Defer Processing via Staging Branch

When you want to load data into HPE Machine Learning Data Management without triggering a pipeline, you can upload it to a staging branch and then submit accumulated changes in one batch by re-pointing the HEAD of your master branch to a commit in the staging branch. Let’s see how this works.

How to Use a Staging Branch

  1. Create a repository. For example, data.

    pachctl create repo data
  2. Create a master branch.

    pachctl create branch data@master
  3. View the created branch:

    pachctl list commit data
    REPO BRANCH COMMIT                           FINISHED           SIZE  ORIGIN DESCRIPTION
    data master 8090bfb4d4fe44158eac12199c37a591 About a minute ago   0B  AUTO

    HPE Machine Learning Data Management automatically created an empty HEAD commit on the new branch, as you can see from the 0B (zero-byte) size and AUTO commit origin.

  4. Commit a file to a staging branch:

    pachctl put file data@staging -f <file>

    HPE Machine Learning Data Management automatically creates the staging branch. Your repo now has 2 branches, staging and master. In this example, the staging name is used, but you can name the branch as you want – and have as many staging branches as you need.

  5. Verify that the branches were created:

    pachctl list branch data
    BRANCH  HEAD                              TRIGGER
    staging f3506f0fab6e483e8338754081109e69   -
    master  8090bfb4d4fe44158eac12199c37a591   -

    The master branch still has the same HEAD commit. No jobs have started to process the new file, because there are no pipelines that take staging as inputs. You can continue to commit to staging to add new data to the branch, and the pipeline will not process anything.

  6. When you are ready to process the data, update the master branch to point it to the head of the staging branch:

    pachctl create branch data@master --head staging
  7. List your branches to verify that the master branch’s HEAD commit has changed:

    pachctl list branch data
    staging f3506f0fab6e483e8338754081109e69
    master  f3506f0fab6e483e8338754081109e69

    The master and staging branches now have the same HEAD commit. This means that your pipeline has data to process.

  8. Verify that the pipeline has new jobs:

    pachctl list job data@f3506f0fab6e483e8338754081109e69
    ID                               PIPELINE STARTED        DURATION           RESTART PROGRESS  DL   UL  STATE
    f3506f0fab6e483e8338754081109e69 data     32 seconds ago Less than a second 0       6 + 0 / 6 108B 24B success

    You should see one job that HPE Machine Learning Data Management created for all the changes you have submitted to the staging branch, with the same ID. While the commits to the staging branch are ancestors of the current HEAD in master, they were never the actual HEAD of master themselves, so they do not get processed. This behavior works for most of the use cases because commits in HPE Machine Learning Data Management are generally additive, so processing the HEAD commit also processes data from previous commits.