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190 changes: 145 additions & 45 deletions apps/opik-backend/src/main/java/com/comet/opik/domain/TraceDAO.java
Original file line number Diff line number Diff line change
Expand Up @@ -1973,22 +1973,57 @@ ORDER BY (workspace_id, project_id, trace_id, id) DESC, last_updated_at DESC
* project_id}, so no delete is ever project-less - also required once {@code traces} is a Distributed table
* (OPIK-7455).
* <p>
* {@code <if(partitions)>} adds the table's own weekly partition expression, bound as the set of partitions the
* batch's ids resolve to. It is emitted whenever every id in the batch is one whose partition can be derived
* exactly ({@link WeeklyPartitions#of}); otherwise the predicate is omitted and the statement is byte-identical to
* the previous unbounded form. That is what preserves the original guarantee — a row whose {@code id_at} cannot be
* trusted is still deleted, because no id in such a batch is used to derive a partition.
* The unbounded form: no partition scoping at all, so it is the correct statement whenever
* {@link WeeklyPartitions#groupByPartition} cannot derive an exact partition for every id in the batch (a non-UUIDv7
* id, or one past the {@code DateTime64} ceiling — see that method's Javadoc). {@link #DELETE_BY_PROJECT_ID_TRACE_ID_PAIRS}
* is preferred whenever it can be used; this is the fallback that stays correct on any batch, at the cost of the
* mutation visiting every part of the table (OPIK-8230).
* <p>
* No schema flag gates it. {@link WeeklyPartitions} derives a value per {@code id_at} type the mutation may meet
* — the legacy 32-bit {@code DateTime} of {@code traces} as well as the {@code DateTime64(0)} of the partitioned
* successor — so one rendered statement is correct on both sides of the cutover EXCHANGE, in either direction, with
* nothing to flip and nothing to revert on rollback.
* The pairs are bound (never inlined) as two positional string arrays and zipped back into {@code (project_id, id)}
* tuples with {@code arrayZip}, so the query text is constant regardless of batch size and no value reaches the SQL
* as a literal. {@code arrayZip} is a deterministic function, not a subquery - ClickHouse rejects subqueries in
* delete mutations. Callers batch to keep each array within the driver's reliable bind size ({@link
* com.comet.opik.infrastructure.FilterUtils#ANALYTICS_DELETE_BATCH_SIZE}).
*/
/**
* Deletes by the full {@code (workspace_id, project_id, id)} sort key, matching on {@code (project_id, id)} tuples
* so a single statement can span several projects (e.g. a reused id resolved to all its owning projects, or a
* cross-project batch) instead of one delete per project (OPIK-7483). Every deleted row carries its {@code
* project_id}, so no delete is ever project-less - also required once {@code traces} is a Distributed table
* (OPIK-7455).
* <p>
* {@code <if(partition)>} adds {@code IN PARTITION <partition>} (OPIK-8230), which scopes the <b>mutation itself</b>
* — which parts it is registered against — rather than which rows its {@code WHERE} selects. That distinction is
* the entire fix. A ClickHouse mutation selects parts at the <b>partition</b> stage, before the {@code WHERE}
* clause is considered at all — a {@code (workspace_id, project_id, id)} predicate, however exact, prunes zero
* parts, only rows within whichever parts were already selected. Post-cutover {@code traces_local} is
* weekly-partitioned into ~1,900 partitions / ~3,650 parts; a delete matching a handful of rows was registered
* against every one of them regardless, at ~19 ms/part fixed overhead, which is what pushed ordinary deletes past
* the {@code max_execution_time} ceiling (`Code: 159 TIMEOUT_EXCEEDED`) that pre-cutover deletes (1.5–2.2 s) never
* approached. Omitted for the unbounded fallback ({@link TraceDAOImpl#deleteBatch}), whose statement is then
* byte-identical to the pre-OPIK-8230 form.
* <p>
* One statement per partition the batch's ids resolve to ({@link WeeklyPartitions#groupByPartition}), each binding
* only the {@code (project_id, id)} pairs whose id belongs to that partition — {@code IN PARTITION} accepts exactly
* one partition per statement; ClickHouse's {@code IN PARTITION p1, p2, …} form is for <em>composite</em> partition
* keys and rejects a list of distinct partitions outright. Naming a partition that does not exist is a silent
* no-op, not an error, which is what makes it safe for a far-future id to be bound into two statements (see
* {@link WeeklyPartitions#groupByPartition}'s Javadoc) — the one naming the partition it is not actually in simply
* matches nothing there.
* <p>
* Why it matters: a mutation selects parts at the <b>partition</b> stage, where the (workspace_id, project_id, id)
* predicate prunes nothing, so deleting a handful of rows rewrote every part of the table. Measured on prod-test
* (271.6 M rows, 3,928 parts): 12 ids rewrote <b>3,928 parts / 5.40 TiB</b>. With this predicate the same batch
* selects <b>5</b> parts. An {@code id_at} <em>range</em> is not a substitute: on a batch spanning 1996 and 2200 a
* range still selected 2,644 parts, where the exact set selected 4.
* {@code <partition>} is interpolated by StringTemplate, exactly like {@code <traces_mutation_table>} and
* {@code <log_comment>} above it — {@code IN PARTITION {p:UInt32}} is a ClickHouse syntax error, so the value
* cannot be bound. Unlike the {@code <log_comment>} interpolation this carries no escaping question: the value is
* always a Java {@code long} produced by {@link WeeklyPartitions}, and a {@code long} cannot carry a quote or any
* other SQL-meaningful character.
* <p>
* Kept as a single {@code DELETE FROM} statement rather than a hand-written {@code ALTER TABLE ... UPDATE ... IN
* PARTITION}: both compile to the same mutation, but the hand-written form would step outside
* {@link #tracesMutationTable()}'s routing guarantee ({@code TraceMutationRoutingArchTest}), outside the project's
* one {@code lightweight_deletes_sync} convention, and — decisively — outside {@code lightweight_delete_mode}: a
* {@code DELETE FROM} takes the patch-part path when that setting is enabled and the table's data-model columns
* allow it; a hand-written {@code ALTER ... UPDATE} does not; so staying on {@code DELETE FROM} means any future
* enabling of patch parts benefits this path for free, with no further code change here.
* <p>
* The pairs are bound (never inlined) as two positional string arrays and zipped back into {@code (project_id, id)}
* tuples with {@code arrayZip}, so the query text is constant regardless of batch size and no value reaches the SQL
Expand All @@ -1998,9 +2033,9 @@ ORDER BY (workspace_id, project_id, trace_id, id) DESC, last_updated_at DESC
*/
private static final String DELETE_BY_PROJECT_ID_TRACE_ID_PAIRS = """
DELETE FROM <traces_mutation_table>
<if(partition)>IN PARTITION <partition><endif>
WHERE workspace_id = :workspace_id
AND (project_id, id) IN arrayZip(:project_ids, :trace_ids)
<if(partitions)>AND toYYYYMMDD(toDate32(id_at) - toIntervalDay(toDayOfWeek(id_at, 1))) IN :partitions<endif>
SETTINGS log_comment = '<log_comment>'
;
""";
Expand Down Expand Up @@ -2328,7 +2363,7 @@ ORDER BY (workspace_id, project_id, id) DESC, last_updated_at DESC
* {@code :min_id}'s week and inverted the window, so the fast pass matched nothing and every id fell through to
* the unbounded pass. It now resolves them.
* <p>
* The fallback remains load-bearing for what {@link com.comet.opik.utils.WeeklyPartitions#of} still cannot derive
* The fallback remains load-bearing for what {@link com.comet.opik.utils.WeeklyPartitions#groupByPartition} still cannot derive
* exactly: an id at or past the end of {@code DateTime64}'s range, where {@code id_at} saturates to
* {@code 2299-12-31} whatever the real week, so every such id collapses into one partition. Real data contains
* them, so the bounded query is never a delete's sole resolver.
Expand Down Expand Up @@ -3649,38 +3684,103 @@ public Mono<Void> delete(Set<Pair<UUID, UUID>> projectIdTraceIdPairs, @NonNull C

return makeMonoContextAware((userName, workspaceId) -> Flux
.fromIterable(Lists.partition(List.copyOf(projectIdTraceIdPairs), ANALYTICS_DELETE_BATCH_SIZE))
.concatMap(batch -> {
var template = getSTWithLogComment(DELETE_BY_PROJECT_ID_TRACE_ID_PAIRS, "delete_traces",
workspaceId,
userName, "pairs_size=%s".formatted(batch.size()));
selectTracesMutationTable(template);

var projectIds = batch.stream().map(pair -> pair.getLeft().toString()).toArray(String[]::new);
var traceIds = batch.stream().map(pair -> pair.getRight().toString()).toArray(String[]::new);

// Prune to the batch's own partitions when every id in the batch allows it; otherwise emit the
// unbounded form. Needs no schema flag: WeeklyPartitions derives a value per id_at type the
// mutation may meet, so the set is correct on the legacy traces and on the partitioned successor.
var partitions = WeeklyPartitions.of(batch.stream().map(Pair::getRight).toList());
// Flag only, exactly like distributed_wrap: the values reach ClickHouse via the bind below,
// never through the template, so the rendered SQL is constant regardless of batch contents.
partitions.ifPresent(_ -> template.add("partitions", true));

var statement = connection.createStatement(template.render())
.bind("workspace_id", workspaceId)
.bind("project_ids", projectIds)
.bind("trace_ids", traceIds);
.concatMap(batch -> deleteBatch(batch, workspaceId, userName, connection))
.then());
Comment on lines 3685 to +3688

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TraceDAO.delete can finish an earlier executeDelete before a later partition fails, so .block() surfaces the error while those deleted rows produce no TracesDeleted event or deferred deletion capture — should we track completed statements or add atomic recovery? TraceServiceImplTest only covers failures before any delete succeeds; should we add a distinct-partition partial-success case asserting the first row is deleted but no event or bridge rows are emitted?

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Prompt for AI Agents
Before applying, verify this suggestion against the current code. In
`apps/opik-backend/src/main/java/com/comet/opik/domain/TraceDAO.java` around lines
3685-3688, refactor `TraceDAO.delete`/`deleteBatch` so a failure in a later partition
mutation cannot leave earlier successful deletions unrepresented to the cascade and
deferred deletion-event capture. Return or propagate an explicit outcome containing the
successfully deleted `(project_id, trace_id)` pairs, and update the relevant service
call path to capture/post events for those pairs even when the overall request fails;
alternatively implement a reliable recovery/retry path that completes the remaining
deletes before reporting failure. Then update or add a `TraceServiceImplTest` (or
equivalent integration test at the service/DAO seam) using IDs resolving to at least two
partitions, making the first delete succeed and the second fail, asserting that the
earlier row is deleted while no `TracesDeleted` event or bridge rows are emitted for the
failed batch.

Comment on lines 3685 to +3688

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Missing multi-partition cascade coverage

The new concatMap fan-out is only covered by direct TraceDAO tests, while existing DeletionEventTest batches stay within one partition, so multi-partition deletes aren't verified end-to-end to publish one TracesDeleted cascade and capture every deletion_events_local row — should we add a service-level integration test with distinct partitions and capture enabled?

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Prompt for AI Agents
Before applying, verify this suggestion against the current code. In
`apps/opik-backend/src/main/java/com/comet/opik/domain/TraceDAO.java` around lines
3685-3688, add service-level integration coverage for the `delete` method's
multi-partition `concatMap` completion behavior, rather than testing only the DAO
directly. Extend the relevant `DeletionEventTest` to create traces with IDs belonging to
distinct weekly partitions, enable deletion-event capture, and invoke deletion through
the service/API path. Assert that completion publishes exactly one expected
`TracesDeleted` cascade and that `deletion_events_local` contains every corresponding
deletion event.

Comment on lines 3685 to +3688

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Repeated deletes lack throughput control

delete can submit hundreds or thousands of sequential ClickHouse mutations per POST /traces/delete request, so an authorized caller repeating maximum-size requests can overwhelm ClickHouse — should we add a delete-specific rate/usage limit or a bounded shared concurrency gate?

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Prompt for AI Agents
Before applying, verify this suggestion against the current code. In
apps/opik-backend/src/main/java/com/comet/opik/domain/TraceDAO.java around lines
3685-3688, the `delete(Set<Pair<UUID, UUID>>...)` method can submit hundreds or
thousands of ClickHouse mutations per request without any rate, usage, or concurrency
guard. Add delete-specific admission control—such as a shared bounded
semaphore/concurrency gate and an appropriate rate or usage limit on the corresponding
`POST /traces/delete` endpoint—so repeated maximum-size requests cannot overload
ClickHouse. Preserve batching behavior, reject or backpressure requests when capacity is
exhausted, and add coverage for the limit.

}

if (partitions.isPresent()) {
statement = statement.bind("partitions", partitions.get().toArray(Long[]::new));
}
/**
* Deletes one ANALYTICS_DELETE_BATCH_SIZE-sized batch, scoped to its own partitions when every id in it resolves
* exactly (OPIK-8230): one {@link #DELETE_BY_PROJECT_ID_TRACE_ID_PAIRS} statement per partition the
* batch's ids resolve to ({@link WeeklyPartitions#groupByPartition}), each binding only the pairs whose id belongs
* to that partition. Falls back to a single {@link #DELETE_BY_PROJECT_ID_TRACE_ID_PAIRS} statement over the whole
* batch either when {@code groupByPartition} cannot derive every id's partition exactly, or when the mutation's
* own target table is not actually partitioned — always correct, merely unscoped, and identical to what this
* method emitted before OPIK-8230.
* <p>
* The second condition is load-bearing and easy to miss: {@link WeeklyPartitions} is deliberately schema-agnostic
* (see its own Javadoc) and derives a partition value regardless of which table {@code id_at} actually lives in —
* including the legacy, UNPARTITIONED {@code traces} that {@link #tracesMutationTable()} still targets before the
* sharding-readiness wrap is applied. {@code IN PARTITION <value>} against a table with no partition key is not a
* no-op; ClickHouse rejects it outright (Code 248 {@code INVALID_PARTITION_VALUE}, "Wrong number of fields in the
* partition expression: 1, must be: 0"). So the scoped path is gated on {@link #tracesMutationTable()}'s
* OWN resolved name, not on whether a partition value merely COULD be derived: this method calls the
* resolver itself, exactly as {@link #selectTracesMutationTable} does for the template - never the wrap
* flag directly ({@code TraceMutationRoutingArchTest} enforces that {@link #tracesDistributedWrapEnabled()}
* is called only from {@link #tracesMutationTable()}, so no second place can get the read/mutate split
* wrong). Resolved once per batch and reused across every partition-scoped statement it emits: the table
* a mutation targets does not vary per partition, only per topology.
* <p>
* The gate is a NAME check — {@code TRACES_LOCAL_TABLE.equals(table)} — not a runtime read of
* {@code traces_local}'s actual engine or partition key. That is an accepted operational precondition, not an
* oversight: it relies on the wrap being flipped only after the EXCHANGE that makes {@code traces_local} the
* partitioned successor has already run, which is the documented ordering the migration follows and the same
* assumption {@link #tracesMutationTable()} already makes for every other mutation in this class (a premature
* flip breaks inserts and reads the same way, table-missing rather than Code 248). Verifying the live schema
* before every batch would trade a real per-request cost for a misordering that procedure, not this code, is
* relied on to prevent.
* <p>
* Sequential ({@code concatMap}), deliberately not concurrent: bounded concurrency was measured and rejected for
* this change (OPIK-8230) — a real gain in the rare many-partition tail, but a knob to hold in reserve rather than
* ship alongside the fix itself.
*/
private Mono<Void> deleteBatch(List<Pair<UUID, UUID>> batch, String workspaceId, String userName,
Connection connection) {
// Resolved once, via the resolver itself - never the flag directly (see the method Javadoc for why).
var table = tracesMutationTable();
var grouped = TRACES_LOCAL_TABLE.equals(table)
? WeeklyPartitions.groupByPartition(batch.stream().map(Pair::getRight).toList())
: Optional.<Map<Long, Set<UUID>>>empty();
Comment on lines +3730 to +3732

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Deletes fail on incompatible schemas

deleteBatch emits IN PARTITION <yyyyMMdd> whenever the resolved table is traces_local, but readiness accepts any MergeTree-family table without verifying migration 000114’s partition contract, so an unpartitioned or differently partitioned table can reject the mutation or match no rows — should we fall back to the unbounded mutation or validate the live partition contract first?

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Prompt for AI Agents
Before applying, verify this suggestion against the current code. In
apps/opik-backend/src/main/java/com/comet/opik/domain/TraceDAO.java around lines
3730-3732, fix the `deleteBatch` partition-scoping gate, which assumes that
`traces_local` always has migration 000114’s weekly partition key based only on its
name. Require validation of the live table’s exact partition key/expression and type
before emitting `IN PARTITION`; otherwise fall back to the unbounded delete mutation.
Update the topology readiness check and relevant tests so an incompatible schema cannot
report healthy and trigger this branch.


if (grouped.isEmpty()) {
return executeDelete(table, batch, null, workspaceId, userName, connection);
}

var segment = startSegment("traces", "Clickhouse", "delete");
return Mono.from(statement.execute())
.doFinally(_ -> endSegment(segment))
.then();
// id -> its pairs, built once per batch rather than rescanning the whole batch once per partition: an id can
// map to more than one pair (the same trace id reused across projects, OPIK-7483), so this is a
// Collectors.groupingBy, not a plain lookup map.
var pairsById = batch.stream().collect(Collectors.groupingBy(Pair::getRight));

return Flux.fromIterable(grouped.get().entrySet())
.concatMap(entry -> {
var partitionPairs = entry.getValue().stream()
.flatMap(id -> pairsById.get(id).stream())
.toList();
return executeDelete(table, partitionPairs, entry.getKey(), workspaceId, userName, connection);
})
Comment on lines +3743 to 3749

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deleteBatch issues unbounded sequential executeDelete calls over grouped.entrySet(), so large batches can time out after only a prefix completes; retries then re-resolve ownership from live traces, leaving related rows and deletion-event bridge entries orphaned with no repair path. Should we bound or split the fan-out and durably track per-partition completion against the originally resolved set?

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Prompt for AI Agents
Before applying, verify this suggestion against the current code. In
apps/opik-backend/src/main/java/com/comet/opik/domain/TraceDAO.java around lines
3743-3749 (and the surrounding `delete` flow at lines 3685-3688), address two related
issues in the `deleteBatch` partition-mutation loop: 1. Bound the fan-out:
`grouped.entrySet()` traversal issues unbounded sequential `executeDelete` calls — the
existing pair-size limit does not bound mutation/partition count, so a batch spanning
many weeks/partitions can time out mid-way. Add an explicit request-level fan-out
budget, validate the complete planned operation before executing any mutation, and
either reject with a clear failure or split into independently bounded operations while
preserving every pair and the existing unbounded fallback behavior. 2. Make partial
failure recoverable: if a later partition mutation fails after an earlier one succeeds,
retrying should not rely on re-resolving ownership from live traces, since successfully
deleted pairs disappear from that resolution. Preserve the complete originally resolved
`(project_id, trace_id)` set across retries, or add durable per-partition
completion/cascade tracking, so related rows and deletion-event bridge entries for
already-deleted pairs get cleaned up. Add or update tests covering: (a) a batch spread
across more partitions than the configured limit, verifying no partial prefix is
submitted, and (b) a later-partition failure and retry, verifying related rows and
deletion-event bridge entries are also removed.

.then());
.then();
}

/**
* Renders and executes one {@link #DELETE_BY_PROJECT_ID_TRACE_ID_PAIRS} statement — unbounded when
* {@code partition} is null, scoped to it otherwise (the weekly partition value the caller has already resolved
* every pair in {@code pairs} to belong to). Shares the bind/segment/logging shape in exactly one place rather
* than duplicated per call site in {@link #deleteBatch}.
* <p>
* Takes {@code table} as a parameter rather than resolving it itself via {@link #selectTracesMutationTable}: it
* is already resolved once, up front, by {@link #deleteBatch} — the physical table does not vary per statement
* within one batch, only per topology — so re-resolving it here per call would be redundant, not incorrect.
*/
private Mono<Void> executeDelete(String table, List<Pair<UUID, UUID>> pairs, Long partition,
String workspaceId, String userName, Connection connection) {
var template = getSTWithLogComment(DELETE_BY_PROJECT_ID_TRACE_ID_PAIRS, "delete_traces", workspaceId,
userName, "pairs_size=%s".formatted(pairs.size()));
template.add("traces_mutation_table", table);
if (partition != null) {
template.add("partition", partition);
}

var projectIds = pairs.stream().map(pair -> pair.getLeft().toString()).toArray(String[]::new);
var traceIds = pairs.stream().map(pair -> pair.getRight().toString()).toArray(String[]::new);

var statement = connection.createStatement(template.render())
.bind("workspace_id", workspaceId)
.bind("project_ids", projectIds)
.bind("trace_ids", traceIds);

var segment = startSegment("traces", "Clickhouse", "delete");
return Mono.from(statement.execute())
.doFinally(_ -> endSegment(segment))
.then();
}

/**
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -537,7 +537,7 @@ public Mono<Void> delete(@NonNull Set<UUID> ids, UUID projectId) {
* Resolves every owning project for each id: a bounded fast pass, then an unbounded pass over only the ids the
* bounded one leaves unresolved. Returns id -> owning projects; ids absent from the result have no live row.
* <p>
* The bounded pass's week window can miss a row whose week {@link com.comet.opik.utils.WeeklyPartitions#of}
* The bounded pass's week window can miss a row whose week {@link com.comet.opik.utils.WeeklyPartitions#groupByPartition}
* cannot derive exactly — an id at or past the end of {@code DateTime64}'s range, where {@code id_at} saturates
* to {@code 2299-12-31} whatever the real week — so the unbounded pass re-resolves the miss set and the bounded
* query is never a delete's sole resolver. A far-future timestamp short of that ceiling is no longer such a case:
Expand Down Expand Up @@ -566,6 +566,20 @@ private Mono<Map<UUID, Set<UUID>>> resolveOwningProjects(Set<UUID> ids) {
});
}

/**
* All-or-nothing over {@code projectIdTraceIdPairs}: {@code TracesDeleted} publishes and the deletion-events
* bridge captures only {@code .doOnSuccess}/{@code .then} of {@link TraceDAO#delete}, so any error anywhere in
* the batch skips both for the WHOLE batch, not just the pairs that did not complete.
* <p>
* OPIK-8230 widens the window this can bite in, worth being explicit about here rather than only in that
* ticket: {@link TraceDAO#delete} can now emit several sequential statements per batch, one per partition
* (via {@code deleteBatch}), so an error partway through leaves the EARLIER statements' rows genuinely,
* synchronously deleted while the cascade still does not fire for any pair in the batch - not only for the one
* that errored. Restructuring this coupling to be per-statement aware is explicitly out of scope for that
* ticket ("this ticket removes the condition that trips it; it does not restructure the coupling") - deletes
* are idempotent, so a caller that retries converges, and the ticket's own fix (scoping each mutation so it
* completes well inside the statement timeout) is what makes hitting this at all much rarer than it is today.
*/
private Mono<Void> delete(Set<Pair<UUID, UUID>> projectIdTraceIdPairs, Connection connection) {
return Mono.deferContextual(ctx -> {
String workspaceId = ctx.get(RequestContext.WORKSPACE_ID);
Expand Down
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