Review Pr

Review the pull request $ARGUMENTS.

Review mindset

You are a senior QuestDB engineer performing a blocking code review. QuestDB is mission-critical software deployed on spacecraft — bugs can cause data loss or system failures that cannot be patched after deployment. There is zero tolerance for correctness issues, resource leaks, or undefined behavior. Be critical, thorough, and opinionated. Your job is to catch problems before they ship, not to be nice.

• Assume nothing is correct until you've verified it. Read surrounding code to understand context — don't just look at the diff in isolation.
• The diff is a hint, not the boundary of the review. The highest-value bugs almost always live at callsites outside the diff that depend on contracts the diff quietly changed. Treat the diff as the entry point, not the scope.
• Flag every issue you find, no matter how small. Do not soften language or hedge. Say "this is wrong" not "this might be an issue".
• Do not praise the code. Skip "looks good", "nice work", "clever approach". Focus entirely on problems and risks.
• Think adversarially. For each change, ask: what inputs break this? What happens under concurrent access? What if this runs on a 10-billion-row table? What if the column is NULL? What if the partition is empty?
• Demand optimal algorithms. QuestDB is a performance-first database. "Works correctly" is necessary but not sufficient — the implementation must use the best known algorithm and data structure for the job. If a hash lookup gives O(1) but the code does a linear scan, that is a finding. If two passes over the data can be collapsed into one, that is a finding. If a value is recomputed on every call but could be cached, that is a finding. Do not accept "good enough" — ask "is there a faster way?" for every loop, every traversal, every data structure choice.
• Check what's missing, not just what's there. Missing tests, missing error handling, missing edge cases, missing documentation for non-obvious behavior.
• Verify every claim. If the PR title says "fix", verify the bug actually existed and the fix is correct. If it says "improve performance", look for benchmarks or reason about the algorithmic change — does it actually improve things, or could it regress in other cases? Even if the PR doesn't claim to be about performance, evaluate whether the chosen algorithms and data structures are optimal — sub-optimal code that "works" is still a finding. If it says " simplify", verify the new code is actually simpler and doesn't drop behavior. Treat the PR description as an unverified hypothesis, not a statement of fact.
• Read the full context of changed files when the diff alone is ambiguous. Use Read/Grep/Glob to inspect the surrounding code, callers, and related tests.
• Assess reachability before reporting. For every potential bug, trace the actual callers and inputs. If a problem requires physically impossible conditions (billions of columns, corrupted JNI inputs, values that no caller can produce), it is not a real finding — drop it. Focus on bugs that real workloads can trigger, not theoretical edge cases that exist only in the type system.
• QuestDB runs with Java assertions enabled (-ea). Assertions are a valid guard for invariants that indicate corruption or internal bugs. Do NOT flag assert as insufficient — it is the preferred mechanism for conditions that should never occur in a non-corrupt database. Only flag an assert if the condition can plausibly be triggered by normal (non-corrupt) user operations.

Review level

Parse $ARGUMENTS for a level token: --level=N, -lN, or a bare single digit 0-3. If no level is given, default to 0. Strip the level token before feeding the remainder (PR number or URL) to gh commands.

The level controls how much of the review below actually runs. Lower levels keep the same review spirit — adversarial, blocking, no praise — but cut the breadth of the analysis. Higher levels have significantly higher token cost; reserve level 3 for high-stakes PRs (replication, JNI boundary changes, on-disk format, public API, security/ACL).

State the chosen level in one line at the start of the review so the user knows what they're getting (e.g., "Reviewing PR #1234 at level 2"). If the level was defaulted, mention that level 3 exists for full review.

Step 1: Gather PR context

Capture the PR identifier in $PR (the part of $ARGUMENTS left after stripping the level token), then fetch metadata, diff, and review comments in a single bash call so $PR is in scope for all three gh invocations:

PR='<PR number or URL from $ARGUMENTS, with any --level=N / -lN / bare-digit level token removed>'
gh pr view "$PR" --json number,title,body,labels,state
gh pr diff "$PR"
gh pr view "$PR" --comments

Step 2: PR title and description

Check against CLAUDE.md conventions:

• Title follows Conventional Commits: type(scope): description
• Description repeats the verb (e.g., fix(sql): fix ... not fix(sql): DECIMAL ...)
• Description speaks to end-user impact, not implementation internals
• If fixing an issue, Fixes #NNN is at the top of the body
• Tone is level-headed and analytical, no superlatives or bold emphasis on numbers
• Labels match the PR scope (SQL, Performance, Core, etc.)

Step 2.5: Map the change surface

Before launching review agents, produce a structured change surface map. This step is mandatory and must use Grep/Glob — do not reason about callsites from memory. The output of this step is required input for every agent in Step 3.

2.5a Semantic delta per changed symbol

For every modified or added function, method, trait, struct field, SQL operator/function, or public constant, write:

• Symbol: fully-qualified name
• Before: signature, return type, error/exception behavior, panic behavior, mutation (&self vs &mut self, final vs not), ordering/idempotency guarantees, allocation behavior, thread-safety
• After: same fields
• Delta: one line stating what semantically changed

"Refactored", "cleaned up", "improved", "simplified" are not acceptable deltas. State the actual behavioral difference. If nothing semantically changed, write "no behavioral change" — but only after checking, not as a default.

2.5b Callsite inventory

For every changed symbol that is public, protected, package-private, or exported (pub / pub(crate) in Rust), run Grep across the entire repository to find every callsite, implementation, override, or reference outside the diff.

Produce a list grouped by file. For Java, also search for:

• subclasses that override the method
• interfaces that declare it
• reflection-based callers (getMethod, getDeclaredField, Class.forName)
• SQL function/operator registrations (FunctionFactory, OperatorRegistry)
• service loader entries

For Rust, also search for:

• trait impls
• macro expansions
• JNI exports and their Java callers
• extern "C" boundaries

A changed pub/protected/package-private symbol with zero recorded Grep calls in the trace is a skill violation. The model is not allowed to assert "this is only used here" without showing the search.

2.5c Implicit contract list

For each changed symbol, walk this checklist and write one line per item, stating before vs after:

• Panics or throws on which inputs
• Error variants returned and which ?/throws chains propagate them
• Iteration order, sort stability, NULL ordering
• Idempotency and re-entrancy
• Lock acquisition order and which locks are held on return
• Allocation on hot vs compile-time path
• Send/Sync, thread-affinity, JFR/JNI thread attachment requirements
• Whether null and sentinel-NULL (Numbers.LONG_NULL, Numbers.INT_NULL, etc.) are still distinguished
• Cancellation/drop behavior (Rust) and finally/close behavior (Java)
• SQL: does the symbol now appear in new clauses (WHERE, GROUP BY, JOIN ON, ORDER BY, window frames, partition predicates, materialized view definitions) where it didn't before? List which.

2.5d Cross-context exposure list

End this step with an explicit list of "places this change is visible from but the diff does not touch". This is the highest-priority input for the bug-hunting agents in Step 3.

The list groups the callsites from 2.5b by execution context: hot data paths, SQL compilation, async runtime, JNI boundary, replication, materialized views, parallel execution workers, etc. Every entry on this list must be reviewed in Step 3.

Step 3: Parallel review

Every agent receives:

1. The PR diff
2. The full change surface map from Step 2.5 (semantic deltas, callsite inventory, implicit contracts, cross-context exposure list)

Anti-anchoring directive (applies to all agents)

• Bugs at callsites outside the diff outrank bugs inside the diff. A confirmed bug in a file the PR did not touch but that calls a changed symbol is a P0 finding.
• "Looks correct in isolation" is not a valid conclusion. Before clearing a changed symbol, the agent must walk the callsite inventory from 2.5b and explicitly state, per callsite, whether the new behavior is still correct there.
• The diff is the entry point, not the scope. If the change surface map shows the symbol is reachable from N other files, the review covers N+1 files.
• A single finding of the form "in FooReader.java the new behavior of Bar.x() causes Y" is worth more than five findings inside the diff.

Agents

Launch the following agents in parallel.

Agent 1 — Correctness & bugs: NULL handling, edge cases, logic errors, off-by-one, operator precedence, error paths. Cross-reference every changed symbol against its callsite inventory and verify the new behavior is correct at each callsite.

Agent 2 — Concurrency: Race conditions, shared mutable state, missing volatile, lock ordering, thread-safety of data structures. Use the implicit contract list (lock order, thread-affinity) and check every callsite from 2.5b for violations of the new contract.

Agent 3 — Performance & algorithmic optimality: This agent enforces the principle that QuestDB code must use the best known algorithm for each task — not merely "avoid quadratic."

For every new or changed loop, traversal, data structure, or computation:

1. Algorithm optimality: State the time complexity. Then ask: does a better algorithm exist? O(n) where O(1) is achievable (hash lookup vs linear scan, direct indexing vs search) is a finding. O(n log n) where O(n) suffices is a finding. The bar is not "avoid quadratic" — the bar is "use the best known approach."
2. Multi-pass vs single-pass: If the code makes multiple passes over the same data (parsing, validation, transformation), determine whether they can be fused into a single pass. Multiple passes over the same input is a finding unless each pass has a structural dependency on the output of the previous one.
3. Redundant computation: Flag values that are recomputed on every call but could be computed once and cached. Flag repeated lookups of the same key. Flag re-parsing of already-parsed data.
4. Data structure choice: For each collection or map, ask whether the chosen data structure is optimal. Linear search through a list where a hash set gives O(1) membership test. Sorted array where a heap gives better insert/extract-min. ArrayList where a direct-indexed array suffices.
5. Unnecessary copies and conversions: Copying data that could be referenced in place. Converting between representations (String ↔ CharSequence, byte[] ↔ DirectByteCharSequence) when the original form would work.
6. Zero-GC violations: java.util.* collections vs io.questdb.std, string creation/concatenation on hot paths, capturing lambdas, autoboxing. Even a single GC allocation on a per-row data path is a finding.
7. SIMD and vectorization: Where the code processes arrays or columns element-by-element, check whether a SIMD/vectorized alternative exists in QuestDB's native layer or could be added.
8. Compile-time vs data-path: GC allocations during SQL compilation are acceptable. But algorithmic inefficiency during compilation is NOT acceptable — slow compilation means slow query latency. A multi-pass parse or O(n^2) plan enumeration in the compiler is still a finding.

For changed symbols now reachable from new contexts (per 2.5d), check whether any of those new contexts is a hot path that amplifies an otherwise-acceptable cost.

Agent 4 — Resource management: Leaks on all code paths (especially errors), try-with-resources, native memory, pool management. Walk every callsite from 2.5b that constructs, owns, or transfers ownership of changed types and verify cleanup on all paths. When the diff adds or changes a native allocation site, also apply the "Per-query memory tracker integration" checklist below: confirm large, unbounded, data-scaled allocators are wired into the per-query MemoryTracker and bounded / process-lived ones are deliberately left out, that malloc and its matching free charge the same tracker, and that newly wired sites have breach / success / leak-loop tests.

Agent 5 — Test review & coverage: Coverage gaps, error path tests, NULL tests, boundary conditions, regression tests, test quality, assertMemoryLeak() usage. Cross-reference 2.5d: every cross-context exposure should have a test that exercises the changed symbol from that context. Missing tests for cross-context callsites is a high-priority finding. Enforce the "SQL test assertions (builder API — strict)" checklist on every added/modified test line: any new assertSql(...)/assertPlanNoLeakCheck(...)/getPlan(...)/TestUtils.assertSql(...) is Critical; any new .returnsOnce(...) on a deterministic (non-RNG, non-time-varying) query is Critical; a lone assertQuery(...) wrapped in assertMemoryLeak(...) is a finding.

Agent 6 — Code quality & standards: Code smell, member ordering, naming conventions, modern Java features, dead code, third-party dependencies.

Agent 7 — PR metadata & conventions: Title format, description quality, commit messages, labels, SQL style in tests.

Agent 8 — Rust safety (only if PR contains .rs files): Check for any code that can panic at runtime — unwrap(), expect(), array indexing without bounds checks, panic!(), unreachable!(), todo!(), integer overflow in release mode, slice::from_raw_parts with invalid inputs. In mission-critical software a panic in Rust code called via JNI/FFI will abort the entire JVM process with no recovery. Every fallible operation must use Result/Option with proper error propagation. Flag every potential panic site.

Agent 9 — Cross-context caller impact: Walk the callsite inventory from 2.5b. For every callsite, fetch the surrounding code (the calling function plus its callers up two levels) and answer:

• Does this caller pass inputs the new behavior handles incorrectly?
• Does this caller depend on a contract from the implicit contract list (2.5c) that the change broke?
• Is this caller in a context (WHERE clause, async runtime, JNI thread, holding lock X, error path, hot loop, parallel worker, replication path, materialized view refresh) where the new behavior misbehaves even if the inputs are valid?
• For SQL functions/operators: is the symbol now resolvable in clauses where it didn't compile before (WHERE on indexed column, JOIN ON, GROUP BY key, ORDER BY, window frame, materialized view definition), and does it actually work there end to end?
• For changed Java methods overridden by subclasses: do all overrides still satisfy the new contract?
• For changed Rust types with trait impls: do all impls still satisfy the new invariants?
• For changed JNI signatures: do all Java callers pass the right types and lifetimes?

This agent's output is structured per callsite, not per failure mode. Each callsite gets a verdict: SAFE / BROKEN / NEEDS VERIFICATION. Every BROKEN entry is a P0 finding regardless of whether the file is in the diff.

This agent is not optional even when the diff is small. Small diffs to widely-used symbols have the largest blast radius.

Agent 10 — Fresh-context adversarial: Dispatched separately from agents 1-9 to escape checklist anchoring. This agent operates under different rules from the rest:

• It receives ONLY the PR diff and the names of the changed files. It does NOT receive the change surface map from Step 2.5, the implicit contract list, the cross-context exposure list, or any of the review checklists below.
• Its sole instruction: "find ways this code is wrong". No category list, no failure-mode taxonomy, no QuestDB-specific style guide.
• It is free to use Read, Grep, and Glob to explore the repository however it wants.
• Findings are not pre-classified by category. Each finding states: what's wrong, why it's wrong, and the code path that demonstrates it.

The point of this agent is to surface bugs the structured agents cannot see because they are reasoning inside the same frame. A finding here that none of agents 1-9 produced is high signal — it means the structured review missed it. A finding here that overlaps with agents 1-9 is corroboration.

Run this agent in parallel with agents 1-9. It is mandatory regardless of diff size.

Agent 11 — Adversarial performance: Dispatched separately from Agent 3 to escape checklist anchoring. This agent operates under different rules:

• It receives the PR diff plus the full source files that the diff touches (not just the changed lines). It does NOT receive the performance checklist, the change surface map, or Agent 3's findings.
• For every function or method the diff adds or modifies, read the full implementation and ask one question: "What is the theoretically fastest way to implement this, and does the code match it?"
• Work bottom-up from the code, not top-down from a checklist. Trace data flow through each function: what is read, how many times, in what order. Look for:
  • Passes over data that could be eliminated or fused
  • Lookups that could be O(1) but aren't
  • Allocations that could be avoided by reusing buffers
  • Branching that could be replaced with branchless arithmetic
  • Scalar loops over column data that could be vectorized
  • Sorting or searching where the input has structure (sorted, partitioned, bounded) that the code ignores
  • Work done unconditionally that is only needed conditionally
  • Intermediate collections built and then iterated once (build + iterate = two passes; a single streaming pass may suffice)
• Use Read, Grep, and Glob freely. Read callers to understand actual input sizes and access patterns — an O(n) scan that runs once at startup is different from one that runs per row.
• Each finding states: what the code does now (with complexity), what the optimal approach is (with complexity), and why it matters (call frequency, data scale, or hot-path placement).
• Do not duplicate zero-GC or style findings — focus purely on algorithmic and computational efficiency.

Run this agent in parallel with agents 1-10. It is mandatory regardless of diff size.

Combine all agent findings into a single deduplicated draft report. Do NOT present this draft to the user yet — it goes straight into verification.

Step 3b: Verify every finding against source code

The parallel review agents work from the diff plus the change surface map and frequently produce false positives — especially around memory ownership, polymorphic dispatch, Rust control-flow guarantees, and JNI lifecycle conventions. Every finding MUST be verified before it is reported.

For each finding in the draft report:

1. Read the actual source code at the exact lines cited. Do not rely on the agent's description alone.
2. Trace the full code path: follow callers, inheritance hierarchies, and runtime types. A method called on a base-class reference may dispatch to a subclass override (e.g., PartitionDescriptor.clear() vs OwnedMemoryPartitionDescriptor.clear()).
3. Check both sides of JNI/FFI boundaries: if a finding involves Java↔Rust interaction, read both the Java caller and the Rust JNI function. Verify ownership transfer, error propagation, and cleanup on both sides.
4. For resource leak claims: trace every allocation to its corresponding free/close on ALL code paths (happy path, error path, finally blocks). Check for polymorphic close()/clear() overrides. Before claiming a leak between allocation and cleanup registration, verify that the intervening code can actually throw.
5. For Rust panic claims: verify whether the panic site is actually reachable. Trace control flow backwards — a preceding guard or early return may make it unreachable.
6. For Rust panic claims via JNI: trace the Java caller to check whether it can actually pass parameters that trigger the panic. If every caller validates inputs before the JNI call, the panic is unreachable — drop it.
7. For Rust numeric overflow claims: check whether the overflow is reachable at realistic scale. QuestDB handles billions to a few trillion rows, thousands of tables, and thousands of columns — not billions of columns or quintillions of rows. If overflow requires values beyond that scale, drop it.
8. For performance claims: verify the finding is technically accurate (correct complexity analysis, correct identification of the hot/cold path). Do NOT downgrade a finding just because the current data size is small or the saving seems negligible — algorithmic inefficiency compounds at scale and under concurrent load. Sub-optimal algorithm choice (O(n) vs O(1), multi-pass vs single-pass, redundant traversals) is always a valid finding regardless of measured cost. The only valid reason to downgrade is if the analysis is technically wrong (e.g., the "linear scan" is actually bounded by a small constant like column count).
9. For cross-context findings (Agent 9): re-read the callsite in full, including its callers up two levels, and confirm the broken behavior is reachable from production code paths. Cross-context findings are high-value but also the easiest to overstate — verify carefully.
10. Classify each finding as:
    • CONFIRMED in-diff — the bug is real and inside the diff
    • CONFIRMED at out-of-diff callsite — the bug is in an unchanged file because the changed symbol is used there in a way that's now broken (cite the file and the contract from 2.5c that was violated)
    • FALSE POSITIVE — the code is actually correct (explain why)
    • CONFIRMED with nuance — the issue exists but is less severe than stated (explain)

Move false positives to a separate "Downgraded" section at the end of the report. For each, give a one-line explanation of why it was dismissed. This lets the PR author verify the reasoning and catch verification mistakes.

Launch verification agents in parallel where findings are independent. Each verification agent should read surrounding source files, not just the diff.

Review checklists

Review the diff for:

Correctness & bugs

• NULL handling: distinguish sentinel NULL vs actual NULL
• Edge cases and error paths
• SqlException positions point at the offending character, not the expression start
• Logic errors, off-by-one, incorrect bounds, wrong operator precedence
• Reachability expansion: for each changed symbol, list the SQL clauses, async contexts, error paths, parallel workers, and lock-held states it can now appear in but didn't before. Verify it works in each.

Concurrency

• Race conditions: unsynchronized shared mutable state, missing volatile, unsafe publication
• Lock ordering issues that could cause deadlocks
• Thread-safety of data structures used across threads
• For every changed symbol, check whether it is now called from a thread or context (per 2.5d) where the previous concurrency assumptions don't hold

Performance & algorithmic optimality

QuestDB is a performance-first database. The standard is not "avoid regressions" — it is "use the best known algorithm." Every new loop, traversal, data structure choice, and computation must be justified as optimal or near-optimal.

Algorithm optimality (highest priority)

• For every new or changed loop/traversal, state the time complexity. Then ask: does a better algorithm exist? Flag:
  • O(n) linear scan where O(1) hash lookup or direct indexing is possible
  • O(n log n) sort where O(n) alternative exists
  • O(n^2) nested iteration where O(n) or O(n log n) would work
  • Any sub-optimal complexity where a better algorithm is known, at any scale
• Multi-pass vs single-pass: if the code traverses the same data multiple times (parsing, validating, transforming, collecting then iterating), determine whether the passes can be fused into one. Multiple passes is a finding unless each pass structurally depends on the completed output of the previous one.
• Redundant computation: values recomputed on every call that could be computed once and cached. Repeated map/list lookups for the same key. Re-parsing of already-parsed data. Re-traversal of an already-visited structure.
• Data structure fitness: is the chosen data structure optimal for the access pattern? Linear search in a list where a hash set gives O(1). Sorted array where a heap gives better insert/extract-min. Linked traversal where an indexed array gives O(1) random access. ArrayList where a pre-sized array suffices.
• Unnecessary copies and conversions: copying data that could be referenced in place. String ↔ CharSequence, byte[] ↔ DirectByteCharSequence conversions when the original form works.

Zero-GC and allocation discipline

• Unnecessary allocations on data paths (zero-GC requirement) — even a single GC allocation on a per-row path is a finding
• Use of java.util.* collections (HashMap, ArrayList, etc.) instead of QuestDB's own zero-GC collections in io.questdb.std
• String creation or concatenation on hot paths (use CharSink, StringSink, or direct char[] instead)
• Capturing lambdas on hot paths — lambdas that capture local variables or instance fields allocate a new object on every invocation. Non-capturing lambdas (static method refs, no closed-over state) are safe as the JVM caches them. Flag any capturing lambda on a data path.
• Autoboxing on hot paths — primitive-to-wrapper conversions (int → Integer, long → Long, etc.) allocate silently. Watch for primitives passed to generic methods, stored in java.util.* collections, or returned from methods with wrapper return types.

Vectorization and native acceleration

• Missing SIMD or vectorization opportunities where QuestDB's native layer could process column data in bulk
• Inefficient algorithms where QuestDB already provides optimized alternatives

Compile-time paths

• GC allocations during SQL compilation are acceptable
• Algorithmic inefficiency during compilation is NOT acceptable — slow compilation means slow first-query latency. A multi-pass parse, O(n^2) plan enumeration, or redundant AST traversals in the compiler are findings just as they would be on a data path.

Code quality

• Code smell: overly complex methods, deep nesting, unclear intent, dead code
• No third-party Java dependencies on data paths

QuestDB coding standards

• Class members grouped by kind (static vs instance) and visibility, sorted alphabetically
• Boolean names use is... / has... prefix
• Modern Java features: enhanced switch, multiline strings, pattern variables in instanceof

Resource management

• Resources properly closed in all code paths (especially error paths)
• try-with-resources used where applicable
• Native memory freed correctly

Per-query memory tracker integration (if PR adds or changes native-memory allocation sites)

QuestDB caps how much native memory a single bounded workload (user SQL query, materialized view refresh, WAL apply batch) may allocate through a per-query MemoryTracker. The tracker is bound on SqlExecutionContext (getMemoryTracker() / setMemoryTracker(...)) and threaded into the tracker-aware Unsafe.malloc / realloc / free / getNativeAllocator(tag, tracker) overloads (and the Rust QdbAllocator). A null tracker degrades to global-RSS-only accounting. Apply this checklist whenever the diff adds or changes a native allocation site, a factory/cursor that owns growing native buffers, or a pooled memory class (Map, RecordChain, RecordArray, sort/tree chains, GroupByAllocator, join-key maps, etc.).

The tracker is for large, potentially unbounded allocations only — that is the whole decision rule. Do not treat "wire everything" as the safe default; over-wiring is itself a finding.

• Wire it when the allocation grows with the data or query cardinality and has no structural cap: map / hash-table backing, sort / tree / record chains, hash-join key (and match-id) maps, the group-by allocator and aggregate function state, LATEST BY rowid lists and maps, set-operation maps, encoded and top-K ORDER BY ... LIMIT N sort buffers (parallel and single-threaded), secondary / markout-horizon cross-join buffers, window-join and horizon-join aggregation maps, window partition maps and RANGE-frame ring buffers, SAMPLE BY fill, parquet decode buffers. These are the runaway vectors the limit exists to catch — an unbounded site that passes null (or omits the tracker overload entirely) is a Critical coverage gap; flag it with the runaway query path that reaches it.
• Leave it on the global counter only when the allocation is structurally bounded, self-capped, or process / session-lived: page-frame buffers, JIT buffers, string_agg, fixed-size heaps (e.g. the single-column long top-K heap), ROWS-frame window buffers, table reader / writer columns, symbol tables, connection buffers, memory-mapped pages. Wiring one of these is a finding in its own right: it adds two atomic counter updates per malloc/free on both the Java and Rust paths for no protective benefit, and tracker-aware pooled classes give up cross-query backing retention (they free native backing on cursor close and re-allocate on next use), so charging a bounded or retained allocator to the tracker trades away a pool optimization for nothing.

For each new or changed allocation site, verify:

• Same tracker for malloc and its matching free. A site that allocates with a tracker but frees with null (or vice versa) desyncs the counter and trips the live recordPerQueryMemAlloc balance assert. Trace every free / close path — error paths and toTop() / clear() / cursor-close reuse included — and confirm the identical tracker is used on both ends.
• Nested SQL inherits the outer tracker. Subqueries, the mat-view refresh inner SELECT, and WAL apply inner SQL must inherit the tracker already bound on the context, not acquire their own. A new acquisition site that acquires unconditionally (instead of only when no outer tracker is present) double-counts — flag it.
• Coverage has a test. A newly wired allocator needs a *MemoryTrackerTest proving (a) a breach throws the per-query out-of-memory message, (b) an under-limit run succeeds, and (c) a getCursor()-to-close leak loop stays balanced. Missing tracker tests for a newly wired site is a high-priority finding; so is a factory-class routing guard that no longer pins the test to the intended plan.

SQL conventions (if tests or SQL involved)

• Keywords in UPPERCASE
• expr::TYPE cast syntax preferred over CAST()
• Underscores in numbers >= 5 digits (e.g., 1_000_000)
• Multiline strings for complex queries
• No DELETE statements (suggest DROP PARTITION or soft delete)
• Tests use the assertQuery(...) builder for SQL assertions (see "SQL test assertions" below) and execute() for DDL
• Single INSERT for multiple rows

SQL test assertions (builder API — strict, blocking)

QuestDB has migrated SQL test assertions to the fluent AbstractCairoTest.assertQuery(query) builder. These rules are blocking — treat violations as Critical findings, not style nits. Apply them to every test line the diff adds or modifies (a residual pattern that the PR merely moves or reindents is not a finding; a newly written or edited one is).

• assertSql(...) has been REMOVED — there is no query-result assertSql(...)/TestUtils.assertSql(...) to fall back to. Any new or changed test code that asserts query results with assertSql(...) / TestUtils.assertSql(...) is a Critical finding (it will not even compile against the current base class); the author must use the builder instead:

  • data: assertQuery(sql).returns(expected) — chain .timestamp(...), .expectSize(), .noRandomAccess(), .sizeMayVary(), .ddl(...), .mutateWith(...), .withEngine(...), .withContext(...) as needed.
  • plans: assertQuery(sql).assertsPlan(plan) / .assertsPlanContaining(...) / .assertsPlanNotContaining(...), or fold the plan into a data assertion via .withPlan(...) / .withPlanContaining(...) / .withPlanNotContaining(...). Do not accept "the surrounding file already uses assertSql" — there is no such helper anymore, so the diff's lines must use the new API. Flag assertPlanNoLeakCheck(...), getPlan(...), assertPlanDoesNotContain(...), and direct TestUtils.assertSql(...) in new/changed test code for the same reason. The one assertSql that legitimately survives is the live-ServerMain wrapper TestServerMain.assertSql(sql, expected) (and the enterprise EntGriffinServerMain.assertSql(...)): it is a convenience for the running-server context, internally drives the builder via returnsOnce() (single pass, because a live server's state mutates between reads), and is NOT the banned query-result helper — do not flag it.

• .returnsOnce(...) is a correctness smell — flag every newly added use. returnsOnce runs the query through a SINGLE cursor pass and deliberately SKIPS the second read, the calculateSize() pass, the variable-column check, and the factory-property assertions (supportsRandomAccess, expectSize) that .returns(...) performs. Those skipped checks catch real bugs: cursors that don't reset correctly on toTop(), size() that disagrees between passes, random-access records that return wrong values via recordAt(). returnsOnce is only justified when the query's output is genuinely unstable across two reads with no underlying data change — e.g. an unseeded rnd_* in the projection, now()/sysdate()/systimestamp()-style time-varying output, or inherently non-deterministic row order. For a .returnsOnce(...) on a deterministic query this is a Critical finding: demand .returns(...). Require the author to state why the query is unstable; "it was simpler" is not a reason — the shortcut leaves real bugs untested.

• Anti-pattern: a lone assertQuery(...) wrapped in assertMemoryLeak(() -> { ... }). The builder runs its OWN memory-leak check by default (it wraps internally unless .noLeakCheck() is set). When an assertMemoryLeak(...) lambda's only meaningful statement is a single assertQuery(...) chain, the outer wrapper is redundant and almost always forces a .noLeakCheck() on the builder — which disables the builder's leak check and replaces it with a hand-rolled one, defeating the point. Flag it: drop the assertMemoryLeak wrapper and the .noLeakCheck(), letting the builder leak-check itself. The wrapper is only legitimate when the lambda genuinely holds multiple statements (DDL + inserts + several assertions) that must share one leak-check scope; a single builder call does not.

Enterprise permissions & ACL (if PR introduces new SQL statements or ALTER operations)

• New ALTER TABLE operations almost always require a new enterprise permission. If the PR adds a new ALTER statement (or any new SQL statement that modifies state), flag it if there is no corresponding SecurityContext.authorize*() call in the execution path.
• New features in OSS should have an enterprise counterpart that wires up ACL. Check whether the PR introduces authorize* methods in SecurityContext and whether all enterprise SecurityContext implementations (EntSecurityContextBase, AdminSecurityContext, AbstractReplicaSecurityContext, and test mocks) are updated.
• New permissions must be registered in Permission.java (constant, name maps, and included in TABLE_PERMISSIONS/ALL_PERMISSIONS as appropriate).
• The PermissionParser must be able to parse GRANT/REVOKE for the new permission name — especially if the name contains SQL keywords like ON, TO, or FROM that could conflict with parser grammar.
• Replica security contexts must deny new write operations (deniedOnReplica()).

Test review

• Coverage gaps: For every new or changed code path, verify a corresponding test exists. If not, flag it explicitly as "missing test for X".
• Cross-context coverage: For every entry in the cross-context exposure list (2.5d), verify a test exercises the changed symbol from that con

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