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perf(ddxdb): mirror ddx's own tables on DuckDB without reading the catalog - #210
With #206 and #207, xarray-sql's GPT training step runs on DuckDB, but a cached SGD step took 1.28–1.36 s, against about 0.37 s on DataFusion. Most of it was the backend's catalog work for ddx's own tables:
select_all, 325 ms (56 calls). ddx asks for the schema of each table a step writes. Each call first synced the planner's mirror with the whole catalog, which takes two catalog queries, about 5 ms.
Dropping mirrors, 192 ms (154 calls). Each drop first asked the planner's information_schema whether the object was a table or a view.
How
Every table materialize writes is ddx's (__ddx_, a reserved prefix), and _catalog skips those names, so no mirrored object shares one. So for those tables:
materialize mirrors the table with CREATE OR REPLACE TEMP TABLE on the planner, and records its name.
drop_table drops the planner's copy as a table.
select_all of a recorded table plans SELECT * FROM name on the planner as it stands: a temporary table is found by its name alone, and its plan reads nothing else. Any other name takes the old path.
Edge cases
A name drop_table was given that was never written: DROP TABLE IF EXISTS does nothing, as before.
A user's objects still go through _sync on every plan_sql, plan_key and view_sql, so a change to the catalog between calls is still seen.
Performance
GPT (demo 2), cached sql_all steps on DuckDB, same machine:
before
after
step 1, 2
1.36 s, 1.28 s
0.74 s, 0.77 s
select_all (56 calls)
325 ms
23 ms
drop_table (77 calls)
118 ms
27 ms
DuckDB now spends 394 ms of a step running the 77 steps' SQL (CREATE TEMP TABLE … AS). The rest is mostly planning: _sync (95 ms), and plan_sql, plan_key and view_sql, which overlap with it. #203's prepare skips those.
Tests
The ddxdb suite: 100 passed, 1 xfailed.
The GPT step on DuckDB still matches DataFusion's on all 11 parameter tables, to within 3e-17.
Materialized tables are now available to queries immediately after creation, and dropping them prevents them from being selected by later queries. Queries involving other tables continue to use existing catalog synchronization behavior. These updates make materialized-table reads and cleanup more consistent across DuckDB operations, while leaving workflows for non-materialized tables unchanged. Existing query workflows remain unchanged.
…talog
ddx asks for the schema of each table a step writes (`select_all`), and
DuckDB's backend first made its planner's mirror match the whole catalog:
two catalog queries, about 5 ms each time. It dropped each mirrored table
after asking the planner's information schema whether it was a table or a
view. On xarray-sql's GPT, that was 56 schemas and 154 drops per step.
A table `materialize` writes is ddx's (`__ddx_`), and no object the
planner mirrors has its name. So its mirror is made with `CREATE OR
REPLACE TEMP TABLE` and dropped as a table, and its plan of
`SELECT * FROM name` is asked of the planner as `materialize` left it.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CvszJhH8pn8H9G69wEPMU2
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Walkthrough
DuckDBBackend tracks tables created through materialization. It creates planner-side temporary tables for them, plans tracked table selections from those tables, and drops them directly from the planner.
DuckDBBackend initializes a set of materialized table names. materialize creates a planner-side temporary table from the described columns and records its name. drop_table removes the name from the set and drops the planner-side table.
Plan tracked table selections python/ddxdb/ddxdb/duckdb.py
select_all plans tracked tables from the planner-side temporary table without synchronizing the caller’s catalog. Untracked names retain the existing catalog synchronization and planning path.
xqlsystems/xarray-sql#285: The change reduces DuckDB catalog synchronization for ddx-owned tables, which relates to the issue’s DuckDB performance objective.
xqlsystems/ddx#202: The change avoids catalog synchronization for tracked DuckDB temporary tables, addressing part of the repeated planning overhead described by the issue.
Possibly related PRs
xqlsystems/ddx#192: This PR extends the DuckDB backend’s materialization, selection, and table-drop paths.
xqlsystems/ddx#203: Both PRs affect the backend’s materialization and table-drop contract.
xqlsystems/ddx#206: This PR adds planner-side empty tables, and PR #206 changes planner statistics propagation for empty mirrored tables.
Merge Risk: Minimal · up to a5041
The materialized-table planning changes preserve the relevant table lifecycle and selection behavior, with no merge-blocking risk identified.
Security architecture risk: Low · up to a5041
The optimization remains within the caller’s existing database authority. Remaining uncertainty concerns temporary-table consistency after failures or concurrent use, rather than a demonstrated new security exposure.
Retained concerns
No architecture-level concerns identified.
Security review details
Security Blast Radius
inferred — The changed operations affect temporary tables and schema planning associated with one backend and its caller connection. Execution continues under that connection’s existing database authority; the inspected change introduces no additional credential or cross-service execution path.
Trust Boundaries and Controls
observed — Names remain quoted as SQL identifiers. Untracked selections still synchronize and apply catalog ambiguity checks. Direct low-level callers can materialize non-reserved names and enter the fast path, but that interface already executes supplied plans on the caller connection; it is not an independent authorization boundary.
Resilience and Maintainability Implications
observed — The existing rewritten-statement context attempts cleanup on normal exit and exceptions. Its release contract attempts every table drop and reports failure, but does not promise transactional restoration of caller and planner state.
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The title clearly and concisely describes the main change: avoiding catalog reads by mirroring ddx-owned tables on DuckDB.
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Review comments at @python/ddxdb/ddxdb/duckdb.py:
- Around line 356-359: Update `drop_table` so `_written.discard(name)` runs even
if the planner-side `execute` raises after the caller-side drop succeeds;
preserve propagation of the planner error.
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A drop that failed on the planner left the name recorded as written, so
a later select_all of it would skip reading the catalog.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CvszJhH8pn8H9G69wEPMU2
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Why
With #206 and #207, xarray-sql's GPT training step runs on DuckDB, but a cached SGD step took 1.28–1.36 s, against about 0.37 s on DataFusion. Most of it was the backend's catalog work for ddx's own tables:
select_all, 325 ms (56 calls). ddx asks for the schema of each table a step writes. Each call first synced the planner's mirror with the whole catalog, which takes two catalog queries, about 5 ms.information_schemawhether the object was a table or a view.How
Every table
materializewrites is ddx's (__ddx_, a reserved prefix), and_catalogskips those names, so no mirrored object shares one. So for those tables:materializemirrors the table withCREATE OR REPLACE TEMP TABLEon the planner, and records its name.drop_tabledrops the planner's copy as a table.select_allof a recorded table plansSELECT * FROM nameon the planner as it stands: a temporary table is found by its name alone, and its plan reads nothing else. Any other name takes the old path.Edge cases
drop_tablewas given that was never written:DROP TABLE IF EXISTSdoes nothing, as before._syncon everyplan_sql,plan_keyandview_sql, so a change to the catalog between calls is still seen.Performance
GPT (demo 2), cached
sql_allsteps on DuckDB, same machine:select_all(56 calls)drop_table(77 calls)DuckDB now spends 394 ms of a step running the 77 steps' SQL (
CREATE TEMP TABLE … AS). The rest is mostly planning:_sync(95 ms), andplan_sql,plan_keyandview_sql, which overlap with it. #203'sprepareskips those.Tests
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https://claude.ai/code/session_01CvszJhH8pn8H9G69wEPMU2
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