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1740e315f4
Adds batching to structured data row imports. Previously each row was sent as a separate WebSocket message, causing one embeddings service call per row. The bulk import clients now accumulate rows into batches (default 40) before sending, and the --batch-size CLI argument is wired through from tg-load-structured-data to the import call. Splits the schema "indexes" field into two separate configurations: - "query-indexes": fields for Cassandra exact-match retrieval - "vector-indexes": fields for vector embedding and semantic search This avoids wasting compute embedding primary key integers that only need exact lookup, while allowing each field to independently opt in to either or both retrieval patterns. Adds row_id (the primary key value) as a clustering column in the Cassandra rows table so multiple rows sharing the same index value no longer silently overwrite each other. Tests updated for new Cassandra schema.
75 lines
2.8 KiB
Markdown
75 lines
2.8 KiB
Markdown
# Structured Data: Index Split and Composite Keys
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## Problem
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The schema `"indexes"` field serves two purposes:
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- **Cassandra row storage**: creates denormalised copies for exact-match retrieval
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- **Vector embeddings**: sends field values to the embeddings service for semantic search
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These are different capabilities with different costs. Embedding integer IDs is wasted compute; conversely, a field may benefit from semantic search without needing a Cassandra retrieval copy.
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Additionally, the current Cassandra `rows` table uses `index_value` as the sole clustering column. Two rows sharing the same value for an indexed field (e.g. two stars named "Sirius") silently overwrite each other.
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## Changes
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### 1. Split index configuration
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Replace `"indexes"` with:
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- `"query-indexes"` -- fields for Cassandra exact-match lookup
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- `"vector-indexes"` -- fields for vector embedding and semantic search
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A field can appear in both lists.
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### 2. Composite clustering key
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Add `row_id` (the value of the schema's primary key field) as a second clustering column:
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```
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Before:
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PRIMARY KEY ((collection, schema_name, index_name), index_value)
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After:
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PRIMARY KEY ((collection, schema_name, index_name), index_value, row_id)
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```
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This allows multiple rows to share the same index value. Queries by `index_value` alone still work -- Cassandra allows omitting trailing clustering columns.
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### 3. Schema config example
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```json
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{
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"name": "stars",
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"fields": [
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{"name": "id", "type": "integer", "primary_key": true},
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{"name": "proper", "type": "string"},
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{"name": "spect", "type": "string"},
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{"name": "con", "type": "string"}
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],
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"query-indexes": ["id", "proper", "con"],
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"vector-indexes": ["proper", "spect", "con"]
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}
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```
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## Future considerations
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1. **Composite primary keys**: The primary key could span multiple columns
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(e.g. `"primary_key": ["region", "id"]`). The data model already
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supports this -- `row_id` in Cassandra is `frozen<list<text>>` and
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`index_value` is already a list. The schema config and writer would
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need to resolve multiple fields into the `row_id` value.
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2. **Composite query indexes**: Each query index entry could cover multiple
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columns (e.g. `"query-indexes": [["last_name", "first_name"], "email"]`).
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This would allow exact-match lookup on field combinations. The Cassandra
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storage already uses `index_value` as a `frozen<list<text>>`, so the
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data model supports it -- the config parsing and index value construction
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would need updating.
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### Affected components
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- `storage/rows/cassandra/write.py` -- table schema, reads `query-indexes`, writes `row_id`
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- `embeddings/row_embeddings/embeddings.py` -- reads `vector-indexes`
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- `query/row_embeddings/qdrant/service.py` -- no change (consumes embeddings output)
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- Row query service -- must handle multiple rows per index value
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