Distribute Task Execution (DTE)
Nx speeds up your average CI time with caching and the affected command. But neither of these features help with the worst case scenario. When something at the core of your repo has been modified and every task needs to be run in CI, the only way to improve the performance is by adding more agent jobs and efficiently parallelizing the tasks.
The most obvious way to parallelize tasks is to split tasks up by type: running all tests on one job, all builds on another and all lint tasks on a third. This strategy is called binning. This can be made difficult if some test tasks have build tasks as prerequisites, but assuming you figure out some way to handle that, a typical set up can look like the diagram below. Here the test tasks are delayed until all necessary build artifacts are ready, but the build and lint tasks can start right away.
The problem with the binning approach is you'll end up with some idle time on one or more jobs. Nx's distributed task execution reduces that idle time to the minimum possible by assigning each individual task to agent jobs based on the task's average run time. Nx also guarantees that tasks are executed in the correct order and uses distributed caching to make sure that build artifacts from previous tasks are present on every agent job that needs them.
When you set up Nx's distributed task execution, your task graph will look more like this:
And not only will CI finish faster, but the debugging experience is the same as if you ran all of your CI on a single job. That's because Nx uses distributed caching to recreate all of the logs and build artifacts on the main job.
Find more information in this guide to parallelization and distribution in CI.
To distribute your task execution, you need to (1) connect to Nx Cloud and (2) enable DTE in your CI workflow. Each of these steps can be enabled with a single command:
This command installs the latest version of
nx-cloud. The latest version works with any version of Nx >= 13.0.
If you have a new workspace, you can generate the CI configuration as follows:
--ci flag can be
For existing workspaces you would probably want to adjust your configuration by hand. See below for examples.
CI Execution Flow
Distributed task execution can work on any CI provider. You are responsible for launching jobs in your CI system. Nx Cloud then coordinates the way those jobs work together. There are two different kinds of jobs that you'll need to create in your CI system. If you would like Nx Cloud to dynamically provision agents for you, check out Nx Agents
- Main job that controls what is going to be executed
- Multiple agent jobs that actually execute the tasks
The main CI job execution flow looks like this:
1# Coordinate the agents to run the tasks 2- npx nx-cloud start-ci-run --stop-agents-after="build" # makes all nx commands distributed by default 3# Run any commands you want here 4- nx affected -t test lint build # run on agents in a distributed fashion 5- nx affected -t deploy --no-dte # run the main job 6
The agent job execution flow is simple:
1# Wait for tasks to execute 2- npx nx-cloud start-agent 3
Depending on your CI setup, you might want to stop the agents explicitly. You can do it as follows
1# Stop any run away agents 2- npx nx-cloud stop-all-agents 3
For most CI providers, Nx Cloud is able to able to match the main and the agents automatically but for some you might need to provision the
NX_CI_EXECUTION_IDenv variables ( see Environment Variables for more info).
The main job looks more or less the same way as if you haven't used any distribution. The only thing you need to do is to invoke
npx nx-cloud start-ci-run at the beginning and, optionally, invoke
npx nx-cloud stop-all-agents at the end.
The agent jobs run long-running
start-agent processes that execute all the tasks associated with a given CI run. The only thing you need to do to set them up is to invoke
npx nx-cloud start-agent. This process will keep running until Nx Cloud tells it to terminate.
Note it's important that the main job and the agent jobs have the same environment and the same source code. They start around the same time. And, once the main job completes, all the agents will be stopped.
It's also important to note that an Nx Cloud agent isn't a machine but rather a long-running process that runs on a machine. I.e., Nx Cloud doesn't manage your agents--you need to do it in your CI config (check out CI examples below).
Nx Cloud is an orchestrator. The main job tells Nx Cloud what you want to run, and Nx Cloud will distribute those tasks across the agents. Nx Cloud will automatically move files from one agent to another, from the agents to the main job.
The end result is that when say
nx affected -t build completes on the main job, all the file artifacts created on agents are copied over to the main job, as if the main job had built everything locally.
Running Things in Parallel
--parallel is propagated to the agents. E.g.,
npx nx affected -t build --parallel=3 tells Nx Cloud to run up to 3 build targets in parallel on each agent. So if you have say 10 agents, you will run up to 30 builds in parallel across all of them.
You also want to run as many commands in parallel as you can. For instance,
1- nx affected -t build 2- nx affected -t test 3- nx affected -t lint 4
is worse than
1- nx affected -t build & nx affected -t test & nx affected -t lint 2
1- nx affected -t build test lint 2
The latter two are going to schedule all the three commands at the same time, so if an agent cannot find anything to build, they will start running tests and lints. The result is better agent utilization and shorter CI time.
The examples below show how to set up CI using Nx and Nx Cloud using distributed task execution and distributed caching.
Every organization manages their CI/CD pipelines differently, so the examples don't cover org-specific aspects of CI/CD (e.g., deployment). They mainly focus on configuring Nx correctly.
Read the guides for more information on how to configure them in CI.
Note that only cacheable operations can be distributed because they have to be replayed on the main job.