Data Management Best Practices¶
This document discusses best practices for minimizing the space needed to store your Pachyderm data, increasing the performance of your data processing as related to data organization, and general good ideas when you are using Pachyderm to version/process your data.
Certain pipelines simply shuffle files around (e.g., organizing files into buckets). If you find yourself writing a pipeline that does a lot of copying, such as Time Windowing, it probably falls into this category.
The best way to shuffle files, especially large files, is to create symlinks in the output directory that point to files in the input directory.
For instance, to move a file
logs/log.txt, you might be tempted to write a
transform like this:
cp /pfs/input/log.txt /pfs/out/logs/log.txt
However, it’s more efficient to create a symlink:
ln -s /pfs/input/log.txt /pfs/out/logs/log.txt
Under the hood, Pachyderm is smart enough to recognize that the output file simply symlinks to a file that already exists in Pachyderm, and therefore skips the upload altogether.
Note that if your shuffling pipeline only needs the names of the input files but not their content, you can use
lazy input. That way, your shuffling pipeline can skip both the download and the upload.
When a file/commit/repo is deleted, the data is not immediately removed from the underlying storage system (e.g. S3) for performance and architectural reasons. This is similar to how when you delete a file on your computer, the file is not necessarily wiped from disk immediately.
To actually remove the data, you may need to manually invoke garbage collection. The easiest way to do it is through
pachctl garbage-collect. Currently
pachctl garbage-collect can only be started when there are no active jobs running. You also need to ensure that there’s no ongoing
put-file. Garbage collection puts the cluster into a readonly mode where no new jobs can be created and no data can be added.
Setting a root volume size¶
When planning and configuring your Pachyderm deploy, you need to make sure that each node’s root volume is big enough to accommodate your total processing bandwidth. Specifically, you should calculate the bandwidth for your expected running jobs as follows:
(storage needed per datum) x (number of datums being processed simultaneously) / (number of nodes)
Here, the storage needed per datum should be the storage needed for the largest “datum” you expect to process anywhere on your DAG plus the size of the output files that will be written for that datum. If your root volume size is not large enough, pipelines might fail when downloading the input. The pod would get evicted and rescheduled to a different node, where the same thing will happen (assuming that node had a similar volume). This scenario is further discussed here.