclasses.dataclass +class _TableKey: + """Unique identifier for a Doris table.""" + + fe_host: str + database: str + table: str + + +@dataclasses.dataclass +class _VectorIndex: + """Vector index configuration.""" + + name: str + field_name: str + index_type: str # "hnsw" or "ivf" + metric_type: str # "l2_distance" or "inner_product" + dimension: int + # HNSW params + max_degree: int | None = None + ef_construction: int | None = None + # IVF params + nlist: int | None = None + + +@dataclasses.dataclass +class _InvertedIndex: + """Inverted index for text search.""" + + name: str + field_name: str + parser: str | None = None # "chinese", "english", etc. + + +@dataclasses.dataclass +class _State: + """Setup state for Doris target.""" + + key_fields_schema: list[FieldSchema] + value_fields_schema: list[FieldSchema] + vector_indexes: list[_VectorIndex] | None = None + inverted_indexes: list[_InvertedIndex] | None = None + replication_num: int = 1 + buckets: int | str = "auto" # int for fixed count, or "auto" for automatic + # Connection credentials (needed for apply_setup_change) + fe_http_port: int = 8080 + query_port: int = 9030 + username: str = "root" + password: str = "" + max_retries: int = 3 + retry_base_delay: float = 1.0 + retry_max_delay: float = 30.0 + # Timeout configuration + schema_change_timeout: int = 60 + index_build_timeout: int = 300 + # Table creation behavior + auto_create_table: bool = True + # Schema evolution mode + schema_evolution: Literal["extend", "strict"] = "extend" + + +@dataclasses.dataclass +class _MutateContext: + """Context for mutation operations.""" + + spec: DorisTarget + session: "aiohttp.ClientSession" + state: _State + lock: asyncio.Lock + + +# ============================================================ +# ERROR CLASSES +# ============================================================ + + +class DorisError(Exception): + """Base class for Doris connector errors.""" + + +class DorisConnectionError(DorisError): + """Connection-related errors (network, auth, timeout).""" + + def __init__( + self, message: str, host: str, port: int, cause: Exception | None = None + ): + self.host = host + self.port = port + self.cause = cause + super().__init__(f"{message} (host={host}:{port})") + + +class DorisAuthError(DorisConnectionError): + """Authentication failed.""" + + +class DorisStreamLoadError(DorisError): + """Stream Load operation failed.""" + + def __init__( + self, + message: str, + status: str, + error_url: str | None = None, + loaded_rows: int = 0, + filtered_rows: int = 0, + ): + self.status = status + self.error_url = error_url + self.loaded_rows = loaded_rows + self.filtered_rows = filtered_rows + super().__init__(f"Stream Load {status}: {message}") + + +class DorisSchemaError(DorisError): + """Schema-related errors (type mismatch, invalid column).""" + + def __init__(self, message: str, field_name: str | None = None): + self.field_name = field_name + super().__init__(message) + + +# ============================================================ +# RETRY LOGIC +# ============================================================ + + +@dataclasses.dataclass +class RetryConfig: + """Retry configuration for Doris operations.""" + + max_retries: int = 3 + base_delay: float = 1.0 + max_delay: float = 30.0 + exponential_base: float = 2.0 + + +def _is_retryable_mysql_error(e: Exception) -> bool: + """Check if a MySQL error is retryable (transient connection issue).""" + try: + import pymysql # type: ignore + + if isinstance(e, pymysql.err.OperationalError): + # Check error code - only retry connection-related errors + if e.args and len(e.args) > 0: + error_code = e.args[0] + # Retryable error codes (connection issues): + # 2003: Can't connect to MySQL server + # 2006: MySQL server has gone away + # 2013: Lost connection to MySQL server during query + # 1040: Too many connections + # 1205: Lock wait timeout + retryable_codes = {2003, 2006, 2013, 1040, 1205} + return error_code in retryable_codes + if isinstance(e, pymysql.err.InterfaceError): + return True # Interface errors are usually connection issues + except ImportError: + pass + return False + + +def _get_retryable_errors() -> tuple[type[Exception], ...]: + """Get tuple of retryable error types including aiohttp errors when available.""" + base_errors: tuple[type[Exception], ...] = ( + asyncio.TimeoutError, + ConnectionError, + ConnectionResetError, + ConnectionRefusedError, + ) + try: + aiohttp = _get_aiohttp() + return base_errors + ( + aiohttp.ClientConnectorError, + aiohttp.ServerDisconnectedError, + ) + except ImportError: + # aiohttp not installed - return only base network errors + # MySQL-only paths will still work via _is_retryable_mysql_error + return base_errors + + +async def with_retry( + operation: Callable[[], Awaitable[T]], + config: RetryConfig = RetryConfig(), + operation_name: str = "operation", + retryable_errors: tuple[type[Exception], ...] | None = None, +) -> T: + """Execute operation with exponential backoff retry. + + Handles both aiohttp errors (via retryable_errors tuple) and MySQL/aiomysql + connection errors (via _is_retryable_mysql_error helper). + """ + if retryable_errors is None: + retryable_errors = _get_retryable_errors() + + last_error: Exception | None = None + + for attempt in range(config.max_retries + 1): + try: + return await operation() + except Exception as e: + # Check if error is retryable (either aiohttp or MySQL error) + is_retryable = isinstance(e, retryable_errors) or _is_retryable_mysql_error( + e + ) + if not is_retryable: + raise # Re-raise non-retryable errors immediately + + last_error = e + if attempt < config.max_retries: + delay = min( + config.base_delay * (config.exponential_base**attempt), + config.max_delay, + ) + _logger.warning( + "%s failed (attempt %d/%d), retrying in %.1fs: %s", + operation_name, + attempt + 1, + config.max_retries + 1, + delay, + e, + ) + await asyncio.sleep(delay) + + raise DorisConnectionError( + f"{operation_name} failed after {config.max_retries + 1} attempts", + host="", + port=0, + cause=last_error, + ) + + +# ============================================================ +# TYPE CONVERSION +# ============================================================ + + +def _convert_value_type_to_doris_type(value_type: EnrichedValueType) -> str: + """Convert EnrichedValueType to Doris SQL type.""" + base_type: ValueType = value_type.type + + if isinstance(base_type, StructType): + return "JSON" + + if isinstance(base_type, TableType): + return "JSON" + + if isinstance(base_type, BasicValueType): + kind: str = base_type.kind + + if kind == "Vector": + # Only vectors with fixed dimension can be stored as ARRAY + # for index creation. Others fall back to JSON. + if _is_vector_indexable(value_type): + return "ARRAY" + else: + return "JSON" + + if kind in _DORIS_TYPE_MAPPING: + return _DORIS_TYPE_MAPPING[kind] + + # Fallback to JSON for unsupported types + return "JSON" + + # Fallback to JSON for unknown value types + return "JSON" + + +def _convert_value_for_doris(value: Any) -> Any: + """Convert Python value to Doris-compatible format.""" + if value is None: + return None + + if isinstance(value, uuid.UUID): + return str(value) + + if isinstance(value, float) and math.isnan(value): + return None + + if isinstance(value, (list, tuple)): + return [_convert_value_for_doris(v) for v in value] + + if isinstance(value, dict): + return {k: _convert_value_for_doris(v) for k, v in value.items()} + + if hasattr(value, "isoformat"): + return value.isoformat() + + if isinstance(value, bytes): + return value.decode("utf-8", errors="replace") + + return value + + +def _get_vector_dimension( + value_fields_schema: list[FieldSchema], field_name: str +) -> int | None: + """Get the dimension of a vector field. + + Returns None if the field is not found, not a vector type, or doesn't have a dimension. + This allows fallback to JSON storage for vectors without fixed dimensions. + """ + for field in value_fields_schema: + if field.name == field_name: + base_type = field.value_type.type + if isinstance(base_type, BasicValueType) and base_type.kind == "Vector": + if ( + base_type.vector is not None + and base_type.vector.dimension is not None + ): + return base_type.vector.dimension + # Field exists but is not a vector with dimension + return None + # Field not found + return None + + +def _get_doris_metric_type(metric: VectorSimilarityMetric) -> str: + """Convert CocoIndex metric to Doris metric type.""" + if metric not in _DORIS_VECTOR_METRIC: + raise ValueError(f"Unsupported vector metric for Doris: {metric}") + doris_metric = _DORIS_VECTOR_METRIC[metric] + # Note: cosine_distance doesn't support index in Doris 4.0 + if doris_metric == "cosine_distance": + _logger.warning( + "Cosine distance does not support vector index in Doris 4.0. " + "Queries will use full table scan. Consider using L2 distance or inner product." + ) + return doris_metric + + +def _extract_vector_dimension(value_type: EnrichedValueType) -> int | None: + """Extract dimension from a vector value type.""" + base_type = value_type.type + if isinstance(base_type, BasicValueType) and base_type.kind == "Vector": + if base_type.vector is not None: + return base_type.vector.dimension + return None + + +def _is_vector_indexable(value_type: EnrichedValueType) -> bool: + """Check if a vector type can be indexed (has fixed dimension).""" + return _extract_vector_dimension(value_type) is not None + + +def _extract_array_element_type(type_str: str) -> str | None: + """Extract element type from ARRAY type string.""" + type_upper = type_str.upper().strip() + if type_upper.startswith("ARRAY<") and type_upper.endswith(">"): + return type_upper[6:-1].strip() + if type_upper.startswith("ARRAY(") and type_upper.endswith(")"): + return type_upper[6:-1].strip() + return None + + +def _extract_varchar_length(type_str: str) -> int | None: + """Extract length from VARCHAR(N) type string. Returns None if no length specified.""" + type_upper = type_str.upper().strip() + if type_upper.startswith("VARCHAR(") and type_upper.endswith(")"): + try: + return int(type_upper[8:-1].strip()) + except ValueError: + return None + return None + + +def _types_compatible(expected: str, actual: str) -> bool: + """Check if two Doris types are compatible. + + This performs strict type checking to avoid data corruption: + - ARRAY types must have matching element types (ARRAY != ARRAY) + - VARCHAR lengths are checked to ensure actual can hold expected data + - TEXT/STRING types are treated as interchangeable + """ + # Normalize for comparison + expected_norm = expected.upper().strip() + actual_norm = actual.upper().strip() + + # Exact match + if expected_norm == actual_norm: + return True + + # Handle ARRAY types - must check element type + expected_elem = _extract_array_element_type(expected_norm) + actual_elem = _extract_array_element_type(actual_norm) + if expected_elem is not None or actual_elem is not None: + if expected_elem is None or actual_elem is None: + # One is ARRAY, one is not + return False + # Both are ARRAY - check element types match + # Allow FLOAT vs DOUBLE as they're commonly interchangeable in Doris + float_types = {"FLOAT", "DOUBLE"} + if expected_elem in float_types and actual_elem in float_types: + return True + return expected_elem == actual_elem + + # Handle VARCHAR - check length compatibility + expected_len = _extract_varchar_length(expected_norm) + actual_len = _extract_varchar_length(actual_norm) + if expected_norm.startswith("VARCHAR") and actual_norm.startswith("VARCHAR"): + if expected_len is not None and actual_len is not None: + # Actual must be able to hold expected length + return actual_len >= expected_len + # If either has no explicit length, accept (Doris defaults to large) + return True + + # Handle TEXT vs STRING (both are text types in Doris) + # These are essentially unlimited text types + text_types = {"TEXT", "STRING"} + expected_base = expected_norm.split("(")[0] + actual_base = actual_norm.split("(")[0] + if expected_base in text_types and actual_base in text_types: + return True + + # TEXT/STRING can hold any VARCHAR content + if expected_norm.startswith("VARCHAR") and actual_base in text_types: + return True + if expected_base in text_types and actual_norm.startswith("VARCHAR"): + # VARCHAR may truncate TEXT - this is a warning case but we allow it + return True + + return False + + +# ============================================================ +# SQL GENERATION +# ============================================================ + + +def _validate_identifier(name: str) -> None: + """Validate SQL identifier to prevent injection.""" + if not re.match(r"^[a-zA-Z_][a-zA-Z0-9_]*$", name): + raise DorisSchemaError(f"Invalid identifier: {name}") + + +def _convert_to_key_column_type(doris_type: str) -> str: + """Convert a Doris type to be compatible with key columns. + + Doris DUPLICATE KEY model doesn't allow TEXT or STRING as key columns. + Convert them to VARCHAR with appropriate length. + """ + if doris_type in ("TEXT", "STRING"): + # Use VARCHAR(512) for key columns - reasonable default for identifiers + return "VARCHAR(512)" + return doris_type + + +def _build_vector_index_properties(idx: "_VectorIndex") -> list[str]: + """Build PROPERTIES list for vector index DDL. + + This helper is shared between CREATE TABLE and CREATE INDEX statements. + + Args: + idx: Vector index definition + + Returns: + List of property strings like '"index_type" = "HNSW"' + """ + props = [ + f'"index_type" = "{idx.index_type}"', + f'"metric_type" = "{idx.metric_type}"', + f'"dim" = "{idx.dimension}"', + ] + if idx.max_degree is not None: + props.append(f'"max_degree" = "{idx.max_degree}"') + if idx.ef_construction is not None: + props.append(f'"ef_construction" = "{idx.ef_construction}"') + if idx.nlist is not None: + props.append(f'"nlist" = "{idx.nlist}"') + return props + + +def _generate_create_table_ddl(key: _TableKey, state: _State) -> str: + """Generate CREATE TABLE DDL for Doris.""" + _validate_identifier(key.database) + _validate_identifier(key.table) + + columns = [] + key_column_names = [] + + # Key columns - must use VARCHAR instead of TEXT/STRING + for field in state.key_fields_schema: + _validate_identifier(field.name) + doris_type = _convert_value_type_to_doris_type(field.value_type) + key_type = _convert_to_key_column_type(doris_type) + columns.append(f" {field.name} {key_type} NOT NULL") + key_column_names.append(field.name) + + # Value columns + for field in state.value_fields_schema: + _validate_identifier(field.name) + doris_type = _convert_value_type_to_doris_type(field.value_type) + # Vector columns must be NOT NULL for index creation + base_type = field.value_type.type + if isinstance(base_type, BasicValueType) and base_type.kind == "Vector": + nullable = "NOT NULL" + else: + nullable = "NULL" if field.value_type.nullable else "NOT NULL" + columns.append(f" {field.name} {doris_type} {nullable}") + + # Vector indexes (inline definition) + for idx in state.vector_indexes or []: + _validate_identifier(idx.name) + _validate_identifier(idx.field_name) + props = _build_vector_index_properties(idx) + columns.append( + f" INDEX {idx.name} ({idx.field_name}) USING ANN PROPERTIES ({', '.join(props)})" + ) + + # Inverted indexes + for inv_idx in state.inverted_indexes or []: + _validate_identifier(inv_idx.name) + _validate_identifier(inv_idx.field_name) + if inv_idx.parser: + columns.append( + f' INDEX {inv_idx.name} ({inv_idx.field_name}) USING INVERTED PROPERTIES ("parser" = "{inv_idx.parser}")' + ) + else: + columns.append( + f" INDEX {inv_idx.name} ({inv_idx.field_name}) USING INVERTED" + ) + + key_cols = ", ".join(key_column_names) + + # Handle "auto" buckets or fixed integer count + buckets_clause = ( + "AUTO" if str(state.buckets).lower() == "auto" else str(state.buckets) + ) + return f"""CREATE TABLE IF NOT EXISTS {key.database}.{key.table} ( +{("," + chr(10)).join(columns)} +) +ENGINE = OLAP +DUPLICATE KEY({key_cols}) +DISTRIBUTED BY HASH({key_cols}) BUCKETS {buckets_clause} +PROPERTIES ( + "replication_num" = "{state.replication_num}" +)""" + + +def _generate_stream_load_label() -> str: + """Generate a unique label for Stream Load.""" + return f"cocoindex_{int(time.time() * 1000)}_{uuid.uuid4().hex[:8]}" + + +def _build_stream_load_headers( + label: str, columns: list[str] | None = None +) -> dict[str, str]: + """Build headers for Stream Load request.""" + headers = { + "format": "json", + "strip_outer_array": "true", + "label": label, + "Expect": "100-continue", + } + + if columns: + headers["columns"] = ", ".join(columns) + + return headers + + +# ============================================================ +# STREAM LOAD +# ============================================================ + + +async def _stream_load( + session: "aiohttp.ClientSession", + spec: DorisTarget, + rows: list[dict[str, Any]], +) -> dict[str, Any]: + """Execute Stream Load for bulk data ingestion. + + Note: Deletes are handled via SQL DELETE (_execute_delete) instead of Stream Load + because Doris 4.0 vector indexes only support DUPLICATE KEY tables, and Stream Load + DELETE requires UNIQUE KEY model. + """ + aiohttp = _get_aiohttp() + + if not rows: + return {"Status": "Success", "NumberLoadedRows": 0} + + protocol = "https" if spec.enable_https else "http" + url = f"{protocol}://{spec.fe_host}:{spec.fe_http_port}/api/{spec.database}/{spec.table}/_stream_load" + + label = _generate_stream_load_label() + # Collect ALL unique columns across all rows to avoid data loss + # (first row may not have all optional fields) + all_columns: set[str] = set() + for row in rows: + all_columns.update(row.keys()) + columns = sorted(all_columns) # Sort for consistent ordering + headers = _build_stream_load_headers(label, columns) + + data = json.dumps(rows, ensure_ascii=False) + + async def do_stream_load() -> dict[str, Any]: + async with session.put( + url, + data=data, + headers=headers, + timeout=aiohttp.ClientTimeout(total=spec.stream_load_timeout), + ) as response: + # Check for auth errors + if response.status in (401, 403): + raise DorisAuthError( + f"Authentication failed: HTTP {response.status}", + host=spec.fe_host, + port=spec.fe_http_port, + ) + + # Parse response - VeloDB/Doris may return wrong Content-Type + text = await response.text() + try: + result: dict[str, Any] = json.loads(text) + except json.JSONDecodeError: + raise DorisStreamLoadError( + message=f"Invalid JSON response: {text[:200]}", + status="ParseError", + ) + + # Use case-insensitive status check for robustness + # (different Doris versions may return different case) + status = result.get("Status", "Unknown") + status_upper = status.upper() if isinstance(status, str) else "" + if status_upper not in ("SUCCESS", "PUBLISH TIMEOUT"): + raise DorisStreamLoadError( + message=result.get("Message", "Unknown error"), + status=status, + error_url=result.get("ErrorURL"), + loaded_rows=result.get("NumberLoadedRows", 0), + filtered_rows=result.get("NumberFilteredRows", 0), + ) + + return result + + retry_config = RetryConfig( + max_retries=spec.max_retries, + base_delay=spec.retry_base_delay, + max_delay=spec.retry_max_delay, + ) + return await with_retry( + do_stream_load, + config=retry_config, + operation_name="Stream Load", + ) + + +# ============================================================ +# MYSQL CONNECTION (for DDL) +# ============================================================ + + +async def _execute_ddl( + spec: DorisTarget, + sql: str, +) -> list[dict[str, Any]]: + """Execute DDL via MySQL protocol using aiomysql.""" + try: + import aiomysql # type: ignore + except ImportError: + raise ImportError( + "aiomysql is required for Doris DDL operations. " + "Install it with: pip install aiomysql" + ) + + async def do_execute() -> list[dict[str, Any]]: + conn = await aiomysql.connect( + host=spec.fe_host, + port=spec.query_port, + user=spec.username, + password=spec.password, + db=spec.database if spec.database else None, + autocommit=True, + ) + try: + async with conn.cursor(aiomysql.DictCursor) as cursor: + await cursor.execute(sql) + try: + result = await cursor.fetchall() + return list(result) + except aiomysql.ProgrammingError as e: + # "no result set" error is expected for DDL statements + # that don't return results (CREATE, DROP, ALTER, etc.) + if "no result set" in str(e).lower(): + return [] + raise # Re-raise other programming errors + finally: + conn.close() + await conn.ensure_closed() + + retry_config = RetryConfig( + max_retries=spec.max_retries, + base_delay=spec.retry_base_delay, + max_delay=spec.retry_max_delay, + ) + return await with_retry( + do_execute, + config=retry_config, + operation_name="DDL execution", + ) + + +async def _table_exists(spec: DorisTarget, database: str, table: str) -> bool: + """Check if a table exists.""" + try: + result = await _execute_ddl(spec, f"SHOW TABLES FROM {database} LIKE '{table}'") + return len(result) > 0 + except Exception as e: # pylint: disable=broad-except + _logger.warning("Failed to check table existence: %s", e) + return False + + +async def _get_table_schema( + spec: DorisTarget, database: str, table: str +) -> dict[str, _ColumnInfo] | None: + """ + Query the actual table schema from Doris using DESCRIBE. + + Returns a dict mapping column_name -> _ColumnInfo, or None if table doesn't exist. + Raises DorisError for other query failures (connection errors, permission issues, etc.). + """ + try: + import aiomysql # type: ignore + except ImportError: + raise ImportError( + "aiomysql is required for Doris operations. " + "Install it with: pip install aiomysql" + ) + + try: + result = await _execute_ddl(spec, f"DESCRIBE `{database}`.`{table}`") + if not result: + return None + + columns: dict[str, _ColumnInfo] = {} + for row in result: + col_name = row.get("Field", "") + col_type = row.get("Type", "") + nullable = row.get("Null", "YES") == "YES" + is_key = row.get("Key", "") == "true" + + # Extract dimension from ARRAY type if present + # Format: ARRAY or ARRAY(384) + dimension: int | None = None + if col_type.upper().startswith("ARRAY"): + dim_match = re.search(r"\((\d+)\)", col_type) + if dim_match: + dimension = int(dim_match.group(1)) + + columns[col_name] = _ColumnInfo( + name=col_name, + doris_type=col_type, + nullable=nullable, + is_key=is_key, + dimension=dimension, + ) + return columns + except aiomysql.Error as e: + # MySQL error 1146: Table doesn't exist + # MySQL error 1049: Unknown database + # VeloDB also uses error 1105 with "Unknown table" message + error_code = e.args[0] if e.args else 0 + error_msg = str(e.args[1]) if len(e.args) > 1 else "" + if error_code in (1146, 1049): + _logger.debug("Table not found: %s.%s", database, table) + return None + if error_code == 1105 and "unknown table" in error_msg.lower(): + _logger.debug("Table not found (VeloDB): %s.%s", database, table) + return None + # Re-raise other MySQL errors (connection issues, permissions, etc.) + raise DorisError(f"Failed to get table schema: {e}") from e + + +async def _get_table_model(spec: DorisTarget, database: str, table: str) -> str | None: + """ + Get the table model (DUPLICATE KEY, UNIQUE KEY, or AGGREGATE KEY) from + SHOW CREATE TABLE. + + Returns the model type string or None if table doesn't exist or can't be determined. + """ + try: + result = await _execute_ddl(spec, f"SHOW CREATE TABLE `{database}`.`{table}`") + if not result: + return None + + create_stmt = result[0].get("Create Table", "") + + # Parse the table model from CREATE TABLE statement + # Format: DUPLICATE KEY(`col1`, `col2`) + # Format: UNIQUE KEY(`col1`) + # Format: AGGREGATE KEY(`col1`) + if "DUPLICATE KEY" in create_stmt.upper(): + return "DUPLICATE KEY" + elif "UNIQUE KEY" in create_stmt.upper(): + return "UNIQUE KEY" + elif "AGGREGATE KEY" in create_stmt.upper(): + return "AGGREGATE KEY" + else: + return None + except Exception as e: # pylint: disable=broad-except + _logger.debug("Failed to get table model: %s", e) + return None + + +async def _create_database_if_not_exists(spec: DorisTarget, database: str) -> None: + """Create database if it doesn't exist.""" + _validate_identifier(database) + # Create a spec with no database to execute CREATE DATABASE + temp_spec = dataclasses.replace(spec, database="") + await _execute_ddl(temp_spec, f"CREATE DATABASE IF NOT EXISTS {database}") + + +async def _execute_delete( + spec: DorisTarget, + key_field_names: list[str], + key_values: list[dict[str, Any]], +) -> int: + """ + Execute DELETE via SQL for DUPLICATE KEY tables. + + Stream Load DELETE requires UNIQUE KEY model, but Doris 4.0 vector indexes + only support DUPLICATE KEY model. So we use standard SQL DELETE instead. + + Uses parameterized queries via aiomysql for safe value escaping. + For single keys, uses efficient IN clause. + For composite keys, deletes one row at a time (Doris doesn't support + OR with AND predicates in DELETE WHERE clause). + + Args: + spec: Doris connection spec + key_field_names: Names of the key columns + key_values: List of key dictionaries to delete + + Returns: + Number of rows deleted + """ + if not key_values: + return 0 + + try: + import aiomysql # type: ignore + except ImportError: + raise ImportError( + "aiomysql is required for Doris delete operations. " + "Install it with: pip install aiomysql" + ) + + # Validate identifiers to prevent SQL injection (values are parameterized) + _validate_identifier(spec.database) + _validate_identifier(spec.table) + for field_name in key_field_names: + _validate_identifier(field_name) + + total_deleted = 0 + is_composite_key = len(key_field_names) > 1 + + retry_config = RetryConfig( + max_retries=spec.max_retries, + base_delay=spec.retry_base_delay, + max_delay=spec.retry_max_delay, + ) + + if not is_composite_key: + # Single key column - use efficient IN clause for batch delete + async def do_single_key_delete() -> int: + conn = await aiomysql.connect( + host=spec.fe_host, + port=spec.query_port, + user=spec.username, + password=spec.password, + db=spec.database, + autocommit=True, + ) + try: + async with conn.cursor() as cursor: + field_name = key_field_names[0] + placeholders = ", ".join(["%s"] * len(key_values)) + sql = f"DELETE FROM `{spec.database}`.`{spec.table}` WHERE `{field_name}` IN ({placeholders})" + params = tuple(kv.get(field_name) for kv in key_values) + _logger.debug( + "Executing batched DELETE: %s with %d keys", + sql[:100], + len(key_values), + ) + await cursor.execute(sql, params) + return int(cursor.rowcount) if cursor.rowcount else 0 + finally: + conn.close() + await conn.ensure_closed() + + total_deleted = await with_retry( + do_single_key_delete, + config=retry_config, + operation_name="SQL DELETE", + ) + else: + # Composite key - Doris DELETE doesn't support OR with AND predicates, + # so we must delete one row at a time. We reuse a single connection + # for efficiency and wrap the whole batch in retry logic. + condition_parts = [f"`{name}` = %s" for name in key_field_names] + sql_template = ( + f"DELETE FROM `{spec.database}`.`{spec.table}` " + f"WHERE {' AND '.join(condition_parts)}" + ) + + # Prepare all params upfront + all_params = [ + tuple(kv.get(name) for name in key_field_names) for kv in key_values + ] + + async def do_composite_deletes() -> int: + """Execute all composite key deletes using a single connection.""" + conn = await aiomysql.connect( + host=spec.fe_host, + port=spec.query_port, + user=spec.username, + password=spec.password, + db=spec.database, + autocommit=True, + ) + try: + deleted_count = 0 + async with conn.cursor() as cursor: + for params in all_params: + _logger.debug("Executing DELETE for composite key: %s", params) + await cursor.execute(sql_template, params) + deleted_count += int(cursor.rowcount) if cursor.rowcount else 0 + return deleted_count + finally: + conn.close() + await conn.ensure_closed() + + # Retry the entire batch - DELETE is idempotent so safe to retry + total_deleted = await with_retry( + do_composite_deletes, + config=retry_config, + operation_name=f"SQL DELETE (composite keys, {len(all_params)} rows)", + ) + + return total_deleted + + +async def _wait_for_schema_change( + spec: DorisTarget, + key: "_TableKey", + timeout: int = 60, +) -> bool: + """ + Wait for ALTER TABLE schema changes to complete. + + Doris tables go through SCHEMA_CHANGE state during DDL operations. + We need to wait for the table to return to NORMAL before issuing + another DDL command. + + Returns True if schema change completed successfully. + Raises DorisSchemaError if the schema change was cancelled/failed. + Returns False on timeout. + """ + start_time = time.time() + poll_interval = 1.0 + + while time.time() - start_time < timeout: + try: + result = await _execute_ddl( + spec, + f"SHOW ALTER TABLE COLUMN FROM `{key.database}` " + f"WHERE TableName = '{key.table}' ORDER BY CreateTime DESC LIMIT 1", + ) + if not result: + # No ongoing ALTER operations + return True + + state = result[0].get("State", "FINISHED") + if state == "FINISHED": + return True + elif state == "CANCELLED": + msg = result[0].get("Msg", "Unknown reason") + raise DorisSchemaError( + f"Schema change on table {key.table} was cancelled: {msg}" + ) + + _logger.debug("Waiting for schema change on %s: %s", key.table, state) + except DorisSchemaError: + raise + except Exception as e: # pylint: disable=broad-except + _logger.debug("Error checking schema change state: %s", e) + + await asyncio.sleep(poll_interval) + + _logger.warning("Timeout waiting for schema change on table %s", key.table) + return False + + +async def _wait_for_index_build( + spec: DorisTarget, + key: "_TableKey", + index_name: str, + timeout: int = 300, +) -> bool: + """ + Wait for BUILD INDEX to complete using SHOW BUILD INDEX. + + Index builds can take significant time on large tables. + This properly monitors the index build progress. + + Returns True if build completed successfully. + Raises DorisSchemaError if the build was cancelled/failed. + Returns False on timeout. + """ + start_time = time.time() + poll_interval = 2.0 + + while time.time() - start_time < timeout: + try: + # SHOW BUILD INDEX shows the status of index build jobs + result = await _execute_ddl( + spec, + f"SHOW BUILD INDEX FROM `{key.database}` " + f"WHERE TableName = '{key.table}' ORDER BY CreateTime DESC LIMIT 5", + ) + + if not result: + # No build jobs found - might have completed quickly + return True + + # Check if any build job for our index is still running + for row in result: + # Check if this is our index (exact match to avoid idx_emb matching idx_emb_v2) + row_index_name = str(row.get("IndexName", "")).strip() + if row_index_name == index_name: + state = row.get("State", "FINISHED") + if state == "FINISHED": + return True + elif state in ("CANCELLED", "FAILED"): + msg = row.get("Msg", "Unknown reason") + raise DorisSchemaError( + f"Index build {index_name} failed with state {state}: {msg}" + ) + else: + _logger.debug("Index build %s state: %s", index_name, state) + break + else: + # No matching index found in results, assume completed + return True + + except DorisSchemaError: + raise + except Exception as e: # pylint: disable=broad-except + _logger.debug("Error checking index build state: %s", e) + + await asyncio.sleep(poll_interval) + + _logger.warning( + "Timeout waiting for index build %s on table %s", index_name, key.table + ) + return False + + +async def _sync_indexes( + spec: DorisTarget, + key: "_TableKey", + previous: "_State | None", + current: "_State", + actual_schema: dict[str, "_ColumnInfo"] | None = None, +) -> None: + """ + Synchronize indexes when table already exists. + + Handles adding/removing vector and inverted indexes. + Waits for schema changes and index builds to complete before proceeding. + + Args: + spec: Doris target specification + key: Table identifier + previous: Previous state (may be None) + current: Current state + actual_schema: Actual table schema from database (for validation in extend mode) + """ + # Determine which indexes to drop and which to add + prev_vec_idx = { + idx.name: idx for idx in (previous.vector_indexes if previous else []) or [] + } + curr_vec_idx = {idx.name: idx for idx in (current.vector_indexes or [])} + + prev_inv_idx = { + idx.name: idx for idx in (previous.inverted_indexes if previous else []) or [] + } + curr_inv_idx = {idx.name: idx for idx in (current.inverted_indexes or [])} + + # Find indexes to drop (in previous but not in current, or changed) + vec_to_drop = set(prev_vec_idx.keys()) - set(curr_vec_idx.keys()) + inv_to_drop = set(prev_inv_idx.keys()) - set(curr_inv_idx.keys()) + + # Also drop if index definition changed + for name in set(prev_vec_idx.keys()) & set(curr_vec_idx.keys()): + if prev_vec_idx[name] != curr_vec_idx[name]: + vec_to_drop.add(name) + + for name in set(prev_inv_idx.keys()) & set(curr_inv_idx.keys()): + if prev_inv_idx[name] != curr_inv_idx[name]: + inv_to_drop.add(name) + + # Find indexes to add (in current but not in previous, or changed) + vec_to_add = set(curr_vec_idx.keys()) - set(prev_vec_idx.keys()) + inv_to_add = set(curr_inv_idx.keys()) - set(prev_inv_idx.keys()) + + # Also add if index definition changed + for name in set(prev_vec_idx.keys()) & set(curr_vec_idx.keys()): + if prev_vec_idx[name] != curr_vec_idx[name]: + vec_to_add.add(name) + + for name in set(prev_inv_idx.keys()) & set(curr_inv_idx.keys()): + if prev_inv_idx[name] != curr_inv_idx[name]: + inv_to_add.add(name) + + # Drop old indexes + dropped_any = False + for idx_name in vec_to_drop | inv_to_drop: + try: + await _execute_ddl( + spec, + f"DROP INDEX `{idx_name}` ON `{key.database}`.`{key.table}`", + ) + _logger.info("Dropped index %s", idx_name) + dropped_any = True + except Exception as e: # pylint: disable=broad-except + _logger.warning("Failed to drop index %s: %s", idx_name, e) + + # Wait for schema change to complete before creating new indexes + if dropped_any and (vec_to_add or inv_to_add): + if not await _wait_for_schema_change( + spec, key, timeout=spec.schema_change_timeout + ): + raise DorisSchemaError( + f"Timeout waiting for DROP INDEX to complete on table {key.table}" + ) + + # Add new vector indexes with column validation + for idx_name in vec_to_add: + idx = curr_vec_idx[idx_name] + + # Validate column compatibility if actual schema is provided + if actual_schema is not None: + _validate_vector_index_column(idx, actual_schema) + + try: + # Wait for any pending schema changes before CREATE INDEX + if not await _wait_for_schema_change( + spec, key, timeout=spec.schema_change_timeout + ): + raise DorisSchemaError( + f"Timeout waiting for schema change before creating index {idx_name}" + ) + + # Create vector index + props = _build_vector_index_properties(idx) + await _execute_ddl( + spec, + f"CREATE INDEX `{idx.name}` ON `{key.database}`.`{key.table}` (`{idx.field_name}`) " + f"USING ANN PROPERTIES ({', '.join(props)})", + ) + + # Build index and wait for completion + await _execute_ddl( + spec, + f"BUILD INDEX `{idx.name}` ON `{key.database}`.`{key.table}`", + ) + + # Wait for index build to complete using SHOW BUILD INDEX + if not await _wait_for_index_build( + spec, key, idx.name, timeout=spec.index_build_timeout + ): + raise DorisSchemaError( + f"Timeout waiting for index build {idx.name} to complete" + ) + + _logger.info("Created and built vector index %s", idx.name) + except DorisSchemaError: + raise + except Exception as e: # pylint: disable=broad-except + _logger.warning("Failed to create vector index %s: %s", idx.name, e) + + # Add new inverted indexes with column validation + for idx_name in inv_to_add: + inv_idx = curr_inv_idx[idx_name] + + # Validate column compatibility if actual schema is provided + if actual_schema is not None: + _validate_inverted_index_column(inv_idx, actual_schema) + + try: + # Wait for any pending schema changes before CREATE INDEX + if not await _wait_for_schema_change( + spec, key, timeout=spec.schema_change_timeout + ): + raise DorisSchemaError( + f"Timeout waiting for schema change before creating index {idx_name}" + ) + + if inv_idx.parser: + await _execute_ddl( + spec, + f"CREATE INDEX `{inv_idx.name}` ON `{key.database}`.`{key.table}` (`{inv_idx.field_name}`) " + f'USING INVERTED PROPERTIES ("parser" = "{inv_idx.parser}")', + ) + else: + await _execute_ddl( + spec, + f"CREATE INDEX `{inv_idx.name}` ON `{key.database}`.`{key.table}` (`{inv_idx.field_name}`) " + f"USING INVERTED", + ) + _logger.info("Created inverted index %s", inv_idx.name) + except DorisSchemaError: + raise + except Exception as e: # pylint: disable=broad-except + _logger.warning("Failed to create inverted index %s: %s", inv_idx.name, e) + + +def _validate_vector_index_column( + idx: "_VectorIndex", actual_schema: dict[str, "_ColumnInfo"] +) -> None: + """Validate that a column is compatible with a vector index. + + Raises DorisSchemaError if the column is missing or incompatible. + """ + # Check 1: Column must exist + if idx.field_name not in actual_schema: + raise DorisSchemaError( + f"Cannot create vector index '{idx.name}': " + f"column '{idx.field_name}' does not exist in table. " + f"Available columns: {list(actual_schema.keys())}" + ) + + col = actual_schema[idx.field_name] + + # Check 2: Must be ARRAY type + if not col.doris_type.upper().startswith("ARRAY"): + raise DorisSchemaError( + f"Cannot create vector index '{idx.name}': " + f"column '{idx.field_name}' has type '{col.doris_type}', " + f"expected ARRAY. Vector indexes require array types." + ) + + # Check 3: Must be NOT NULL + if col.nullable: + raise DorisSchemaError( + f"Cannot create vector index '{idx.name}': " + f"column '{idx.field_name}' is nullable. " + f"Vector indexes require NOT NULL columns. " + f"Use ALTER TABLE to set the column to NOT NULL." + ) + + # Check 4: Dimension must match (if we know it) + if col.dimension is not None and col.dimension != idx.dimension: + raise DorisSchemaError( + f"Cannot create vector index '{idx.name}': " + f"dimension mismatch - column has {col.dimension} dimensions, " + f"but index expects {idx.dimension} dimensions." + ) + + +def _validate_inverted_index_column( + idx: "_InvertedIndex", actual_schema: dict[str, "_ColumnInfo"] +) -> None: + """Validate that a column is compatible with an inverted index. + + Raises DorisSchemaError if the column is missing or incompatible. + """ + # Check 1: Column must exist + if idx.field_name not in actual_schema: + raise DorisSchemaError( + f"Cannot create inverted index '{idx.name}': " + f"column '{idx.field_name}' does not exist in table. " + f"Available columns: {list(actual_schema.keys())}" + ) + + col = actual_schema[idx.field_name] + + # Check 2: Must be a text-compatible type + text_types = {"TEXT", "STRING", "VARCHAR", "CHAR"} + col_type_upper = col.doris_type.upper() + is_text_type = any(col_type_upper.startswith(t) for t in text_types) + + if not is_text_type: + raise DorisSchemaError( + f"Cannot create inverted index '{idx.name}': " + f"column '{idx.field_name}' has type '{col.doris_type}', " + f"expected TEXT, VARCHAR, or STRING. " + f"Inverted indexes require text-compatible column types." + ) + + +# ============================================================ +# CONNECTOR IMPLEMENTATION +# ============================================================ + + +@op.target_connector( + spec_cls=DorisTarget, persistent_key_type=_TableKey, setup_state_cls=_State +) +class _Connector: + @staticmethod + def get_persistent_key(spec: DorisTarget) -> _TableKey: + return _TableKey( + fe_host=spec.fe_host, + database=spec.database, + table=spec.table, + ) + + @staticmethod + def get_setup_state( + spec: DorisTarget, + key_fields_schema: list[FieldSchema], + value_fields_schema: list[FieldSchema], + index_options: IndexOptions, + ) -> _State: + if len(key_fields_schema) == 0: + raise ValueError("Doris requires at least one key field") + + # Extract vector indexes + vector_indexes: list[_VectorIndex] | None = None + if index_options.vector_indexes: + vector_indexes = [] + for idx in index_options.vector_indexes: + metric_type = _get_doris_metric_type(idx.metric) + # Skip cosine similarity as it doesn't support index + if metric_type == "cosine_distance": + continue + + dimension = _get_vector_dimension(value_fields_schema, idx.field_name) + + # Skip vector index if dimension is not available + # The field will be stored as JSON instead + if dimension is None: + _logger.warning( + "Field '%s' does not have a fixed vector dimension. " + "It will be stored as JSON and vector index will not be created. " + "Only vectors with fixed dimensions support ARRAY storage " + "and vector indexing in Doris.", + idx.field_name, + ) + continue + + # Determine index type and parameters from method + index_type = "hnsw" # Default to HNSW + max_degree: int | None = None + ef_construction: int | None = None + nlist: int | None = None + + if idx.method is not None: + if isinstance(idx.method, HnswVectorIndexMethod): + index_type = "hnsw" + # m in HNSW corresponds to max_degree in Doris + max_degree = idx.method.m + ef_construction = idx.method.ef_construction + elif isinstance(idx.method, IvfFlatVectorIndexMethod): + index_type = "ivf" + # lists in IVFFlat corresponds to nlist in Doris + nlist = idx.method.lists + + vector_indexes.append( + _VectorIndex( + name=f"idx_{idx.field_name}_ann", + field_name=idx.field_name, + index_type=index_type, + metric_type=metric_type, + dimension=dimension, + max_degree=max_degree, + ef_construction=ef_construction, + nlist=nlist, + ) + ) + if not vector_indexes: + vector_indexes = None + + # Extract FTS indexes + inverted_indexes: list[_InvertedIndex] | None = None + if index_options.fts_indexes: + inverted_indexes = [ + _InvertedIndex( + name=f"idx_{idx.field_name}_inv", + field_name=idx.field_name, + parser=idx.parameters.get("parser") if idx.parameters else None, + ) + for idx in index_options.fts_indexes + ] + + return _State( + key_fields_schema=key_fields_schema, + value_fields_schema=value_fields_schema, + vector_indexes=vector_indexes, + inverted_indexes=inverted_indexes, + replication_num=spec.replication_num, + buckets=spec.buckets, + # Store connection credentials for apply_setup_change + fe_http_port=spec.fe_http_port, + query_port=spec.query_port, + username=spec.username, + password=spec.password, + max_retries=spec.max_retries, + retry_base_delay=spec.retry_base_delay, + retry_max_delay=spec.retry_max_delay, + schema_change_timeout=spec.schema_change_timeout, + index_build_timeout=spec.index_build_timeout, + auto_create_table=spec.auto_create_table, + schema_evolution=spec.schema_evolution, + ) + + @staticmethod + def describe(key: _TableKey) -> str: + return f"Doris table {key.database}.{key.table}@{key.fe_host}" + + @staticmethod + def check_state_compatibility( + previous: _State, current: _State + ) -> op.TargetStateCompatibility: + # Key schema change → always incompatible (requires table recreation) + if previous.key_fields_schema != current.key_fields_schema: + return op.TargetStateCompatibility.NOT_COMPATIBLE + + # Check schema evolution mode + is_extend_mode = current.schema_evolution == "extend" + + # Value schema: check for removed columns + prev_field_names = {f.name for f in previous.value_fields_schema} + curr_field_names = {f.name for f in current.value_fields_schema} + + # Columns removed from schema (in previous but not in current) + removed_columns = prev_field_names - curr_field_names + if removed_columns: + if is_extend_mode: + # In extend mode: columns removed from schema are OK + # (we'll keep them in DB, just won't manage them) + _logger.info( + "Extend mode: columns removed from schema will be kept in DB: %s", + removed_columns, + ) + else: + # In strict mode: removing columns is incompatible + return op.TargetStateCompatibility.NOT_COMPATIBLE + + # Check type changes for columns that exist in BOTH schemas + prev_fields = {f.name: f for f in previous.value_fields_schema} + for field in current.value_fields_schema: + if field.name in prev_fields: + # Type changes are always incompatible (can't ALTER column type) + if prev_fields[field.name].value_type.type != field.value_type.type: + return op.TargetStateCompatibility.NOT_COMPATIBLE + + # Index changes (vector or inverted) don't require table recreation. + # They are handled in apply_setup_change via _sync_indexes(). + + return op.TargetStateCompatibility.COMPATIBLE + + @staticmethod + async def apply_setup_change( + key: _TableKey, previous: _State | None, current: _State | None + ) -> None: + if current is None and previous is None: + return + + # Get a spec for DDL execution - use current or previous state + state = current or previous + if state is None: + return + + is_extend_mode = state.schema_evolution == "extend" + + # Create a spec for DDL execution with credentials from state + spec = DorisTarget( + fe_host=key.fe_host, + database=key.database, + table=key.table, + fe_http_port=state.fe_http_port, + query_port=state.query_port, + username=state.username, + password=state.password, + max_retries=state.max_retries, + retry_base_delay=state.retry_base_delay, + retry_max_delay=state.retry_max_delay, + ) + + # Handle target removal + if current is None: + # In extend mode, we don't drop tables on target removal + if not is_extend_mode: + try: + await _execute_ddl( + spec, f"DROP TABLE IF EXISTS `{key.database}`.`{key.table}`" + ) + except Exception as e: # pylint: disable=broad-except + _logger.warning("Failed to drop table: %s", e) + return + + # Check if we need to drop and recreate (key schema change) + key_schema_changed = ( + previous is not None + and previous.key_fields_schema != current.key_fields_schema + ) + + if key_schema_changed: + # Key schema change always requires table recreation + try: + await _execute_ddl( + spec, f"DROP TABLE IF EXISTS `{key.database}`.`{key.table}`" + ) + except Exception as e: # pylint: disable=broad-except + _logger.warning("Failed to drop table: %s", e) + + # Create database if not exists (only if auto_create_table is enabled) + if current.auto_create_table: + await _create_database_if_not_exists(spec, key.database) + + # Query actual table schema from database + actual_schema = await _get_table_schema(spec, key.database, key.table) + + if actual_schema is None: + # Table doesn't exist - create it + if not current.auto_create_table: + raise DorisSchemaError( + f"Table {key.database}.{key.table} does not exist and " + f"auto_create_table is disabled" + ) + + ddl = _generate_create_table_ddl(key, current) + _logger.info("Creating table with DDL:\n%s", ddl) + await _execute_ddl(spec, ddl) + + # Build vector indexes (async operation in Doris) + for idx in current.vector_indexes or []: + try: + await _execute_ddl( + spec, + f"BUILD INDEX {idx.name} ON `{key.database}`.`{key.table}`", + ) + except Exception as e: # pylint: disable=broad-except + _logger.warning("Failed to build index %s: %s", idx.name, e) + return + + # Table exists - validate table model first + # Vector indexes require DUPLICATE KEY model in Doris 4.0+ + table_model = await _get_table_model(spec, key.database, key.table) + if table_model and table_model != "DUPLICATE KEY": + raise DorisSchemaError( + f"Table {key.database}.{key.table} uses {table_model} model, " + f"but Doris requires DUPLICATE KEY model for vector index support. " + f"Please drop the table and recreate it with DUPLICATE KEY model." + ) + + # Validate key columns + desired_key_names = {f.name for f in current.key_fields_schema} + actual_key_names = {name for name, col in actual_schema.items() if col.is_key} + + # Check key column mismatch + if desired_key_names != actual_key_names: + raise DorisSchemaError( + f"Key column mismatch for table {key.database}.{key.table}: " + f"expected keys {sorted(desired_key_names)}, " + f"but table has keys {sorted(actual_key_names)}. " + f"To fix this, either update the schema to match or drop the table." + ) + + # Validate key column types + for field in current.key_fields_schema: + if field.name in actual_schema: + expected_type = _convert_value_type_to_doris_type(field.value_type) + expected_type = _convert_to_key_column_type(expected_type) + actual_type = actual_schema[field.name].doris_type + if not _types_compatible(expected_type, actual_type): + raise DorisSchemaError( + f"Key column '{field.name}' type mismatch: " + f"expected '{expected_type}', but table has '{actual_type}'" + ) + + # Now handle value columns based on schema evolution mode + actual_columns = set(actual_schema.keys()) + desired_columns = { + f.name for f in current.key_fields_schema + current.value_fields_schema + } + + # Check extra columns in DB + extra_columns = actual_columns - desired_columns + if extra_columns: + if is_extend_mode: + _logger.info( + "Extend mode: keeping extra columns in DB not in schema: %s", + extra_columns, + ) + else: + # Strict mode: extra columns are not allowed + raise DorisSchemaError( + f"Strict mode: table {key.database}.{key.table} has extra columns " + f"not in schema: {sorted(extra_columns)}. " + f"Either add these columns to the schema or drop the table." + ) + + # Add missing columns (only value columns can be added) + missing_columns = desired_columns - actual_columns + missing_key_columns = missing_columns & desired_key_names + if missing_key_columns: + raise DorisSchemaError( + f"Table {key.database}.{key.table} is missing key columns: " + f"{sorted(missing_key_columns)}. Key columns cannot be added via ALTER TABLE." + ) + + for field in current.value_fields_schema: + if field.name in missing_columns: + _validate_identifier(field.name) + doris_type = _convert_value_type_to_doris_type(field.value_type) + base_type = field.value_type.type + + # Determine nullable and default value for ALTER TABLE + # When adding columns to existing tables, NOT NULL columns need defaults + default_clause = "" + if isinstance(base_type, BasicValueType) and base_type.kind == "Vector": + # Vector columns must be NOT NULL for index creation + # Use empty array as default for existing rows + nullable = "NOT NULL" + default_clause = " DEFAULT '[]'" + _logger.warning( + "Adding vector column %s with empty default. " + "Existing rows will have empty vectors until data is populated.", + field.name, + ) + elif field.value_type.nullable: + nullable = "NULL" + else: + # NOT NULL columns need DEFAULT for existing rows + nullable = "NOT NULL" + # Set appropriate defaults based on type + if doris_type in ("TEXT", "STRING"): + default_clause = " DEFAULT ''" + elif doris_type == "BIGINT": + default_clause = " DEFAULT 0" + elif doris_type in ("FLOAT", "DOUBLE"): + default_clause = " DEFAULT 0.0" + elif doris_type == "BOOLEAN": + default_clause = " DEFAULT FALSE" + elif doris_type == "JSON": + default_clause = " DEFAULT '{}'" + else: + # For complex types, use NULL instead + nullable = "NULL" + + try: + await _execute_ddl( + spec, + f"ALTER TABLE `{key.database}`.`{key.table}` " + f"ADD COLUMN `{field.name}` {doris_type} {nullable}{default_clause}", + ) + _logger.info("Added column %s to table %s", field.name, key.table) + + # Wait for schema change to complete before proceeding + # Doris tables go through SCHEMA_CHANGE state during ALTER TABLE + # and reject writes to newly added columns until complete + await _wait_for_schema_change( + spec, key, timeout=spec.schema_change_timeout + ) + + # Update actual_schema with the new column + actual_schema[field.name] = _ColumnInfo( + name=field.name, + doris_type=doris_type, + nullable=nullable == "NULL", + is_key=False, + dimension=_extract_vector_dimension(field.value_type), + ) + except Exception as e: # pylint: disable=broad-except + _logger.warning("Failed to add column %s: %s", field.name, e) + + # Verify type compatibility for existing columns + for field in current.value_fields_schema: + if field.name in actual_schema and field.name not in missing_columns: + expected_type = _convert_value_type_to_doris_type(field.value_type) + actual_type = actual_schema[field.name].doris_type + # Normalize types for comparison (basic check) + if not _types_compatible(expected_type, actual_type): + raise DorisSchemaError( + f"Column '{field.name}' type mismatch: " + f"DB has '{actual_type}', schema expects '{expected_type}'" + ) + + # Handle index changes with actual schema validation + await _sync_indexes(spec, key, previous, current, actual_schema) + + @staticmethod + async def prepare( + spec: DorisTarget, + setup_state: _State, + ) -> _MutateContext: + aiohttp = _get_aiohttp() + session = aiohttp.ClientSession( + auth=aiohttp.BasicAuth(spec.username, spec.password), + ) + return _MutateContext( + spec=spec, + session=session, + state=setup_state, + lock=asyncio.Lock(), + ) + + @staticmethod + async def mutate( + *all_mutations: tuple[_MutateContext, dict[Any, dict[str, Any] | None]], + ) -> None: + for context, mutations in all_mutations: + upserts: list[dict[str, Any]] = [] + deletes: list[dict[str, Any]] = [] + + key_field_names = [f.name for f in context.state.key_fields_schema] + + for key, value in mutations.items(): + # Build key dict + if isinstance(key, tuple): + key_dict = { + name: _convert_value_for_doris(k) + for name, k in zip(key_field_names, key) + } + else: + key_dict = {key_field_names[0]: _convert_value_for_doris(key)} + + if value is None: + deletes.append(key_dict) + else: + # Build full row + row = {**key_dict} + for field in context.state.value_fields_schema: + if field.name in value: + row[field.name] = _convert_value_for_doris( + value[field.name] + ) + upserts.append(row) + + async with context.lock: + # For DUPLICATE KEY tables, we must delete existing rows before inserting + # to prevent accumulating duplicate rows with the same key. + # This ensures idempotent upsert behavior. + # + # WARNING: This is NOT atomic. If stream load fails after deletes, + # the deleted rows are lost. Use smaller batch_size to minimize risk. + if upserts: + # Extract keys from upserts for deletion + upsert_keys = [ + {name: row[name] for name in key_field_names} for row in upserts + ] + + # Delete existing rows first + deleted_count = 0 + for i in range(0, len(upsert_keys), context.spec.batch_size): + batch = upsert_keys[i : i + context.spec.batch_size] + deleted_count += await _execute_delete( + context.spec, key_field_names, batch + ) + + # Process inserts in batches via Stream Load + # If this fails, deleted rows cannot be recovered + try: + for i in range(0, len(upserts), context.spec.batch_size): + batch = upserts[i : i + context.spec.batch_size] + await _stream_load(context.session, context.spec, batch) + except Exception as e: + if deleted_count > 0: + _logger.error( + "Stream Load failed after deleting %d rows. " + "Data loss may have occurred. Error: %s", + deleted_count, + e, + ) + raise + + # Process explicit deletes in batches via SQL DELETE + for i in range(0, len(deletes), context.spec.batch_size): + batch = deletes[i : i + context.spec.batch_size] + await _execute_delete(context.spec, key_field_names, batch) + + @staticmethod + async def cleanup(context: _MutateContext) -> None: + """Clean up resources used by the mutation context. + + This closes the aiohttp session that was created in prepare(). + """ + if context.session is not None: + await context.session.close() + + +# ============================================================ +# PUBLIC HELPERS +# ============================================================ + + +async def connect_async( + fe_host: str, + query_port: int = 9030, + username: str = "root", + password: str = "", + database: str | None = None, +) -> Any: + """ + Helper function to connect to a Doris database via MySQL protocol. + Returns an aiomysql connection for query operations. + + Usage: + conn = await connect_async("localhost", database="my_db") + try: + async with conn.cursor() as cursor: + await cursor.execute("SELECT * FROM my_table LIMIT 10") + rows = await cursor.fetchall() + finally: + conn.close() + await conn.ensure_closed() + """ + try: + import aiomysql # type: ignore + except ImportError: + raise ImportError( + "aiomysql is required for Doris connections. " + "Install it with: pip install aiomysql" + ) + + return await aiomysql.connect( + host=fe_host, + port=query_port, + user=username, + password=password, + db=database, + autocommit=True, + ) + + +def build_vector_search_query( + table: str, + vector_field: str, + query_vector: list[float], + metric: str = "l2_distance", + limit: int = 10, + select_columns: list[str] | None = None, + where_clause: str | None = None, +) -> str: + """ + Build a vector search query for Doris. + + Args: + table: Table name (database.table format supported). Names are + validated and quoted with backticks to prevent SQL injection. + vector_field: Name of the vector column (validated and quoted) + query_vector: Query vector as a list of floats + metric: Distance metric ("l2_distance" or "inner_product") + limit: Number of results to return (must be positive integer) + select_columns: Columns to select (validated and quoted, default: all) + where_clause: Optional WHERE clause for filtering. + WARNING: This is NOT escaped. Caller must ensure proper + escaping of any user input to prevent SQL injection. + + Returns: + SQL query string + + Raises: + ValueError: If table, vector_field, or select_columns contain + invalid characters that could indicate SQL injection. + + Note: + Uses _approximate suffix for functions to leverage vector index. + """ + # Validate and quote table name (supports database.table format) + table_parts = table.split(".") + if len(table_parts) == 2: + _validate_identifier(table_parts[0]) + _validate_identifier(table_parts[1]) + quoted_table = f"`{table_parts[0]}`.`{table_parts[1]}`" + elif len(table_parts) == 1: + _validate_identifier(table) + quoted_table = f"`{table}`" + else: + raise ValueError(f"Invalid table name format: {table}") + + # Validate and quote vector field + _validate_identifier(vector_field) + quoted_vector_field = f"`{vector_field}`" + + # Validate limit + if not isinstance(limit, int) or limit <= 0: + raise ValueError(f"limit must be a positive integer, got: {limit}") + + # Use approximate functions to leverage index + if metric == "l2_distance": + distance_fn = "l2_distance_approximate" + order = "ASC" # Smaller distance = more similar + elif metric == "inner_product": + distance_fn = "inner_product_approximate" + order = "DESC" # Larger product = more similar + else: + # Validate metric for safety + if not metric.isidentifier(): + raise ValueError(f"Invalid metric name: {metric}") + distance_fn = metric + order = "ASC" if "distance" in metric else "DESC" + + # Format vector as array literal (safe - only floats) + vector_literal = "[" + ", ".join(str(float(v)) for v in query_vector) + "]" + + # Build SELECT clause + if select_columns: + # Validate and quote each column + quoted_columns = [] + for col in select_columns: + _validate_identifier(col) + quoted_columns.append(f"`{col}`") + select = ", ".join(quoted_columns) + else: + select = "*" + + # Build query + query = f"""SELECT {select}, {distance_fn}({quoted_vector_field}, {vector_literal}) as _distance +FROM {quoted_table}""" + + if where_clause: + # WARNING: where_clause is NOT escaped + query += f"\nWHERE {where_clause}" + + query += f"\nORDER BY _distance {order}\nLIMIT {limit}" + + return query diff --git a/python/cocoindex/tests/targets/__init__.py b/python/cocoindex/tests/targets/__init__.py new file mode 100644 index 000000000..cb3e68dd3 --- /dev/null +++ b/python/cocoindex/tests/targets/__init__.py @@ -0,0 +1 @@ +# Tests for target connectors diff --git a/python/cocoindex/tests/targets/test_doris_integration.py b/python/cocoindex/tests/targets/test_doris_integration.py new file mode 100644 index 000000000..873b46a49 --- /dev/null +++ b/python/cocoindex/tests/targets/test_doris_integration.py @@ -0,0 +1,3226 @@ +# mypy: disable-error-code="no-untyped-def" +""" +Integration tests for Doris connector with VeloDB Cloud. + +Run with: pytest python/cocoindex/tests/targets/test_doris_integration.py -v + +Environment variables for custom Doris setup: +- DORIS_FE_HOST: FE host (required) +- DORIS_PASSWORD: Password (required) +- DORIS_HTTP_PORT: HTTP port for Stream Load (default: 8080) +- DORIS_QUERY_PORT: MySQL protocol port (default: 9030) +- DORIS_USERNAME: Username (default: root) +- DORIS_DATABASE: Test database (default: cocoindex_test) +- DORIS_ASYNC_TIMEOUT: Timeout in seconds for async operations like index changes (default: 15) +""" + +import asyncio +import os +import time +import uuid +import pytest +from typing import Any, Generator, Literal + +# Skip all tests if dependencies not available +try: + import aiohttp + import aiomysql # type: ignore[import-untyped] # noqa: F401 + + DEPS_AVAILABLE = True +except ImportError: + DEPS_AVAILABLE = False + +from cocoindex.targets.doris import ( + DorisTarget, + _Connector, + _TableKey, + _State, + _VectorIndex, + _InvertedIndex, + _stream_load, + _execute_ddl, + _table_exists, + _generate_create_table_ddl, + connect_async, + build_vector_search_query, + DorisConnectionError, + RetryConfig, + with_retry, +) +from cocoindex.engine_type import ( + FieldSchema, + EnrichedValueType, + BasicValueType, + VectorTypeSchema, +) +from cocoindex import op +from cocoindex.index import IndexOptions + +# ============================================================ +# TEST CONFIGURATION +# ============================================================ + +# All configuration via environment variables - no defaults for security +# Required env vars: +# DORIS_FE_HOST - FE host address +# DORIS_PASSWORD - Password for authentication +# Optional env vars: +# DORIS_HTTP_PORT - HTTP port (default: 8080) +# DORIS_QUERY_PORT - MySQL port (default: 9030) +# DORIS_USERNAME - Username (default: root) +# DORIS_DATABASE - Test database (default: cocoindex_test) +# DORIS_ASYNC_TIMEOUT - Timeout for async operations (default: 15) + +# Timeout for Doris async operations (index creation/removal, schema changes) +ASYNC_OPERATION_TIMEOUT = int(os.getenv("DORIS_ASYNC_TIMEOUT", "15")) + + +def get_test_config() -> dict[str, Any] | None: + """Get test configuration from environment variables. + + Returns None if required env vars are not set. + """ + fe_host = os.getenv("DORIS_FE_HOST") + password = os.getenv("DORIS_PASSWORD") + + # Required env vars + if not fe_host or not password: + return None + + return { + "fe_host": fe_host, + "fe_http_port": int(os.getenv("DORIS_HTTP_PORT", "8080")), + "query_port": int(os.getenv("DORIS_QUERY_PORT", "9030")), + "username": os.getenv("DORIS_USERNAME", "root"), + "password": password, + "database": os.getenv("DORIS_DATABASE", "cocoindex_test"), + } + + +# Check if Doris is configured +_TEST_CONFIG = get_test_config() +DORIS_CONFIGURED = _TEST_CONFIG is not None + +# Skip tests if deps not available or Doris not configured +pytestmark = [ + pytest.mark.skipif(not DEPS_AVAILABLE, reason="aiohttp/aiomysql not installed"), + pytest.mark.skipif( + not DORIS_CONFIGURED, + reason="Doris not configured (set DORIS_FE_HOST and DORIS_PASSWORD)", + ), + pytest.mark.integration, +] + + +# ============================================================ +# FIXTURES +# ============================================================ + + +@pytest.fixture(scope="module") +def test_config() -> dict[str, Any]: + """Test configuration.""" + assert _TEST_CONFIG is not None, "Doris not configured" + return _TEST_CONFIG + + +@pytest.fixture(scope="module") +def event_loop() -> Generator[asyncio.AbstractEventLoop, None, None]: + """Create event loop for async tests.""" + loop = asyncio.new_event_loop() + yield loop + loop.close() + + +@pytest.fixture +def unique_table_name() -> str: + """Generate unique table name for each test.""" + return f"test_{int(time.time())}_{uuid.uuid4().hex[:8]}" + + +@pytest.fixture +def doris_spec(test_config: dict[str, Any], unique_table_name: str) -> DorisTarget: + """Create DorisTarget spec for testing.""" + return DorisTarget( + fe_host=test_config["fe_host"], + fe_http_port=test_config["fe_http_port"], + query_port=test_config["query_port"], + username=test_config["username"], + password=test_config["password"], + database=test_config["database"], + table=unique_table_name, + replication_num=1, + buckets=1, # Small for testing + ) + + +@pytest.fixture +def cleanup_table( + doris_spec: DorisTarget, event_loop: asyncio.AbstractEventLoop +) -> Generator[None, None, None]: + """Cleanup table after test.""" + yield + try: + event_loop.run_until_complete( + _execute_ddl( + doris_spec, + f"DROP TABLE IF EXISTS {doris_spec.database}.{doris_spec.table}", + ) + ) + except Exception as e: + print(f"Cleanup failed: {e}") + + +@pytest.fixture +def ensure_database( + doris_spec: DorisTarget, event_loop: asyncio.AbstractEventLoop +) -> None: + """Ensure test database exists.""" + # Create a spec without database to create the database + temp_spec = DorisTarget( + fe_host=doris_spec.fe_host, + fe_http_port=doris_spec.fe_http_port, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + database="", # No database for CREATE DATABASE + table="dummy", + ) + try: + event_loop.run_until_complete( + _execute_ddl( + temp_spec, + f"CREATE DATABASE IF NOT EXISTS {doris_spec.database}", + ) + ) + except Exception as e: + print(f"Database creation failed: {e}") + + +# Type alias for BasicValueType kind +_BasicKind = Literal[ + "Bytes", + "Str", + "Bool", + "Int64", + "Float32", + "Float64", + "Range", + "Uuid", + "Date", + "Time", + "LocalDateTime", + "OffsetDateTime", + "TimeDelta", + "Json", + "Vector", + "Union", +] + + +def _mock_field( + name: str, kind: _BasicKind, nullable: bool = False, dim: int | None = None +) -> FieldSchema: + """Create mock FieldSchema for testing.""" + if kind == "Vector": + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), + dimension=dim, + ) + basic_type = BasicValueType(kind=kind, vector=vec_schema) + else: + basic_type = BasicValueType(kind=kind) + return FieldSchema( + name=name, + value_type=EnrichedValueType(type=basic_type, nullable=nullable), + ) + + +def _mock_state( + key_fields: list[str] | None = None, + value_fields: list[str] | None = None, + vector_fields: list[tuple[str, int]] | None = None, + spec: DorisTarget | None = None, + schema_evolution: str = "extend", +) -> _State: + """Create mock State for testing.""" + key_fields = key_fields or ["id"] + value_fields = value_fields or ["content"] + + key_schema = [_mock_field(f, "Int64") for f in key_fields] + value_schema = [_mock_field(f, "Str") for f in value_fields] + + if vector_fields: + for name, dim in vector_fields: + value_schema.append(_mock_field(name, "Vector", dim=dim)) + + # Use spec credentials if provided + if spec: + return _State( + key_fields_schema=key_schema, + value_fields_schema=value_schema, + fe_http_port=spec.fe_http_port, + query_port=spec.query_port, + username=spec.username, + password=spec.password, + max_retries=spec.max_retries, + retry_base_delay=spec.retry_base_delay, + retry_max_delay=spec.retry_max_delay, + schema_evolution=schema_evolution, # type: ignore[arg-type] + ) + + return _State( + key_fields_schema=key_schema, + value_fields_schema=value_schema, + schema_evolution=schema_evolution, # type: ignore[arg-type] + ) + + +# ============================================================ +# CONNECTION TESTS +# ============================================================ + + +class TestConnection: + """Test connection to VeloDB Cloud.""" + + @pytest.mark.asyncio + async def test_mysql_connection(self, doris_spec: DorisTarget): + """Test MySQL protocol connection.""" + conn = await connect_async( + fe_host=doris_spec.fe_host, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + ) + try: + async with conn.cursor() as cursor: + await cursor.execute("SELECT 1") + result = await cursor.fetchone() + assert result[0] == 1 + finally: + conn.close() + await conn.ensure_closed() + + @pytest.mark.asyncio + async def test_execute_ddl_show_databases(self, doris_spec: DorisTarget): + """Test DDL execution with SHOW DATABASES.""" + result = await _execute_ddl(doris_spec, "SHOW DATABASES") + assert isinstance(result, list) + # Should have at least some system databases + db_names = [r.get("Database") for r in result] + assert "information_schema" in db_names or len(db_names) > 0 + + @pytest.mark.asyncio + async def test_http_connection_for_stream_load( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test HTTP endpoint is reachable for Stream Load.""" + # First create a simple table + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + # Wait for table to be ready + await asyncio.sleep(2) + + # Try Stream Load with empty data + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + result = await _stream_load(session, doris_spec, []) + assert result.get("Status") == "Success" + + +# ============================================================ +# TABLE LIFECYCLE TESTS +# ============================================================ + + +class TestTableLifecycle: + """Test table creation, modification, and deletion.""" + + @pytest.mark.asyncio + async def test_create_simple_table( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test creating a simple table.""" + key = _Connector.get_persistent_key(doris_spec) + state = _mock_state(spec=doris_spec) + + # Apply setup change (create table) + await _Connector.apply_setup_change(key, None, state) + + # Verify table exists + exists = await _table_exists(doris_spec, doris_spec.database, doris_spec.table) + assert exists, "Table should exist after creation" + + @pytest.mark.asyncio + async def test_create_table_with_vector_column( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test creating table with vector column.""" + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=384), + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + + await _execute_ddl(doris_spec, ddl) + + # Verify table exists + exists = await _table_exists(doris_spec, doris_spec.database, doris_spec.table) + assert exists + + @pytest.mark.asyncio + async def test_create_table_with_vector_index( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test creating table with vector index (HNSW).""" + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=384), + ], + vector_indexes=[ + _VectorIndex( + name="idx_embedding_ann", + field_name="embedding", + index_type="hnsw", + metric_type="l2_distance", + dimension=384, + ) + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + + await _execute_ddl(doris_spec, ddl) + + # Verify table exists + exists = await _table_exists(doris_spec, doris_spec.database, doris_spec.table) + assert exists + + @pytest.mark.asyncio + async def test_create_table_with_ivf_vector_index( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test creating table with IVF vector index.""" + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=384), + ], + vector_indexes=[ + _VectorIndex( + name="idx_embedding_ivf", + field_name="embedding", + index_type="ivf", + metric_type="l2_distance", + dimension=384, + nlist=128, # IVF-specific parameter + ) + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + + # Verify DDL contains IVF-specific parameters + assert '"index_type" = "ivf"' in ddl + assert '"nlist" = "128"' in ddl + + await _execute_ddl(doris_spec, ddl) + + # Verify table exists + exists = await _table_exists(doris_spec, doris_spec.database, doris_spec.table) + assert exists + + # Verify index was created by checking SHOW CREATE TABLE + result = await _execute_ddl( + doris_spec, + f"SHOW CREATE TABLE `{doris_spec.database}`.`{doris_spec.table}`", + ) + create_stmt = result[0].get("Create Table", "") + assert "idx_embedding_ivf" in create_stmt, "IVF index should be created" + + @pytest.mark.asyncio + async def test_drop_table( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test dropping a table in strict mode. + + Note: In extend mode (default), tables are NOT dropped when target is removed. + This test uses strict mode to verify table dropping works. + """ + # Create table first - use strict mode so table will be dropped + key = _Connector.get_persistent_key(doris_spec) + state = _mock_state(spec=doris_spec, schema_evolution="strict") + await _Connector.apply_setup_change(key, None, state) + + # Verify exists + exists = await _table_exists(doris_spec, doris_spec.database, doris_spec.table) + assert exists + + # Drop table (only works in strict mode) + await _Connector.apply_setup_change(key, state, None) + + # Verify dropped + exists = await _table_exists(doris_spec, doris_spec.database, doris_spec.table) + assert not exists + + @pytest.mark.asyncio + async def test_vector_without_dimension_stored_as_json( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that vector fields without dimension are stored as JSON. + + When a vector field doesn't have a fixed dimension, it cannot be stored + as ARRAY or have a vector index. Instead, it falls back to JSON + storage, which is consistent with how other targets (Postgres, Qdrant) + handle this case. + """ + # Create a vector field without dimension + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), + dimension=None, # No dimension specified + ) + basic_type = BasicValueType(kind="Vector", vector=vec_schema) + key_fields = [_mock_field("id", "Int64")] + value_fields = [ + _mock_field("content", "Str"), + FieldSchema( + name="embedding", + value_type=EnrichedValueType(type=basic_type), + ), + ] + + state = _State( + key_fields_schema=key_fields, + value_fields_schema=value_fields, + fe_http_port=doris_spec.fe_http_port, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + vector_indexes=None, # No vector index since no dimension + ) + + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + # Verify table was created + exists = await _table_exists(doris_spec, doris_spec.database, doris_spec.table) + assert exists + + # Verify the embedding column is JSON (not ARRAY) + result = await _execute_ddl( + doris_spec, + f"SHOW CREATE TABLE `{doris_spec.database}`.`{doris_spec.table}`", + ) + create_stmt = result[0].get("Create Table", "") + # JSON columns are stored as JSON type in Doris + assert ( + "`embedding` JSON" in create_stmt + or "`embedding` json" in create_stmt.lower() + ), f"embedding should be JSON type, got: {create_stmt}" + assert "ARRAY" not in create_stmt, ( + f"embedding should NOT be ARRAY, got: {create_stmt}" + ) + + # Test that we can insert JSON data into the vector column + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + # Insert data with embedding as a JSON array + data = [ + {"id": 1, "content": "test", "embedding": [0.1, 0.2, 0.3]}, + { + "id": 2, + "content": "test2", + "embedding": [0.4, 0.5, 0.6, 0.7], + }, # Different length is OK for JSON + ] + load_result = await _stream_load(session, doris_spec, data) + assert load_result.get("Status") == "Success", ( + f"Stream Load failed: {load_result}" + ) + + # Verify data was inserted + await asyncio.sleep(2) # Wait for data to be visible + query_result = await _execute_ddl( + doris_spec, + f"SELECT id, embedding FROM `{doris_spec.database}`.`{doris_spec.table}` ORDER BY id", + ) + assert len(query_result) == 2 + # JSON stored vectors can have different lengths + assert query_result[0]["id"] == 1 + assert query_result[1]["id"] == 2 + + +# ============================================================ +# DATA MUTATION TESTS +# ============================================================ + + +class TestDataMutation: + """Test upsert and delete operations.""" + + @pytest.mark.asyncio + async def test_insert_single_row( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test inserting a single row via Stream Load.""" + # Create table + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert row + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + result = await _stream_load( + session, + doris_spec, + [{"id": 1, "content": "Hello, Doris!"}], + ) + assert result.get("Status") in ("Success", "Publish Timeout") + + # Wait for data to be visible + await asyncio.sleep(2) + + # Verify data + query_result = await _execute_ddl( + doris_spec, + f"SELECT * FROM {doris_spec.database}.{doris_spec.table} WHERE id = 1", + ) + assert len(query_result) == 1 + assert query_result[0]["content"] == "Hello, Doris!" + + @pytest.mark.asyncio + async def test_insert_multiple_rows( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test inserting multiple rows in batch.""" + # Create table + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert rows + rows = [{"id": i, "content": f"Row {i}"} for i in range(1, 101)] + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + result = await _stream_load(session, doris_spec, rows) + assert result.get("Status") in ("Success", "Publish Timeout") + + # Wait for data + await asyncio.sleep(2) + + # Verify count + query_result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert query_result[0]["cnt"] == 100 + + @pytest.mark.asyncio + async def test_upsert_row( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test upserting (update on duplicate key).""" + # Create table + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + # Insert initial row + await _stream_load( + session, + doris_spec, + [{"id": 1, "content": "Original"}], + ) + + await asyncio.sleep(2) + + # Upsert (update same key) + await _stream_load( + session, + doris_spec, + [{"id": 1, "content": "Updated"}], + ) + + await asyncio.sleep(2) + + # Verify updated - with DUPLICATE KEY model, may have multiple versions + query_result = await _execute_ddl( + doris_spec, + f"SELECT content FROM {doris_spec.database}.{doris_spec.table} WHERE id = 1 ORDER BY content DESC LIMIT 1", + ) + # Note: DUPLICATE KEY keeps all versions, so we check latest + assert len(query_result) >= 1 + + @pytest.mark.asyncio + async def test_delete_row( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test deleting a row via SQL DELETE (works with DUPLICATE KEY tables).""" + # Create table + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + # Insert row + await _stream_load( + session, + doris_spec, + [{"id": 1, "content": "To be deleted"}], + ) + + await asyncio.sleep(2) + + # Verify row exists + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table} WHERE id = 1", + ) + assert result[0]["cnt"] >= 1 + + # Delete row using SQL DELETE (not Stream Load) + from cocoindex.targets.doris import _execute_delete + + await _execute_delete( + doris_spec, + key_field_names=["id"], + key_values=[{"id": 1}], + ) + # Note: cursor.rowcount may return 0 in Doris even for successful deletes + # so we verify deletion by checking the actual row count + + await asyncio.sleep(2) + + # Verify row is deleted + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table} WHERE id = 1", + ) + assert result[0]["cnt"] == 0 + + @pytest.mark.asyncio + async def test_upsert_idempotent_no_duplicates( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that repeated upserts do NOT accumulate duplicate rows. + + This tests the fix for the issue where DUPLICATE KEY tables would + accumulate multiple rows with the same key. The fix uses delete-before-insert + to ensure idempotent upsert behavior. + """ + from cocoindex.targets.doris import _MutateContext, _execute_delete + + # Create table + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + context = _MutateContext( + spec=doris_spec, + session=session, + state=state, + lock=asyncio.Lock(), + ) + + # First upsert + mutations1: dict[Any, dict[str, Any] | None] = { + 1: {"content": "Version 1"}, + } + await _Connector.mutate((context, mutations1)) + + await asyncio.sleep(2) + + # Second upsert - same key, different value + mutations2: dict[Any, dict[str, Any] | None] = { + 1: {"content": "Version 2"}, + } + await _Connector.mutate((context, mutations2)) + + await asyncio.sleep(2) + + # Third upsert - same key, yet another value + mutations3: dict[Any, dict[str, Any] | None] = { + 1: {"content": "Version 3"}, + } + await _Connector.mutate((context, mutations3)) + + await asyncio.sleep(2) + + # Verify EXACTLY ONE row exists (not 3) + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table} WHERE id = 1", + ) + assert result[0]["cnt"] == 1, ( + f"Expected exactly 1 row, but found {result[0]['cnt']}. " + "Delete-before-insert fix may not be working." + ) + + # Verify the content is the latest version + content_result = await _execute_ddl( + doris_spec, + f"SELECT content FROM {doris_spec.database}.{doris_spec.table} WHERE id = 1", + ) + assert content_result[0]["content"] == "Version 3" + + +# ============================================================ +# INDEX LIFECYCLE TESTS +# ============================================================ + + +class TestIndexLifecycle: + """Test index creation, modification, and removal on existing tables.""" + + @pytest.mark.asyncio + async def test_add_vector_index_to_existing_table( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test adding a vector index to an existing table without index.""" + from cocoindex.targets.doris import _sync_indexes + + # Create table without vector index + state_no_index = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=4), + ], + vector_indexes=None, # No index initially + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state_no_index) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert some data first + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + rows = [ + {"id": 1, "content": "Test", "embedding": [1.0, 0.0, 0.0, 0.0]}, + ] + await _stream_load(session, doris_spec, rows) + + await asyncio.sleep(2) + + # Now add a vector index + state_with_index = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=4), + ], + vector_indexes=[ + _VectorIndex( + name="idx_embedding_ann", + field_name="embedding", + index_type="hnsw", + metric_type="l2_distance", + dimension=4, + ) + ], + ) + + # Sync indexes - should add the new index + await _sync_indexes(doris_spec, key, state_no_index, state_with_index) + + await asyncio.sleep(3) + + # Verify index was created by checking SHOW CREATE TABLE + result = await _execute_ddl( + doris_spec, + f"SHOW CREATE TABLE {doris_spec.database}.{doris_spec.table}", + ) + create_stmt = result[0].get("Create Table", "") + assert "idx_embedding_ann" in create_stmt, "Vector index should be created" + + @pytest.mark.asyncio + async def test_add_inverted_index_to_existing_table( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test adding an inverted (FTS) index to an existing table.""" + from cocoindex.targets.doris import _sync_indexes + + # Create table without inverted index + state_no_index = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + inverted_indexes=None, + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state_no_index) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert some data + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + rows = [{"id": 1, "content": "Hello world test document"}] + await _stream_load(session, doris_spec, rows) + + await asyncio.sleep(2) + + # Add inverted index + state_with_index = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + inverted_indexes=[ + _InvertedIndex( + name="idx_content_inv", + field_name="content", + parser="unicode", + ) + ], + ) + + await _sync_indexes(doris_spec, key, state_no_index, state_with_index) + + await asyncio.sleep(3) + + # Verify index was created + result = await _execute_ddl( + doris_spec, + f"SHOW CREATE TABLE {doris_spec.database}.{doris_spec.table}", + ) + create_stmt = result[0].get("Create Table", "") + assert "idx_content_inv" in create_stmt, "Inverted index should be created" + + @pytest.mark.asyncio + async def test_remove_index_from_existing_table( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test removing an index from an existing table.""" + from cocoindex.targets.doris import _sync_indexes + + # Create table with inverted index + state_with_index = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + inverted_indexes=[ + _InvertedIndex( + name="idx_content_inv", + field_name="content", + parser="unicode", + ) + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state_with_index) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Verify index exists + result = await _execute_ddl( + doris_spec, + f"SHOW CREATE TABLE {doris_spec.database}.{doris_spec.table}", + ) + create_stmt = result[0].get("Create Table", "") + assert "idx_content_inv" in create_stmt, "Index should exist initially" + + # Remove the index + state_no_index = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + inverted_indexes=None, + ) + + await _sync_indexes(doris_spec, key, state_with_index, state_no_index) + + # Wait for index removal with retry (Doris async operation) + for i in range(ASYNC_OPERATION_TIMEOUT): + await asyncio.sleep(1) + result = await _execute_ddl( + doris_spec, + f"SHOW CREATE TABLE {doris_spec.database}.{doris_spec.table}", + ) + create_stmt = result[0].get("Create Table", "") + if "idx_content_inv" not in create_stmt: + break + else: + pytest.fail( + f"Index was not removed after {ASYNC_OPERATION_TIMEOUT}s. " + f"Current schema: {create_stmt}" + ) + + @pytest.mark.asyncio + async def test_change_index_parameters( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test changing index parameters (recreates index).""" + from cocoindex.targets.doris import _sync_indexes + + # Create table with inverted index using english parser + state_english = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + inverted_indexes=[ + _InvertedIndex( + name="idx_content_inv", + field_name="content", + parser="english", + ) + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state_english) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(3) + + # Change to unicode parser (same name, different params) + state_unicode = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + inverted_indexes=[ + _InvertedIndex( + name="idx_content_inv", + field_name="content", + parser="unicode", # Changed parser + ) + ], + ) + + await _sync_indexes(doris_spec, key, state_english, state_unicode) + + # Wait longer for schema change to complete (Doris needs time for index operations) + await asyncio.sleep(5) + + # Index should still exist (was dropped and recreated) + result = await _execute_ddl( + doris_spec, + f"SHOW CREATE TABLE {doris_spec.database}.{doris_spec.table}", + ) + create_stmt = result[0].get("Create Table", "") + assert "idx_content_inv" in create_stmt, ( + "Index should exist after parameter change" + ) + # Note: Verifying the parser changed would require parsing SHOW CREATE TABLE output + + +# ============================================================ +# CONNECTOR MUTATION TESTS +# ============================================================ + + +class TestConnectorMutation: + """Test the full connector mutation flow using _Connector.mutate().""" + + @pytest.mark.asyncio + async def test_mutate_insert_multiple_rows( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test inserting multiple rows via connector mutation.""" + from cocoindex.targets.doris import _MutateContext + + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + context = _MutateContext( + spec=doris_spec, + session=session, + state=state, + lock=asyncio.Lock(), + ) + + # Insert multiple rows + mutations: dict[Any, dict[str, Any] | None] = { + 1: {"content": "First row"}, + 2: {"content": "Second row"}, + 3: {"content": "Third row"}, + } + await _Connector.mutate((context, mutations)) + + await asyncio.sleep(2) + + # Verify all rows were inserted + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert result[0]["cnt"] == 3 + + @pytest.mark.asyncio + async def test_mutate_delete_rows( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test deleting rows via connector mutation (value=None).""" + from cocoindex.targets.doris import _MutateContext + + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + context = _MutateContext( + spec=doris_spec, + session=session, + state=state, + lock=asyncio.Lock(), + ) + + # Insert rows first + insert_mutations: dict[Any, dict[str, Any] | None] = { + 1: {"content": "Row 1"}, + 2: {"content": "Row 2"}, + 3: {"content": "Row 3"}, + } + await _Connector.mutate((context, insert_mutations)) + + await asyncio.sleep(2) + + # Verify rows exist + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert result[0]["cnt"] == 3 + + # Delete row 2 (value=None means delete) + delete_mutations: dict[Any, dict[str, Any] | None] = { + 2: None, # Delete + } + await _Connector.mutate((context, delete_mutations)) + + await asyncio.sleep(2) + + # Verify row was deleted + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert result[0]["cnt"] == 2 + + # Verify specific row is gone + result = await _execute_ddl( + doris_spec, + f"SELECT id FROM {doris_spec.database}.{doris_spec.table} ORDER BY id", + ) + ids = [row["id"] for row in result] + assert ids == [1, 3], "Row 2 should be deleted" + + @pytest.mark.asyncio + async def test_mutate_mixed_operations( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test mixed insert/update/delete in single mutation batch.""" + from cocoindex.targets.doris import _MutateContext + + state = _mock_state(spec=doris_spec) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + context = _MutateContext( + spec=doris_spec, + session=session, + state=state, + lock=asyncio.Lock(), + ) + + # Initial insert + await _Connector.mutate( + ( + context, + { + 1: {"content": "Original 1"}, + 2: {"content": "Original 2"}, + 3: {"content": "Original 3"}, + }, + ) + ) + + await asyncio.sleep(2) + + # Mixed operations in single batch: + # - Update row 1 + # - Delete row 2 + # - Insert row 4 + mixed_mutations: dict[Any, dict[str, Any] | None] = { + 1: {"content": "Updated 1"}, # Update + 2: None, # Delete + 4: {"content": "New row 4"}, # Insert + } + await _Connector.mutate((context, mixed_mutations)) + + await asyncio.sleep(2) + + # Verify final state + result = await _execute_ddl( + doris_spec, + f"SELECT id, content FROM {doris_spec.database}.{doris_spec.table} ORDER BY id", + ) + + # Should have rows 1, 3, 4 + assert len(result) == 3 + rows_by_id = {row["id"]: row["content"] for row in result} + assert rows_by_id[1] == "Updated 1" + assert rows_by_id[3] == "Original 3" + assert rows_by_id[4] == "New row 4" + assert 2 not in rows_by_id + + @pytest.mark.asyncio + async def test_mutate_composite_key( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test mutation with composite (multi-column) primary key.""" + from cocoindex.targets.doris import _MutateContext + + # Create state with composite key + state = _State( + key_fields_schema=[ + _mock_field("tenant_id", "Int64"), + _mock_field("doc_id", "Str"), + ], + value_fields_schema=[_mock_field("content", "Str")], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + context = _MutateContext( + spec=doris_spec, + session=session, + state=state, + lock=asyncio.Lock(), + ) + + # Insert with composite keys (tuple keys) + mutations: dict[Any, dict[str, Any] | None] = { + (1, "doc_a"): {"content": "Tenant 1, Doc A"}, + (1, "doc_b"): {"content": "Tenant 1, Doc B"}, + (2, "doc_a"): {"content": "Tenant 2, Doc A"}, + } + await _Connector.mutate((context, mutations)) + + await asyncio.sleep(2) + + # Update one row + await _Connector.mutate( + ( + context, + { + (1, "doc_a"): {"content": "Updated content"}, + }, + ) + ) + + await asyncio.sleep(2) + + # Verify + result = await _execute_ddl( + doris_spec, + f"SELECT tenant_id, doc_id, content FROM {doris_spec.database}.{doris_spec.table} ORDER BY tenant_id, doc_id", + ) + + assert len(result) == 3 + # Find the updated row + updated_row = next( + r for r in result if r["tenant_id"] == 1 and r["doc_id"] == "doc_a" + ) + assert updated_row["content"] == "Updated content" + + @pytest.mark.asyncio + async def test_mutate_composite_key_delete( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test deleting rows with composite (multi-column) primary key. + + This tests the fix for composite-key DELETE which uses OR conditions + instead of tuple IN syntax (which Doris doesn't support). + """ + from cocoindex.targets.doris import _MutateContext + + # Create state with composite key + state = _State( + key_fields_schema=[ + _mock_field("tenant_id", "Int64"), + _mock_field("doc_id", "Str"), + ], + value_fields_schema=[_mock_field("content", "Str")], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + context = _MutateContext( + spec=doris_spec, + session=session, + state=state, + lock=asyncio.Lock(), + ) + + # Insert multiple rows with composite keys + mutations: dict[Any, dict[str, Any] | None] = { + (1, "doc_a"): {"content": "Tenant 1, Doc A"}, + (1, "doc_b"): {"content": "Tenant 1, Doc B"}, + (1, "doc_c"): {"content": "Tenant 1, Doc C"}, + (2, "doc_a"): {"content": "Tenant 2, Doc A"}, + (2, "doc_b"): {"content": "Tenant 2, Doc B"}, + } + await _Connector.mutate((context, mutations)) + + await asyncio.sleep(2) + + # Verify all rows exist + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert result[0]["cnt"] == 5 + + # Delete multiple rows with composite keys in a single mutation + # This tests the OR chain DELETE: WHERE (t=1 AND d='a') OR (t=1 AND d='c') OR (t=2 AND d='b') + delete_mutations: dict[Any, dict[str, Any] | None] = { + (1, "doc_a"): None, # Delete + (1, "doc_c"): None, # Delete + (2, "doc_b"): None, # Delete + } + await _Connector.mutate((context, delete_mutations)) + + await asyncio.sleep(2) + + # Verify correct rows were deleted + result = await _execute_ddl( + doris_spec, + f"SELECT tenant_id, doc_id FROM {doris_spec.database}.{doris_spec.table} ORDER BY tenant_id, doc_id", + ) + + # Should have 2 rows remaining: (1, doc_b) and (2, doc_a) + assert len(result) == 2 + remaining_keys = [(r["tenant_id"], r["doc_id"]) for r in result] + assert (1, "doc_b") in remaining_keys + assert (2, "doc_a") in remaining_keys + # Deleted keys should not exist + assert (1, "doc_a") not in remaining_keys + assert (1, "doc_c") not in remaining_keys + assert (2, "doc_b") not in remaining_keys + + @pytest.mark.asyncio + async def test_mutate_composite_key_delete_large_batch( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test deleting many rows with composite key. + + This tests that deleting multiple rows with composite keys works correctly. + Doris doesn't support OR with AND predicates in DELETE, so composite keys + are deleted one row at a time. + """ + from cocoindex.targets.doris import _MutateContext + + # Create state with composite key + state = _State( + key_fields_schema=[ + _mock_field("tenant_id", "Int64"), + _mock_field("doc_id", "Str"), + ], + value_fields_schema=[_mock_field("content", "Str")], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Use a moderate batch size (deleting one-by-one takes time) + num_rows = 20 + + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + context = _MutateContext( + spec=doris_spec, + session=session, + state=state, + lock=asyncio.Lock(), + ) + + # Insert many rows + insert_mutations: dict[Any, dict[str, Any] | None] = { + (i // 5, f"doc_{i % 5}"): {"content": f"Content {i}"} + for i in range(num_rows) + } + await _Connector.mutate((context, insert_mutations)) + + await asyncio.sleep(3) + + # Verify all rows inserted + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert result[0]["cnt"] == num_rows + + # Delete all rows + delete_mutations: dict[Any, dict[str, Any] | None] = { + (i // 5, f"doc_{i % 5}"): None for i in range(num_rows) + } + await _Connector.mutate((context, delete_mutations)) + + await asyncio.sleep(3) + + # Verify all rows were deleted + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert result[0]["cnt"] == 0, ( + f"Expected 0 rows after delete, but found {result[0]['cnt']}. " + "Composite key delete may not be working correctly." + ) + + +# ============================================================ +# VECTOR SEARCH TESTS +# ============================================================ + + +class TestVectorSearch: + """Test vector search functionality.""" + + @pytest.mark.asyncio + async def test_insert_and_query_vectors( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test inserting vectors and querying with similarity search.""" + # Create table with vector column (no index for simpler test) + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=4), # Small dim for testing + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert vectors + rows = [ + {"id": 1, "content": "Apple", "embedding": [1.0, 0.0, 0.0, 0.0]}, + {"id": 2, "content": "Banana", "embedding": [0.0, 1.0, 0.0, 0.0]}, + {"id": 3, "content": "Cherry", "embedding": [0.0, 0.0, 1.0, 0.0]}, + ] + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + load_result = await _stream_load(session, doris_spec, rows) + assert load_result.get("Status") in ("Success", "Publish Timeout") + + await asyncio.sleep(2) + + # Query with vector similarity (using non-approximate function for test) + # Note: approximate functions require index + query = f""" + SELECT id, content, l2_distance(embedding, [1.0, 0.0, 0.0, 0.0]) as distance + FROM {doris_spec.database}.{doris_spec.table} + ORDER BY distance ASC + LIMIT 3 + """ + query_result = await _execute_ddl(doris_spec, query) + + assert len(query_result) == 3 + # Apple should be closest (distance ~0) + assert query_result[0]["content"] == "Apple" + + @pytest.mark.asyncio + async def test_ivf_index_vector_search( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test vector search with IVF index. + + IVF requires at least nlist training points, so we must: + 1. Create table without IVF index + 2. Load data first + 3. Add IVF index after data is loaded + 4. Build the index + """ + # Step 1: Create table WITHOUT IVF index (just vector column) + state_no_index = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=4), + ], + vector_indexes=None, # No index initially + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state_no_index) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Step 2: Insert vectors first (IVF needs training data) + rows = [ + {"id": 1, "content": "Apple", "embedding": [1.0, 0.0, 0.0, 0.0]}, + {"id": 2, "content": "Banana", "embedding": [0.0, 1.0, 0.0, 0.0]}, + {"id": 3, "content": "Cherry", "embedding": [0.0, 0.0, 1.0, 0.0]}, + {"id": 4, "content": "Date", "embedding": [0.0, 0.0, 0.0, 1.0]}, + {"id": 5, "content": "Elderberry", "embedding": [0.5, 0.5, 0.0, 0.0]}, + ] + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + load_result = await _stream_load(session, doris_spec, rows) + assert load_result.get("Status") in ("Success", "Publish Timeout") + + await asyncio.sleep(3) + + # Step 3: Add IVF index after data is loaded + # nlist=2 requires at least 2 data points for training + await _execute_ddl( + doris_spec, + f"""CREATE INDEX idx_embedding_ivf ON `{doris_spec.database}`.`{doris_spec.table}` (embedding) + USING ANN PROPERTIES ( + "index_type" = "ivf", + "metric_type" = "l2_distance", + "dim" = "4", + "nlist" = "2" + )""", + ) + + await asyncio.sleep(2) + + # Step 4: Build the index + try: + await _execute_ddl( + doris_spec, + f"BUILD INDEX idx_embedding_ivf ON `{doris_spec.database}`.`{doris_spec.table}`", + ) + await asyncio.sleep(5) # Wait for index build + except Exception: + pass # Index may already be built + + # Query with l2_distance function (index accelerates ORDER BY queries) + query = f""" + SELECT id, content, l2_distance(embedding, [1.0, 0.0, 0.0, 0.0]) as distance + FROM {doris_spec.database}.{doris_spec.table} + ORDER BY distance ASC + LIMIT 3 + """ + query_result = await _execute_ddl(doris_spec, query) + + assert len(query_result) >= 1 + # Apple should be closest (exact match) + assert query_result[0]["content"] == "Apple" + + @pytest.mark.asyncio + async def test_hnsw_index_vector_search( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test vector search with HNSW index.""" + # Create table with HNSW vector index + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=4), + ], + vector_indexes=[ + _VectorIndex( + name="idx_embedding_hnsw", + field_name="embedding", + index_type="hnsw", + metric_type="l2_distance", + dimension=4, + max_degree=16, + ef_construction=100, + ) + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert vectors + rows = [ + {"id": 1, "content": "Apple", "embedding": [1.0, 0.0, 0.0, 0.0]}, + {"id": 2, "content": "Banana", "embedding": [0.0, 1.0, 0.0, 0.0]}, + {"id": 3, "content": "Cherry", "embedding": [0.0, 0.0, 1.0, 0.0]}, + ] + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + load_result = await _stream_load(session, doris_spec, rows) + assert load_result.get("Status") in ("Success", "Publish Timeout") + + await asyncio.sleep(3) + + # Build the index explicitly + try: + await _execute_ddl( + doris_spec, + f"BUILD INDEX idx_embedding_hnsw ON `{doris_spec.database}`.`{doris_spec.table}`", + ) + await asyncio.sleep(3) + except Exception: + pass + + # Query with l2_distance function (index accelerates ORDER BY queries) + query = f""" + SELECT id, content, l2_distance(embedding, [1.0, 0.0, 0.0, 0.0]) as distance + FROM {doris_spec.database}.{doris_spec.table} + ORDER BY distance ASC + LIMIT 3 + """ + query_result = await _execute_ddl(doris_spec, query) + + assert len(query_result) >= 1 + # Apple should be closest + assert query_result[0]["content"] == "Apple" + + +# ============================================================ +# HYBRID SEARCH TESTS (Vector + Full-Text) +# ============================================================ + + +class TestHybridSearch: + """Test hybrid search combining vector similarity and full-text search.""" + + @pytest.mark.asyncio + async def test_inverted_index_text_search( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test full-text search with inverted index.""" + from cocoindex.targets.doris import _InvertedIndex + + # Create table with inverted index on content + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("title", "Str"), + _mock_field("content", "Str"), + ], + inverted_indexes=[ + _InvertedIndex( + name="idx_content_inv", + field_name="content", + parser="unicode", # Good for mixed language content + ), + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert documents + rows = [ + { + "id": 1, + "title": "Apache Doris", + "content": "Apache Doris is a real-time analytical database", + }, + { + "id": 2, + "title": "Vector Database", + "content": "Vector databases enable semantic search with embeddings", + }, + { + "id": 3, + "title": "Machine Learning", + "content": "Machine learning powers modern AI applications", + }, + { + "id": 4, + "title": "Data Analytics", + "content": "Real-time data analytics for business intelligence", + }, + ] + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + load_result = await _stream_load(session, doris_spec, rows) + assert load_result.get("Status") in ("Success", "Publish Timeout") + + await asyncio.sleep(3) # Wait for index to be built + + # Test MATCH_ANY - any keyword match + query = f""" + SELECT id, title FROM {doris_spec.database}.{doris_spec.table} + WHERE content MATCH_ANY 'real-time analytics' + """ + query_result = await _execute_ddl(doris_spec, query) + assert ( + len(query_result) >= 1 + ) # Should match docs with "real-time" or "analytics" + + # Test MATCH_ALL - all keywords required + query = f""" + SELECT id, title FROM {doris_spec.database}.{doris_spec.table} + WHERE content MATCH_ALL 'real-time analytical' + """ + query_result = await _execute_ddl(doris_spec, query) + assert len(query_result) >= 1 # Should match "Apache Doris" doc + + # Test MATCH_PHRASE - exact phrase + query = f""" + SELECT id, title FROM {doris_spec.database}.{doris_spec.table} + WHERE content MATCH_PHRASE 'semantic search' + """ + query_result = await _execute_ddl(doris_spec, query) + assert len(query_result) >= 1 # Should match "Vector Database" doc + + @pytest.mark.asyncio + async def test_hybrid_vector_and_text_search( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test hybrid search combining vector similarity with text filtering.""" + from cocoindex.targets.doris import _InvertedIndex, _VectorIndex + + # Create table with both vector and inverted indexes + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("category", "Str"), + _mock_field("content", "Str"), + _mock_field("embedding", "Vector", dim=4), + ], + vector_indexes=[ + _VectorIndex( + name="idx_embedding_ann", + field_name="embedding", + index_type="hnsw", + metric_type="l2_distance", + dimension=4, + ), + ], + inverted_indexes=[ + _InvertedIndex( + name="idx_content_inv", + field_name="content", + parser="unicode", + ), + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert documents with embeddings + # Embeddings are simple 4D vectors for testing + rows = [ + { + "id": 1, + "category": "tech", + "content": "Apache Doris real-time database", + "embedding": [1.0, 0.0, 0.0, 0.0], + }, + { + "id": 2, + "category": "tech", + "content": "Vector search with embeddings", + "embedding": [0.9, 0.1, 0.0, 0.0], + }, + { + "id": 3, + "category": "science", + "content": "Machine learning algorithms", + "embedding": [0.0, 1.0, 0.0, 0.0], + }, + { + "id": 4, + "category": "science", + "content": "Deep learning neural networks", + "embedding": [0.0, 0.9, 0.1, 0.0], + }, + { + "id": 5, + "category": "business", + "content": "Real-time analytics dashboard", + "embedding": [0.5, 0.5, 0.0, 0.0], + }, + ] + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + load_result = await _stream_load(session, doris_spec, rows) + assert load_result.get("Status") in ("Success", "Publish Timeout") + + await asyncio.sleep(3) # Wait for indexes + + # Build index + try: + await _execute_ddl( + doris_spec, + f"BUILD INDEX idx_embedding_ann ON {doris_spec.database}.{doris_spec.table}", + ) + except Exception: + pass # Index may already be built + + await asyncio.sleep(2) + + # Hybrid search: Vector similarity + text filter + # Find docs similar to [1.0, 0.0, 0.0, 0.0] that contain "real-time" + query = f""" + SELECT id, category, content, + l2_distance(embedding, [1.0, 0.0, 0.0, 0.0]) as distance + FROM {doris_spec.database}.{doris_spec.table} + WHERE content MATCH_ANY 'real-time' + ORDER BY distance ASC + LIMIT 3 + """ + query_result = await _execute_ddl(doris_spec, query) + assert len(query_result) >= 1 + # Should return docs containing "real-time", ordered by vector similarity + + # Hybrid search: Vector similarity + category filter + text search + query = f""" + SELECT id, category, content, + l2_distance(embedding, [0.0, 1.0, 0.0, 0.0]) as distance + FROM {doris_spec.database}.{doris_spec.table} + WHERE category = 'science' + AND content MATCH_ANY 'learning' + ORDER BY distance ASC + LIMIT 2 + """ + query_result = await _execute_ddl(doris_spec, query) + assert len(query_result) >= 1 + # Should return science docs about learning, ordered by similarity + + @pytest.mark.asyncio + async def test_text_search_operators( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test various text search operators with inverted index.""" + from cocoindex.targets.doris import _InvertedIndex + + # Create table with inverted index + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + _mock_field("content", "Str"), + ], + inverted_indexes=[ + _InvertedIndex( + name="idx_content_inv", + field_name="content", + parser="unicode", + ), + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(doris_spec, ddl) + + await asyncio.sleep(2) + + # Insert test documents + rows = [ + {"id": 1, "content": "data warehouse solutions for enterprise"}, + {"id": 2, "content": "data warehousing best practices"}, + {"id": 3, "content": "big data processing pipeline"}, + {"id": 4, "content": "warehouse inventory management system"}, + ] + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + load_result = await _stream_load(session, doris_spec, rows) + assert load_result.get("Status") in ("Success", "Publish Timeout") + + await asyncio.sleep(3) + + # Test MATCH_PHRASE_PREFIX - prefix matching + query = f""" + SELECT id, content FROM {doris_spec.database}.{doris_spec.table} + WHERE content MATCH_PHRASE_PREFIX 'data ware' + """ + query_result = await _execute_ddl(doris_spec, query) + # Should match "data warehouse" and "data warehousing" + assert len(query_result) >= 1 + + +# ============================================================ +# CONFIGURATION TESTS +# ============================================================ + + +class TestConfiguration: + """Test all configuration options.""" + + def test_default_config_values(self): + """Test default configuration values.""" + spec = DorisTarget( + fe_host="localhost", + database="test", + table="test_table", + ) + assert spec.fe_http_port == 8080 + assert spec.query_port == 9030 + assert spec.username == "root" + assert spec.password == "" + assert spec.enable_https is False + assert spec.batch_size == 10000 + assert spec.stream_load_timeout == 600 + assert spec.auto_create_table is True + assert spec.max_retries == 3 + assert spec.retry_base_delay == 1.0 + assert spec.retry_max_delay == 30.0 + assert spec.replication_num == 1 + assert spec.buckets == "auto" + + def test_custom_config_values(self): + """Test custom configuration values.""" + spec = DorisTarget( + fe_host="custom-host", + database="custom_db", + table="custom_table", + fe_http_port=9080, + query_port=19030, + username="custom_user", + password="custom_pass", + enable_https=True, + batch_size=5000, + stream_load_timeout=300, + auto_create_table=False, + max_retries=5, + retry_base_delay=2.0, + retry_max_delay=60.0, + replication_num=3, + buckets=16, + ) + assert spec.fe_host == "custom-host" + assert spec.fe_http_port == 9080 + assert spec.query_port == 19030 + assert spec.username == "custom_user" + assert spec.password == "custom_pass" + assert spec.enable_https is True + assert spec.batch_size == 5000 + assert spec.stream_load_timeout == 300 + assert spec.auto_create_table is False + assert spec.max_retries == 5 + assert spec.retry_base_delay == 2.0 + assert spec.retry_max_delay == 60.0 + assert spec.replication_num == 3 + assert spec.buckets == 16 + + @pytest.mark.asyncio + async def test_batch_size_respected( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that batch_size configuration is used.""" + # Create spec with small batch size + spec = DorisTarget( + fe_host=doris_spec.fe_host, + fe_http_port=doris_spec.fe_http_port, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + database=doris_spec.database, + table=doris_spec.table, + batch_size=10, # Small batch + ) + + # Create table + state = _mock_state(spec=doris_spec) + key = _TableKey(spec.fe_host, spec.database, spec.table) + ddl = _generate_create_table_ddl(key, state) + await _execute_ddl(spec, ddl) + + await asyncio.sleep(2) + + # The batch_size is used in mutate() to batch rows + # This test verifies the spec is accepted + assert spec.batch_size == 10 + + +# ============================================================ +# RETRY LOGIC TESTS +# ============================================================ + + +class TestRetryLogic: + """Test retry configuration and behavior.""" + + def test_retry_config_defaults(self): + """Test RetryConfig default values.""" + config = RetryConfig() + assert config.max_retries == 3 + assert config.base_delay == 1.0 + assert config.max_delay == 30.0 + assert config.exponential_base == 2.0 + + def test_retry_config_custom(self): + """Test custom RetryConfig values.""" + config = RetryConfig( + max_retries=5, + base_delay=0.5, + max_delay=60.0, + exponential_base=3.0, + ) + assert config.max_retries == 5 + assert config.base_delay == 0.5 + assert config.max_delay == 60.0 + assert config.exponential_base == 3.0 + + @pytest.mark.asyncio + async def test_retry_succeeds_on_first_try(self): + """Test retry logic when operation succeeds immediately.""" + call_count = 0 + + async def successful_op(): + nonlocal call_count + call_count += 1 + return "success" + + result = await with_retry( + successful_op, + config=RetryConfig(max_retries=3), + retryable_errors=(Exception,), + ) + + assert result == "success" + assert call_count == 1 + + @pytest.mark.asyncio + async def test_retry_succeeds_after_failures(self): + """Test retry logic with transient failures.""" + call_count = 0 + + async def flaky_op(): + nonlocal call_count + call_count += 1 + if call_count < 3: + raise asyncio.TimeoutError("Transient error") + return "success" + + result = await with_retry( + flaky_op, + config=RetryConfig(max_retries=3, base_delay=0.01), + retryable_errors=(asyncio.TimeoutError,), + ) + + assert result == "success" + assert call_count == 3 + + @pytest.mark.asyncio + async def test_retry_exhausted_raises_error(self): + """Test retry logic when all retries fail.""" + call_count = 0 + + async def always_fails(): + nonlocal call_count + call_count += 1 + raise asyncio.TimeoutError("Always fails") + + with pytest.raises(DorisConnectionError) as exc_info: + await with_retry( + always_fails, + config=RetryConfig(max_retries=2, base_delay=0.01), + retryable_errors=(asyncio.TimeoutError,), + ) + + assert call_count == 3 # Initial + 2 retries + assert "failed after 3 attempts" in str(exc_info.value) + + @pytest.mark.asyncio + async def test_retry_config_from_spec_used(self, doris_spec: DorisTarget): + """Test that spec's retry config is actually used.""" + # Create spec with custom retry settings + spec = DorisTarget( + fe_host=doris_spec.fe_host, + fe_http_port=doris_spec.fe_http_port, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + database=doris_spec.database, + table=doris_spec.table, + max_retries=1, + retry_base_delay=0.1, + retry_max_delay=1.0, + ) + + # Verify config is set + assert spec.max_retries == 1 + assert spec.retry_base_delay == 0.1 + assert spec.retry_max_delay == 1.0 + + +# ============================================================ +# ERROR HANDLING TESTS +# ============================================================ + + +class TestErrorHandling: + """Test error handling scenarios.""" + + @pytest.mark.asyncio + async def test_invalid_host_raises_connection_error(self): + """Test that invalid host raises appropriate error.""" + spec = DorisTarget( + fe_host="invalid-host-that-does-not-exist.example.com", + database="test", + table="test_table", + max_retries=0, # No retries for faster test + ) + + with pytest.raises((DorisConnectionError, Exception)): + await _execute_ddl(spec, "SELECT 1") + + @pytest.mark.asyncio + async def test_invalid_credentials_raises_auth_error(self, doris_spec: DorisTarget): + """Test that invalid credentials raise auth error.""" + spec = DorisTarget( + fe_host=doris_spec.fe_host, + fe_http_port=doris_spec.fe_http_port, + query_port=doris_spec.query_port, + username="invalid_user", + password="invalid_password", + database=doris_spec.database, + table=doris_spec.table, + max_retries=0, + ) + + with pytest.raises(Exception): # May be auth error or connection error + await _execute_ddl(spec, "SELECT 1") + + +# ============================================================ +# FULL CONNECTOR WORKFLOW TEST +# ============================================================ + + +class TestConnectorWorkflow: + """Test complete connector workflow as used by CocoIndex.""" + + @pytest.mark.asyncio + async def test_full_workflow( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test the complete connector workflow: setup -> prepare -> mutate.""" + # 1. Get persistent key + key = _Connector.get_persistent_key(doris_spec) + assert isinstance(key, _TableKey) + + # 2. Get setup state + key_schema = [_mock_field("doc_id", "Str")] + value_schema = [ + _mock_field("title", "Str"), + _mock_field("content", "Str"), + ] + state = _Connector.get_setup_state( + doris_spec, + key_fields_schema=key_schema, + value_fields_schema=value_schema, + index_options=IndexOptions(primary_key_fields=["doc_id"]), + ) + assert isinstance(state, _State) + + # 3. Describe resource + desc = _Connector.describe(key) + assert doris_spec.table in desc + + # 4. Apply setup change (create table) + await _Connector.apply_setup_change(key, None, state) + + # 5. Verify table exists + exists = await _table_exists(doris_spec, doris_spec.database, doris_spec.table) + assert exists + + # 6. Prepare for mutations + context = await _Connector.prepare(doris_spec, state) + assert context.session is not None + + # 7. Perform mutations + mutations: dict[Any, dict[str, Any] | None] = { + "doc1": {"title": "First Document", "content": "This is document 1"}, + "doc2": {"title": "Second Document", "content": "This is document 2"}, + } + await _Connector.mutate((context, mutations)) + + # Wait for data + await asyncio.sleep(2) + + # 8. Verify data + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert result[0]["cnt"] >= 2 + + # 9. Cleanup (drop table) + await _Connector.apply_setup_change(key, state, None) + + # 10. Close session + await context.session.close() + + +# ============================================================ +# HELPER FOR STATE WITH CREDENTIALS +# ============================================================ + + +def _state_with_creds( + spec: DorisTarget, + key_fields: list[FieldSchema], + value_fields: list[FieldSchema], + vector_indexes: list[_VectorIndex] | None = None, + inverted_indexes: list[_InvertedIndex] | None = None, + schema_evolution: str = "extend", +) -> _State: + """Create a _State with credentials from the spec.""" + return _State( + key_fields_schema=key_fields, + value_fields_schema=value_fields, + vector_indexes=vector_indexes, + inverted_indexes=inverted_indexes, + fe_http_port=spec.fe_http_port, + query_port=spec.query_port, + username=spec.username, + password=spec.password, + max_retries=spec.max_retries, + retry_base_delay=spec.retry_base_delay, + retry_max_delay=spec.retry_max_delay, + schema_evolution=schema_evolution, # type: ignore[arg-type] + replication_num=1, + buckets=1, # Small for testing + ) + + +# ============================================================ +# SCHEMA EVOLUTION TESTS +# ============================================================ + + +class TestSchemaEvolution: + """Test schema evolution behavior (extend vs strict mode).""" + + @pytest.mark.asyncio + async def test_extend_mode_keeps_extra_columns( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that extend mode keeps extra columns in DB that aren't in schema. + + Documented behavior: Extra columns in the database that aren't in your + schema are kept untouched. + """ + from cocoindex.targets.doris import _get_table_schema + + # Create initial table with extra column + initial_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + _mock_field("extra_col", "Str"), # Extra column to be kept + ], + schema_evolution="extend", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, initial_state) + + await asyncio.sleep(2) + + # Now apply a new state WITHOUT the extra column + new_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + # extra_col is removed from schema + ], + schema_evolution="extend", + ) + + # Apply the setup change - should NOT drop the extra column + await _Connector.apply_setup_change(key, initial_state, new_state) + + await asyncio.sleep(2) + + # Verify extra_col still exists in the database + actual_schema = await _get_table_schema( + doris_spec, doris_spec.database, doris_spec.table + ) + assert actual_schema is not None, "Table should exist" + assert "extra_col" in actual_schema, ( + "Extra column should be kept in extend mode. " + f"Available columns: {list(actual_schema.keys())}" + ) + assert "content" in actual_schema + + @pytest.mark.asyncio + async def test_extend_mode_adds_missing_columns( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that extend mode adds missing columns via ALTER TABLE. + + Documented behavior: Missing columns are added via ALTER TABLE ADD COLUMN. + """ + from cocoindex.targets.doris import _get_table_schema + + # Create initial table without the new column + initial_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + ], + schema_evolution="extend", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, initial_state) + + await asyncio.sleep(2) + + # Insert some data + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + await _stream_load(session, doris_spec, [{"id": 1, "content": "Test"}]) + + await asyncio.sleep(2) + + # Now apply a new state WITH an additional column + new_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + _mock_field("new_column", "Str"), # New column to add + ], + schema_evolution="extend", + ) + + # Apply the setup change - should add the new column + await _Connector.apply_setup_change(key, initial_state, new_state) + + await asyncio.sleep(2) + + # Verify new_column was added + actual_schema = await _get_table_schema( + doris_spec, doris_spec.database, doris_spec.table + ) + assert actual_schema is not None, "Table should exist" + assert "new_column" in actual_schema, ( + "New column should be added in extend mode. " + f"Available columns: {list(actual_schema.keys())}" + ) + assert "content" in actual_schema + + # Verify existing data is preserved + result = await _execute_ddl( + doris_spec, + f"SELECT * FROM {doris_spec.database}.{doris_spec.table} WHERE id = 1", + ) + assert len(result) == 1 + assert result[0]["content"] == "Test" + + @pytest.mark.asyncio + async def test_extend_mode_never_drops_table_except_key_change( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that extend mode never drops table except for primary key changes. + + Documented behavior: Tables are never dropped except for primary key changes. + """ + # Create initial table with data + initial_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + _mock_field("old_column", "Str"), + ], + schema_evolution="extend", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, initial_state) + + await asyncio.sleep(2) + + # Insert data + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + await _stream_load( + session, + doris_spec, + [ + {"id": 1, "content": "Row 1", "old_column": "Old data"}, + {"id": 2, "content": "Row 2", "old_column": "Old data 2"}, + ], + ) + + await asyncio.sleep(2) + + # Apply new state that removes a column (NOT a key change) + new_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], # Same key + value_fields=[ + _mock_field("content", "Str"), + # old_column removed + ], + schema_evolution="extend", + ) + + await _Connector.apply_setup_change(key, initial_state, new_state) + + await asyncio.sleep(2) + + # Verify data is still there (table wasn't dropped) + result = await _execute_ddl( + doris_spec, + f"SELECT COUNT(*) as cnt FROM {doris_spec.database}.{doris_spec.table}", + ) + assert result[0]["cnt"] == 2, ( + "Data should be preserved - table should not be dropped" + ) + + @pytest.mark.asyncio + async def test_strict_mode_drops_table_on_column_removal( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that strict mode drops and recreates table when columns are removed. + + Documented behavior: In strict mode, schema changes (removing columns) + cause the table to be dropped and recreated. + """ + # Create initial table with data + initial_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + _mock_field("old_column", "Str"), + ], + schema_evolution="strict", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, initial_state) + + await asyncio.sleep(2) + + # Insert data + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + await _stream_load( + session, + doris_spec, + [ + {"id": 1, "content": "Row 1", "old_column": "Old data"}, + ], + ) + + await asyncio.sleep(2) + + # Apply new state that removes a column + new_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + # old_column removed + ], + schema_evolution="strict", + ) + + # Check compatibility - should be NOT_COMPATIBLE in strict mode + compat = _Connector.check_state_compatibility(initial_state, new_state) + assert compat == op.TargetStateCompatibility.NOT_COMPATIBLE, ( + "Removing columns should be NOT_COMPATIBLE in strict mode" + ) + + @pytest.mark.asyncio + async def test_key_change_drops_table_even_in_extend_mode( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that key schema changes drop table even in extend mode. + + Documented behavior: Tables are never dropped except for primary key changes. + """ + # Create initial table + initial_state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[_mock_field("content", "Str")], + schema_evolution="extend", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, initial_state) + + await asyncio.sleep(2) + + # Insert data + async with aiohttp.ClientSession( + auth=aiohttp.BasicAuth(doris_spec.username, doris_spec.password), + ) as session: + await _stream_load(session, doris_spec, [{"id": 1, "content": "Test"}]) + + await asyncio.sleep(2) + + # New state with different key schema + new_state = _state_with_creds( + doris_spec, + key_fields=[ + _mock_field("id", "Int64"), + _mock_field("version", "Int64"), # Added to key + ], + value_fields=[_mock_field("content", "Str")], + schema_evolution="extend", + ) + + # Check compatibility - should be NOT_COMPATIBLE even in extend mode + compat = _Connector.check_state_compatibility(initial_state, new_state) + assert compat == op.TargetStateCompatibility.NOT_COMPATIBLE, ( + "Key schema change should be NOT_COMPATIBLE even in extend mode" + ) + + @pytest.mark.asyncio + async def test_table_model_validation( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that tables with correct DUPLICATE KEY model pass validation. + + Documented behavior: Tables are created using DUPLICATE KEY model, + which is required for vector index support in Doris 4.0+. + """ + from cocoindex.targets.doris import _get_table_model + + # Create a table via CocoIndex (should be DUPLICATE KEY) + state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[_mock_field("content", "Str")], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, state) + + await asyncio.sleep(2) + + # Verify table model is DUPLICATE KEY + table_model = await _get_table_model( + doris_spec, doris_spec.database, doris_spec.table + ) + assert table_model == "DUPLICATE KEY", ( + f"Table should use DUPLICATE KEY model, got: {table_model}" + ) + + # Apply same state again (should succeed since model is correct) + await _Connector.apply_setup_change(key, state, state) + + +# ============================================================ +# INDEX VALIDATION FAILURE TESTS +# ============================================================ + + +class TestIndexValidationFailures: + """Test index creation failures when columns are incompatible. + + These tests verify the documented behavior: Indexes are created only if + the referenced column exists and has a compatible type. Incompatible + columns should raise DorisSchemaError. + """ + + @pytest.mark.asyncio + async def test_vector_index_on_missing_column_raises_error( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that creating a vector index on a missing column raises DorisSchemaError.""" + from cocoindex.targets.doris import ( + _sync_indexes, + DorisSchemaError, + _get_table_schema, + ) + + # Create table WITHOUT embedding column + state_no_vector = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + # No embedding column + ], + schema_evolution="extend", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, state_no_vector) + + await asyncio.sleep(2) + + # Get actual schema from DB + actual_schema = await _get_table_schema( + doris_spec, doris_spec.database, doris_spec.table + ) + assert actual_schema is not None + + # Try to create vector index on non-existent column + state_with_index = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + ], + vector_indexes=[ + _VectorIndex( + name="idx_embedding_ann", + field_name="embedding", # This column doesn't exist! + index_type="hnsw", + metric_type="l2_distance", + dimension=384, + ) + ], + schema_evolution="extend", + ) + + # Should raise DorisSchemaError + with pytest.raises(DorisSchemaError) as exc_info: + await _sync_indexes( + doris_spec, key, state_no_vector, state_with_index, actual_schema + ) + + assert "embedding" in str(exc_info.value) + assert "does not exist" in str(exc_info.value) + + @pytest.mark.asyncio + async def test_vector_index_on_wrong_type_raises_error( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that creating a vector index on a non-array column raises DorisSchemaError.""" + from cocoindex.targets.doris import ( + _sync_indexes, + DorisSchemaError, + _get_table_schema, + ) + + # Create table with TEXT column instead of ARRAY + state_wrong_type = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + _mock_field( + "embedding", "Str" + ), # Wrong type - TEXT instead of ARRAY + ], + schema_evolution="extend", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, state_wrong_type) + + await asyncio.sleep(2) + + # Get actual schema from DB + actual_schema = await _get_table_schema( + doris_spec, doris_spec.database, doris_spec.table + ) + assert actual_schema is not None + + # Try to create vector index on TEXT column + state_with_index = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("content", "Str"), + _mock_field("embedding", "Str"), + ], + vector_indexes=[ + _VectorIndex( + name="idx_embedding_ann", + field_name="embedding", # This column is TEXT, not ARRAY + index_type="hnsw", + metric_type="l2_distance", + dimension=384, + ) + ], + schema_evolution="extend", + ) + + # Should raise DorisSchemaError + with pytest.raises(DorisSchemaError) as exc_info: + await _sync_indexes( + doris_spec, key, state_wrong_type, state_with_index, actual_schema + ) + + assert "embedding" in str(exc_info.value) + assert "ARRAY" in str(exc_info.value) or "type" in str(exc_info.value).lower() + + @pytest.mark.asyncio + async def test_inverted_index_on_missing_column_raises_error( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that creating an inverted index on a missing column raises DorisSchemaError.""" + from cocoindex.targets.doris import ( + _sync_indexes, + DorisSchemaError, + _get_table_schema, + ) + + # Create table WITHOUT content column + state_no_content = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("title", "Str"), + # No content column + ], + schema_evolution="extend", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, state_no_content) + + await asyncio.sleep(2) + + # Get actual schema from DB + actual_schema = await _get_table_schema( + doris_spec, doris_spec.database, doris_spec.table + ) + assert actual_schema is not None + + # Try to create inverted index on non-existent column + state_with_index = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("title", "Str"), + ], + inverted_indexes=[ + _InvertedIndex( + name="idx_content_inv", + field_name="content", # This column doesn't exist! + parser="unicode", + ) + ], + schema_evolution="extend", + ) + + # Should raise DorisSchemaError + with pytest.raises(DorisSchemaError) as exc_info: + await _sync_indexes( + doris_spec, key, state_no_content, state_with_index, actual_schema + ) + + assert "content" in str(exc_info.value) + assert "does not exist" in str(exc_info.value) + + @pytest.mark.asyncio + async def test_inverted_index_on_wrong_type_raises_error( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Test that creating an inverted index on a non-text column raises DorisSchemaError.""" + from cocoindex.targets.doris import ( + _sync_indexes, + DorisSchemaError, + _get_table_schema, + ) + + # Create table with INT column instead of TEXT + state_wrong_type = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("title", "Str"), + _mock_field("count", "Int64"), # Wrong type - INT instead of TEXT + ], + schema_evolution="extend", + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, state_wrong_type) + + await asyncio.sleep(2) + + # Get actual schema from DB + actual_schema = await _get_table_schema( + doris_spec, doris_spec.database, doris_spec.table + ) + assert actual_schema is not None + + # Try to create inverted index on INT column + state_with_index = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("title", "Str"), + _mock_field("count", "Int64"), + ], + inverted_indexes=[ + _InvertedIndex( + name="idx_count_inv", + field_name="count", # This column is BIGINT, not TEXT + parser="unicode", + ) + ], + schema_evolution="extend", + ) + + # Should raise DorisSchemaError + with pytest.raises(DorisSchemaError) as exc_info: + await _sync_indexes( + doris_spec, key, state_wrong_type, state_with_index, actual_schema + ) + + assert "count" in str(exc_info.value) + assert "type" in str(exc_info.value).lower() or "TEXT" in str(exc_info.value) + + +# ============================================================ +# QUERY HELPER TESTS +# ============================================================ + + +class TestQueryHelpers: + """Test query helper functions documented in the docs.""" + + @pytest.mark.asyncio + async def test_connect_async_with_proper_cleanup(self, doris_spec: DorisTarget): + """Test connect_async helper with proper cleanup using ensure_closed(). + + Documented usage: + conn = await connect_async(...) + try: + async with conn.cursor() as cursor: + await cursor.execute("SELECT * FROM table") + rows = await cursor.fetchall() + finally: + conn.close() + await conn.ensure_closed() + """ + conn = await connect_async( + fe_host=doris_spec.fe_host, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + database=doris_spec.database, + ) + try: + async with conn.cursor() as cursor: + await cursor.execute("SELECT 1 as value") + rows = await cursor.fetchall() + assert len(rows) == 1 + assert rows[0][0] == 1 + finally: + conn.close() + await conn.ensure_closed() + + def test_build_vector_search_query_l2_distance(self): + """Test build_vector_search_query with L2 distance metric.""" + sql = build_vector_search_query( + table="test_db.embeddings", + vector_field="embedding", + query_vector=[0.1, 0.2, 0.3, 0.4], + metric="l2_distance", + limit=10, + select_columns=["id", "content"], + ) + + assert "l2_distance_approximate" in sql + assert "`embedding`" in sql # Backtick-quoted + assert "[0.1, 0.2, 0.3, 0.4]" in sql + assert "LIMIT 10" in sql + assert "ORDER BY _distance ASC" in sql + assert "`id`, `content`" in sql # Backtick-quoted + + def test_build_vector_search_query_inner_product(self): + """Test build_vector_search_query with inner product metric.""" + sql = build_vector_search_query( + table="test_db.embeddings", + vector_field="embedding", + query_vector=[0.1, 0.2], + metric="inner_product", + limit=5, + ) + + assert "inner_product_approximate" in sql + assert "ORDER BY _distance DESC" in sql # Larger = more similar + assert "LIMIT 5" in sql + assert "`test_db`.`embeddings`" in sql # Backtick-quoted table + + def test_build_vector_search_query_with_where_clause(self): + """Test build_vector_search_query with WHERE clause filter.""" + sql = build_vector_search_query( + table="test_db.docs", + vector_field="embedding", + query_vector=[1.0, 0.0], + metric="l2_distance", + limit=10, + where_clause="category = 'tech'", + ) + + assert "WHERE category = 'tech'" in sql + assert "`test_db`.`docs`" in sql # Backtick-quoted table + + +# ============================================================ +# DOCUMENTED BEHAVIOR VERIFICATION +# ============================================================ + + +class TestDocumentedBehavior: + """Verify all documented behavior works as specified.""" + + @pytest.mark.asyncio + async def test_vector_type_mapped_to_array_float( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Verify: Vectors are mapped to ARRAY columns in Doris.""" + from cocoindex.targets.doris import _get_table_schema + + state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("embedding", "Vector", dim=4), + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, state) + + await asyncio.sleep(2) + + actual_schema = await _get_table_schema( + doris_spec, doris_spec.database, doris_spec.table + ) + assert actual_schema is not None + assert "embedding" in actual_schema + assert "ARRAY" in actual_schema["embedding"].doris_type.upper() + + @pytest.mark.asyncio + async def test_vector_columns_are_not_null( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Verify: Vector columns are automatically created as NOT NULL.""" + from cocoindex.targets.doris import _get_table_schema + + state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[ + _mock_field("embedding", "Vector", dim=4), + ], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, state) + + await asyncio.sleep(2) + + actual_schema = await _get_table_schema( + doris_spec, doris_spec.database, doris_spec.table + ) + assert actual_schema is not None + assert "embedding" in actual_schema + assert actual_schema["embedding"].nullable is False, ( + "Vector columns should be NOT NULL for vector index support" + ) + + @pytest.mark.asyncio + async def test_duplicate_key_table_model( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """Verify: Tables are created using DUPLICATE KEY model.""" + state = _state_with_creds( + doris_spec, + key_fields=[_mock_field("id", "Int64")], + value_fields=[_mock_field("content", "Str")], + ) + key = _TableKey(doris_spec.fe_host, doris_spec.database, doris_spec.table) + await _Connector.apply_setup_change(key, None, state) + + await asyncio.sleep(2) + + result = await _execute_ddl( + doris_spec, + f"SHOW CREATE TABLE {doris_spec.database}.{doris_spec.table}", + ) + create_stmt = result[0].get("Create Table", "") + assert "DUPLICATE KEY" in create_stmt.upper(), ( + "Table should use DUPLICATE KEY model for vector index support" + ) + + @pytest.mark.asyncio + async def test_default_config_values_match_docs(self): + """Verify default config values match documentation.""" + spec = DorisTarget( + fe_host="localhost", + database="test", + table="test_table", + ) + + # Connection defaults from docs + assert spec.fe_http_port == 8080, "Default fe_http_port should be 8080" + assert spec.query_port == 9030, "Default query_port should be 9030" + assert spec.username == "root", "Default username should be 'root'" + assert spec.password == "", "Default password should be empty string" + assert spec.enable_https is False, "Default enable_https should be False" + + # Behavior defaults from docs + assert spec.batch_size == 10000, "Default batch_size should be 10000" + assert spec.stream_load_timeout == 600, ( + "Default stream_load_timeout should be 600" + ) + assert spec.auto_create_table is True, ( + "Default auto_create_table should be True" + ) + assert spec.schema_evolution == "extend", ( + "Default schema_evolution should be 'extend'" + ) + + # Retry defaults from docs + assert spec.max_retries == 3, "Default max_retries should be 3" + assert spec.retry_base_delay == 1.0, "Default retry_base_delay should be 1.0" + assert spec.retry_max_delay == 30.0, "Default retry_max_delay should be 30.0" + + # Table property defaults from docs + assert spec.replication_num == 1, "Default replication_num should be 1" + assert spec.buckets == "auto", "Default buckets should be 'auto'" + + +# ============================================================ +# TEXT EMBEDDING EXAMPLE INTEGRATION TEST +# ============================================================ + + +class TestTextEmbeddingExample: + """Integration tests for the text_embedding_doris example pattern.""" + + @pytest.mark.asyncio + async def test_text_embedding_flow_pattern( + self, doris_spec: DorisTarget, ensure_database, cleanup_table + ): + """ + Test the complete text_embedding_doris example flow pattern: + 1. Create table with vector index + 2. Insert document chunks with embeddings + 3. Query using vector similarity search + """ + import uuid + from cocoindex.targets.doris import ( + _execute_ddl, + connect_async, + build_vector_search_query, + ) + + # Step 1: Create table with vector and FTS index (matching example pattern) + table_name = doris_spec.table + database = doris_spec.database + + # Create table via connector + vector_indexes = [ + _VectorIndex( + name="idx_text_embedding_ann", + field_name="text_embedding", + index_type="hnsw", + metric_type="l2_distance", + dimension=4, # Small dimension for testing + max_degree=16, + ef_construction=200, + ) + ] + inverted_indexes = [ + _InvertedIndex( + name="idx_text_inv", + field_name="text", + parser="unicode", + ) + ] + + state = _State( + key_fields_schema=[_mock_field("id", "Str")], + value_fields_schema=[ + _mock_field("filename", "Str"), + _mock_field("location", "Str"), + _mock_field("text", "Str"), + _mock_field("text_embedding", "Vector", dim=4), + ], + vector_indexes=vector_indexes, + inverted_indexes=inverted_indexes, + replication_num=1, + buckets="auto", + fe_http_port=doris_spec.fe_http_port, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + max_retries=3, + retry_base_delay=1.0, + retry_max_delay=30.0, + auto_create_table=True, + schema_evolution="extend", + ) + + key = _TableKey(doris_spec.fe_host, database, table_name) + await _Connector.apply_setup_change(key, None, state) + await asyncio.sleep(3) + + # Step 2: Insert document chunks (matching example data format) + # Build mutations dict: key -> {field: value, ...} + mutations: dict[Any, dict[str, Any] | None] = { + "doc1_chunk_0": { + "filename": "doc1.md", + "location": "0:100", + "text": "Vector databases are specialized database systems designed for similarity search.", + "text_embedding": [0.1, 0.2, 0.3, 0.4], + }, + "doc2_chunk_0": { + "filename": "doc2.md", + "location": "0:80", + "text": "Apache Doris is a high-performance analytical database with vector support.", + "text_embedding": [0.2, 0.3, 0.4, 0.5], + }, + "doc3_chunk_0": { + "filename": "doc3.md", + "location": "0:90", + "text": "Semantic search uses embeddings to find relevant results.", + "text_embedding": [0.3, 0.4, 0.5, 0.6], + }, + } + + context = await _Connector.prepare(doris_spec, state) + await _Connector.mutate((context, mutations)) + await _Connector.cleanup(context) + + # Wait for data to be visible + await asyncio.sleep(3) + + # Step 3: Build index (required after data load for IVF, good practice for HNSW) + try: + await _execute_ddl( + doris_spec, + f"BUILD INDEX idx_text_embedding_ann ON `{database}`.`{table_name}`", + ) + await asyncio.sleep(2) + except Exception: + pass # Index may already be built or not require explicit build + + # Step 4: Query using vector similarity (matching example query pattern) + query_vector = [0.15, 0.25, 0.35, 0.45] # Similar to doc1 + + sql = build_vector_search_query( + table=f"{database}.{table_name}", + vector_field="text_embedding", + query_vector=query_vector, + metric="l2_distance", + limit=3, + select_columns=["id", "filename", "text"], + ) + + conn = await connect_async( + fe_host=doris_spec.fe_host, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + database=database, + ) + + try: + async with conn.cursor() as cursor: + await cursor.execute(sql) + results = await cursor.fetchall() + finally: + conn.close() + await conn.ensure_closed() + + # Verify results + assert len(results) == 3, "Should return 3 results" + # Results should be ordered by distance (closest first) + # doc1 [0.1, 0.2, 0.3, 0.4] should be closest to query [0.15, 0.25, 0.35, 0.45] + assert results[0][1] == "doc1.md", ( + "First result should be doc1.md (closest vector)" + ) + + # Step 5: Verify full-text search works (optional part of example) + fts_sql = f""" + SELECT id, filename, text + FROM {database}.{table_name} + WHERE text MATCH_ANY 'vector' + LIMIT 5 + """ + + conn = await connect_async( + fe_host=doris_spec.fe_host, + query_port=doris_spec.query_port, + username=doris_spec.username, + password=doris_spec.password, + database=database, + ) + + try: + async with conn.cursor() as cursor: + await cursor.execute(fts_sql) + fts_results = await cursor.fetchall() + finally: + conn.close() + await conn.ensure_closed() + + assert len(fts_results) >= 1, ( + "Should find at least one document containing 'vector'" + ) diff --git a/python/cocoindex/tests/targets/test_doris_unit.py b/python/cocoindex/tests/targets/test_doris_unit.py new file mode 100644 index 000000000..186ce00f9 --- /dev/null +++ b/python/cocoindex/tests/targets/test_doris_unit.py @@ -0,0 +1,493 @@ +""" +Unit tests for Doris connector (no database connection required). +""" +# mypy: disable-error-code="no-untyped-def" + +import uuid +import math +from typing import Literal +import pytest + +from cocoindex.targets.doris import ( + DorisTarget, + _TableKey, + _State, + _VectorIndex, + _Connector, + _convert_value_type_to_doris_type, + _convert_value_for_doris, + _validate_identifier, + _generate_create_table_ddl, + _get_vector_dimension, + _is_vector_indexable, + _is_retryable_mysql_error, + DorisSchemaError, + DorisConnectionError, + RetryConfig, + with_retry, +) +from cocoindex.engine_type import ( + FieldSchema, + EnrichedValueType, + BasicValueType, + VectorTypeSchema, +) +from cocoindex import op +from cocoindex.index import ( + IndexOptions, + VectorIndexDef, + VectorSimilarityMetric, +) + +_BasicKind = Literal[ + "Bytes", + "Str", + "Bool", + "Int64", + "Float32", + "Float64", + "Range", + "Uuid", + "Date", + "Time", + "LocalDateTime", + "OffsetDateTime", + "TimeDelta", + "Json", + "Vector", + "Union", +] + + +def _mock_field( + name: str, kind: _BasicKind, nullable: bool = False, dim: int | None = None +) -> FieldSchema: + """Create mock FieldSchema for testing.""" + if kind == "Vector": + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), + dimension=dim, + ) + basic_type = BasicValueType(kind=kind, vector=vec_schema) + else: + basic_type = BasicValueType(kind=kind) + return FieldSchema( + name=name, + value_type=EnrichedValueType(type=basic_type, nullable=nullable), + ) + + +# ============================================================ +# TYPE MAPPING AND JSON FALLBACK TESTS +# ============================================================ + + +class TestTypeMapping: + """Test CocoIndex type -> Doris SQL type conversion.""" + + @pytest.mark.parametrize( + "kind,expected_doris", + [ + ("Str", "TEXT"), + ("Bool", "BOOLEAN"), + ("Int64", "BIGINT"), + ("Float32", "FLOAT"), + ("Float64", "DOUBLE"), + ("Uuid", "VARCHAR(36)"), + ("Json", "JSON"), + ], + ) + def test_basic_type_mapping(self, kind: _BasicKind, expected_doris: str) -> None: + basic_type = BasicValueType(kind=kind) + enriched = EnrichedValueType(type=basic_type) + assert _convert_value_type_to_doris_type(enriched) == expected_doris + + def test_vector_with_dimension_maps_to_array(self) -> None: + """Vector with dimension should map to ARRAY.""" + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), dimension=384 + ) + basic_type = BasicValueType(kind="Vector", vector=vec_schema) + enriched = EnrichedValueType(type=basic_type) + assert _convert_value_type_to_doris_type(enriched) == "ARRAY" + + def test_vector_without_dimension_falls_back_to_json(self) -> None: + """Vector without dimension should fall back to JSON (like Postgres/Qdrant).""" + # No dimension + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), dimension=None + ) + basic_type = BasicValueType(kind="Vector", vector=vec_schema) + enriched = EnrichedValueType(type=basic_type) + assert _convert_value_type_to_doris_type(enriched) == "JSON" + + # No vector schema at all + basic_type = BasicValueType(kind="Vector", vector=None) + enriched = EnrichedValueType(type=basic_type) + assert _convert_value_type_to_doris_type(enriched) == "JSON" + + def test_unsupported_type_falls_back_to_json(self) -> None: + """Unsupported types should fall back to JSON.""" + basic_type = BasicValueType(kind="Union") + enriched = EnrichedValueType(type=basic_type) + assert _convert_value_type_to_doris_type(enriched) == "JSON" + + +class TestVectorIndexability: + """Test vector indexability and dimension extraction.""" + + def test_vector_indexability(self) -> None: + """Only vectors with fixed dimension are indexable.""" + # With dimension - indexable + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), dimension=384 + ) + basic_type = BasicValueType(kind="Vector", vector=vec_schema) + enriched = EnrichedValueType(type=basic_type) + assert _is_vector_indexable(enriched) is True + + # Without dimension - not indexable + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), dimension=None + ) + basic_type = BasicValueType(kind="Vector", vector=vec_schema) + enriched = EnrichedValueType(type=basic_type) + assert _is_vector_indexable(enriched) is False + + def test_get_vector_dimension(self) -> None: + """Test dimension extraction returns None for non-indexable vectors.""" + fields = [_mock_field("embedding", "Vector", dim=384)] + assert _get_vector_dimension(fields, "embedding") == 384 + + # No dimension + fields = [_mock_field("embedding", "Vector", dim=None)] + assert _get_vector_dimension(fields, "embedding") is None + + # Field not found + fields = [_mock_field("other", "Str")] + assert _get_vector_dimension(fields, "embedding") is None + + +# ============================================================ +# VALUE CONVERSION TESTS +# ============================================================ + + +class TestValueConversion: + """Test Python value -> Doris-compatible format conversion.""" + + def test_special_value_handling(self) -> None: + """Test handling of special values (UUID, NaN, None).""" + test_uuid = uuid.uuid4() + assert _convert_value_for_doris(test_uuid) == str(test_uuid) + assert _convert_value_for_doris(math.nan) is None + assert _convert_value_for_doris(None) is None + + def test_collection_conversion(self) -> None: + """Test list and dict conversion.""" + assert _convert_value_for_doris([1.0, 2.0, 3.0]) == [1.0, 2.0, 3.0] + assert _convert_value_for_doris({"key": "value"}) == {"key": "value"} + + +# ============================================================ +# DDL AND SCHEMA TESTS +# ============================================================ + + +class TestDDLGeneration: + """Test DDL generation for Doris.""" + + def test_create_table_structure(self) -> None: + """Test basic table DDL generation.""" + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + ) + key = _TableKey("localhost", "test_db", "test_table") + ddl = _generate_create_table_ddl(key, state) + + assert "DUPLICATE KEY" in ddl # Required for vector index support + assert "id BIGINT NOT NULL" in ddl + assert "content TEXT" in ddl + + def test_vector_column_ddl(self) -> None: + """Test vector column DDL with and without dimension.""" + # With dimension - ARRAY + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("embedding", "Vector", dim=768)], + ) + key = _TableKey("localhost", "test_db", "test_table") + ddl = _generate_create_table_ddl(key, state) + assert "embedding ARRAY NOT NULL" in ddl + + # Without dimension - JSON + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), dimension=None + ) + basic_type = BasicValueType(kind="Vector", vector=vec_schema) + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[ + FieldSchema( + name="embedding", value_type=EnrichedValueType(type=basic_type) + ) + ], + ) + ddl = _generate_create_table_ddl(key, state) + assert "embedding JSON" in ddl + + def test_vector_index_ddl(self) -> None: + """Test vector index DDL generation.""" + state = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("embedding", "Vector", dim=768)], + vector_indexes=[ + _VectorIndex( + name="idx_embedding_ann", + field_name="embedding", + index_type="hnsw", + metric_type="l2_distance", + dimension=768, + ) + ], + ) + key = _TableKey("localhost", "test_db", "test_table") + ddl = _generate_create_table_ddl(key, state) + + assert "INDEX idx_embedding_ann (embedding) USING ANN" in ddl + assert '"index_type" = "hnsw"' in ddl + + +class TestIdentifierValidation: + """Test SQL identifier validation.""" + + def test_valid_identifiers(self) -> None: + _validate_identifier("valid_table_name") + _validate_identifier("MyTable123") + + def test_invalid_identifiers(self) -> None: + with pytest.raises(DorisSchemaError): + _validate_identifier("invalid-name") + with pytest.raises(DorisSchemaError): + _validate_identifier("'; DROP TABLE users; --") + + +# ============================================================ +# CONNECTOR LOGIC TESTS +# ============================================================ + + +class TestConnectorLogic: + """Test connector business logic.""" + + def test_vector_index_skipped_for_no_dimension(self) -> None: + """Test that vector index is skipped when dimension is not available.""" + spec = DorisTarget(fe_host="localhost", database="test", table="test_table") + key_fields = [_mock_field("id", "Int64")] + + # Vector without dimension + vec_schema = VectorTypeSchema( + element_type=BasicValueType(kind="Float32"), dimension=None + ) + basic_type = BasicValueType(kind="Vector", vector=vec_schema) + value_fields = [ + FieldSchema(name="embedding", value_type=EnrichedValueType(type=basic_type)) + ] + + # Request vector index on field without dimension + index_options = IndexOptions( + primary_key_fields=["id"], + vector_indexes=[ + VectorIndexDef( + field_name="embedding", + metric=VectorSimilarityMetric.L2_DISTANCE, + ) + ], + ) + + state = _Connector.get_setup_state( + spec, key_fields, value_fields, index_options + ) + + # Vector index should be skipped + assert state.vector_indexes is None or len(state.vector_indexes) == 0 + + def test_state_compatibility(self) -> None: + """Test schema compatibility checking.""" + state1 = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + ) + state2 = _State( + key_fields_schema=[_mock_field("id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + ) + assert ( + _Connector.check_state_compatibility(state1, state2) + == op.TargetStateCompatibility.COMPATIBLE + ) + + # Key change is incompatible + state3 = _State( + key_fields_schema=[_mock_field("new_id", "Int64")], + value_fields_schema=[_mock_field("content", "Str")], + ) + assert ( + _Connector.check_state_compatibility(state1, state3) + == op.TargetStateCompatibility.NOT_COMPATIBLE + ) + + def test_timeout_config_propagated(self) -> None: + """Test that timeout configs are propagated from DorisTarget to _State.""" + spec = DorisTarget( + fe_host="localhost", + database="test", + table="test_table", + schema_change_timeout=120, # Non-default + index_build_timeout=600, # Non-default + ) + key_fields = [_mock_field("id", "Int64")] + value_fields = [_mock_field("content", "Str")] + index_options = IndexOptions(primary_key_fields=["id"]) + + state = _Connector.get_setup_state( + spec, key_fields, value_fields, index_options + ) + + assert state.schema_change_timeout == 120 + assert state.index_build_timeout == 600 + + +# ============================================================ +# RETRY LOGIC TESTS +# ============================================================ + + +class TestRetryLogic: + """Test retry configuration and behavior.""" + + @pytest.mark.asyncio + async def test_retry_succeeds_on_first_try(self) -> None: + """Test retry logic when operation succeeds immediately.""" + call_count = 0 + + async def successful_op() -> str: + nonlocal call_count + call_count += 1 + return "success" + + result = await with_retry( + successful_op, + config=RetryConfig(max_retries=3), + retryable_errors=(Exception,), + ) + + assert result == "success" + assert call_count == 1 + + @pytest.mark.asyncio + async def test_retry_succeeds_after_failures(self) -> None: + """Test retry logic with transient failures.""" + import asyncio + + call_count = 0 + + async def flaky_op() -> str: + nonlocal call_count + call_count += 1 + if call_count < 3: + raise asyncio.TimeoutError("Transient error") + return "success" + + result = await with_retry( + flaky_op, + config=RetryConfig(max_retries=3, base_delay=0.01), + retryable_errors=(asyncio.TimeoutError,), + ) + + assert result == "success" + assert call_count == 3 + + @pytest.mark.asyncio + async def test_retry_exhausted_raises_error(self) -> None: + """Test retry logic when all retries fail.""" + import asyncio + + call_count = 0 + + async def always_fails() -> str: + nonlocal call_count + call_count += 1 + raise asyncio.TimeoutError("Always fails") + + with pytest.raises(DorisConnectionError) as exc_info: + await with_retry( + always_fails, + config=RetryConfig(max_retries=2, base_delay=0.01), + retryable_errors=(asyncio.TimeoutError,), + ) + + assert call_count == 3 # Initial + 2 retries + assert "failed after 3 attempts" in str(exc_info.value) + + +class TestMySQLErrorRetry: + """Test MySQL error retry functionality.""" + + def test_retryable_mysql_error_codes(self) -> None: + """Test that specific MySQL error codes are identified as retryable.""" + try: + import pymysql + except ImportError: + pytest.skip("pymysql not installed") + + # Retryable error codes (connection issues) + retryable_codes = [2003, 2006, 2013, 1040, 1205] + for code in retryable_codes: + error = pymysql.err.OperationalError(code, f"Test error {code}") + assert _is_retryable_mysql_error(error), ( + f"Error code {code} should be retryable" + ) + + # Non-retryable error codes + non_retryable_codes = [ + 1064, + 1146, + 1045, + ] # Syntax error, table not found, access denied + for code in non_retryable_codes: + error = pymysql.err.OperationalError(code, f"Test error {code}") + assert not _is_retryable_mysql_error(error), ( + f"Error code {code} should not be retryable" + ) + + def test_non_mysql_error_not_retryable(self) -> None: + """Test that non-MySQL errors are not identified as retryable.""" + assert not _is_retryable_mysql_error(ValueError("test")) + assert not _is_retryable_mysql_error(RuntimeError("test")) + + @pytest.mark.asyncio + async def test_with_retry_handles_mysql_errors(self) -> None: + """Test that with_retry retries on MySQL connection errors.""" + try: + import pymysql + except ImportError: + pytest.skip("pymysql not installed") + + call_count = 0 + + async def mysql_flaky_op() -> str: + nonlocal call_count + call_count += 1 + if call_count < 3: + raise 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