graphile-worker

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;
  helpers.logger.info(`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=true"
# 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'));

Success!

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 } = 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;
        helpers.logger.info(`Hello, ${name}`);
      },
    },
    // or:
    //   taskDirectory: `${__dirname}/tasks`,
  });

  // Immediately await (or otherwise handled) the resulting promise, to avoid
  // "unhandled rejection" errors causing a process crash in the event of
  // something going wrong.
  await runner.promise;

  // If the worker exits (whether through fatal error or otherwise), the above
  // promise will resolve/reject.
}

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

You can also use the library to quickly add a job:

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

quickAddJob(
  // makeWorkerUtils options
  { connectionString: "postgres:///my_db" },

  // Task identifier
  "hello",

  // Payload
  { name: "Bobby Tables" },
).catch((err) => {
  console.error(err);
  process.exit(1);
});

Running these two examples 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)

Support

You can ask for help on Discord at http://discord.gg/graphile

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.

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

Features

• 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)
• Crontab-like scheduling feature for recurring tasks (with optional backfill)
• Task de-duplication via unique job_key
• Append data to already enqueued jobs with "batch jobs"
• Flexible runtime controls that can be used for complex rate limiting (e.g. via graphile-worker-rate-limiter)
• 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.

Status

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: http://discord.gg/graphile

Requirements

PostgreSQL 12+ and Node 14+*.

Note: graphile-worker versions before 0.13.0 installed the pgcrypto extension into the public schema of your database (if it wasn't already installed). As of version 0.13.0 we no longer use pgcrypto. Existing users may want to uninstall it - see the release notes for instructions.

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

Note: Postgres 12 is required for the generated always as (expression) feature; if you need to use earlier versions of Postgres or Node, please use version 0.13.x or earlier.

Installation

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

Running

graphile-worker manages its 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.)

Note: Graphile Worker expects the Postgres role used at runtime to be the same as the role used while running the migrations. If you need to run your migrations as a different role, one solution is to explicitly change the owner of the graphile_worker.* tables to be the same role as is used at runtime.

The following CLI options are available:

Options:
      --help                    Show help                              [boolean]
      --version                 Show version number                    [boolean]
  -c, --connection              Database connection string, defaults to the
                                'DATABASE_URL' envvar                   [string]
  -s, --schema                  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]
  -w, --watch                   [EXPERIMENTAL] Watch task files for changes,
                                automatically reloading the task code without
                                restarting worker     [boolean] [default: false]
      --crontab                 override path to crontab file           [string]
  -j, --jobs                    number of jobs to run concurrently
                                                           [number] [default: 1]
  -m, --max-pool-size           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]
      --no-prepared-statements  set this flag if you want to disable prepared
                                statements, e.g. for compatibility with
                                pgBouncer             [boolean] [default: false]

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() method on the resolved object.

The resolved 'Runner' object has a number of helpers on it, see Runner object for more information.

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.

RunnerOptions

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)
• forbiddenFlags see Forbidden flags below
• events: pass your own new EventEmitter() if you want to customize the options, get earlier events (before the runner object resolves), or want to get events from alternative Graphile Worker entrypoints.
• noPreparedStatements: Set true if you want to prevent the use of prepared statements, for example if you wish to use Graphile Worker with an external PostgreSQL connection pool. Enabling this setting may have a small performance impact.

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

One of these must be provided (in order of priority):

• pgPool pg.Pool instance
• connectionString setting
• DATABASE_URL envvar
• PostgreSQL environmental variables, including at least PGDATABASE (NOTE: not all envvars are supported)

Runner object

The run method above resolves to a 'Runner' object that has the following methods and properties:

• stop(): Promise<void> - stops the runner from accepting new jobs, and returns a promise that resolves when all the in progress tasks (if any) are complete.
• addJob: AddJobFunction - see addJob.
• promise: Promise<void> - a promise that resolves once the runner has completed.
• events: WorkerEvents - a Node.js EventEmitter that exposes certain events within the runner (see WorkerEvents).

Example: adding a job with runner.addJob

See addJob for more details.

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

Example: listening to an event with runner.events

See WorkerEvents for more details.

runner.events.on("job:success", ({ worker, job }) => {
  console.log(`Hooray! Worker ${worker.workerId} completed job ${job.id}`);
});

WorkerEvents

We support a large number of events via an EventEmitter. You can either retrieve the event emitter via the events property on the Runner object, or you can create your own event emitter and pass it to Graphile Worker via the WorkerOptions.events option (this is primarily useful for getting events from the other Graphile Worker entrypoints).

Details of what events we support and what data is available on the event payload is detailed below in TypeScript syntax:

export type WorkerEvents = TypedEventEmitter<{
  /**
   * When a worker pool is created
   */
  "pool:create": { workerPool: WorkerPool };

  /**
   * When a worker pool attempts to connect to PG ready to issue a LISTEN
   * statement
   */
  "pool:listen:connecting": { workerPool: WorkerPool };

  /**
   * When a worker pool starts listening for jobs via PG LISTEN
   */
  "pool:listen:success": { workerPool: WorkerPool; client: PoolClient };

  /**
   * When a worker pool faces an error on their PG LISTEN client
   */
  "pool:listen:error": {
    workerPool: WorkerPool;
    error: any;
    client: PoolClient;
  };

  /**
   * When a worker pool is released
   */
  "pool:release": { pool: WorkerPool };

  /**
   * When a worker pool starts a graceful shutdown
   */
  "pool:gracefulShutdown": { pool: WorkerPool; message: string };

  /**
   * When a worker pool graceful shutdown throws an error
   */
  "pool:gracefulShutdown:error": { pool: WorkerPool; error: any };

  /**
   * When a worker is created
   */
  "worker:create": { worker: Worker; tasks: TaskList };

  /**
   * When a worker release is requested
   */
  "worker:release": { worker: Worker };

  /**
   * When a worker stops (normally after a release)
   */
  "worker:stop": { worker: Worker; error?: any };

  /**
   * When a worker is about to ask the database for a job to execute
   */
  "worker:getJob:start": { worker: Worker };

  /**
   * When a worker calls get_job but there are no available jobs
   */
  "worker:getJob:error": { worker: Worker; error: any };

  /**
   * When a worker calls get_job but there are no available jobs
   */
  "worker:getJob:empty": { worker: Worker };

  /**
   * When a worker is created
   */
  "worker:fatalError": { worker: Worker; error: any; jobError: any | null };

  /**
   * When a job is retrieved by get_job
   */
  "job:start": { worker: Worker; job: Job };

  /**
   * When a job completes successfully
   */
  "job:success": { worker: Worker; job: Job };

  /**
   * When a job throws an error
   */
  "job:error": { worker: Worker; job: Job; error: any };

  /**
   * When a job fails permanently (emitted after job:error when appropriate)
   */
  "job:failed": { worker: Worker; job: Job; error: any };

  /**
   * When a job has finished executing and the result (success or failure) has
   * been written back to the database
   */
  "job:complete": { worker: Worker; job: Job; error: any  };

  /**
   * When the runner is terminated by a signal
   */
  gracefulShutdown: { signal: Signal };

  /**
   * When the runner is stopped
   */
  stop: {};
}>;

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
      "calculate-life-meaning",

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

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

WorkerUtilsOptions

• 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)

WorkerUtils

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
    "calculate-life-meaning",

    // Payload
    { value: 42 },

    // Optionally, add further task spec details here
  );
}

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

addJob

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 and jobKeyMode 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, or a list of these objects in "batch job" mode
• 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 replace, update or remove it later if needed (see Replacing, updating and removing jobs); can be used for de-duplication (i.e. throttling or debouncing)
  • jobKeyMode: controls the behavior of jobKey when a matching job is found (see Replacing, updating and removing jobs)

Example:

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

Definitions:

export type AddJobFunction = (
  /**
   * The name of the task that will be executed for this job.
   */
  identifier: string,

  /**
   * 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 (only specify if you want jobs in this
   * queue to run serially). (Default: null)
   */
  queueName?: string;

  /**
   * A Date to schedule this task to run in the future. (Default: now)
   */
  runAt?: Date;

  /**
   * Jobs are executed in numerically ascending order of priority (jobs with a
   * numerically smaller priority are run first). (Default: 0)
   */
  priority?: number;

  /**
   * How many attempts should this task get? The minimum is 1, in which case the
   * task will only be attempted once and won't be retried. (Default: 25)
   */
  maxAttempts?: number;

  /**
   * Unique identifier for the job, can be used to update or remove it later if
   * needed. (Default: null)
   */
  jobKey?: string;

  /**
   * Modifies the behavior of `jobKey`; when 'replace' all attributes will be
   * updated, when 'preserve_run_at' all attributes except 'run_at' will be
   * updated, when 'unsafe_dedupe' a new job will only be added if no existing
   * job (including locked jobs and permanently failed jobs) with matching job
   * key exists. (Default: 'replace')
   */
  jobKeyMode?: "replace" | "preserve_run_at" | "unsafe_dedupe";

  /**
   * Flags for the job, can be used to dynamically filter which jobs can and
   * cannot run at runtime. (Default: null)
   */
  flags?: string[];
}

Batch jobs

Normally a job's payload is an object; however we also allow for jobs to have a payload that is an array of objects. When payload is an array of objects, we call this a "batch job" and it has a few special behaviours:

1. when you use job_key in replace or preserve_run_at mode, when a job is replaced/updated, instead of overwriting the payload, the existing an new payloads will be merged into a larger array (this only occurs when the existing and new payloads are both arrays, otherwise the payload is simply replaced).
2. when a task executes a batch job, it may return a list of promises that is the same length as the payload array. If any of these promises reject, then the job is said to have "partial success," the result of which is it being sent back to the queue for a retry, but with the successful objects removed from the payload so only the failed objects will be retried.

Batch jobs can be useful where you need to aggregate multiple tasks together over time for efficiency; for example if you have a notification system you might schedule a notification to be sent to a user in 2 minutes time that they received a DM. Over the next 2 minutes if any other DMs are received, these can be appended to the job payload such that when the job executes it can inform the user of all of these DMs, not just the latest one.

Logger

We use @graphile/logger as a log abstraction so that you can log to whatever logging facilities you like. By default this will log to console, and debug-level messages are not output unless you have the environmental variable GRAPHILE_LOGGER_DEBUG=1. You can override this by passing a custom logger.

It's recommended that your tasks always use the methods on helpers.logger for logging so that you can later route your messages to a different log store if you want to. There are 4 methods, one for each level of severity (error, warn, info, debug), and each accept a string as the first argument and optionally an arbitrary object as the second argument:

• helpers.logger.error(message: string, meta?: LogMeta)
• helpers.logger.warn(message: string, meta?: LogMeta)
• helpers.logger.info(message: string, meta?: LogMeta)
• helpers.logger.debug(message: string, meta?: LogMeta)

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 the @graphile/logger documentation for more information.

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

NOTE: some log messages are gated behind the GRAPHILE_ENABLE_DANGEROUS_LOGS=1 environmental variable - to see them you will need to enable that envvar AND enable debug logging (e.g. with GRAPHILE_LOGGER_DEBUG=1 as mentioned above) - do not do this in production as these logs may include incredibly sensitive details such as your full database connection string including password.

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 (unless this is a "batch job"). 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

Handling batch jobs

If the payload is an array, then optionally your task may choose to return an array of the same length, the entries in which are promises. Should any of these promises reject, then the job will be re-enqueued, but the payload will be overwritten to only contain the entries associated with the rejected promises - i.e. the successful entries will be removed.

helpers

helpers.logger

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 (see Logger). 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

helpers.withPgClient(callback)

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"));

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 (or the experimental graphile_worker.add_jobs function for bulk inserts, see below).

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, or an array of one or more of these objects for "batch jobs" (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 null)
• 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. Must be castable to smallint.
• job_key - unique identifier for the job, used to replace, update or remove it later if needed (see Replacing, updating and removing jobs); can also be used for de-duplication
• priority - an integer representing the jobs priority. Jobs are executed in numerically ascending order of priority (jobs with a numerically smaller priority are run first). Default: 0. Must be castable to smallint.
• flags - an optional text array (text[]) representing a flags to attach to the job. Can be used alongside the forbiddenFlags option in library mode to implement complex rate limiting or other behaviors which requiring skipping jobs at runtime (see Forbidden flags).
• job_key_mode - when job_key is specified, this setting indicates what should happen when an existing job is found with the same job key:
  • replace (default) - all job parameters are updated to the new values, including the run_at (inserts new job if matching job is locked)
  • preserve_run_at - all job parameters are updated to the new values, except for run_at which maintains the previous value (inserts new job if matching job is locked)
  • unsafe_dedupe - only inserts the job if no existing job (whether or not it is locked or has failed permanently) with matching key is found; does not update the existing job

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'
  )
);

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(
  $1,
  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.)

add_jobs

Experimental: this API may change in a semver minor release.

For bulk insertion of jobs, we've introduced the graphile_worker.add_jobs function. It accepts the following options:

• specs - an array of graphile_worker.job_spec objects
• job_key_preserve_run_at - an optional boolean detailing if the run_at should be preserved when the same job_key is seen again

The job_spec object has the following properties, all of which correspond with the add_job option of the same name above.

• identifier
• payload
• queue_name
• run_at
• max_attempts
• job_key
• priority
• flags

Note: job_key_mode='unsafe_dedupe' is not supported in add_jobs - you must add jobs one at a time using add_job to use that. The equivalent of job_key_mode='replace' is enabled by default, to change this to the same behavior as job_key_mode='preserve_run_at' you should set job_key_preserve_run_at to true.

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

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

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

Replacing, updating and removing jobs

Replacing/updating jobs

Jobs scheduled with a job_key parameter may be replaced/updated by calling add_job again with the same job_key value. This can be used for rescheduling jobs, to ensure only one of a given job is scheduled at a time, or to update other settings for the job.

For example after the below SQL transaction, the send_email job will run only once, with the payload '{"count": 2}':

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

In all cases if no match is found then a new job will be created; behavior when an existing job with the same job key is found is controlled by the job_key_mode setting:

• replace (default) - overwrites the unlocked job with the new values. This is primarily useful for rescheduling, updating, or debouncing (delaying execution until there have been no events for at least a certain time period). Locked jobs will cause a new job to be scheduled instead.
• preserve_run_at - overwrites the unlocked job with the new values, but preserves run_at. This is primarily useful for throttling (executing at most once over a given time period). Locked jobs will cause a new job to be scheduled instead.
• unsafe_dedupe - if an existing job is found, even if it is locked or permanently failed, then it won't be updated. This is very dangerous as it means that the event that triggered this add_job call may not result in any action. It is strongly advised you do not use this mode unless you are certain you know what you are doing.

The full job_key_mode algorithm is roughly as follows:

• If no existing job with the same job key is found:
  • a new job will be created with the new attributes.
• Otherwise, if job_key_mode is unsafe_dedupe:
  • stop and return the existing job.
• Otherwise, if the existing job is locked:
  • it will have its key cleared
  • it will have its attempts set to max_attempts to avoid it running again
  • a new job will be created with the new attributes.
• Otherwise, if the existing job has previously failed:
  • it will have its attempts reset to 0 (as if it were newly scheduled)
  • it will have its last_error cleared
  • it will have all other attributes updated to their new values, including run_at (even when job_key_mode is preserve_run_at).
• Otherwise, if job_key_mode is preserve_run_at:
  • the job will have all its attributes except for run_at updated to their new values.
• Otherwise:
  • the job will have all its attributes updated to their new values.

Removing jobs

Pending jobs may also be removed using job_key:

SELECT graphile_worker.remove_job('abc');

job_key caveats

IMPORTANT: jobs that complete successfully are deleted, there is no permanent job_key log, i.e. remove_job on a completed job_key is a no-op as no row exists.

IMPORTANT: the job_key is treated as universally unique (whilst the job is pending/failed), so you can update a job to have a completely different task_identifier or payload. You must be careful to ensure that your job_key is sufficiently unique to prevent you accidentally replacing or deleting unrelated jobs by mistake; one way to approach this is to incorporate the task_identifier into the job_key.

IMPORTANT: If a job is updated using add_job when it is currently locked (i.e. running), a second job will be scheduled separately (unless job_key_mode = 'unsafe_dedupe'), meaning both will run.

IMPORTANT: calling remove_job for a locked (i.e. running) job will not actually remove it, but will prevent it from running again on failure.

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

SQL:

SELECT * FROM graphile_worker.reschedule_jobs(
  ARRAY[7, 99, 38674, ...],
  run_at := NOW() + interval '5 minutes',
  priority := 5,
  attempts := 5,
  max_attempts := 25
);

JS:

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).

Recurring tasks (crontab)

Graphile Worker supports triggering recurring tasks according to a cron-like schedule. This is designed for recurring tasks such as sending a weekly email, running database maintenance tasks every day, performing data roll-ups hourly, downloading external data every 20 minutes, etc.

Graphile Worker's crontab support:

• guarantees (thanks to ACID-compliant transactions) that no duplicate task schedules will occur
• can backfill missed jobs if desired (e.g. if the Worker wasn't running when the job was due to be scheduled)
• schedules tasks using Graphile Worker's regular job queue, so you get all the regular features such as exponential back-off on failure.
• works reliably even if you're running multiple workers (see "Distributed crontab" below)

NOTE: It is not intended that you add recurring tasks for each of your individual application users, instead you should have relatively few recurring tasks, and those tasks can create additional jobs for the individual users (or process multiple users) if necessary.

Tasks are by default read from a crontab file next to the tasks/ folder (but this is configurable in library mode). Please note that our syntax is not 100% compatible with cron's, and our task payload differs. We only handle timestamps in UTC. The following diagram details the parts of a Graphile Worker crontab schedule:

# ┌───────────── UTC minute (0 - 59)
# │ ┌───────────── UTC hour (0 - 23)
# │ │ ┌───────────── UTC day of the month (1 - 31)
# │ │ │ ┌───────────── UTC month (1 - 12)
# │ │ │ │ ┌───────────── UTC day of the week (0 - 6) (Sunday to Saturday)
# │ │ │ │ │ ┌───────────── task (identifier) to schedule
# │ │ │ │ │ │    ┌────────── optional scheduling options
# │ │ │ │ │ │    │     ┌────── optional payload to merge
# │ │ │ │ │ │    │     │
# │ │ │ │ │ │    │     │
# * * * * * task ?opts {payload}

Comment lines start with a #.

For the first 5 fields we support an explicit numeric value, * to represent all valid values, */n (where n is a positive integer) to represent all valid values divisible by n, range syntax such as 1-5, and any combination of these separated by commas.

The task identifier should match the following regexp /^[_a-zA-Z][_a-zA-Z0-9:_-]*$/ (namely it should start with an alphabetic character and it should only contain alphanumeric characters, colon, underscore and hyphen). It should be the name of one of your Graphile Worker tasks.

The opts must always be prefixed with a ? if provided and details configuration for the task such as what should be done in the event that the previous event was not scheduled (e.g. because the Worker wasn't running). Options are specified using HTTP query string syntax (with & separator).

Currently we support the following opts:

• id=UID where UID is a unique alphanumeric case-sensitive identifier starting with a letter - specify an identifier for this crontab entry; by default this will use the task identifier, but if you want more than one schedule for the same task (e.g. with different payload, or different times) then you will need to supply a unique identifier explicitly.
• fill=t where t is a "time phrase" (see below) - backfill any entries from the last time period t, for example if the worker was not running when they were due to be executed (by default, no backfilling).
• max=n where n is a small positive integer - override the max_attempts of the job.
• queue=name where name is an alphanumeric queue name - add the job to a named queue so it executes serially.
• priority=n where n is a relatively small integer - override the priority of the job.

NOTE: changing the identifier (e.g. via id) can result in duplicate executions, so we recommend that you explicitly set it and never change it.

NOTE: using fill will not backfill new tasks, only tasks that were previously known.

NOTE: the higher you set the fill parameter, the longer the worker startup time will be; when used you should set it to be slightly larger than the longest period of downtime you expect for your worker.

Time phrases are comprised of a sequence of number-letter combinations, where the number represents a quantity and the letter represents a time period, e.g. 5d for five days, or 3h for three hours; e.g. 4w3d2h1m represents 4 weeks, 3 days, 2 hours and 1 minute (i.e. a period of 44761 minutes). The following time periods are supported:

• s - one second (1000 milliseconds)
• m - one minute (60 seconds)
• h - one hour (60 minutes)
• d - one day (24 hours)
• w - one week (7 days)

The payload is a JSON5 object; it must start with a {, must not contain newlines or carriage returns (\n or \r), and must not contain trailing whitespace. It will be merged into the default crontab payload properties.

Each crontab job will have a JSON object payload containing the key _cron with the value being an object with the following entries:

• ts - ISO8601 timestamp representing when this job was due to execute
• backfilled - true if the task was "backfilled" (i.e. it wasn't scheduled on time), false otherwise

Distributed crontab

TL;DR: when running identical crontabs on multiple workers no special action is necessary - it Just Works :tm:

When you run multiple workers with the same crontab files then the first worker that attempts to queue a particular cron job will succeed and the other workers will take no action - this is thanks to SQL ACID-compliant transactions and our known_crontabs lock table.

If your workers have different crontabs then you must be careful to ensure that the cron items each have unique identifiers; the easiest way to do this is to specify the identifiers yourself (see the id= option above). Should you forget to do this then for any overlapping timestamps for items that have the same derived identifier one of the cron tasks will schedule but the others will not.

Crontab examples

The following schedules the send_weekly_email task at 4:30am (UTC) every Monday:

30 4 * * 1 send_weekly_email

The following does similar, but also will backfill any tasks over the last two days (2d), sets max attempts to 10 and merges in {"onboarding": false} into the task payload:

30 4 * * 1 send_weekly_email ?fill=2d&max=10 {onboarding:false}

The following triggers the rollup task every 4 hours on the hour:

0 */4 * * * rollup

Limiting backfill

When you ask Graphile Worker to backfill jobs, it will do so for all jobs matching that specification that should have been scheduled over the backfill period. Other than the period itself, you cannot place limits on the backfilling (for example, you cannot say "backfill at most one job" or "only backfill if the next job isn't due within the next 3 hours"); this is because we've determined that there's many situations (back-off, overloaded worker, serially executed jobs, etc.) in which the result of this behaviour might result in outcomes that the user did not expect.

If you need these kinds of constraints on backfilled jobs, you should implement them at runtime (rather than at scheduling time) in the task executor itself, which could use the payload._cron.ts property to determine whether execution should continue or not.

Specifying cron items in library mode

You've three options for specifying cron tasks in library mode:

1. crontab: a crontab string (like the contents of a crontab file)
2. crontabFile: the (string) path to a crontab file, from which to read the rules
3. parsedCronItems: explicit parsed cron items (see below)

parsedCronItems

The Graphile Worker internal format for cron items lists all the matching minutes/hours/etc uniquely and in numerically ascending order. It also has other requirements and is to be treated as an opaque type, so you must not construct this value manually.

Instead, you may specify the parsedCronItems using one of the helper functions:

1. parseCrontab: pass a crontab string and it will be converted into a list of ParsedCronItems
2. parseCronItems: pass a list of CronItems and it will be converted into a list of ParsedCronItems

The CronItem type is designed to be written by humans (and their scripts) and has the following properties:

• task (required): the string identifier of the task that should be executed (same as the first argument to add_job)
• pattern (required): a cron pattern (e.g. * * * * *) describing when to run this task
• options: optional options influencing backfilling, etc
  • backfillPeriod: how long (in milliseconds) to backfill (see above)
  • maxAttempts: the maximum number of attempts we'll give the job
  • queueName: if you want the job to run serially, you can add it to a named queue
  • priority: optionally override the priority of the job
• payload: an optional payload object to merge into the generated payload for the job
• identifier: an optional string to give this cron item a permanent identifier; if not given we will use the task. This is particularly useful if you want to schedule the same task multiple times, perhaps on different time patterns or with different payloads or other options (since every cron item must have a unique identifier).

Forbidden flags

When a job is created (or updated via job_key), you may set its flags to a list of strings. When the worker is run in library mode, you may pass the forbiddenFlags option to indicate that jobs with any of the given flags should not be executed.

await run({
  // ...
  forbiddenFlags: forbiddenFlags,
});

The forbiddenFlags option can be:

• null
• an array of strings
• a function returning null or an array of strings
• an (async) function returning a promise that resolve to null or an array of strings

If forbiddenFlags is a function, graphile-worker will invoke it each time a worker looks for a job to run, and will skip over any job that has any flag returned by your function. You should ensure that forbiddenFlags resolves quickly; it's advised that you maintain a cache you update periodically (e.g. once a second) rather than always calculating on the fly, or use pub/sub or a similar technique to maintain the forbidden flags list.

For an example of how this can be used to achieve rate-limiting logic, see the graphile-worker-rate-limiter project and the discussion on issue #118.

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)

Uninstallation

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

DROP SCHEMA graphile_worker CASCADE;

Performance

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 queue around 99,600 jobs per second and can process around 11,800 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. Note, however, that Graphile Worker is limited by the performance of the underlying Postgres database, and when you hit this limit performance will start to go down (rather than up) as you add more workers.

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 running both the tests and the database locally.)
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.

Conclusion:

• Startup/shutdown: 110ms
• Jobs per second: 11,851
• Average latency: 2.66ms (min: 2.39ms, max: 12.09ms)

Timing startup/shutdown time...
... it took 110ms

Scheduling 20000 jobs
Adding jobs: 200.84ms
... it took 287ms


Timing 20000 job execution...
Found 999!

... it took 1797ms
Jobs per second: 11851.90


Testing latency...
[core] INFO: Worker connected and looking for jobs... (task names: 'latency')
Beginning latency test
Latencies - min: 2.39ms, max: 12.09ms, avg: 2.66ms

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

Exponential-backoff

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

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 the jobs that that worker was executing remain locked for at least 4 hours. Every 8-10 minutes a worker will sweep for jobs that have been locked for more than 4 hours and will make them available to be processed again automatically. If you run many workers, each worker will do this, so it's likely that jobs will be released closer to the 4 hour mark. You can unlock jobs earlier than this by clearing the locked_at and locked_by columns on the relevant tables.

If the worker schema has not yet been installed into your database, the following error may appear in your PostgreSQL server logs. This is completely harmless and should only appear once as the worker will create the schema for you.

ERROR: relation "graphile_worker.migrations" does not exist at character 16
STATEMENT: select id from "graphile_worker".migrations order by id desc limit 1;

Error codes

• GWBKM - Invalid job_key_mode value, expected 'replace', 'preserve_run_at' or 'unsafe_dedupe'.

Development

yarn
yarn watch

In another terminal:

createdb graphile_worker_test
yarn test

Using the official Docker image

docker pull graphile/worker

When using the Docker image you can pass any supported options to the command line or use the supported environment variables. For the current list of supported command line options you can run:

docker run --init --rm -it graphile/worker --help

Adding tasks to execute is done by mounting the tasks directory as a volume into the /worker directory.

The following example has a tasks directory in the current directory on the Docker host. The PostgreSQL server is also running on the same host.

docker run \
  --init \
  --rm -it \
  --network=host \
  -v "$PWD/tasks":/worker/tasks \
  graphile/worker \
    -c "postgres://postgres:postgres@localhost:5432/postgres"

Using Docker to develop this module

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

Database migrations

New database migrations must be accompanied by an updated db dump. This can be generated using the command yarn db:dump, and requires a running postgres 12 server. Using docker:

docker run -e POSTGRES_HOST_AUTH_METHOD=trust -d -p 5432:5432 postgres:12

then run

PGUSER=postgres PGHOST=localhost yarn db:dump

Thanks for reading!

If this project h