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feat: add analyze_plan api (#2280)
add analyze plan api to allow executing the queries and see runtime metrics. Which help identify the query IO overhead and help identify query slowness
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@@ -36,6 +36,49 @@ protected inner: NativeQueryType | Promise<NativeQueryType>;
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## Methods
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### analyzePlan()
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```ts
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analyzePlan(): Promise<string>
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```
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Executes the query and returns the physical query plan annotated with runtime metrics.
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This is useful for debugging and performance analysis, as it shows how the query was executed
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and includes metrics such as elapsed time, rows processed, and I/O statistics.
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#### Returns
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`Promise`<`string`>
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A query execution plan with runtime metrics for each step.
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#### Example
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```ts
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import * as lancedb from "@lancedb/lancedb"
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const db = await lancedb.connect("./.lancedb");
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const table = await db.createTable("my_table", [
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{ vector: [1.1, 0.9], id: "1" },
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]);
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const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
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Example output (with runtime metrics inlined):
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AnalyzeExec verbose=true, metrics=[]
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ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
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Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
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CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
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GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
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FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
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SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
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KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
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LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
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```
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***
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### execute()
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```ts
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