| title | Upgrading to v4 |
|---|---|
| description | What's new in v4, how to upgrade, and breaking changes. |
import NodeVersions from "/snippets/node-versions.mdx";
Read our blog post for an overview of the new features.
In addition to waiting for a specific duration, or waiting for a child task to complete, you can now create and wait for a token to be completed, giving you more flexibility and the ability to wait for arbitrary conditions. For example, you can send the token to a Slack channel, and only complete the token when the user has clicked an "Approve" button.
To wait for a token, you need to first create one using the wait.createToken function:
import { wait } from "@trigger.dev/sdk";
// Somewhere in your code, either your backend or inside a task
const token = await wait.createToken({
timeout: "10m", // you can optionally specify a timeout for the token
});
await sendTokenToSlack(token.id);Wait tokens are completed with a payload that you can specify when you complete the token:
// When the user clicks the "Approve" button, you can complete the token
await wait.completeToken(tokenId, {
status: "approved",
});You can wait for the token using the token ID:
type ApprovalToken = {
status: "approved" | "rejected";
};
// Inside a task
const result = await wait.forToken<ApprovalToken>(tokenId);
if (result.ok) {
console.log("Token completed", result.output.status); // "approved" or "rejected"
} else {
console.log("Token timed out", result.error);
}You can now pass an idempotency key to any wait function, allowing you to skip waits if the same idempotency key is used again. This can be useful if you want to skip waits when retrying a task, for example:
// Specify the idempotency key and TTL when creating a wait token
const token = await wait.createToken({
idempotencyKey: "my-idempotency-key",
idempotencyKeyTTL: "1h",
});
// Specify the idempotency key and TTL when waiting for a duration:
await wait.for({ seconds: 10 }, { idempotencyKey: "my-idempotency-key", idempotencyKeyTTL: "1h" });
// Specify the idempotency key and TTL when waiting for a child task:
await childTask.triggerAndWait(
{ foo: "bar" },
{
idempotencyKey: "my-idempotency-key",
idempotencyKeyTTL: "1h",
}
);idempotencyKeyTTL allows you to specify how long the idempotency key should be valid for. The default is 30 days.
You can now specify a priority when triggering a task. This allows you to prioritize certain tasks over others, and is useful if you want to ensure that certain tasks are executed before others.
await task.trigger({ foo: "bar" }, { priority: 1 });The priority value is a time duration in seconds, which offsets the timestamp of the run in the queue. If you specify a priority of 10, the run will win over runs with a priority of 0 that were triggered within the last 10 seconds. A more concrete example:
// Triggered at 12:00:00, into a queue with a large number of queued runs
await task.trigger({ foo: "bar" }, { priority: 0 });
// Triggered at 12:00:09, into the same queue
await task.trigger({ foo: "bar" }, { priority: 10 });In this case, the second run will be executed first, because it's priority moved it 1 second ahead of the first run.
We purposefully chose to use a time duration as the priority value instead of specifying priority levels, because priority levels can cause "level starvation" where lower priority runs are never executed because there are always higher priority runs in the queue.We've added a new way to register global lifecycle hooks that are executed for all runs, regardless of the task. Previously, this was only possible in the trigger.config.ts file, but now you can register them anywhere in your codebase:
import { tasks } from "@trigger.dev/sdk";
tasks.onStart(({ ctx, payload, task }) => {
console.log("Run started", ctx.run);
});
tasks.onSuccess(({ ctx, output }) => {
console.log("Run finished", ctx.run);
});
tasks.onFailure(({ ctx, error }) => {
console.log("Run failed", ctx.run);
});If you create a init.ts file at the root of your trigger directory, it will be automatically loaded when a task is executed. This is useful if you want to register global lifecycle hooks, or initialize a database connection, etc.
import { tasks } from "@trigger.dev/sdk";
tasks.onStart(({ ctx, payload, task }) => {
console.log("Run started", ctx.run);
});We've added two new lifecycle hooks that allow you to run code when a run is paused or resumed because of a wait:
export const myTask = task({
id: "my-task",
onWait: async ({ wait }) => {
console.log("Run paused", wait);
},
onResume: async ({ wait }) => {
console.log("Run resumed", wait);
},
run: async (payload: any, { ctx }) => {
console.log("Run started", ctx.run);
await wait.for({ seconds: 10 });
console.log("Run finished", ctx.run);
},
});We've added a new lifecycle hook that is executed when a run completes, regardless of whether it succeeded or failed:
tasks.onComplete(({ ctx, result }) => {
if (result.ok) {
console.log("Run succeeded", result.data);
} else {
console.log("Run failed", result.error);
}
});Available in v4.0.0-beta.12 and later.
You can now define an onCancel hook that is called when a run is cancelled. This is useful if you want to clean up any resources that were allocated for the run.
tasks.onCancel(({ ctx, signal }) => {
console.log("Run cancelled", signal);
});You can use the onCancel hook along with the signal passed into the run function to interrupt a call to an external service, for example using the streamText function from the AI SDK:
import { logger, tasks, schemaTask } from "@trigger.dev/sdk";
import { streamText } from "ai";
import { z } from "zod";
export const interruptibleChat = schemaTask({
id: "interruptible-chat",
description: "Chat with the AI",
schema: z.object({
prompt: z.string().describe("The prompt to chat with the AI"),
}),
run: async ({ prompt }, { signal }) => {
const chunks: TextStreamPart<{}>[] = [];
// 👇 This is a global onCancel hook, but it's inside of the run function
tasks.onCancel(async () => {
// We have access to the chunks here, and can save them to the database
await saveChunksToDatabase(chunks);
});
try {
const result = streamText({
model: getModel(),
prompt,
experimental_telemetry: {
isEnabled: true,
},
tools: {},
abortSignal: signal, // 👈 Pass the signal to the streamText function, which aborts with the run is cancelled
onChunk: ({ chunk }) => {
chunks.push(chunk);
},
});
const textParts = [];
for await (const part of result.textStream) {
textParts.push(part);
}
return textParts.join("");
} catch (error) {
if (error instanceof Error && error.name === "AbortError") {
// streamText will throw an AbortError if the signal is aborted, so we can handle it here
} else {
throw error;
}
}
},
});The onCancel hook can optionally wait for the run function to finish, and access the output of the run:
import { logger, task } from "@trigger.dev/sdk";
import { setTimeout } from "node:timers/promises";
export const cancelExampleTask = task({
id: "cancel-example",
// Signal will be aborted when the task is cancelled 👇
run: async (payload: { message: string }, { signal }) => {
try {
// We pass the signal to setTimeout to abort the timeout if the task is cancelled
await setTimeout(10_000, undefined, { signal });
} catch (error) {
// Ignore the abort error
}
// Do some more work here
return {
message: "Hello, world!",
};
},
onCancel: async ({ runPromise }) => {
// You can await the runPromise to get the output of the task
const output = await runPromise;
},
});Our task middleware system is now much more useful. Previously it only ran "around" the run function, but now we've hoisted it to the top level and it now runs before/after all the other hooks.
We've also added a new locals API that allows you to share data between middleware and hooks.
import { locals } from "@trigger.dev/sdk";
import { logger, tasks } from "@trigger.dev/sdk";
// This would be type of your database client here
const DbLocal = locals.create<{ connect: () => Promise<void>; disconnect: () => Promise<void> }>(
"db"
);
export function getDb() {
return locals.getOrThrow(DbLocal);
}
export function setDb(db: { connect: () => Promise<void> }) {
locals.set(DbLocal, db);
}
tasks.middleware("db", async ({ ctx, payload, next, task }) => {
// This would be your database client here
const db = locals.set(DbLocal, {
connect: async () => {
logger.info("Connecting to the database");
},
disconnect: async () => {
logger.info("Disconnecting from the database");
},
});
await db.connect();
await next();
await db.disconnect();
});
// Disconnect when the run is paused
tasks.onWait("db", async ({ ctx, payload, task }) => {
const db = getDb();
await db.disconnect();
});
// Reconnect when the run is resumed
tasks.onResume("db", async ({ ctx, payload, task }) => {
const db = getDb();
await db.connect();
});Now in your tasks run function and all your hooks (global or task specific) you can access the database client using getDb():
import { getDb } from "./db";
export const myTask = task({
run: async (payload: any, { ctx }) => {
const db = getDb();
await db.query("SELECT 1");
},
});Hidden tasks
Previously, you were required to export the task from a file in your trigger directory to be able to execute it. We've changed the way tasks are indexed and this requirement has been removed. So you can now just define a task without exporting it, and everything will still work:
import { task } from "@trigger.dev/sdk";
const myTask = task({
run: async (payload: any, { ctx }) => {},
});You can use this to define "hidden" tasks that should only ever be triggered by other tasks in the same file:
import { task } from "@trigger.dev/sdk";
const myTask = task({
run: async (payload: any, { ctx }) => {},
});
export const myTask2 = task({
run: async (payload: any, { ctx }) => {
await myTask.trigger(payload);
},
});Or you can create a package of reusable tasks that can be imported and used in your tasks, without having to re-export them:
import { task } from "@trigger.dev/sdk";
import { sendToSlack } from "@repo/tasks";
export const myTask = task({
run: async (payload: any, { ctx }) => {
await sendToSlack.trigger(payload);
},
});We've added a new useWaitToken react hook that allows you to complete a wait token from a React component, using a Public Access Token.
import { wait } from "@trigger.dev/sdk";
// Somewhere in your code, you'll need to create the token and then pass the token ID and the public token to the frontend
const token = await wait.createToken({
timeout: "10m",
});
return {
tokenId: token.id,
publicToken: token.publicAccessToken, // An automatically generated public access token that expires in 1 hour
};Now you can use the useWaitToken hook in your frontend code:
import { useWaitToken } from "@trigger.dev/react-hooks";
export function MyComponent({ publicToken, tokenId }: { publicToken: string; tokenId: string }) {
const { complete } = useWaitToken(tokenId, {
accessToken: publicToken,
});
return <button onClick={() => complete({ foo: "bar" })}>Complete</button>;
}We've added a new ai.tool function that allows you to create an AI tool from an existing schemaTask to use with the Vercel AI SDK:
import { ai } from "@trigger.dev/sdk/ai";
import { schemaTask } from "@trigger.dev/sdk";
import { z } from "zod";
import { generateText } from "ai";
const myToolTask = schemaTask({
id: "my-tool-task",
schema: z.object({
foo: z.string(),
}),
run: async (payload: any, { ctx }) => {},
});
const myTool = ai.tool(myToolTask);
export const myAiTask = schemaTask({
id: "my-ai-task",
schema: z.object({
text: z.string(),
}),
run: async (payload, { ctx }) => {
const { text } = await generateText({
prompt: payload.text,
model: openai("gpt-4o"),
tools: {
myTool,
},
});
},
});You can also pass the experimental_toToolResultContent option to the ai.tool function to customize the content of the tool result:
import { openai } from "@ai-sdk/openai";
import { Sandbox } from "@e2b/code-interpreter";
import { ai } from "@trigger.dev/sdk/ai";
import { schemaTask } from "@trigger.dev/sdk/v3";
import { generateObject } from "ai";
import { z } from "zod";
const chartTask = schemaTask({
id: "chart",
description: "Generate a chart using natural language",
schema: z.object({
input: z.string().describe("The chart to generate"),
}),
run: async ({ input }) => {
const code = await generateObject({
model: openai("gpt-4o"),
schema: z.object({
code: z.string().describe("The Python code to execute"),
}),
system: `
You are a helpful assistant that can generate Python code to be executed in a sandbox, using matplotlib.pyplot.
For example:
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()
Make sure the code ends with plt.show()
`,
prompt: input,
});
const sandbox = await Sandbox.create();
const execution = await sandbox.runCode(code.object.code);
const firstResult = execution.results[0];
if (firstResult.png) {
return {
chart: firstResult.png,
};
} else {
throw new Error("No chart generated");
}
},
});
// This is useful if you want to return an image from the tool
export const chartTool = ai.tool(chartTask, {
experimental_toToolResultContent: (result) => {
return [
{
type: "image",
data: result.chart,
mimeType: "image/png",
},
];
},
});You can also now get access to the current tool execution options inside the task run function using the ai.currentToolOptions() function:
import { ai } from "@trigger.dev/sdk/ai";
import { schemaTask } from "@trigger.dev/sdk";
import { z } from "zod";
const myToolTask = schemaTask({
id: "my-tool-task",
schema: z.object({
foo: z.string(),
}),
run: async (payload, { ctx }) => {
const toolOptions = ai.currentToolOptions();
console.log(toolOptions);
},
});
export const myAiTask = ai.tool(myToolTask);See the AI SDK tool execution options docs for more details on the tool execution options.
`ai.tool` is compatible with `schemaTask`'s defined with Zod and ArkType schemas, or any schemas that implement a `.toJsonSchema()` function.First read the deprecations, breaking changes, and known issues sections below.
We recommend the following steps to migrate to v4:
- Install the v4 package.
- Run the
trigger devCLI command and test your tasks locally, fixing any breaking changes. - Deploy to the staging environment and test your tasks in staging, fixing any breaking changes. (this step is optional, but highly recommended)
- Once you've verified that v4 is working as expected, you should deploy your application backend with the updated v4 package.
- Once you've deployed your application backend, you should deploy your tasks to the production environment.
Note that between steps 4 and 5, runs triggered with the v4 package will continue using v3, and only new runs triggered after step 5 is complete will use v4.
Once v4 is activated in your environment, there will be a period of time where old runs will continue to execute using v3, while new runs will use v4. Because these engines use completely different underlying queues and concurrency models, it's possible you may have up to double the amount of concurrently executing runs. Once the runs drain from the old run engine, the concurrency will return to normal.To opt-in to using v4, you will need to update your dependencies to the latest version of the v4-beta tag.
npm add @trigger.dev/sdk@v4-beta -Eyarn add @trigger.dev/sdk@v4-beta -Epnpm add @trigger.dev/sdk@v4-beta -E You will need to do this for all your @trigger.dev/* packages.
You'll also need to use the v4-beta version of the trigger.dev CLI package:
npx trigger.dev@v4-beta devyarn dlx trigger.dev@v4-beta devpnpm dlx trigger.dev@v4-beta devDuring the beta we will be tracking issues and releasing regular fixes.
We've deprecated the following APIs:
We've deprecated the @trigger.dev/sdk/v3 import path and moved to a new path:
// This still works, but will be removed in a future version
import { task } from "@trigger.dev/sdk/v3";
// This is the new path
import { task } from "@trigger.dev/sdk";We've renamed the handleError hook to catchError to better reflect that it can catch and react to errors. handleError will be removed in a future version.
init was previously used to initialize data used in the run function:
import { task } from "@trigger.dev/sdk";
const myTask = task({
init: async () => {
return {
myClient: new MyClient(),
};
},
run: async (payload: any, { ctx, init }) => {
const client = init.myClient;
await client.doSomething();
},
});This has now been deprecated in favor of the locals API and middleware. See the Improved middleware and locals section for more details.
We've deprecated the toolTask function, which created both a Trigger.dev task and a tool compatible with the Vercel AI SDK:
import { toolTask, schemaTask } from "@trigger.dev/sdk";
import { z } from "zod";
import { generateText } from "ai";
const myToolTask = toolTask({
id: "my-tool-task",
run: async (payload: any, { ctx }) => {},
});
export const myAiTask = schemaTask({
id: "my-ai-task",
schema: z.object({
text: z.string(),
}),
run: async (payload, { ctx }) => {
const { text } = await generateText({
prompt: payload.text,
model: openai("gpt-4o"),
tools: {
myToolTask,
},
});
},
});We've replaced the toolTask function with the ai.tool function, which creates an AI tool from an existing schemaTask. See the ai.tool section for more details.
Previously, it was possible to specify a queue name of a queue that did not exist, along with a concurrency limit. The queue would then be created "on-demand" with the specified concurrency limit. If the queue did exist, the concurrency limit of the queue would be updated to the specified value:
await myTask.trigger({ foo: "bar" }, { queue: { name: "my-queue", concurrencyLimit: 10 } });This is no longer possible, and queues must now be defined ahead of time using the queue function:
import { queue } from "@trigger.dev/sdk";
const myQueue = queue({
name: "my-queue",
concurrencyLimit: 10,
});Now when you trigger a task, you can only specify the queue by name:
await myTask.trigger({ foo: "bar" }, { queue: "my-queue" });Or you can set the queue on the task:
import { queue, task } from "@trigger.dev/sdk";
const myQueue = queue({
name: "my-queue",
concurrencyLimit: 10,
});
export const myTask = task({
id: "my-task",
queue: myQueue,
run: async (payload: any, { ctx }) => {},
});
// You can optionally specify the queue directly on the task
export const myTask2 = task({
id: "my-task-2",
queue: {
name: "my-queue-2",
concurrencyLimit: 50,
},
run: async (payload: any, { ctx }) => {},
});Now you can trigger these tasks without having to specify the queue name in the trigger options:
await myTask.trigger({ foo: "bar" }); // Will use the queue defined on the task
await myTask2.trigger({ foo: "bar" }); // Will use the queue defined on the taskWe've changed a few things around how concurrency is managed at the environment and queue level:
- Environment concurrency limits are now "burstable" above the base concurrency limit. The default burst factor is 2.0, meaning that the environment concurrency limit can be up to 2x the base concurrency limit. So if your base concurrency limit is 10, the environment concurrency limit can be up to 20.
- Each individual queue has a maximum concurrency limit of the environment base concurrency limit, NOT the burstable limit. So if your base concurrency limit is 10, the queue concurrency limit can be up to 10. This means if you don't set the queue concurrency limit, it will default to the environment base concurrency limit.
- The only time we "release" concurrency is when a run is checkpointed. This means that if you have a run that is waiting on a waitpoint, and the run is checkpointed, the concurrency will be released back into the queue, allowing other runs to execute/resume. We release the concurrency back to the queue and the environment.
This means that if you have a queue with a concurrencyLimit of 1, you can only have exactly 1 run executing at a time, but you may have more than 1 run in the WAITING state that belongs to that queue. Runs are only transitioned to the WAITING state when they are waiting on a waitpoint and have been checkpointed.
We've done some work cleaning up the run statuses. The new statuses are:
PENDING_VERSION: Task is waiting for a version update because it cannot execute without additional information (task, queue, etc.).QUEUED: Task is waiting to be executed by a worker.DEQUEUED: Task has been dequeued and is being sent to a worker to start executing.EXECUTING: Task is currently being executed by a worker.WAITING: Task has been paused by the system, and will be resumed by the system.COMPLETED: Task has been completed successfully.CANCELED: Task has been canceled by the user.FAILED: Task has failed to complete, due to an error in the task code.CRASHED: Task has crashed and won't be retried, most likely the worker ran out of resources, e.g. memory or storage.SYSTEM_FAILURE: Task has failed to complete, due to an error in the systemDELAYED: Task has been scheduled to run at a specific time.EXPIRED: Task has expired and won't be executed,TIMED_OUT: Task has reached its maxDuration and has been stopped.
We've removed the following statuses:
WAITING_FOR_DEPLOY: This is no longer used, and is replaced byPENDING_VERSIONFROZEN: This is no longer used, and is replaced byWAITINGINTERRUPTED: This is no longer usedREATTEMPTING: This is no longer used, and is replaced byEXECUTING
We've also added "boolean" helpers to runs returned via the API and from Realtime:
isQueued: Returns true when the status isQUEUED,PENDING_VERSION, orDELAYEDisExecuting: Returns true when the status isEXECUTING,DEQUEUED. These count against your concurrency limits.isWaiting: Returns true when the status isWAITING. These do not count against your concurrency limits.isCompleted: Returns true when the status is any of the completed statuses.isCanceled: Returns true when the status isCANCELEDisFailed: Returns true when the status is any of the failed statuses.isSuccess: Returns true when the status isCOMPLETED
We've changed the function signatures of the lifecycle hooks to be more consistent and easier to use, by unifying all the parameters into a single object that can be destructured.
Previously, hooks received a payload as the first argument and then an additional object as the second argument:
import { task } from "@trigger.dev/sdk";
export const myTask = task({
id: "my-task",
onStart: (payload, { ctx }) => {},
run: async (payload, { ctx }) => {},
});Now, all the parameters are passed in a single object:
import { task } from "@trigger.dev/sdk";
export const myTask = task({
id: "my-task",
onStart: ({ payload, ctx }) => {},
// The run function still uses separate parameters
run: async (payload, { ctx }) => {},
});This is true for all the lifecycle hooks:
import { task } from "@trigger.dev/sdk";
export const myTask = task({
id: "my-task",
onStart: ({ payload, ctx, task }) => {},
onSuccess: ({ payload, ctx, task, output }) => {},
onFailure: ({ payload, ctx, task, error }) => {},
onWait: ({ payload, ctx, task, wait }) => {},
onResume: ({ payload, ctx, task, wait }) => {},
onComplete: ({ payload, ctx, task, result }) => {},
catchError: ({ payload, ctx, task, error, retry, retryAt, retryDelayInMs }) => {},
run: async (payload, { ctx }) => {},
});We've made a few small changes to the ctx object:
ctx.attempt.idandctx.attempt.statushave been removed.ctx.attempt.numberis still available.ctx.task.exportNamehas been removed (since we no longer require tasks to be exported to be triggered).
The batchTrigger function no longer returns a runs list directly. In v3, you could access the runs directly from the batch handle:
// In v3
const batchHandle = await tasks.batchTrigger([
[myTask, { foo: "bar" }],
[myOtherTask, { baz: "qux" }],
]);
// You could access runs directly
console.log(batchHandle.runs);In v4, you now need to use the runs.list() method to get the list of runs:
// In v4
const batchHandle = await tasks.batchTrigger([
[myTask, { foo: "bar" }],
[myOtherTask, { baz: "qux" }],
]);
// Now you need to call runs.list()
const runs = await batchHandle.runs.list();
console.log(runs);This release also includes a new experimental processKeepAlive option, which allows you to
keep the process alive after the run has completed for the next warm start, which makes warm starts even faster.
Currently during a warm start, we still recreate the actual task run process between runs, leading to a completely fresh global environment for each run. This experimental option will keep the task run process alive between run executions, leading to even faster warm starts. This option is respected in both the dev CLI and in deployed tasks.
To enable this option, add this to your trigger.config.ts:
import { defineConfig } from "@trigger.dev/sdk";
export default defineConfig({
project: "<project ref>",
// This is false by default
experimental_processKeepAlive: true,
maxDuration: 60,
});You can also pass an object to experimental_processKeepAlive to provide more options:
import { defineConfig } from "@trigger.dev/sdk";
export default defineConfig({
project: "<project ref>",
experimental_processKeepAlive: {
enabled: true,
// After 20 runs execute with a single process, we'll restart the process and start fresh
maxExecutionsPerProcess: 20,
// In dev, you can combine this option with setting a max pool size, giving you the ability to limit the number of processes created on your local dev machine. Has no effect on deployed tasks
devMaxPoolSize: 10,
},
maxDuration: 60,
});- Be careful with memory usage and memory leaks, as this will cause memory to persist across run executions.
- It's possible different tasks get executed in the same persisted process.
- If you configure any 3rd party SDKs globally using env vars for API keys, those SDKs will not change between runs. So if you change an env var between runs, the SDK will be "stale" and continue using the old env var value. Instead, you should initialize SDKs using env vars at runtime (in any of the lifecycle hooks or inside the
runfunction of a task. - Cancelling a task will cause the task run process to be restarted. Exiting the process is the only reliable way to actually stop a running function from stopping.
- This DOES NOT effect cold starts, warm starts only will be improved.
We recommend enabling this option and testing in a staging or preview environment before trying it out in prod, as there could be unknown issues depending on what you are doing in your tasks. #2183
[Release v4.0.0-beta.23](https://github.com/triggerdotdev/trigger.dev/releases/tag/trigger.dev%404.0.0-v4-beta.23).-
fix: Logging large objects is now much more performant and uses less memory (#2263)
-
New internal idempotency implementation for trigger and batch trigger to prevent request retries from duplicating work (#2256)
-
Enhance deploy command output to better distinguish between local and remote builds (#2254)
-
Fixes a bug that would allow processes that had OOM errors to be incorrectly reused when experimental_processKeepAlive was enabled (#2261)
-
Add runtime version detection for display in the dashboard (#2254)
-
Update base images to latest compatible versions. The
node-22runtime now uses v22.16.0 andbunuses the latest v1.2.18 release. The defaultnoderuntime is unchanged and points at v21.7.3. (#2254) -
Fail fast in CI when running deploy with missing
TRIGGER_ACCESS_TOKENand add useful error message with link to docs (#2258)
-
Removes the
releaseConcurrencyOnWaitpointoption on queues and thereleaseConcurrencyoption on various wait functions. Replaced with the following default behavior: (#2284)- Concurrency is never released when a run is first blocked via a waitpoint, at either the env or queue level.
- Concurrency is always released when a run is checkpointed and shutdown, at both the env and queue level.
Additionally, environment concurrency limits now have a new "Burst Factor", defaulting to 2.0x. The "Burst Factor" allows the environment-wide concurrency limit to be higher than any individual queue's concurrency limit. For example, if you have an environment concurrency limit of 100, and a Burst Factor of 2.0x, then you can execute up to 200 runs concurrently, but any one task/queue can still only execute 100 runs concurrently.
We've done some work cleaning up the run statuses. The new statuses are:
PENDING_VERSION: Task is waiting for a version update because it cannot execute without additional information (task, queue, etc.)QUEUED: Task is waiting to be executed by a workerDEQUEUED: Task has been dequeued and is being sent to a worker to start executing.EXECUTING: Task is currently being executed by a workerWAITING: Task has been paused by the system, and will be resumed by the systemCOMPLETED: Task has been completed successfullyCANCELED: Task has been canceled by the userFAILED: Task has failed to complete, due to an error in the systemCRASHED: Task has crashed and won't be retried, most likely the worker ran out of resources, e.g. memory or storageSYSTEM_FAILURE: Task has failed to complete, due to an error in the systemDELAYED: Task has been scheduled to run at a specific timeEXPIRED: Task has expired and won't be executedTIMED_OUT: Task has reached it's maxDuration and has been stopped
We've removed the following statuses:
WAITING_FOR_DEPLOY: This is no longer used, and is replaced byPENDING_VERSIONFROZEN: This is no longer used, and is replaced byWAITINGINTERRUPTED: This is no longer usedREATTEMPTING: This is no longer used, and is replaced byEXECUTING
We've also added "boolean" helpers to runs returned via the API and from Realtime:
isQueued: Returns true when the status isQUEUED,PENDING_VERSION, orDELAYEDisExecuting: Returns true when the status isEXECUTING,DEQUEUED. These count against your concurrency limits.isWaiting: Returns true when the status isWAITING. These do not count against your concurrency limits.isCompleted: Returns true when the status is any of the completed statuses.isCanceled: Returns true when the status isCANCELEDisFailed: Returns true when the status is any of the failed statuses.isSuccess: Returns true when the status isCOMPLETED
This change adds the ability to easily detect which runs are being counted against your concurrency limit by filtering for both
EXECUTINGorDEQUEUED. -
Added runs.list filtering for queue and machine (#2277)