The global rate limiter was being applied at the FairQueue claim phase,
consuming 1 token per queue-claim-attempt rather than per item
processed.
With many small queues (each batch is its own queue), consumers burned
through tokens on empty or single-item queues, causing aggressive
throttling well below the intended items/sec limit.
Changes:
- Move rate limiter from FairQueue claim phase to BatchQueue worker
queue
consumer loop (before blockingPop), so each token = 1 item processed
- Replace the FairQueue rate limiter with a worker queue depth cap to
prevent unbounded growth that could cause visibility timeouts
- Add BATCH_QUEUE_WORKER_QUEUE_MAX_DEPTH env var (optional, disabled by
default)
Input streams enable sending typed data to executing tasks from external
callers — backends, frontends, or other tasks. This unlocks interactive
use cases like approval UIs, cancel buttons, chat interfaces, and
human-in-the-loop AI workflows where the task needs to receive data
while running.
Three consumption patterns inside a task:
* `.wait()` — Suspend the task until data arrives (process freed, most
efficient)
* `.once()` — Wait for the next message (process stays alive)
* `.on()` — Subscribe to a continuous stream of messages
One send pattern from outside:
* `.send(runId, data)` — Send typed data to a specific run's input
stream
## User-facing API
### Define a typed input stream
```ts
import { streams, task } from "@trigger.dev/sdk";
const approval = streams.input<{ approved: boolean; reviewer: string }>({ id: "approval" });
```
### Consume inside a task
```ts
export const myTask = task({
id: "my-task",
run: async () => {
// Pattern 1: Suspend until data arrives (most efficient — frees the process)
const result = await approval.wait({ timeout: "5m" });
// Pattern 2: Wait for next message (process stays alive)
const data = await approval.once().unwrap();
// Pattern 3: Subscribe to multiple messages
approval.on((data) => { /* handle each message */ });
},
});
```
### Send from outside
```ts
// From a backend (using secret API key)
await approval.send(runId, { approved: true, reviewer: "alice" });
// From a frontend (using public JWT token from trigger response)
const { send } = useInputStreamSend("approval", runId, { accessToken });
send({ approved: true, reviewer: "alice" });
```
---------
Co-authored-by: Claude <noreply@anthropic.com>
- New User onboarding questions added and stored in a new
`onboardingData` col
- Keeps the same Org creation screen and stores the data in the same
format in same DB column
- New Org onboarding questions addded and stored in a new
`onboardingData` col
https://github.com/user-attachments/assets/244e4bae-f74d-4ed4-a545-92c9b927e98b
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Gracefully handle oversized batch items instead of aborting the stream.
When an NDJSON batch item exceeds the maximum size, the parser now emits
an error marker instead of throwing, allowing the batch to seal
normally. The oversized item becomes a pre-failed run with
`PAYLOAD_TOO_LARGE` error code, while other items in the batch process
successfully. This prevents `batchTriggerAndWait` from seeing connection
errors and retrying with exponential backoff.
Also fixes the NDJSON parser not consuming the remainder of an oversized
line split across multiple chunks, which caused "Invalid JSON" errors on
subsequent lines.
Split the CTE in blockRunWithWaitpoint so the pending waitpoint check
is a separate SQL statement. In READ COMMITTED isolation, each statement
gets its own snapshot, so a separate SELECT sees the latest committed
state from concurrent completeWaitpoint calls.
Previously, the CTE did INSERT + pending check in one statement (one
snapshot). If completeWaitpoint committed between the CTE start and
the SELECT, the SELECT would still see PENDING due to the stale
snapshot. Neither side would enqueue continueRunIfUnblocked, leaving
the run stuck forever.
Fix slow fair queue processing by removing spurious cooloff on
concurrency blocks and fixing a race condition where retry attempt
counts were not atomically updated during message re-queue.
Removed cooloff entirely from the batch queue
What changed
- Fixed some functions like dateAdd, toString, ifNotFinite
- Removed all functions that accept lambdas as they're not supported
(yet)
- Added tests for all TRQL functions that use ClickHouse
This PR implements a new run TTL system and queue size limits to prevent
unbounded queue growth which should help prevent situations where queues
enter a "death spiral" where the queue will never be able to catch up.
The main/correct way to battle this situation is to enforce a maximum
TTL on all runs (e.g. up to 14 days) where runs that have been queued
for that maximum TTL will get auto-expired, making room for newer runs
to execute. This required creating a new TTL system that can handle
higher workloads and is now deeply integrated into the RunQueue. When
runs are enqueued with a TTL, they are added to their normal queue as
well as to the TTL queue. When runs are dequeued, they are removed from
both their normal queue and the TTL queue. If runs are dequeued by the
TTL system, they are removed from their normal queue. Both these
dequeues happen automatically so there is no race condition.
The TTL expiration system is also made reliable by expiring runs via a
Redis worker, which is enqueued to atomically inside the TTL dequeue lua
script.
### Optional associated waitpoints
Additionally, this PR implements an optimization where runs that aren't
triggered with a dependent parent run will no longer create an
associated waitpoint. Associated waitpoints are then lazily created if a
dependent run wants to wait for the child run post-facto (via debounce
or idempotency), which is a rare situation but is possible. This means
fewer waitpoint creations but also fewer waitpoint completions for runs
with no dependencies.
### Environment Queue Limits
Prevents any single queue growing too large by enforcing queue size
limits at trigger time.
- Queue size checks happen at trigger time - runs are rejected if queue
would exceed limit
- Dashboard UI shows queue limits on both the Queues page and a new
Limits page
- In-memory caching for queue size checks to reduce Redis load
### Batch trigger fixes
Currently when a batch item cannot be created for whatever reason (e.g.
queue limits) the run will never get created, which means a stalled run
if using `batchTriggerAndWait`. We've updated the system to handle this
differently: now when a batch item cannot be triggered and converted
into a run, we will eventually (after retrying 8 times up to 30s) we
will create a "pre-failed" run with the error details, correctly
resolving the batchTriggerAndWait.
- Adds an end-to-end OTEL metrics pipeline: task workers collect and
export metrics via OpenTelemetry, the webapp ingests them into
ClickHouse, and they're queryable through the existing dashboard query
engine
- Workers emit process CPU/memory metrics (via
`@opentelemetry/host-metrics`) and Node.js runtime metrics (event loop
utilization, event loop delay, heap usage)
- Users can create custom metrics in their tasks via
`otel.metrics.getMeter()` from `@trigger.dev/sdk`
- Metrics are automatically tagged with run context (run ID, task slug,
machine, worker version) so they can be sliced per-run, per-task, or
per-machine
- The TSQL query engine gains metrics table support with typed attribute
columns, `prettyFormat()` for human-readable values, and per-schema time
bucket thresholds
- Includes reference tasks
(`references/hello-world/src/trigger/metrics.ts`) demonstrating
CPU-intensive, memory-ramp, bursty workload, and custom metrics patterns
## What changed
### Metrics collection (packages/core, packages/cli-v3)
- **Metrics export pipeline** — `TracingSDK` now sets up a
`MeterProvider` with a `PeriodicExportingMetricReader` that chains
through `TaskContextMetricExporter` (adds run context attributes) and
`BufferingMetricExporter` (batches exports to reduce overhead)
- **Host metrics** — Enabled `@opentelemetry/host-metrics` for process
CPU, memory, and system-level metrics
- **Node.js runtime metrics** — New `nodejsRuntimeMetrics.ts` module
using `performance.eventLoopUtilization()`, `monitorEventLoopDelay()`,
and `process.memoryUsage()` to emit 6 observable gauges
- File system and diskio metrics
- **Custom metrics** — Exposed `otel.metrics` from `@trigger.dev/sdk` so
users can create counters, histograms, and gauges in their tasks
- **Machine ID** — Stable per-worker machine identifier for grouping
metrics
- **Dev worker** — Drops `system.*` metrics to reduce noise, keeps
sending metrics between runs in warm workers
### Metrics ingestion (apps/webapp)
- **OTEL endpoint** — `otel.v1.metrics.ts` accepts OTEL metric export
requests (JSON and protobuf), converts to ClickHouse rows
- **ClickHouse schema** — `017_create_metrics_v1.sql` with 10-second
aggregation buckets, JSON attributes column, 60-day TTLs
### Query engine (internal-packages/tsql, apps/webapp)
- **Metrics query schema** — Typed columns for metric attributes
(`task_identifier`, `run_id`, `machine_name`, `worker_version`, etc.)
extracted from the JSON attributes column
- **`prettyFormat()`** — TSQL function that annotates columns with
format hints (`bytes`, `percent`, `durationSeconds`) for frontend
rendering without changing the underlying data
- **Per-schema time buckets** — Different tables can define their own
time bucket thresholds (metrics uses tighter intervals than runs)
- **AI query integration** — The AI query service knows about the
metrics table and can generate metric queries
- **Chart improvements** — Better formatting for byte values,
percentages, and durations in charts and tables
### Reference project
- **`references/hello-world/src/trigger/metrics.ts`** — 6 example tasks:
`cpu-intensive`, `memory-ramp`, `bursty-workload`, `sustained-workload`,
`concurrent-load`, `custom-metrics`
## Test plan
- [ ] Build all packages and webapp
- [ ] Start dev worker with hello-world reference project
- [ ] Run `cpu-intensive`, `memory-ramp`, and `custom-metrics` tasks
- [ ] Verify metrics in ClickHouse: `SELECT DISTINCT metric_name FROM
metrics_v1`
- [ ] Query via dashboard AI: "show me CPU utilization over time"
- [ ] Verify `prettyFormat` renders correctly in chart tooltips and
table cells
- [ ] Confirm dev worker drops `system.*` metrics but keeps `process.*`
and `nodejs.*`
This will prevent internal logs to be added to the
task_events_search_table
Closes #<issue>
## ✅ Checklist
- [x] I have followed every step in the [contributing
guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md)
- [x] The PR title follows the convention.
- [x] I ran and tested the code works
---
## Testing
Ran the migration, deleted the old invalid rows and ran new tasks.
The undesired logs are not added to the table.
---
## Changelog
Updated the MATERIALIZED VIEW to also filter for `trace_id != ''`
---------
Co-authored-by: Matt Aitken <matt@mattaitken.com>
For now we’re going to always add FINAL to TRQL queries for data
correctness.
In the future we will implement an automated optimization where we use
`SELECT argMax(column, _version)` and `WHERE _is_deleted = 0`. But this
is a more complex change and needs more investigation of downsides.
## ✅ Checklist
- [X] I have followed every step in the [contributing
guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md)
- [X] The PR title follows the convention.
- [X] I ran and tested the code works
---
## Testing
Tested the migration on test env and locally.
Tested query and merge performance.
Generated tasks and observed the ingested data and searches.
---
## Changelog
* New ClickHouse table & MV (task_events_search_v1): A search-optimized
materialized view that filters out debug events, partial spans, and
empty span events at ingestion time.
* ClickHouse client updates: New getLogsSearchListQueryBuilder and
taskEventsSearch accessor on the ClickHouse class.
* LogsListPresenter: Switches to the new search table, uses
triggered_timestamp for cursor pagination instead of unixTimestamp.
* Spans route: Also switches to the new search query builder.
* Seed spanSpammer task: Adds a 10s trace with events and metadata
operations for testing.
Summary
- Implemented metrics dashboards with a built-in dashboard and custom
dashboards
- Added a "Big number” display type
What changed
- New data format for metric layouts and saving/editing layouts
(editing, saving, cancel revert)
- QueryWidget usable on Query page and Metrics dashboards
- Time filtering, auto-reloading and timeBucket() auto-bin support
- Filters added to metrics; widget popover/improved history and blank
states
- Side menu:
- Metrics/Insights section with icons, colors, padding, collapsible
behavior and reordering of custom dashboards
- Move action logic into service for reuse and API querying; refactor
reordering for reuse
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---------
Co-authored-by: James Ritchie <james@trigger.dev>
Bundle superjson and its dependency (copy-anything) during build to
avoid
ERR_REQUIRE_ESM errors on Node.js versions that don't support
require(ESM)
by default (< 22.12.0) and AWS Lambda which intentionally disables it.
- Add scripts/bundle-superjson.mjs to bundle superjson with esbuild
- Update build script to bundle vendor files before tshy compilation
- Move superjson from dependencies to devDependencies
- Update imports to use vendored bundles
Fixes#2937
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---------
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Eric Allam <ericallam@users.noreply.github.com>
Summary
- Query: add time limits, performance improvements, and styling updates
Changes
- Add ClickHouse output_text and error_text columns with indexes
- Automatically use _text columns for JSON based on query pattern;
support JSON column data prefixes
- Add idempotency key and scope columns
- Add enforcedWhereClause for tenant and time restrictions, instead of
the old tenant stuff.
- Implement basic time filter limiting and set default time period based
on plan; show message when results are clipped
- UX: resizable code area (including vertical splits), collapsible
sidebar, fix table/chart vertical sizing, max height for chart legend in
fullscreen
- Styling and UI tweaks: improved chart legend styling, more chart
colours, thinner line chart stroke, pricing callout color, improved
layout for callouts
- Features: generate and save AI titles
<!-- devin-review-badge-begin -->
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## ✨ Changes
### UI & UX
- Normalized log level display across table and detail view
- Fixed table header scroll behavior and sidebar positioning
- Improved loading state with taller segment and disabled resizing
- Added "no more logs" message with count
- Enhanced keyboard shortcuts
### Filtering & Search
- Streamlined filters: RunId and Task only (removed run filters)
- Side panel closes when filters change
- Fixed logs from previous search remaining in table
- Fixed table scroll position when changing filters
### Backend
- Added performance indexes on message and attributes
(`014_add_task_runs_v2_search_indexes.sql`)
- Added DEBUG level logging by default
- Removed internal logs from display
- Fixed ServiceValidationError forwarding to frontend
- Removed v1 logs API support
Moving usage updates into the run engine to prevent inefficient &
additional incremental updates to the TaskRun table. Read/Modify/Write
pattern is safe inside of the run engine because of the run lock. We can
also now cap the usageDurationMs value from overflowing and causing an
error.
## Why?
This is preventing at least one update per TaskRun and instead updating
these values piggybacking on other updates.
## Aurora PostgreSQL Reader Consistency Notes
### TL;DR
Aurora readers share the same storage as the writer, but maintain
separate in-memory page caches. This means:
- **Storage is always consistent** - writes are synchronously committed
to shared storage
- **Page cache can lag** - typically <100ms, but can cause stale reads
if data is cached
### How It Works
1. Writer commits to shared storage (synchronous 4/6 quorum)
2. Writer sends cache invalidation messages to readers (asynchronous)
3. If reader has data in cache → returns cached (potentially stale)
value
4. If reader has cache miss → fetches from shared storage (always
current)
### Monitoring
```sql
SELECT server_id,
CASE WHEN session_id = 'MASTER_SESSION_ID' THEN 'Writer' ELSE 'Reader' END AS role,
replica_lag_in_msec
FROM aurora_replica_status();
```
Move expensive findMany queries for PENDING_VERSION and
WAITING_FOR_DEPLOY
runs to read replicas to avoid blocking migrations on the primary
database.
Changes:
- Add readOnlyPrisma to SystemResources type
- Pass readOnlyPrisma to systems in RunEngine constructor
- Update pendingVersionSystem to use readOnlyPrisma for findMany
- Update executeTasksWaitingForDeploy to use _replica for findMany
## Summary
- Adds `IF NOT EXISTS` to the migration that adds
`idempotencyKeyOptions` column to prevent errors if the column already
exists
## Migration Checksum Fix
If you've already applied the previous version of this migration, you'll
need to update the checksum in your `_prisma_migrations` table to match
the new migration file.
**Previous checksum:**
`f8876e274e3f7735312275eb24a9c4b40f512ac12a286b2de3add47f66df5b27`
**New checksum:**
`0620a914ddbaf01279576274432e51c41f41502cd4c8de38621625380750e397`
### Fix instructions
Run this SQL command against your database:
```sql
UPDATE "_prisma_migrations"
SET checksum = '0620a914ddbaf01279576274432e51c41f41502cd4c8de38621625380750e397'
WHERE migration_name = '20260116154810_add_idempotency_key_options_to_task_run';
```
This updates the stored checksum to match the modified migration file,
allowing future migrations to proceed without checksum mismatch errors.
## Test plan
- [x] Verified migration applies cleanly on fresh database
- [ ] Verified checksum update works on database with previous migration
applied
🤖 Generated with [Claude Code](https://claude.com/claude-code)
What changed
- Upgraded recharts to 2.15.2
- Added multiple chart types and components: big number, line, stacked,
bar (including zoomable & reference line), big dataset bar, and usage
graph
- Implemented custom legend with animated values, tooltip showing x-axis
data, and hover/highlight behaviors for stacks and legend
- Added loading, no-data, and invalid chart states plus loading spinners
and improved loading animations/layout
- Storybook integration: initial charts setup, separate chart files,
alphabetized menu, chart state toggles, and story updates
- Interaction & UX improvements: zooming (drag/select), crosshair
pointer, show/select dates while zooming, prevent text selection on
drag, hide mouse wheel zoom, capped legend items, axis/legend styling
tweaks, better spacing, and min-height for charts
- Data & state handling: moved date data to route for unified zooming,
moved chartState to main Chart component, moved hard-coded/mock data out
of components, and set chart data when zooming to start/end dates
- Performance & animation: turned off/reduced chart animations, sped up
animated numbers, removed hover transitions for bars
- New UI primitives and layout: Card component, small card updates, SVG
icons, improved segmented control and popover variants, table
improvements (resizable columns, filtering, sorting, scrolling fixes)
- Various fixes and polish: tooltip style fixes, legend value updates,
hover/leave state resets, bar width fixes for small datasets,
type/import fixes, and numerous small style/typo tweaks
---------
Co-authored-by: James Ritchie <james@trigger.dev>
## Summary
- Store the original user-provided idempotency key and scope alongside
the hash
- Expose `ctx.run.idempotencyKey` as the user-provided key (not the
hash)
- Add `ctx.run.idempotencyKeyScope` to show the scope ("run", "attempt",
or "global")
<img width="539" height="450" alt="CleanShot 2026-01-19 at 11 40 46"
src="https://github.com/user-attachments/assets/b6f42991-697e-4314-a164-aef77b8fd25c"
/>
## Problem
Idempotency keys were hashed (SHA-256) before storage, making debugging
difficult since users couldn't see the value they originally set or
search for runs by idempotency key.
## Solution
Attach metadata to the `String` object returned by
`idempotencyKeys.create()` using a Symbol, extract it in the SDK before
the API call, and store it in the database alongside the hash.
```typescript
const key = await idempotencyKeys.create("my-key", { scope: "global" });
await childTask.triggerAndWait(payload, { idempotencyKey: key });
// In child task:
ctx.run.idempotencyKey // "my-key" (previously showed the hash)
ctx.run.idempotencyKeyScope // "global"
```
Test plan
- Trigger task with idempotencyKeys.create() using different scopes (run, attempt, global)
- Verify ctx.run.idempotencyKey returns user-provided key
- Verify ctx.run.idempotencyKeyScope returns correct scope
- Verify PostgreSQL stores idempotencyKeyOptions JSON
- Verify ClickHouse receives idempotency_key_user and idempotency_key_scope via replication
---------
Co-authored-by: James Ritchie <james@trigger.dev>
Closes #<issue>
## ✅ Checklist
- [ ] I have followed every step in the [contributing
guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md)
- [ ] The PR title follows the convention.
- [ ] I ran and tested the code works
---
## Testing
_[Describe the steps you took to test this change]_
---
## Changelog
_[Short description of what has changed]_
---
## Screenshots
_[Screenshots]_
💯
---------
Co-authored-by: Matt Aitken <matt@mattaitken.com>
Summary
- Add nullable projectId field to TaskScheduleInstance.
- Create an index for TaskScheduleInstance.environmentId (added only if
it doesn’t exist, handled concurrently).
- Ensure TaskScheduleInstance.projectId is set everywhere in the
codebase.
Backfilling projectIds, once this is live
```sql
UPDATE "TaskScheduleInstance" tsi
SET "projectId" = ts."projectId"
FROM "TaskSchedule" ts
WHERE tsi."taskScheduleId" = ts."id";
```
* Removed EVENT_REPOSITORY_CLICKHOUSE_ROLLOUT_PERCENT
* Added hasLogsPageAccess featureFlag for logs page
* Replaced attributes with attributes_text for logs to reduce memory
usage and improve query performance
* Added support for event_v1 for logs, now depending on the settings the
logs are fetched either from `task_events_v1` or `task_events_v2`
* Show an error in the interface in cast the repository store is
`postgres`
It’s useful to know when they were modified for debugging and auditing.
For existing rows createdAt and updatedAt are set to now() during the
migration, to avoid a nullable column.
Don’t allow aliased columns to be queried – it was actually safe but
confusing. We call `created_at` -> `triggered_at` but we still allowed
created_at which was confusing.
Now we have nice errors if you try select columns that aren’t
selectable.
Also removed a ClickHouse setting `allow_experimental_object_type` which
worked fine locally but stopped all queries working on ClickHouse Cloud
🤦♂️
## Summary
Optimizes the runs replication service for better CPU efficiency and
throughput when inserting task runs into ClickHouse.
### Key Changes
- **Switch to compact array format** - Uses
`JSONCompactEachRowWithNames` instead of `JSONEachRow` for ClickHouse
inserts, reducing JSON serialization overhead
- **Type-safe tuple arrays** - Introduces `TaskRunInsertArray` and
`PayloadInsertArray` tuple types with compile-time column order
validation
- **Pre-sorted batch inserts** - Sorts inserts by primary key before
flushing for better ClickHouse insert performance
- **Programmatic index generation** - `TASK_RUN_INDEX` and
`PAYLOAD_INDEX` are generated from column arrays to prevent manual
synchronization errors
### Files Changed
- `runsReplicationService.server.ts` - Core optimization to use compact
array inserts
- `@internal/clickhouse` - Added `insertCompactRaw` method and tuple
types
- `taskRuns.ts` - Column definitions, index constants, and insert
functions
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Closes #<issue>
## ✅ Checklist
- [x] I have followed every step in the [contributing
guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md)
- [x] The PR title follows the convention.
- [x] I ran and tested the code works
---
## Testing
- Verified log detail view displays correctly with message, metadata,
and attributes
- Tested search highlighting functionality in log messages (escapes
special regex characters)
- Confirmed tabs (Details/Run) switch properly with keyboard shortcuts
(d/r)
- Verified run information loads via async fetcher in Run tab
- Tested close button and Escape key for dismissing the panel
- Verified log details display correct information: level badges, kind
badges, timestamps, trace IDs, span IDs
- Confirmed links to parent spans and run pages work correctly
- Tested with various log levels (ERROR, WARN, INFO, DEBUG, TRACE) and
kinds (SPAN, SPAN_EVENT, LOG_*)
- Verified admin-only fields display correctly when user has admin
access
- Tested data loading states and error states (log not found, run not
found)
---
## Changelog
Created new Logs page.
The information shown is gathered from the spans from each run.
The feature supports all run filters with two new filters for level and
logs text search.
---
## Screenshots
<img width="2059" height="1196" alt="Logs page preview"
src="https://github.com/user-attachments/assets/70b667b4-98cc-4728-855a-2766dd5c1aa5"
/>
💯
---------
Co-authored-by: James Ritchie <james@trigger.dev>
Summary
- Improve query experience and safety across ClickHouse and TSQL.
Changes
- Display JSON columns when in non-pretty mode (no longer show [Object
Object]).
- Sanitize ClickHouse errors originating from TSQL.
- Remove tenant details from errors.
- Add AI-assisted error-fixing for queries.
- Improve code quality and readability.
- Provide autocomplete support for enum values.
- Enforce limits on ClickHouse queries (10s query limit).
- Add org-level and global concurrency limits.
- Warn and train AI to avoid SELECT *; when used, only return core
columns and show info.
- If AI suggests no time range, default to past 7 days.
- Format the default query for readability.
- Add an admin-only EXPLAIN button.
- Prevent impersonation queries from being saved to history.
TRQL (pronounced Treacle like the delicious British dark sweet syrup) is
the TRiggerQueryLanguage. It allows users to safely write queries on
their data. The queries are safely turned into ClickHouse queries which
are tenant-safe and not SQL injectable.
https://github.com/user-attachments/assets/bbfca473-b3fc-4150-8fe6-79e8840a2d29
This started out as a translation of HogQL by PostHog from Python to
TypeScript.
Features
- Tenant safe queries.
- Many underlying ClickHouse features including functions and
aggregations.
- Virtual columns, which are exposed to users as real columns but are
actually expressions.
- Transformations of data types and where clauses.
- Simple JSON path querying.
- Limits on execution time.
- Reporting of query statistics.
## Query page
There’s a new Query page (currently behind a feature flag) where you can
write TRQL queries and execute them against your environment, project or
organization.
Features
- Executing TRQL queries
- Syntax highlighting and errors
- Autocomplete
- AI generation/editing of queries
- Help and examples
- Table with auto-inferred data types from the table schema
- Table cell renderers for our special types like Run ids, environments,
machines, tasks, queues, etc.
- Copy/export as CSV/JSON
- Line and bar graphs with grouping and stacking
- History of queries
This PR fixes some issues with the new BatchQueue by implementing the
full two-phase dequeue process in the FairQueue, and moving the
responsibility of consuming the worker queue to the BatchQueue and
independently enabling it via the `BATCH_QUEUE_WORKER_QUEUE_ENABLED` env
var. We've also introduced the `BATCH_QUEUE_SHARD_COUNT` env var to
control the count of master queue shards in the FairQueue. We can also
control how many queues are considered in each iteration of the master
queue consumer via the `BATCH_QUEUE_MASTER_QUEUE_LIMIT` env var.
This PR will also now skip trying to dequeue from tenants that are at
concurrency capacity, which should lead to fewer issues with low
concurrency tenants blocking higher concurrency tenants from processing.
Eric Allam
