bottleneck
Advanced tools
The bottleneck npm package is a rate limiter that allows you to control the rate at which tasks are executed. It is useful for managing resource usage and ensuring that your application does not exceed the rate limits imposed by APIs or other systems. It can be used to throttle function calls, manage task execution concurrency, and queue tasks.
Task Scheduling
This feature allows you to schedule tasks to be executed with a minimum time interval between them and a maximum number of concurrent tasks. The code sample demonstrates how to create a limiter that only allows one task to run at a time with a minimum of 1 second between task starts.
const Bottleneck = require('bottleneck');
const limiter = new Bottleneck({ maxConcurrent: 1, minTime: 1000 });
async function myFunction(id) {
console.log(`Working on task ${id}`);
}
for (let i = 0; i < 5; i++) {
limiter.schedule(() => myFunction(i));
}Priority Queueing
This feature allows you to assign priorities to tasks. Tasks with higher priority (lower priority number) will be executed before those with lower priority. The code sample shows how to schedule tasks with different priorities.
const Bottleneck = require('bottleneck');
const limiter = new Bottleneck({ maxConcurrent: 2 });
async function myFunction(priority, id) {
console.log(`Working on task ${id} with priority ${priority}`);
}
limiter.schedule({ priority: 1 }, myFunction, 1, 'A');
limiter.schedule({ priority: 5 }, myFunction, 5, 'B');
limiter.schedule({ priority: 3 }, myFunction, 3, 'C');Rate Limiting
This feature allows you to limit the rate at which functions are executed. It can be used to avoid hitting rate limits of external APIs. The code sample demonstrates how to set up a limiter with a reservoir that refreshes over time, and how to handle the 'depleted' event when the rate limit is exceeded.
const Bottleneck = require('bottleneck');
const limiter = new Bottleneck({ reservoir: 100, // initial value
reservoirRefreshAmount: 100,
reservoirRefreshInterval: 60 * 1000 // must be divisible by 250
});
limiter.on('depleted', () => console.log('Rate limit exceeded'));
// Function to be rate-limited
async function myFunction() {
console.log('Doing something.');
}
setInterval(() => {
limiter.submit(myFunction);
}, 10);p-throttle is a package that allows you to throttle function calls. It is similar to bottleneck in that it helps to control the rate of function execution, but it is built on top of Promises and does not provide the same level of detailed configuration as bottleneck, such as priority queueing or reservoir-based limiting.
async-ratelimiter is a rate limiting library that uses Redis for storage. It is designed for more distributed use cases where you need to maintain rate limits across multiple processes or servers. Unlike bottleneck, which is more focused on in-process rate limiting, async-ratelimiter is suitable for scenarios where you need a shared rate limit state.
leaky-bucket is another rate limiting library that implements the leaky bucket algorithm. It is similar to bottleneck in providing rate limiting capabilities, but it has a different algorithmic approach. While bottleneck uses a more general approach with configurable reservoirs, leaky-bucket strictly adheres to the leaky bucket algorithm, which may be more suitable for certain use cases.
Bottleneck is a lightweight and zero-dependency Task Scheduler and Rate Limiter for Node.js and the browser.
Bottleneck is an easy solution as it adds very little complexity to your code. It is battle-hardened, reliable and production-ready and used on a large scale in private companies and open source software.
It supports Clustering: it can rate limit jobs across multiple Node.js instances. It uses Redis and strictly atomic operations to stay reliable in the presence of unreliable clients and networks. It also supports Redis Cluster and Redis Sentinel.
submit()schedule()wrap()updateSettings()incrementReservoir()currentReservoir()stop()chain()npm install --save bottleneck
import Bottleneck from "bottleneck";
// Note: To support older browsers and Node <6.0, you must import the ES5 bundle instead.
var Bottleneck = require("bottleneck/es5");
Most APIs have a rate limit. For example, to execute 3 requests per second:
const limiter = new Bottleneck({
minTime: 333
});
If there's a chance some requests might take longer than 333ms and you want to prevent more than 1 request from running at a time, add maxConcurrent: 1:
const limiter = new Bottleneck({
maxConcurrent: 1,
minTime: 333
});
minTime and maxConcurrent are enough for the majority of use cases. They work well together to ensure a smooth rate of requests. If your use case requires executing requests in bursts or every time a quota resets, look into Reservoir Intervals.
Instead of this:
myFunction(arg1, arg2)
.then((result) => {
/* handle result */
});
Do this:
limiter.schedule(() => myFunction(arg1, arg2))
.then((result) => {
/* handle result */
});
Or this:
const wrapped = limiter.wrap(myFunction);
wrapped(arg1, arg2)
.then((result) => {
/* handle result */
});
Instead of this:
const result = await myFunction(arg1, arg2);
Do this:
const result = await limiter.schedule(() => myFunction(arg1, arg2));
Or this:
const wrapped = limiter.wrap(myFunction);
const result = await wrapped(arg1, arg2);
Instead of this:
someAsyncCall(arg1, arg2, callback);
Do this:
limiter.submit(someAsyncCall, arg1, arg2, callback);
Remember...
Bottleneck builds a queue of jobs and executes them as soon as possible. By default, the jobs will be executed in the order they were received.
Read the 'Gotchas' and you're good to go. Or keep reading to learn about all the fine tuning and advanced options available. If your rate limits need to be enforced across a cluster of computers, read the Clustering docs.
Need help debugging your application?
Instead of throttling maybe you want to batch up requests into fewer calls?
schedule() or wrap() only returns once all the work it does has completed.Instead of this:
limiter.schedule(() => {
tasksArray.forEach(x => processTask(x));
// BAD, we return before our processTask() functions are finished processing!
});
Do this:
limiter.schedule(() => {
const allTasks = tasksArray.map(x => processTask(x));
// GOOD, we wait until all tasks are done.
return Promise.all(allTasks);
});
bind() the object:// instead of this:
limiter.schedule(object.doSomething);
// do this:
limiter.schedule(object.doSomething.bind(object));
// or, wrap it in an arrow function instead:
limiter.schedule(() => object.doSomething());
Bottleneck requires Node 6+ to function. However, an ES5 build is included: var Bottleneck = require("bottleneck/es5");.
Make sure you're catching "error" events emitted by your limiters!
Consider setting a maxConcurrent value instead of leaving it null. This can help your application's performance, especially if you think the limiter's queue might become very long.
If you plan on using priorities, make sure to set a maxConcurrent value.
When using submit(), if a callback isn't necessary, you must pass null or an empty function instead. It will not work otherwise.
When using submit(), make sure all the jobs will eventually complete by calling their callback, or set an expiration. Even if you submitted your job with a null callback , it still needs to call its callback. This is particularly important if you are using a maxConcurrent value that isn't null (unlimited), otherwise those not completed jobs will be clogging up the limiter and no new jobs will be allowed to run. It's safe to call the callback more than once, subsequent calls are ignored.
const limiter = new Bottleneck({/* options */});
Basic options:
| Option | Default | Description |
|---|---|---|
maxConcurrent | null (unlimited) | How many jobs can be executing at the same time. Consider setting a value instead of leaving it null, it can help your application's performance, especially if you think the limiter's queue might get very long. |
minTime | 0 ms | How long to wait after launching a job before launching another one. |
highWater | null (unlimited) | How long can the queue be? When the queue length exceeds that value, the selected strategy is executed to shed the load. |
strategy | Bottleneck.strategy.LEAK | Which strategy to use when the queue gets longer than the high water mark. Read about strategies. Strategies are never executed if highWater is null. |
penalty | 15 * minTime, or 5000 when minTime is 0 | The penalty value used by the BLOCK strategy. |
reservoir | null (unlimited) | How many jobs can be executed before the limiter stops executing jobs. If reservoir reaches 0, no jobs will be executed until it is no longer 0. New jobs will still be queued up. |
reservoirRefreshInterval | null (disabled) | Every reservoirRefreshInterval milliseconds, the reservoir value will be automatically updated to the value of reservoirRefreshAmount. The reservoirRefreshInterval value should be a multiple of 250 (5000 for Clustering). |
reservoirRefreshAmount | null (disabled) | The value to set reservoir to when reservoirRefreshInterval is in use. |
reservoirIncreaseInterval | null (disabled) | Every reservoirIncreaseInterval milliseconds, the reservoir value will be automatically incremented by reservoirIncreaseAmount. The reservoirIncreaseInterval value should be a multiple of 250 (5000 for Clustering). |
reservoirIncreaseAmount | null (disabled) | The increment applied to reservoir when reservoirIncreaseInterval is in use. |
reservoirIncreaseMaximum | null (disabled) | The maximum value that reservoir can reach when reservoirIncreaseInterval is in use. |
Promise | Promise (built-in) | This lets you override the Promise library used by Bottleneck. |
Reservoir Intervals let you execute requests in bursts, by automatically controlling the limiter's reservoir value. The reservoir is simply the number of jobs the limiter is allowed to execute. Once the value reaches 0, it stops starting new jobs.
There are 2 types of Reservoir Intervals: Refresh Intervals and Increase Intervals.
In this example, we throttle to 100 requests every 60 seconds:
const limiter = new Bottleneck({
reservoir: 100, // initial value
reservoirRefreshAmount: 100,
reservoirRefreshInterval: 60 * 1000, // must be divisible by 250
// also use maxConcurrent and/or minTime for safety
maxConcurrent: 1,
minTime: 333 // pick a value that makes sense for your use case
});
reservoir is a counter decremented every time a job is launched, we set its initial value to 100. Then, every reservoirRefreshInterval (60000 ms), reservoir is automatically updated to be equal to the reservoirRefreshAmount (100).
In this example, we throttle jobs to meet the Shopify API Rate Limits. Users are allowed to send 40 requests initially, then every second grants 2 more requests up to a maximum of 40.
const limiter = new Bottleneck({
reservoir: 40, // initial value
reservoirIncreaseAmount: 2,
reservoirIncreaseInterval: 1000, // must be divisible by 250
reservoirIncreaseMaximum: 40,
// also use maxConcurrent and/or minTime for safety
maxConcurrent: 5,
minTime: 250 // pick a value that makes sense for your use case
});
Reservoir Intervals are an advanced feature, please take the time to read and understand the following warnings.
Reservoir Intervals are not a replacement for minTime and maxConcurrent. It's strongly recommended to also use minTime and/or maxConcurrent to spread out the load. For example, suppose a lot of jobs are queued up because the reservoir is 0. Every time the Refresh Interval is triggered, a number of jobs equal to reservoirRefreshAmount will automatically be launched, all at the same time! To prevent this flooding effect and keep your application running smoothly, use minTime and maxConcurrent to stagger the jobs.
The Reservoir Interval starts from the moment the limiter is created. Let's suppose we're using reservoirRefreshAmount: 5. If you happen to add 10 jobs just 1ms before the refresh is triggered, the first 5 will run immediately, then 1ms later it will refresh the reservoir value and that will make the last 5 also run right away. It will have run 10 jobs in just over 1ms no matter what your reservoir interval was!
Reservoir Intervals prevent a limiter from being garbage collected. Call limiter.disconnect() to clear the interval and allow the memory to be freed. However, it's not necessary to call .disconnect() to allow the Node.js process to exit.
Adds a job to the queue. This is the callback version of schedule().
limiter.submit(someAsyncCall, arg1, arg2, callback);
You can pass null instead of an empty function if there is no callback, but someAsyncCall still needs to call its callback to let the limiter know it has completed its work.
submit() can also accept advanced options.
Adds a job to the queue. This is the Promise and async/await version of submit().
const fn = function(arg1, arg2) {
return httpGet(arg1, arg2); // Here httpGet() returns a promise
};
limiter.schedule(fn, arg1, arg2)
.then((result) => {
/* ... */
});
In other words, schedule() takes a function fn and a list of arguments. schedule() returns a promise that will be executed according to the rate limits.
schedule() can also accept advanced options.
Here's another example:
// suppose that `client.get(url)` returns a promise
const url = "https://wikipedia.org";
limiter.schedule(() => client.get(url))
.then(response => console.log(response.body));
Takes a function that returns a promise. Returns a function identical to the original, but rate limited.
const wrapped = limiter.wrap(fn);
wrapped()
.then(function (result) {
/* ... */
})
.catch(function (error) {
// Bottleneck might need to fail the job even if the original function can never fail.
// For example, your job is taking longer than the `expiration` time you've set.
});
submit(), schedule(), and wrap() all accept advanced options.
// Submit
limiter.submit({/* options */}, someAsyncCall, arg1, arg2, callback);
// Schedule
limiter.schedule({/* options */}, fn, arg1, arg2);
// Wrap
const wrapped = limiter.wrap(fn);
wrapped.withOptions({/* options */}, arg1, arg2);
| Option | Default | Description |
|---|---|---|
priority | 5 | A priority between 0 and 9. A job with a priority of 4 will be queued ahead of a job with a priority of 5. Important: You must set a low maxConcurrent value for priorities to work, otherwise there is nothing to queue because jobs will be be scheduled immediately! |
weight | 1 | Must be an integer equal to or higher than 0. The weight is what increases the number of running jobs (up to maxConcurrent) and decreases the reservoir value. |
expiration | null (unlimited) | The number of milliseconds a job is given to complete. Jobs that execute for longer than expiration ms will be failed with a BottleneckError. |
id | <no-id> | You should give an ID to your jobs, it helps with debugging. |
A strategy is a simple algorithm that is executed every time adding a job would cause the number of queued jobs to exceed highWater. Strategies are never executed if highWater is null.
When adding a new job to a limiter, if the queue length reaches highWater, drop the oldest job with the lowest priority. This is useful when jobs that have been waiting for too long are not important anymore. If all the queued jobs are more important (based on their priority value) than the one being added, it will not be added.
Same as LEAK, except it will only drop jobs that are less important than the one being added. If all the queued jobs are as or more important than the new one, it will not be added.
When adding a new job to a limiter, if the queue length reaches highWater, do not add the new job. This strategy totally ignores priority levels.
When adding a new job to a limiter, if the queue length reaches highWater, the limiter falls into "blocked mode". All queued jobs are dropped and no new jobs will be accepted until the limiter unblocks. It will unblock after penalty milliseconds have passed without receiving a new job. penalty is equal to 15 * minTime (or 5000 if minTime is 0) by default. This strategy is ideal when bruteforce attacks are to be expected. This strategy totally ignores priority levels.
minTime setting.Note: By default, Bottleneck does not keep track of DONE jobs, to save memory. You can enable this feature by passing trackDoneStatus: true as an option when creating a limiter.
const counts = limiter.counts();
console.log(counts);
/*
{
RECEIVED: 0,
QUEUED: 0,
RUNNING: 0,
EXECUTING: 0,
DONE: 0
}
*/
Returns an object with the current number of jobs per status in the limiter.
console.log(limiter.jobStatus("some-job-id"));
// Example: QUEUED
Returns the status of the job with the provided job id in the limiter. Returns null if no job with that id exist.
console.log(limiter.jobs("RUNNING"));
// Example: ['id1', 'id2']
Returns an array of all the job ids with the specified status in the limiter. Not passing a status string returns all the known ids.
const count = limiter.queued(priority);
console.log(count);
priority is optional. Returns the number of QUEUED jobs with the given priority level. Omitting the priority argument returns the total number of queued jobs in the limiter.
const count = await limiter.clusterQueued();
console.log(count);
Returns the number of QUEUED jobs in the Cluster.
if (limiter.empty()) {
// do something...
}
Returns a boolean which indicates whether there are any RECEIVED or QUEUED jobs in the limiter.
limiter.running()
.then((count) => console.log(count));
Returns a promise that returns the total weight of the RUNNING and EXECUTING jobs in the Cluster.
limiter.done()
.then((count) => console.log(count));
Returns a promise that returns the total weight of DONE jobs in the Cluster. Does not require passing the trackDoneStatus: true option.
limiter.check()
.then((wouldRunNow) => console.log(wouldRunNow));
Checks if a new job would be executed immediately if it was submitted now. Returns a promise that returns a boolean.
'error'
limiter.on("error", function (error) {
/* handle errors here */
});
The two main causes of error events are: uncaught exceptions in your event handlers, and network errors when Clustering is enabled.
'failed'
limiter.on("failed", function (error, jobInfo) {
// This will be called every time a job fails.
});
'retry'
See Retries to learn how to automatically retry jobs.
limiter.on("retry", function (message, jobInfo) {
// This will be called every time a job is retried.
});
'empty'
limiter.on("empty", function () {
// This will be called when `limiter.empty()` becomes true.
});
'idle'
limiter.on("idle", function () {
// This will be called when `limiter.empty()` is `true` and `limiter.running()` is `0`.
});
'dropped'
limiter.on("dropped", function (dropped) {
// This will be called when a strategy was triggered.
// The dropped request is passed to this event listener.
});
'depleted'
limiter.on("depleted", function (empty) {
// This will be called every time the reservoir drops to 0.
// The `empty` (boolean) argument indicates whether `limiter.empty()` is currently true.
});
'debug'
limiter.on("debug", function (message, data) {
// Useful to figure out what the limiter is doing in real time
// and to help debug your application
});
'received' 'queued' 'scheduled' 'executing' 'done'
limiter.on("queued", function (info) {
// This event is triggered when a job transitions from one Lifecycle stage to another
});
See Jobs Lifecycle for more information.
These Lifecycle events are not triggered for jobs located on another limiter in a Cluster, for performance reasons.
Use removeAllListeners() with an optional event name as first argument to remove listeners.
Use .once() instead of .on() to only receive a single event.
The following example:
const limiter = new Bottleneck();
// Listen to the "failed" event
limiter.on("failed", async (error, jobInfo) => {
const id = jobInfo.options.id;
console.warn(`Job ${id} failed: ${error}`);
if (jobInfo.retryCount === 0) { // Here we only retry once
console.log(`Retrying job ${id} in 25ms!`);
return 25;
}
});
// Listen to the "retry" event
limiter.on("retry", (error, jobInfo) => console.log(`Now retrying ${jobInfo.options.id}`));
const main = async function () {
let executions = 0;
// Schedule one job
const result = await limiter.schedule({ id: 'ABC123' }, async () => {
executions++;
if (executions === 1) {
throw new Error("Boom!");
} else {
return "Success!";
}
});
console.log(`Result: ${result}`);
}
main();
will output
Job ABC123 failed: Error: Boom!
Retrying job ABC123 in 25ms!
Now retrying ABC123
Result: Success!
To re-run your job, simply return an integer from the 'failed' event handler. The number returned is how many milliseconds to wait before retrying it. Return 0 to retry it immediately.
IMPORTANT: When you ask the limiter to retry a job it will not send it back into the queue. It will stay in the EXECUTING state until it succeeds or until you stop retrying it. This means that it counts as a concurrent job for maxConcurrent even while it's just waiting to be retried. The number of milliseconds to wait ignores your minTime settings.
limiter.updateSettings(options);
The options are the same as the limiter constructor.
Note: Changes don't affect SCHEDULED jobs.
limiter.incrementReservoir(incrementBy);
Returns a promise that returns the new reservoir value.
limiter.currentReservoir()
.then((reservoir) => console.log(reservoir));
Returns a promise that returns the current reservoir value.
The stop() method is used to safely shutdown a limiter. It prevents any new jobs from being added to the limiter and waits for all EXECUTING jobs to complete.
limiter.stop(options)
.then(() => {
console.log("Shutdown completed!")
});
stop() returns a promise that resolves once all the EXECUTING jobs have completed and, if desired, once all non-EXECUTING jobs have been dropped.
| Option | Default | Description |
|---|---|---|
dropWaitingJobs | true | When true, drop all the RECEIVED, QUEUED and RUNNING jobs. When false, allow those jobs to complete before resolving the Promise returned by this method. |
dropErrorMessage | This limiter has been stopped. | The error message used to drop jobs when dropWaitingJobs is true. |
enqueueErrorMessage | This limiter has been stopped and cannot accept new jobs. | The error message used to reject a job added to the limiter after stop() has been called. |
Tasks that are ready to be executed will be added to that other limiter. Suppose you have 2 types of tasks, A and B. They both have their own limiter with their own settings, but both must also follow a global limiter G:
const limiterA = new Bottleneck( /* some settings */ );
const limiterB = new Bottleneck( /* some different settings */ );
const limiterG = new Bottleneck( /* some global settings */ );
limiterA.chain(limiterG);
limiterB.chain(limiterG);
// Requests added to limiterA must follow the A and G rate limits.
// Requests added to limiterB must follow the B and G rate limits.
// Requests added to limiterG must follow the G rate limits.
To unchain, call limiter.chain(null);.
The Group feature of Bottleneck manages many limiters automatically for you. It creates limiters dynamically and transparently.
Let's take a DNS server as an example of how Bottleneck can be used. It's a service that sees a lot of abuse and where incoming DNS requests need to be rate limited. Bottleneck is so tiny, it's acceptable to create one limiter for each origin IP, even if it means creating thousands of limiters. The Group feature is perfect for this use case. Create one Group and use the origin IP to rate limit each IP independently. Each call with the same key (IP) will be routed to the same underlying limiter. A Group is created like a limiter:
const group = new Bottleneck.Group(options);
The options object will be used for every limiter created by the Group.
The Group is then used with the .key(str) method:
// In this example, the key is an IP
group.key("77.66.54.32").schedule(() => {
/* process the request */
});
str : The key to use. All jobs added with the same key will use the same underlying limiter. Default: ""The return value of .key(str) is a limiter. If it doesn't already exist, it is generated for you. Calling key() is how limiters are created inside a Group.
Limiters that have been idle for longer than 5 minutes are deleted to avoid memory leaks, this value can be changed by passing a different timeout option, in milliseconds.
group.on("created", (limiter, key) => {
console.log("A new limiter was created for key: " + key)
// Prepare the limiter, for example we'll want to listen to its "error" events!
limiter.on("error", (err) => {
// Handle errors here
})
});
Listening for the "created" event is the recommended way to set up a new limiter. Your event handler is executed before key() returns the newly created limiter.
const group = new Bottleneck.Group({ maxConcurrent: 2, minTime: 250 });
group.updateSettings({ minTime: 500 });
After executing the above commands, new limiters will be created with { maxConcurrent: 2, minTime: 500 }.
str: The key for the limiter to delete.Manually deletes the limiter at the specified key. When using Clustering, the Redis data is immediately deleted and the other Groups in the Cluster will eventually delete their local key automatically, unless it is still being used.
Returns an array containing all the keys in the Group.
Same as group.keys(), but returns all keys in this Group ID across the Cluster.
const limiters = group.limiters();
console.log(limiters);
// [ { key: "some key", limiter: <limiter> }, { key: "some other key", limiter: <some other limiter> } ]
Some APIs can accept multiple operations in a single call. Bottleneck's Batching feature helps you take advantage of those APIs:
const batcher = new Bottleneck.Batcher({
maxTime: 1000,
maxSize: 10
});
batcher.on("batch", (batch) => {
console.log(batch); // ["some-data", "some-other-data"]
// Handle batch here
});
batcher.add("some-data");
batcher.add("some-other-data");
batcher.add() returns a Promise that resolves once the request has been flushed to a "batch" event.
| Option | Default | Description |
|---|---|---|
maxTime | null (unlimited) | Maximum acceptable time (in milliseconds) a request can have to wait before being flushed to the "batch" event. |
maxSize | null (unlimited) | Maximum number of requests in a batch. |
Batching doesn't throttle requests, it only groups them up optimally according to your maxTime and maxSize settings.
Clustering lets many limiters access the same shared state, stored in Redis. Changes to the state are Atomic, Consistent and Isolated (and fully ACID with the right Durability configuration), to eliminate any chances of race conditions or state corruption. Your settings, such as maxConcurrent, minTime, etc., are shared across the whole cluster, which means —for example— that { maxConcurrent: 5 } guarantees no more than 5 jobs can ever run at a time in the entire cluster of limiters. 100% of Bottleneck's features are supported in Clustering mode. Enabling Clustering is as simple as changing a few settings. It's also a convenient way to store or export state for later use.
Bottleneck will attempt to spread load evenly across limiters.
First, add redis or ioredis to your application's dependencies:
# NodeRedis (https://github.com/NodeRedis/node_redis)
npm install --save redis
# or ioredis (https://github.com/luin/ioredis)
npm install --save ioredis
Then create a limiter or a Group:
const limiter = new Bottleneck({
/* Some basic options */
maxConcurrent: 5,
minTime: 500
id: "my-super-app" // All limiters with the same id will be clustered together
/* Clustering options */
datastore: "redis", // or "ioredis"
clearDatastore: false,
clientOptions: {
host: "127.0.0.1",
port: 6379
// Redis client options
// Using NodeRedis? See https://github.com/NodeRedis/node_redis#options-object-properties
// Using ioredis? See https://github.com/luin/ioredis/blob/master/API.md#new-redisport-host-options
}
});
| Option | Default | Description |
|---|---|---|
datastore | "local" | Where the limiter stores its internal state. The default ("local") keeps the state in the limiter itself. Set it to "redis" or "ioredis" to enable Clustering. |
clearDatastore | false | When set to true, on initial startup, the limiter will wipe any existing Bottleneck state data on the Redis db. |
clientOptions | {} | This object is passed directly to the redis client library you've selected. |
clusterNodes | null | ioredis only. When clusterNodes is not null, the client will be instantiated by calling new Redis.Cluster(clusterNodes, clientOptions) instead of new Redis(clientOptions). |
timeout | null (no TTL) | The Redis TTL in milliseconds (TTL) for the keys created by the limiter. When timeout is set, the limiter's state will be automatically removed from Redis after timeout milliseconds of inactivity. |
Redis | null | Overrides the import/require of the redis/ioredis library. You shouldn't need to set this option unless your application is failing to start due to a failure to require/import the client library. |
Note: When using Groups, the timeout option has a default of 300000 milliseconds and the generated limiters automatically receive an id with the pattern ${group.id}-${KEY}.
Note: If you are seeing a runtime error due to the require() function not being able to load redis/ioredis, then directly pass the module as the Redis option. Example:
import Redis from "ioredis"
const limiter = new Bottleneck({
id: "my-super-app",
datastore: "ioredis",
clientOptions: { host: '12.34.56.78', port: 6379 },
Redis
});
Unfortunately, this is a side effect of having to disable inlining, which is necessary to make Bottleneck easy to use in the browser.
The first limiter connecting to Redis will store its constructor options on Redis and all subsequent limiters will be using those settings. You can alter the constructor options used by all the connected limiters by calling updateSettings(). The clearDatastore option instructs a new limiter to wipe any previous Bottleneck data (for that id), including previously stored settings.
Queued jobs are NOT stored on Redis. They are local to each limiter. Exiting the Node.js process will lose those jobs. This is because Bottleneck has no way to propagate the JS code to run a job across a different Node.js process than the one it originated on. Bottleneck doesn't keep track of the queue contents of the limiters on a cluster for performance and reliability reasons. You can use something like BeeQueue in addition to Bottleneck to get around this limitation.
Due to the above, functionality relying on the queue length happens purely locally:
highWater and load shedding (strategies) are per limiter. However, one limiter entering Blocked mode will put the entire cluster in Blocked mode until penalty milliseconds have passed. See Strategies."empty" event is triggered when the (local) queue is empty."idle" event is triggered when the (local) queue is empty and no jobs are currently running anywhere in the cluster.You must work around these limitations in your application code if they are an issue to you. The publish() method could be useful here.
The current design guarantees reliability, is highly performant and lets limiters come and go. Your application can scale up or down, and clients can be disconnected at any time without issues.
It is strongly recommended that you give an id to every limiter and Group since it is used to build the name of your limiter's Redis keys! Limiters with the same id inside the same Redis db will be sharing the same datastore.
It is strongly recommended that you set an expiration (See Job Options) on every job, since that lets the cluster recover from crashed or disconnected clients. Otherwise, a client crashing while executing a job would not be able to tell the cluster to decrease its number of "running" jobs. By using expirations, those lost jobs are automatically cleared after the specified time has passed. Using expirations is essential to keeping a cluster reliable in the face of unpredictable application bugs, network hiccups, and so on.
Network latency between Node.js and Redis is not taken into account when calculating timings (such as minTime). To minimize the impact of latency, Bottleneck only performs a single Redis call per lifecycle transition. Keeping the Redis server close to your limiters will help you get a more consistent experience. Keeping the system time consistent across all clients will also help.
It is strongly recommended to set up an "error" listener on all your limiters and on your Groups.
The ready(), publish() and clients() methods also exist when using the local datastore, for code compatibility reasons: code written for redis/ioredis won't break with local.
This method returns a promise that resolves once the limiter is connected to Redis.
As of v2.9.0, it's no longer necessary to wait for .ready() to resolve before issuing commands to a limiter. The commands will be queued until the limiter successfully connects. Make sure to listen to the "error" event to handle connection errors.
const limiter = new Bottleneck({/* options */});
limiter.on("error", (err) => {
// handle network errors
});
limiter.ready()
.then(() => {
// The limiter is ready
});
This method broadcasts the message string to every limiter in the Cluster. It returns a promise.
const limiter = new Bottleneck({/* options */});
limiter.on("message", (msg) => {
console.log(msg); // prints "this is a string"
});
limiter.publish("this is a string");
To send objects, stringify them first:
limiter.on("message", (msg) => {
console.log(JSON.parse(msg).hello) // prints "world"
});
limiter.publish(JSON.stringify({ hello: "world" }));
If you need direct access to the redis clients, use .clients():
console.log(limiter.clients());
// { client: <Redis Client>, subscriber: <Redis Client> }
ioredis datastore and the clusterNodes option.ioredis datastore.b_. It also uses pubsub channels starting with b_ It will not interfere with any other data stored on the server.SCRIPT LOAD command. These scripts only take up a few Kb of memory. Running the SCRIPT FLUSH command will cause any connected limiters to experience critical errors until a new limiter connects to Redis and loads the scripts again.Bottleneck needs to create 2 Redis Clients to function, one for normal operations and one for pubsub subscriptions. These 2 clients are kept in a Bottleneck.RedisConnection (NodeRedis) or a Bottleneck.IORedisConnection (ioredis) object, referred to as the Connection object.
By default, every Group and every standalone limiter (a limiter not created by a Group) will create their own Connection object, but it is possible to manually control this behavior. In this example, every Group and limiter is sharing the same Connection object and therefore the same 2 clients:
const connection = new Bottleneck.RedisConnection({
clientOptions: {/* NodeRedis/ioredis options */}
// ioredis also accepts `clusterNodes` here
});
const limiter = new Bottleneck({ connection: connection });
const group = new Bottleneck.Group({ connection: connection });
You can access and reuse the Connection object of any Group or limiter:
const group = new Bottleneck.Group({ connection: limiter.connection });
When a Connection object is created manually, the connectivity "error" events are emitted on the Connection itself.
connection.on("error", (err) => { /* handle connectivity errors here */ });
If you already have a NodeRedis/ioredis client, you can ask Bottleneck to reuse it, although currently the Connection object will still create a second client for pubsub operations:
import Redis from "redis";
const client = new Redis.createClient({/* options */});
const connection = new Bottleneck.RedisConnection({
// `clientOptions` and `clusterNodes` will be ignored since we're passing a raw client
client: client
});
const limiter = new Bottleneck({ connection: connection });
const group = new Bottleneck.Group({ connection: connection });
Depending on your application, using more clients can improve performance.
Use the disconnect(flush) method to close the Redis clients.
limiter.disconnect();
group.disconnect();
If you created the Connection object manually, you need to call connection.disconnect() instead, for safety reasons.
Debugging complex scheduling logic can be difficult, especially when priorities, weights, and network latency all interact with one another.
If your application is not behaving as expected, start by making sure you're catching "error" events emitted by your limiters and your Groups. Those errors are most likely uncaught exceptions from your application code.
Make sure you've read the 'Gotchas' section.
To see exactly what a limiter is doing in real time, listen to the "debug" event. It contains detailed information about how the limiter is executing your code. Adding job IDs to all your jobs makes the debug output more readable.
When Bottleneck has to fail one of your jobs, it does so by using BottleneckError objects. This lets you tell those errors apart from your own code's errors:
limiter.schedule(fn)
.then((result) => { /* ... */ } )
.catch((error) => {
if (error instanceof Bottleneck.BottleneckError) {
/* ... */
}
});
The internal algorithms essentially haven't changed from v1, but many small changes to the interface were made to introduce new features.
All the breaking changes:
require("bottleneck/es5") if you need ES5 support in v2. Bottleneck v1 will continue to use ES5 only.Cluster feature is now called Group. This is to distinguish it from the new v2 Clustering feature.Group constructor takes an options object to match the limiter constructor.submitPriority(), use submit() with an options object instead.schedulePriority(), use schedule() with an options object instead.rejectOnDrop option is now true by default. It can be set to false if you wish to retain v1 behavior. However this option is left undocumented as enabling it is considered to be a poor practice.null instead of 0 to indicate an unlimited maxConcurrent value.null instead of -1 to indicate an unlimited highWater value.changeSettings() to updateSettings(), it now returns a promise to indicate completion. It takes the same options object as the constructor.nbQueued() to queued().nbRunning to running(), it now returns its result using a promise.isBlocked().changePenalty(), it is now done through the options object like any other limiter setting.changeReservoir(), it is now done through the options object like any other limiter setting.stopAll(). Use the new stop() method.check() now accepts an optional weight argument, and returns its result using a promise.Group changeTimeout() method. Instead, pass a timeout option when creating a Group.Version 2 is more user-friendly and powerful.
After upgrading your code, please take a minute to read the Debugging your application chapter.
This README is always in need of improvements. If wording can be clearer and simpler, please consider forking this repo and submitting a Pull Request, or simply opening an issue.
Suggestions and bug reports are also welcome.
To work on the Bottleneck code, simply clone the repo, makes your changes to the files located in src/ only, then run ./scripts/build.sh && npm test to ensure that everything is set up correctly.
To speed up compilation time during development, run ./scripts/build.sh dev instead. Make sure to build and test without dev before submitting a PR.
The tests must also pass in Clustering mode and using the ES5 bundle. You'll need a Redis server running locally (latency needs to be minimal to run the tests). If the server isn't using the default hostname and port, you can set those in the .env file. Then run ./scripts/build.sh && npm run test-all.
All contributions are appreciated and will be considered.
FAQs
Distributed task scheduler and rate limiter
The npm package bottleneck receives a total of 2,287,033 weekly downloads. As such, bottleneck popularity was classified as popular.
We found that bottleneck demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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