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Job queue for PostgreSQL running on Node.js - allows you to run jobs (e.g. sending emails, performing calculations, generating PDFs, etc) "in the background" so that your HTTP response/application code is not held up. Can be used with any PostgreSQL-backed application. Pairs beautifully with PostGraphile or PostgREST.

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Quickstart: CLI

In your existing Node.js project:

Add the worker to your project:

yarn add graphile-worker
# or: npm install --save graphile-worker

Create tasks:

Create a tasks/ folder, and place in it JS files containing your task specs. The names of these files will be the task identifiers, e.g. hello below:

// tasks/hello.js
module.exports = async (payload, helpers) => {
  const { name } = payload;`Hello, ${name}`);

Run the worker

(Make sure you're in the folder that contains the tasks/ folder.)

npx graphile-worker -c "my_db"
# or, if you have a remote database, something like: 
#   npx graphile-worker -c "postgres://user:pass@host:port/db?ssl=1" 
# or, if you prefer envvars 
#   DATABASE_URL="..." npx graphile-worker 

(Note: npx runs the local copy of an npm module if it is installed, when you're ready, switch to using the package.json "scripts" entry instead.)

Schedule a job via SQL

Connect to your database and run the following SQL:

SELECT graphile_worker.add_job('hello', json_build_object('name''Bobby Tables'));


You should see the worker output Hello, Bobby Tables. Gosh, that was fast!

Quickstart: library

Instead of running graphile-worker via the CLI, you may use it directly in your Node.js code. The following is equivalent to the CLI example above:

const { run, quickAddJob } = require("graphile-worker");
async function main() {
  // Run a worker to execute jobs:
  const runner = await run({
    connectionString: "postgres:///my_db",
    concurrency: 5,
    // Install signal handlers for graceful shutdown on SIGINT, SIGTERM, etc
    noHandleSignals: false,
    pollInterval: 1000,
    // you can set the taskList or taskDirectory but not both
    taskList: {
      hello: async (payload, helpers) => {
        const { name } = payload;`Hello, ${name}`);
    // or:
    //   taskDirectory: `${__dirname}/tasks`,
  // Or add a job to be executed:
  await quickAddJob(
    // makeWorkerUtils options
    { connectionString: "postgres:///my_db" },
    // Task identifier
    // Payload
    { name: "Bobby Tables" }
main().catch(err => {

Running this example should output something like:

[core] INFO: Worker connected and looking for jobs... (task names: 'hello')
[job(worker-7327280603017288: hello{1})] INFO: Hello, Bobby Tables
[worker(worker-7327280603017288)] INFO: Completed task 1 (hello) with success (0.16ms)


You can ask for help on Discord at

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  • Standalone and embedded modes
  • Designed to be used both from JavaScript or directly in the database
  • Easy to test (recommended: runTaskListOnce util)
  • Low latency (typically under 3ms from task schedule to execution, uses LISTEN/NOTIFY to be informed of jobs as they're inserted)
  • High performance (uses SKIP LOCKED to find jobs to execute, resulting in faster fetches)
  • Small tasks (uses explicit task names / payloads resulting in minimal serialisation/deserialisation overhead)
  • Parallel by default
  • Adding jobs to same named queue runs them in series
  • Automatically re-attempts failed jobs with exponential back-off
  • Customisable retry count (default: 25 attempts over ~3 days)
  • Task de-duplication via unique job_key
  • Open source; liberal MIT license
  • Executes tasks written in Node.js (these can call out to any other language or networked service)
  • Modern JS with 100% async/await API (no callbacks)
  • Written natively in TypeScript
  • Watch mode for development (experimental - iterate your jobs without restarting worker)
  • If you're running really lean, you can run Graphile Worker in the same Node process as your server to keep costs and devops complexity down.


Production ready (and used in production).

We're still enhancing/iterating the library rapidly, hence the 0.x numbering; updating to a new "minor" version (0.y) may require some small code modifications, particularly to TypeScript type names; these are documented in the changelog.

This specific codebase is fairly young, but it's based on years of implementing similar job queues for Postgres.

To give feedback please raise an issue or reach out on discord:


PostgreSQL 10+* and Node 10+*.

If your database doesn't already include the pgcrypto extension we'll automatically install it into the public schema for you. If the extension is installed in a different schema (unlikely) you may face issues. Making alias functions in the public schema, should solve this issue (see issue #43 for an example).

* Might work with older versions, but has not been tested.


yarn add graphile-worker
# or: npm install --save graphile-worker


graphile-worker manages it's own database schema (graphile_worker). Just point graphile-worker at your database and we handle our own migrations:

npx graphile-worker -c "postgres:///my_db"

(npx looks for the graphile-worker binary locally; it's often better to use the "scripts" entry in package.json instead.)

The following CLI options are available:

  --help               Show help                                       [boolean]
  --version            Show version number                             [boolean]
  --connection, -c     Database connection string, defaults to the
                       'DATABASE_URL' envvar                            [string]
  --schema, -s         The database schema in which Graphile Worker is (to be)
                       located             [string] [default: "graphile_worker"]
  --schema-only        Just install (or update) the database schema, then exit
                                                      [boolean] [default: false]
  --once               Run until there are no runnable jobs left, then exit
                                                      [boolean] [default: false]
  --watch, -w          [EXPERIMENTAL] Watch task files for changes,
                       automatically reloading the task code without restarting
                       worker                         [boolean] [default: false]
  --jobs, -j           number of jobs to run concurrently  [number] [default: 1]
  --max-pool-size, -m  maximum size of the PostgreSQL pool[number] [default: 10]
  --poll-interval      how long to wait between polling for jobs in milliseconds
                       (for jobs scheduled in the future/retries)
                                                        [number] [default: 2000]

Library usage: running jobs

graphile-worker can be used as a library inside your Node.js application. There are two main use cases for this: running jobs, and queueing jobs. Here are the APIs for running jobs.

run(options: RunnerOptions): Promise<Runner>

Runs until either stopped by a signal event like SIGINT or by calling the stop() function on the Runner object run() resolves to.

The Runner object also contains a addJob method (see addJob) that can be used to enqueue jobs:

await runner.addJob("testTask", {
  thisIsThePayload: true,

runOnce(options: RunnerOptions): Promise<void>

Equivalent to running the CLI with the --once flag. The function will run until there are no runnable jobs left, and then resolve.

runMigrations(options: RunnerOptions): Promise<void>

Equivalent to running the CLI with the --schema-only option. Runs the migrations and then resolves.


The following options for these methods are available.

  • concurrency: The equivalent of the CLI --jobs option with the same default value.
  • nohandleSignals: If set true, we won't install signal handlers and it'll be up to you to handle graceful shutdown of the worker if the process receives a signal.
  • pollInterval: The equivalent of the CLI --poll-interval option with the same default value.
  • logger: To change how log messages are output you may provide a custom logger; see Logger below
  • the database is identified through one of these options:
    • connectionString: A PostgreSQL connection string to the database containing the job queue, or
    • pgPool: A pg.Pool instance to use
  • the tasks to execute are identified through one of these options:
    • taskDirectory: A path string to a directory containing the task handlers.
    • taskList: An object with the task names as keys and a corresponding task handler functions as values
  • schema can be used to change the default graphile_worker schema to something else (equivalent to --schema on the CLI)

Exactly one of either taskDirectory or taskList must be provided (except for runMigrations which doesn't require a task list).

Either connectionString or pgPool must be provided, or the DATABASE_URL envvar must be set.

Library usage: queueing jobs

You can also use the graphile-worker library to queue jobs using one of the following APIs.

NOTE: although running the worker will automatically install its schema, the same is not true for queuing jobs. You must ensure that the worker database schema is installed before you attempt to enqueue a job; you can install the database schema into your database with the following command:

yarn graphile-worker -c "postgres:///my_db" --schema-only

Alternatively you can use the WorkerUtils migrate method:

await workerUtils.migrate();

makeWorkerUtils(options: WorkerUtilsOptions): Promise<WorkerUtils>

Useful for adding jobs from within JavaScript in an efficient way.

Runnable example:

const { makeWorkerUtils } = require("graphile-worker");
async function main() {
  const workerUtils = await makeWorkerUtils({
    connectionString: "postgres:///my_db",
  try {
    await workerUtils.migrate();
    await workerUtils.addJob(
      // Task identifier
      // Payload
      { value: 42 }
      // Optionally, add further task spec details here
    // await workerUtils.addJob(...);
    // await workerUtils.addJob(...);
    // await workerUtils.addJob(...);
  } finally {
    await workerUtils.release();
main().catch(err => {

We recommend building one instance of WorkerUtils and sharing it as a singleton throughout your code.


  • exactly one of these keys must be present to determine how to connect to the database:
    • connectionString: A PostgreSQL connection string to the database containing the job queue, or
    • pgPool: A pg.Pool instance to use
  • schema can be used to change the default graphile_worker schema to something else (equivalent to --schema on the CLI)


A WorkerUtils instance has the following methods:

  • addJob(name: string, payload: JSON, spec: TaskSpec) - a method you can call to enqueue a job, see addJob.
  • migrate() - a method you can call to update the graphile-worker database schema; returns a promise.
  • release() - call this to release the WorkerUtils instance. It's typically best to use WorkerUtils as a singleton, so you often won't need this, but it's useful for tests or processes where you want Node to exit cleanly when it's done.

quickAddJob(options: WorkerUtilsOptions, ...addJobArgs): Promise<Job>

If you want to quickly add a job and you don't mind the cost of opening a DB connection pool and then cleaning it up right away for every job added, there's the quickAddJob convenience function. It takes the same options as makeWorkerUtils as the first argument; the remaining arguments are for addJob.

NOTE: you are recommended to use makeWorkerUtils instead where possible, but in one-off scripts this convenience method may be enough.

Runnable example:

const { quickAddJob } = require("graphile-worker");
async function main() {
  await quickAddJob(
    // makeWorkerUtils options
    { connectionString: "postgres:///my_db" },
    // Task identifier
    // Payload
    { value: 42 }
    // Optionally, add further task spec details here
main().catch(err => {


The addJob API exists in many places in graphile-worker, but all the instances have exactly the same call signature. The API is used to add a job to the queue for immediate or delayed execution. With jobKey it can also be used to replace existing jobs.

NOTE: quickAddJob is similar to addJob, but accepts an additional initial parameter describing how to connect to the database).

The addJob arguments are as follows:

  • identifier: the name of the task to be executed
  • payload: an optional JSON-compatible object to give the task more context on what it is doing
  • options: an optional object specifying:
    • queueName: the queue to run this task under
    • runAt: a Date to schedule this task to run in the future
    • maxAttempts: how many retries should this task get? (Default: 25)
    • jobKey: unique identifier for the job, used to update or remove it later if needed (see Updating and removing jobs); can also be used for de-duplication


await addJob("task_2", { foo: "bar" });


export type AddJobFunction = (
   * The name of the task that will be executed for this job.
   * The payload (typically a JSON object) that will be passed to the task executor.
  payload?: any,
   * Additional details about how the job should be handled.
  spec?: TaskSpec
) => Promise<Job>;
export interface TaskSpec {
   * The queue to run this task under (specify if you want the job to run
   * serially).
  queueName?: string;
   * A Date to schedule this task to run in the future
  runAt?: Date;
   * How many retries should this task get? (Default: 25)
  maxAttempts?: number;
   * Unique identifier for the job, can be used to update or remove it later if needed
  jobKey?: string;


You may customise where log messages from graphile-worker (and your tasks) go by supplying a custom Logger instance using your own logFactory.

const { Logger, run } = require("graphile-worker");
/* Replace this function with your own implementation */
function logFactory(scope) {
  return (level, message, meta) => {
    console.log(level, message, scope, meta);
const logger = new Logger(logFactory);
// Pass the logger to the 'run' method as part of options:
  /* pgPool, taskList, etc... */

Your logFactory function will be passed a scope object which may contain the following keys (all optional):

  • label (string): a rough description of the type of action ('watch', 'worker' and 'job' are the currently used values).
  • workerId (string): the ID of the worker instance
  • taskIdentifier (string): the task name (identifier) of the running job
  • jobId (number): the id of the running job

And it should return a logger function which will receive these three arguments:

  • level ('error', 'warning', 'info' or 'debug') - severity of the log message
  • message (string) - the log message itself
  • meta (optional object) - may contain other useful metadata, useful in structured logging systems

The return result of the logger function is currently ignored; but we strongly recommend that for future compatibility you do not return anything from your logger function.

See consoleLogFactory in src/logger.ts for an example logFactory.

NOTE: you do not need to (and should not) customise, inherit or extend the Logger class at all.

Creating task executors

A task executor is a simple async JS function which receives as input the job payload and a collection of helpers. It does the work and then returns. If it returns then the job is deemed a success and is deleted from the queue. If it throws an error then the job is deemed a failure and the task is rescheduled using an exponential-backoff algorithm.

IMPORTANT: your jobs should wait for all asynchronous work to be completed before returning, otherwise we might mistakenly think they were successful.

IMPORTANT: we automatically retry the job if it fails, so it's often sensible to split large jobs into smaller jobs, this also allows them to run in parallel resulting in faster execution. This is particularly important for tasks that are not idempotent (i.e. running them a second time will have extra side effects) - for example sending emails.

Tasks are created in the tasks folder in the directory from which you run graphile-worker; the name of the file (less the .js suffix) is used as the task identifier. Currently only .js files that can be directly loaded by Node.js are supported; if you are using Babel, TypeScript or similar you will need to compile your tasks into the tasks folder.

current directory
├── package.json
├── node_modules
└── tasks
    ├── task_1.js
    └── task_2.js
// tasks/task_1.js
module.exports = async payload => {
  await doMyLogicWith(payload);
// tasks/task_2.js
module.exports = async (payload, helpers) => {
  // async is optional, but best practice
  helpers.logger.debug(`Received ${JSON.stringify(payload)}`);

Each task function is passed two arguments:

  • payload - the payload you passed when calling add_job
  • helpers - an object containing:
    • logger - a scoped Logger instance, to aid tracing/debugging
    • job - the whole job (including uuid, attempts, etc) - you shouldn't need this
    • withPgClient - a helper to use to get a database client
    • query(sql, values) - a convenience wrapper for withPgClient(pgClient => pgClient.query(sql, values))
    • addJob - a helper to schedule a job



So that you may redirect logs to your preferred logging provider, we have enabled you to supply your own logging provider. Overriding this is currently only available in library mode. We then wrap this logging provider with a helper class to ease debugging; the helper class has the following methods:

  • error(message, meta?): for logging errors, similar to console.error
  • warn(message, meta?): for logging warnings, similar to console.warn
  • info(message, meta?): for logging informational messages, similar to
  • debug(message, meta?): to aid with debugging, similar to console.log
  • scope(additionalScope): returns a new Logger instance with additional scope information


withPgClient gets a pgClient from the pool, calls await callback(pgClient), and finally releases the client and returns the result of callback. This workflow makes testing your tasks easier.


const {
  rows: [row],
} = await withPgClient(pgClient => pgClient.query("select 1 as one"));

helpers.addJob(identifier, payload?, options?)

See addJob

More detail on scheduling jobs through SQL

You can schedule jobs directly in the database, e.g. from a trigger or function, or by calling SQL from your application code. You do this using the graphile_worker.add_job function.

NOTE: the addJob JavaScript method simply defers to this underlying add_job SQL function.

add_job accepts the following parameters (in this order):

  • identifier - the only required field, indicates the name of the task executor to run (omit the .js suffix!)
  • payload - a JSON object with information to tell the task executor what to do (defaults to an empty object)
  • queue_name - if you want certain tasks to run one at a time, add them to the same named queue (defaults to a random value)
  • run_at - a timestamp after which to run the job; defaults to now.
  • max_attempts - if this task fails, how many times should we retry it? Default: 25.
  • job_key - unique identifier for the job, used to update or remove it later if needed (see Updating and removing jobs); can also be used for de-duplication

Typically you'll want to set the identifier and payload:

SELECT graphile_worker.add_job(
    'subject''graphile-worker test'

It's recommended that you use PostgreSQL's named parameters for the other parameters so that you only need specify the arguments you're using:

SELECT graphile_worker.add_job('reminder', run_at := NOW() + INTERVAL '2 days');

TIP: if you want to run a job after a variable number of seconds according to the database time (rather than the application time), you can use interval multiplication; see run_at in this example:

SELECT graphile_worker.add_job(
  payload := $2,
  queue_name := $3,
  max_attempts := $4,
  run_at := NOW() + ($5 * INTERVAL '1 second')

NOTE: graphile_worker.add_job(...) requires database owner privileges to execute. To allow lower-privileged users to call it, wrap it inside a PostgreSQL function marked as SECURITY DEFINER so that it will run with the same privileges as the more powerful user that defined it. (Be sure that this function performs any access checks that are necessary.)

Example: scheduling job from trigger

This snippet creates a trigger function which adds a job to execute task_identifier_here when a new row is inserted into my_table.

CREATE FUNCTION my_table_created() RETURNS trigger AS $$
  PERFORM graphile_worker.add_job('task_identifier_here', json_build_object('id';

Example: one trigger function to rule them all

If your tables are all defined with a single primary key named id then you can define a more convenient dynamic trigger function which can be called from multiple triggers for multiple tables to quickly schedule jobs.

CREATE FUNCTION trigger_job() RETURNS trigger AS $$
  PERFORM graphile_worker.add_job(TG_ARGV[0], json_build_object(
    'schema', TG_TABLE_SCHEMA,
    'table', TG_TABLE_NAME,
    'op', TG_OP,

You might use this trigger like this:

CREATE TRIGGER send_verification_email
  AFTER INSERT ON user_emails
  WHEN (NEW.verified is false)
  EXECUTE PROCEDURE trigger_job('send_verification_email');
CREATE TRIGGER user_changed
  EXECUTE PROCEDURE trigger_job('user_changed');
CREATE TRIGGER generate_pdf
  EXECUTE PROCEDURE trigger_job('generate_pdf');
CREATE TRIGGER generate_pdf_update
  EXECUTE PROCEDURE trigger_job('generate_pdf');

Updating and removing jobs

Jobs scheduled with a job_key parameter may be updated later, provided they are still pending, by calling add_job again with the same job_key value. This can be used for rescheduling jobs or to ensure only one of a given job is scheduled at a time. When a job is updated, any omitted parameters are reset to their defaults, with the exception of queue_name which persists unless overridden. For example after the below SQL transaction, the send_email job will run only once, with the payload '{"count": 2}':

SELECT graphile_worker.add_job('send_email''{"count": 1}', job_key := 'abc');
SELECT graphile_worker.add_job('send_email''{"count": 2}', job_key := 'abc');

Pending jobs may also be removed using job_key:

SELECT graphile_worker.remove_job('abc');

Note: If a job is updated using add_job once it is already running or completed, the second job will be scheduled separately, meaning both will run. Likewise, calling remove_job for a running or completed job is a no-op.

Administration functions

When implementing an administrative UI you may need more control over the jobs. For this we have added a few administrative functions that can be called in SQL or through the JS API. The JS API is exposed via a WorkerUtils instance; see makeWorkerUtils above.

IMPORTANT: if you choose to run UPDATE or DELETE commands against the underlying tables, be sure to NOT manipulate jobs that are locked as this could have unintended consequences. The following administrative functions will automatically ensure that the jobs are not locked before applying any changes.

Complete jobs

SQL: SELECT * FROM graphile_worker.complete_jobs(ARRAY[7, 99, 38674, ...]);

JS: const deletedJobs = await workerUtils.completeJobs([7, 99, 38674, ...]);

Marks the specified jobs (by their ids) as if they were completed, assuming they are not locked. Note that completing a job deletes it. You may mark failed and permanently failed jobs as completed if you wish. The deleted jobs will be returned (note that this may be fewer jobs than you requested).

Permanently fail jobs

SQL: SELECT * FROM graphile_worker.permanently_fail_jobs(ARRAY[7, 99, 38674, ...], 'Enter reason here');

JS: const updatedJobs = await workerUtils.permanentlyFailJobs([7, 99, 38674, ...], 'Enter reason here');

Marks the specified jobs (by their ids) as failed permanently, assuming they are not locked. This means setting their attempts equal to their max_attempts. The updated jobs will be returned (note that this may be fewer jobs than you requested).

Rescheduling jobs


SELECT * FROM graphile_worker.reschedule_jobs(
  ARRAY[79938674, ...],
  run_at := NOW() + interval '5 minutes',
  priority := 5,
  attempts := 5,
  max_attempts := 25


const updatedJobs = await workerUtils.rescheduleJobs(
  [7, 99, 38674, ...],
    runAt: '2020-02-02T02:02:02Z',
    priority: 5,
    attempts: 5,
    maxAttempts: 25

Updates the specified scheduling properties of the jobs (assuming they are not locked). All of the specified options are optional, omitted or null values will left unmodified.

This method can be used to postpone or advance job execution, or to schedule a previously failed or permanently failed job for execution. The updated jobs will be returned (note that this may be fewer jobs than you requested).

Rationality checks

We recommend that you limit queue_name, task_identifier and job_key to printable ASCII characters.

  • queue_name can be at most 128 characters long
  • task_identifier can be at most 128 characters long
  • job_key can be at most 512 characters long
  • schema should be reasonable; max 32 characters is preferred. Defaults to graphile_worker (15 chars)


To delete the worker code and all the tasks from your database, just run this one SQL statement:

DROP SCHEMA graphile_worker CASCADE;


graphile-worker is not intended to replace extremely high performance dedicated job queues, it's intended to be a very easy way to get a reasonably performant job queue up and running with Node.js and PostgreSQL. But this doesn't mean it's a slouch by any means - it achieves an average latency from triggering a job in one process to executing it in another of under 3ms, and a 12-core database server can process around 10,000 jobs per second.

graphile-worker is horizontally scalable. Each instance has a customisable worker pool, this pool defaults to size 1 (only one job at a time on this worker) but depending on the nature of your tasks (i.e. assuming they're not compute-heavy) you will likely want to set this higher to benefit from Node.js' concurrency. If your tasks are compute heavy you may still wish to set it higher and then using Node's child_process (or Node v11's worker_threads) to share the compute load over multiple cores without significantly impacting the main worker's runloop.

To test performance, you can run yarn perfTest. This runs three tests:

  1. a startup/shutdown test to see how fast the worker can startup and exit if there's no jobs queued (this includes connecting to the database and ensuring the migrations are up to date)
  2. a load test - by default this will run 20,000 trivial jobs with a parallelism of 4 (i.e. 4 node processes) and a concurrency of 10 (i.e. 10 concurrent jobs running on each node process), but you can configure this in perfTest/run.js. (These settings were optimised for a 12-core hyperthreading machine.)
  3. a latency test - determining how long between issuing an add_job command and the task itself being executed.

perfTest results:

The test was ran on a 12-core AMD Ryzen 3900 with an M.2 SSD, running both the workers and the database (and a tonne of Chrome tabs, electron apps, and what not). Jobs=20000, parallelism=4, concurrency=10.


  • Startup/shutdown: 66ms
  • Jobs per second: 10,299
  • Average latency: 2.62ms (min: 2.43ms, max: 11.90ms)
Timing startup/shutdown time...
... it took 66ms

Scheduling 20000 jobs

Timing 20000 job execution...
Found 999!

... it took 2008ms
Jobs per second: 10298.81

Testing latency...
[core] INFO: Worker connected and looking for jobs... (task names: 'latency')
Beginning latency test
Latencies - min: 2.43ms, max: 11.90ms, avg: 2.62ms

TODO: post perfTest results in a more reasonable configuration, e.g. using an RDS PostgreSQL server and a worker running on EC2.


We currently use the formula exp(least(10, attempt)) to determine the delays between attempts (the job must fail before the next attempt is scheduled, so the total time elapsed may be greater depending on how long the job runs for before it fails). This seems to handle temporary issues well, after ~4 hours attempts will be made every ~6 hours until the maximum number of attempts is achieved. The specific delays can be seen below:

  exp(least(10, attempt)) * interval '1 second' as delay,
  sum(exp(least(10, attempt)) * interval '1 second') over (order by attempt asc) total_delay
from generate_series(1, 24) as attempt;

 attempt |      delay      |   total_delay
       1 | 00:00:02.718282 | 00:00:02.718282
       2 | 00:00:07.389056 | 00:00:10.107338
       3 | 00:00:20.085537 | 00:00:30.192875
       4 | 00:00:54.598150 | 00:01:24.791025
       5 | 00:02:28.413159 | 00:03:53.204184
       6 | 00:06:43.428793 | 00:10:36.632977
       7 | 00:18:16.633158 | 00:28:53.266135
       8 | 00:49:40.957987 | 01:18:34.224122
       9 | 02:15:03.083928 | 03:33:37.308050
      10 | 06:07:06.465795 | 09:40:43.773845
      11 | 06:07:06.465795 | 15:47:50.239640
      12 | 06:07:06.465795 | 21:54:56.705435
      13 | 06:07:06.465795 | 28:02:03.171230
      14 | 06:07:06.465795 | 34:09:09.637025
      15 | 06:07:06.465795 | 40:16:16.102820
      16 | 06:07:06.465795 | 46:23:22.568615
      17 | 06:07:06.465795 | 52:30:29.034410
      18 | 06:07:06.465795 | 58:37:35.500205
      19 | 06:07:06.465795 | 64:44:41.966000
      20 | 06:07:06.465795 | 70:51:48.431795
      21 | 06:07:06.465795 | 76:58:54.897590
      22 | 06:07:06.465795 | 83:06:01.363385
      23 | 06:07:06.465795 | 89:13:07.829180
      24 | 06:07:06.465795 | 95:20:14.294975

What if something goes wrong?

If a job throws an error, the job is failed and scheduled for retries with exponential back-off. We use async/await so assuming you write your task code well all errors should be cascaded down automatically.

If the worker is terminated (SIGTERM, SIGINT, etc), it triggers a graceful shutdown - i.e. it stops accepting new jobs, waits for the existing jobs to complete, and then exits. If you need to restart your worker, you should do so using this graceful process.

If the worker completely dies unexpectedly (e.g. process.exit(), segfault, SIGKILL) then those jobs remain locked for 4 hours, after which point they're available to be processed again automatically. You can free them up earlier than this by clearing the locked_at and locked_by columns on the relevant tables.


yarn watch

In another terminal:

createdb graphile_worker_test
yarn test

Using Docker

Start the dev db and app in the background

docker-compose up -d

Run the tests

docker-compose exec app yarn jest -i

Reset the test db

cat __tests__/reset-db.sql | docker-compose exec -T db psql -U postgres -v GRAPHILE_WORKER_SCHEMA=graphile_worker graphile_worker_test

Run the perf tests

docker-compose exec app node ./perfTest/run.js

monitor the container logs

docker-compose logs -f db
docker-compose logs -f app

Thanks for reading!

If this project helps you out, please sponsor ongoing development.


npm i graphile-worker

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