AveryQi115 opened a new pull request, #58924:
URL: https://github.com/apache/spark/pull/58924
## TL;DR
Guarantees that a CTE marked non-inlinable (`CTERelationDef.forceSkipInline
= true`) is **materialized exactly once and physically reused** across all of
its references. Each such CTE reference is turned into a `CTEReuseExchange`
node wrapped over a single shuffle of the CTE body. Whether or not Adaptive
Query Execution (AQE) is enabled, execution respects the `CTEReuseExchange`:
every reference reads the **same shuffle files** rather than recomputing the
CTE. This closes the gap where stock `ReuseExchangeAndSubquery` cannot
guarantee reuse for a non-deterministic CTE body — non-determinism breaks
canonical equality, so the stock rule may fail to dedup the copies and silently
recompute.
### Why it is needed
A CTE referenced multiple times can be either inlined (recomputed per
reference) or materialized once. For a **non-deterministic** body (e.g.
`rand()`, `monotonically_increasing_id()`, non-deterministic UDFs),
recomputation is not just wasteful but **semantically wrong** — each reference
would observe different rows. Stock reuse keys on canonical equality of the
physical subtrees, which non-determinism and per-reference `exprId` remapping
defeat. This change introduces a `cteId`-keyed mechanism so all references
provably share one materialized exchange.
---
## Life cycle
The feature threads one identity — the `cteId` — from the logical plan down
to a single physical shuffle:
```
CTERelationDef / CTERelationRef (analyzer; forceSkipInline = true)
│ ReplaceCTERefWithRepartition
▼
RepartitionByExpression (plan-reuse id) (optimizer; body inlined +
deduplicated)
│ ReplaceRepartitionWithCTEReuse
▼
CTEReuseRelation(cteId, partitioning, …) (optimizer; only when refs > 1)
│ SparkStrategies
▼
CTEReuseExchange(cteId, shuffle) (physical leaf; shuffle held as
metadata)
│
├── AQE on ─▶ CTEReuseQueryStageExec ─▶ inner
AdaptiveSparkPlanExec ─▶ Shuffle
└── AQE off ─▶ LOCAL_SHUFFLE_FOR_CTE shuffle ─▶ ReusedExchangeExec
(canonical reuse)
```
### Logical → physical (common to both modes)
- **`ReplaceCTERefWithRepartition`** replaces each reference to a
`forceSkipInline` CTE with a *plan-reuse* `RepartitionByExpression` (a non-zero
`repartitionId`, assigned by `RepartitionIdGenerator`) wrapping the inlined CTE
body. If the def carries `forcePartitioning = Some(HashPartitioning)`, the
reference is materialized with that pinned partitioning; otherwise it falls
back to the normal path (`RepartitionByExpression(Seq.empty, …)`, unless the
body already satisfies the partitioning or the reference is under a subquery).
The inlined body is run through `DeduplicateRelations` so each copy gets fresh
`exprId`s — the `repartitionId`, not `exprId` equality, is what ties the copies
together.
- **`ReplaceRepartitionWithCTEReuse`** (main-query pass only) counts
plan-reuse repartitions by `repartitionId` across the whole plan including
subqueries. Every id with **more than one** consumer is sealed into a
`CTEReuseRelation(cteId, partitioning, sharedSubplan)`; single-consumer ids
stay as ordinary repartitions and simply inline. `sharedSubplan` is
deliberately **not a child** of `CTEReuseRelation`, so outer-plan
re-optimization treats the reference as a leaf and never descends into (or
diverges) the shared body.
- **`SparkStrategies`** plans `CTEReuseRelation` eagerly (not via
`planLater`, since the shuffle lives in metadata that the placeholder collector
cannot see) into a `ShuffleExchangeExec` over the body, re-stamps its origin as
`LOCAL_SHUFFLE_FOR_CTE(cteId)`, and wraps it as a `CTEReuseExchange(cteId,
shuffle)` leaf. The shuffle is stored in `innerChildren`, not `children`, so
AQE's bottom-up stage creation does not redundantly plan its substages.
### AQE on: `CTEReuseExchange → CTEReuseQueryStageExec → inner
AdaptiveSparkPlanExec → Shuffle`
- **`PlanCTEReuse`** (a preprocessing rule alongside
`PlanAdaptiveSubqueries`) builds **one shared inner `AdaptiveSparkPlanExec` per
`cteId`**, registered in `AdaptiveExecutionContext.cteAQERegistry`. The
registry lives on the shared execution context, so the identical inner AQE
instance is reused across every reference — main query and subqueries — and
across re-plans, preventing duplicate replanning or plan divergence of the
shared body.
- **`createNonResultQueryStages`** replaces each `CTEReuseExchange` with a
`CTEReuseQueryStageExec(id, innerAQE, output)`. All references' stages point at
the same inner AQE; each stage remaps the inner AQE's (primary reference's)
attribute ids into its own reference's id space, mirroring
`ReusedExchangeExec`. Execution and statistics delegate to the inner AQE's
post-iteration `executedPlan`, so the shared body is iterated once.
### AQE off: `CTEReuseExchange → LOCAL_SHUFFLE_FOR_CTE shuffle →
ReusedExchangeExec`
- **`UnwrapCTEReuseExchange`** (early in the preparation pipeline, in both
the main and subquery batches) replaces each `CTEReuseExchange` with its
underlying `LOCAL_SHUFFLE_FOR_CTE(cteId)` shuffle. From there all normal
preparation rules run per copy.
- Because every copy of a `cteId` is planned from the same canonicalized
`sharedSubplan` and the shuffle is protected from `EnsureRequirements`, the
copies stay **canonically equal**, and the stock **`ReuseExchangeAndSubquery`**
deduplicates them into `ReusedExchangeExec`.
- **`VerifyCTEReuse`** runs at the very end (final main-query pass only) and
asserts at most one live shuffle per `cteId`. Under
`spark.sql.optimizer.failOnCteReuseWithoutAqe` (test default) a violation
throws; in production it logs. Reuse is a best-effort optimization here, not a
correctness invariant.
---
## Design considerations and details
### When AQE is on
**1. Deadlock avoidance (dedicated thread pool).** The inner AQE is
materialized via `materialize()` / lazy `materializeFuture`, which calls
`withFinalPlanUpdate(skipResultStage = true)` on a **dedicated**
`cteExecutionContext` thread pool (sized by
`spark.sql.cteMaterialization.maxThreadThreshold`, default 1024). The outer
AQE's stage-materialization threads block waiting on the CTE stage to finish;
if the inner AQE materialized on the same shared `QueryStageCreator` pool,
nested materialization could exhaust that pool while outer threads hold slots
waiting on it — a classic thread-pool deadlock. A separate pool breaks the
dependency cycle. `skipResultStage = true` materializes the shared body's
stages without wrapping a `ResultQueryStageExec`, since the CTE stage is an
input to other stages, not a query result.
**2. Runtime filters.** The CTE body is materialized once and shared across
all consumers, so consumer-specific runtime filters (dynamic partition pruning,
bloom filters) must **not** be pushed below the reuse boundary — doing so would
specialize the single shared result to one consumer and corrupt the others.
Runtime filters are applied per consumer *above* the `CTEReuseExchange`; the
shared subplan itself is never specialized. This is a deliberate limitation of
guaranteed reuse: correctness of the shared materialization takes precedence
over consumer-local filter pushdown.
**3. Shuffle planning (`EnsureRequirements`).** The reuse shuffle's
partitioning is fixed at the logical level (`CTEReuseRelation.partitioning`,
derived from the common distribution of all consumers, or a safe fallback).
`EnsureRequirements` must not rewrite it, because re-deriving partitioning per
consumer would make the copies diverge and defeat reuse. The guard is `case s:
ShuffleExchangeExec if !s.isCreatedForSubplanReuse => s.copy(outputPartitioning
= …)`, where `isCreatedForSubplanReuse` is true for the `LOCAL_SHUFFLE_FOR_CTE`
origin. The protected shuffle is an immutable boundary: consumer-dependent
operators are added *above* it, keeping the subtree below canonically identical
across copies. The same origin also keeps the shuffle out of the AQE
`supportedShuffleOrigins` allowlists (coalesce / local read / skew join), so
its partition layout stays frozen.
### When AQE is off
**1. Interaction with `planSubqueries`.** With AQE off, subqueries are
prepared **separately** — `PlanSubqueries` calls
`QueryExecution.prepareExecutedPlan → preparations(subquery = true)`, applying
the preparation rules recursively to each subquery plan on its own. So
`UnwrapCTEReuseExchange` is registered in **both** the main and subquery
preparation batches, ensuring CTE references *inside subqueries* are unwrapped
too. Conversely, `VerifyCTEReuse` runs **only on the final main-query pass**:
during the per-subquery `PlanSubqueries` invocations, reuse across the whole
plan is not yet complete, so verifying there would produce false negatives.
Reuse itself is achieved entirely through the stock `ReuseExchangeAndSubquery`
(canonical matching) — this change adds no new dedup engine for the AQE-off
path, only the protected `LOCAL_SHUFFLE_FOR_CTE` boundary that keeps the copies
canonically equal for the stock rule to find.
## Testing
Adds `CTEReuseWithAQESuite`, `CTEReuseWithoutAQESuite`, and
`ReplaceCTERefAndRepartitionWithCTEReuseSuite` covering the optimizer rewrites,
the AQE-on inner-AQE path, and the AQE-off canonical-reuse path (including
reuse across subqueries). All new confs default to off, so existing behavior is
unchanged.
---
Co-authored-by: Maryann Xue <[email protected]>
Co-authored-by: Juliusz Sompolski <[email protected]>
This pull request and its description were written by Isaac.
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