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Graph/sql-predicate-cardinality-drift

AntiPattern

SQL Predicate and Cardinality Drift

sql-predicate-cardinality-drift

SQL fails to return the intended rows because predicates and cardinality assumptions drift into the wrong level (LIMITed subqueries, misused loops/IFs, and non-sargable date expressions), causing slow scans, wrong duplicates, or missing exclusions across DBs.

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Updated5/30/2026

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30 linked nodes

MATCHESSolution

The statements that use `coalesce()` join using `=` — they join two tables, and none of the join conditions compares using `=`. Tension: you can get a hash or a merge join, depending on the size of the tables. Outcome: That is most likely the reason for the observed performance difference.

MATCHESSolution

the existing validate.ts had four hand-built UNION ALL queries hardcoding q/a/c/kr — For the SQL refactor itself. Tension: each query has different per-table column projections. Outcome: The cleanest pattern.

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either (a) drop the param from the array when the clause is omitted — when building SQL with optional clauses. Tension: requires dynamic position recomputation, error-prone. Outcome: (b) is safer for most cases.

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1000 records is very small amount of records. — At this scale Postgres might decide that sequential scan will be faster. Outcome: sequential scan will be faster.

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More complex, multi-statement queries, or things like `insert..on conflict` and `merge`.

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Drop ORDER BY when there's no LIMIT. Tension: Salience ordering only matters when you're picking the top-K. Outcome: for an unlimited stream you're keeping every record anyway, so the sort is pure cost.

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Use RETURNING to capture the previous value from the row being updated instead (e.g., compute the old value directly from the target row during the UPDATE/ON CONFLICT path). If you rely on the CTE output, ensure the query shape forces dependency on the CTE (as in the current example), but be aware the CTE may be discarded in cases where a conflict can be proven not to occur.

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on 2M rows it takes `575ms` on average, having to run a sequential scan. Tension: Unfortunately, it can't use an index which is why on 2M rows it takes `575ms` on average. Outcome: The skip scan runs 56x faster, under `11ms` using index-onlies.

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You need dynamic SQL to do this properly. — in PostgreSQL, I am trying to have SQL which orders by multiple columns. Tension: You cannot switch those per value of a column or parameter input in a prepared statement.

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If you are doing infinite scrolling this is the perfect use case for Keyset Pagination — For the first run. Tension: On the subsequent runs, add a `WHERE` predicate. Outcome: and `$4` should be the highest ID you got on the previous run.

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Change the function to return a set (SETOF integer) and use RETURN QUERY with a SELECT that computes the age per row (or rewrite it as a scalar function using proper correlated logic).

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I am trying to create a generated always column that takes the Current Date, subtract the previously mentioned int column to get an Age (int). — I am trying to learn Postgresql (12) "on the job" and seem to be stuck on a rather simple issue. Tension: the logic is faulty in that the `asset_age` is calculated when the row is inserted in the table or its underlying columns updated so as time moves on your asset ages go out of sync. Outcome: You can most easily solve your issue by dropping column `asset_age` from your table and create a view that calculates the value whenever you need it:.

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Avoiding duplicates requires a table constraint like: — Concurrency. Tension: you run the risk of getting duplicates. Outcome: UNIQUE (auction_id, max_bid).

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Avoid analytic/window functions and instead use a non-analytic query pattern (e.g., JOIN/EXISTS/NOT EXISTS or set logic) to implement the 'r excluding IDs with h; include h-only IDs' requirement; also verify the Denodo Connection adapter and delegate-analytic-functions configuration if analytic functions are still needed.

MATCHESSolution

Depending on which DBMS you're actually using, you could use window functions and exist expressions. — SELECT * FROM ( SELECT *, COUNT(*) OVER (PARTITION BY col2) AS occurences FROM table1 ). Tension: where col2 = t1.col2. Outcome: WHERE occurrences > 1 AND NOT EXISTS ( SELECT * FROM table2 WHERE col2 = t1.col2 ).

MATCHESSolution

Rewrite the cursor condition using `orWhere` to encode tuple ordering: `created_at > cursor.timestamp OR (created_at = cursor.timestamp AND id > cursor.id)`, keeping the same `orderBy` on `timestamp` and `id` and applying `limit(first)`.

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Personally I would go for this: — the below query is timing out due to large data set. Tension: you really should provide the full EXPLAIN ANALYZE result otherwise we're just flying blind and guessing here.

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Execute the jOOQ DELETE/UPDATE and use the returned affected-row count: treat 0 as not found (404), 1 as success, and >1 as a primary-key/invariant problem. Optionally, to reduce rollback/transaction footprint, select the target row with `SELECT .. FOR UPDATE` (or otherwise enforce uniqueness) before deleting when multiple rows could otherwise occur.

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if you use derived queries.

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Ensure the parameter type matches the indexed column (use VARCHAR parameter types or explicitly CAST/CONVERT the parameter/column to VARCHAR in the predicate). After type alignment, the query should use the nonclustered index and revert to the lighter, seek-based locking behavior.

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the solution shown below — exceeding the SQL parameter limit. Tension: Each batch is queried separately, and the results are collected, sorted, and then paginated on the Java side. Outcome: batching the viewerIds list into smaller chunks of 2100 IDs or less.

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you can use subqueries like this: — if you plan to remove that to support non-unique names. Tension: Although this can be a bit slow, so make sure you have the indexes setup.

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Drizzle supports this cleanly with `or()` + `isNull()` — Drizzle ORM. Tension: no raw SQL needed. Outcome: const rows = await db.select().from(questions).where( or( eq(questions.tenantId, tenantId), isNull(questions.tenantId) ) );.

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Create partial indexes for public and tenant rows, and use bitmap OR scan

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Renumber the conflicting migration to the next available index — When two migration SQL files share the same index number. Tension: Verify no other code references the old filename. Outcome: 0016_seed_leech_ratio.sql → 0017_seed_leech_ratio.sql.

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Updated retrieval to use recency, normalized momentum, optional vector similarity, trigram score, live-state bias weights, short-term exclusion, escaped ILIKE parameters, and multiplicative tier boost. — Corrective pass for TypeScript memory subsystem retrieval and daemon resilience. Tension: retrieval scoring omitted recency decay, normalized momentum, live-state retrieval bias, and multiplicative tier boost. Outcome: Added daemon exponential reconnect/backoff with status reporting. Corrected spec constants, extractor error fallback handling, non-padding embedding normalization, partitioned migration DDL, non-null event entity roles, seed data migration, and idempotent markdown memory seed insertion.

MATCHESSolution

What relationships actually exist? — MATCH ()-[r]->() RETURN type(r), count(r) ORDER BY count(r) DESC. Tension: What your projection expects vs what exists. Outcome: check your projection.

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legacy-imported nodes without `createdAt`. Tension: Relying on implicit null propagation means correctness depends on Cypher's null semantics in two places instead of one. Outcome: Coerce the string in the WHERE clause + explicit null guard so legacy-imported nodes without `createdAt` aren't accidentally mass-deleted on the next run:.

MATCHESSolution

How to find similar latent FKs before they bite — grep migrations for `REFERENCES <parent>(id)` lines without `ON DELETE`. Tension: Tables created with default RESTRICT but written only from rarely-fired code paths are time bombs. Outcome: audit each.

MATCHESSolution

backwards compatibility with persisted legacy data. Tension: indexes are positions; positions are not identity.

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