Speed up your CircleCI pipeline by using the RAM disk

January 21, 2021

During a presentation—held internally at Acast by my colleague Daniel Grefberg—I discovered a one-line change that reduced by 2 minutes the execution time of my current project’s CI pipeline.

- working_directory: ~/my-project-name
+ working_directory: /mnt/ramdisk/my-project-name

CircleCI allows us to mount the project on the RAM disk and use it as a ”temporary file storage paradigm.” That means, when caching a project’s dependencies, CircleCI will use the RAM disk instead of the disk drive.

When working on a Node.js project, it is common to cache the node_modules folder. It allows us to re-use it in the other jobs to avoid running the install command every time. On paper, that should be faster than running npm ci (or yarn install --frozen-lockfile) for each job, but that’s not necessarily the case. It usually takes ~2 minutes to restore the cache of ~400MB. This time is mainly taken for decompressing the thousands of files from the folder on the disk. But, this step gets down to ~20 seconds by moving it to the RAM disk.

This change is a quick win to save a few minutes for running our pipeline. The only drawback is that we are limited to the RAM capacity—defined by the job’s resource_class attribute. By default, it runs on a 4GB RAM (medium resource class), so depending on our project and the dependencies’ size, we might need to increase the resource class.

version: 2.1

aliases:
  - &default_image
    working_directory: /mnt/ramdisk/my-project
    docker:
      - image: cimg/node:12.20

  - &cache_key v1-cache-{{checksum ".circleci/config.yml"}}-{{checksum
    "yarn.lock"}}
  - &restore_cache
    restore_cache:
      keys:
        - *cache_key

jobs:
  initialize:
    <<: *default_image
    steps:
      - checkout
      - *restore_cache
      - run: yarn install --frozen-lockfile
      - save_cache:
          name: Save cache
          key: *cache_key
          paths:
            - node_modules

  test_unit:
    <<: *default_image
    steps:
      - checkout
      - *restore_cache
      - run: yarn test:unit --maxWorkers=2

Feel free to send feedback or ask questions on Twitter.



Written by Antoine Lehurt (a.k.a kewah). Senior front-end engineer living in Stockholm and currently working at Acast.