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.

To help us develop this software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

And please give some love to our featured sponsors 🤩:

Chad Furman

Storyscript

Point72 Ventures

In your existing Node.js project:

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

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;
  helpers.logger.info(`Hello, ${name}`);
};

(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.)

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!

Instead of running graphile-worker via the CLI, you may use it directly in your Node.js code:

const { run } = require("graphile-worker");

async function main() {
  const runner = await run({
    connectionString: "postgres:///",
    concurrency: 5,
    pollInterval: 1000,
    // you can set the taskList or taskDirectory but not both
    taskList: {
      testTask: async (payload, helpers) => {
        helpers.logger.debug("working on task...");
      },
    },
    // or:
    //   taskDirectory: `${__dirname}/tasks`,
  });
}

main().catch(err => {
  console.error(err);
  process.exit(1);
});

You can then add jobs with the addJob method:

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

And stop the job runner with runner.stop().

Please support development of this project via sponsorship. With your support we can improve performance, usability and documentation at a greater rate, leading to reduced running and engineering costs for your organisation, leading to a net ROI.

Support contracts are also available; for more information see: https://www.graphile.org/support/

• Standalone and embedded modes
• Easy to test with (including runTaskListOnce util)
• Low latency (~2ms from task schedule to execution, uses LISTEN/NOTIFY to be informed of jobs as they're inserted)
• High performance (~700 jobs per second on a single node, 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)
• Open source
• Executes tasks written in Node.js (can call out to any other language or networked service)
• Modern JS with async/await
• Watch mode for development (experimental - iterate your jobs without restarting worker)

Solid test suite testing internals, but external interfaces need tests to prevent regressions (get in touch if you'd like to help with this!). This specific codebase is 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: http://discord.gg/graphile

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://localhost/mydb"

(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:

Options:
  --help            Show help                                          [boolean]
  --version         Show version number                                [boolean]
  --connection, -c  Database connection string, defaults to the 'DATABASE_URL'
                    envvar                                              [string]
  --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              [default: 1]
  --poll-interval   how long to wait between polling for jobs in milliseconds
                    (for jobs scheduled in the future/retries)
                                                        [number] [default: 2000]

graphile-worker can be used as a library inside your Node.js application. It exposes the run(options) and runOnce(options) functions.

run(options) will run until either stopped by a signal event like SIGINT or by calling the stop() function on the object returned by run().

runOnce(options) is the equivalent of running the cli with the --once option. The function will run until there are no runnable jobs left.

runMigrations(options) is the equivalent of running the cli with the --schema-only option. Runs the migrations and then returns.

The following options for these methods are available.

• concurrency: The equivalent of the cli --jobs option with the same default value.
• 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

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.

Example

const { Pool } = require("pg");
const { run } = require("graphile-worker");

const pgPool = new Pool({
  connectionString: "postgres://postgres:postgres@localhost:5432/postgres",
});

async function main() {
  const runner = await run({
    pgPool,
    // or: connectionString: process.env.DATABASE_URL,

    concurrency: 1,
    pollInterval: 2000,

    taskList: {
      testTask: async (payload, helpers) => {
        helpers.logger.debug(`Received ${JSON.stringify(payload)}`);
      },
    },
    // or: taskDirectory: `${__dirname}/tasks`,
  });
  // to clean up: runner.stop()
}

main().catch(err => {
  console.error(err);
  process.exit(1);
});

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:
run({
  logger,
  /* 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.

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 console.info
• 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.

Example:

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

Schedules a job; arguments:

• 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)

Example:

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

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. (We'll add a JS helper for this soon...)

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.

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

SELECT graphile_worker.add_job(
  'send_email',
  json_build_object(
    'to', '[email protected]',
    'subject', 'graphile-worker test'
  )
);

You can skip parameters you don't need by using PostgreSQL's named parameter support:

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

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.)

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 $$
BEGIN
  PERFORM graphile_worker.add_job('task_identifier_here', json_build_object('id', NEW.id));
  RETURN NEW;
END;
$$ LANGUAGE plpgsql VOLATILE;

CREATE TRIGGER trigger_name AFTER INSERT ON my_table FOR EACH ROW EXECUTE PROCEDURE my_table_created();

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 $$
BEGIN
  PERFORM graphile_worker.add_job(TG_ARGV[0], json_build_object(
    'schema', TG_TABLE_SCHEMA,
    'table', TG_TABLE_NAME,
    'op', TG_OP,
    'id', (CASE WHEN TG_OP = 'DELETE' THEN OLD.id ELSE NEW.id END)
  ));
  RETURN NEW;
END;
$$ LANGUAGE plpgsql VOLATILE;

You might use this trigger like this:

CREATE TRIGGER send_verification_email
  AFTER INSERT ON user_emails
  FOR EACH ROW
  WHEN (NEW.verified is false)
  EXECUTE PROCEDURE trigger_job('send_verification_email');
CREATE TRIGGER user_changed
  AFTER INSERT OR UPDATE OR DELETE ON users
  FOR EACH ROW
  EXECUTE PROCEDURE trigger_job('user_changed');
CREATE TRIGGER generate_pdf
  AFTER INSERT ON pdfs
  FOR EACH ROW
  EXECUTE PROCEDURE trigger_job('generate_pdf');
CREATE TRIGGER generate_pdf_update
  AFTER UPDATE ON pdfs
  FOR EACH ROW
  WHEN (NEW.title IS DISTINCT FROM OLD.title)
  EXECUTE PROCEDURE trigger_job('generate_pdf');

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 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 just 2ms, and each worker can handle up to 731 jobs per second on modest hardware (2011 iMac).

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 reveals that on a 2011 iMac running both the worker and the database (and a bunch of other stuff) starting the command, checking for jobs, and exiting takes about 0.40s and running 20,000 trivial queued jobs across a single worker pool of size 1 takes 27.35s (~731 jobs per second). Latencies are also measured, from before the call to queue the job is fired until when the job is actually executed. These latencies ranged from 1.39ms to 19.66ms with an average of 1.90ms.

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:

select
  attempt,
  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

yarn
yarn watch

In another terminal:

createdb graphile_worker_test
yarn test