Load Your Data Into Pachyderm¶
The data that you commit to Pachyderm is stored in an object store of your choice, such as Amazon S3, MinIO, Google Cloud Storage, or other. Pachyderm records the cryptographic hash (
SHA) of each portion of your data and stores it as a commit with a unique identifier (ID). Although the data is stored as an unstructured blob, Pachyderm enables you to interact with versioned data as you typically do in a standard file system.
Pachyderm stores versioned data in repositories which can contain one or multiple files, as well as files arranged in directories. Regardless of the repository structure, Pachyderm versions the state of each data repository as the data changes over time.
To put data into Pachyderm, a commit must be started, or opened. Data can then be put into Pachyderm as part of that open commit and is available once the commit is finished or closed.
Pachyderm provides the following options to load data:
- By using the
pachctl put filecommand. This option is great for testing, development, integration with CI/CD, and for users who prefer scripting. See Load Your Data by Using
By creating a pipeline to pull data from an outside source. Because Pachyderm pipelines can be any arbitrary code that runs in a Docker container, you can call out to external APIs or data sources and pull in data from there. Your pipeline code can be triggered on-demand or continuously with the following special types of pipelines:
- Spout: A spout enables you to continuously load streaming data from a streaming data source, such as a messaging system or message queue into Pachyderm. See Spout.
- Cron: A cron triggers your pipeline periodically based on the interval that you configure in your pipeline spec. See Cron.
Note: Pipelines enable you to do much more than just ingressing data into Pachyderm. Pipelines can run all kinds of data transformations on your input data sources, such as a Pachyderm repository, and be configured to run your code automatically as new data is committed. For more information, see Pipeline.
- By using a Pachyderm language client. This option is ideal for Go or Python users who want to push data into Pachyderm from services or applications written in those languages. If you did not find your favorite language in the list of supported language clients, Pachyderm uses a protobuf API which supports many other languages. See Pachyderm Language Clients.
If you are using the Pachyderm Enterprise version, you can use these additional options:
- By using the S3 gateway. This option is great to use with the existing tools and libraries that interact with S3-compatible object stores. See Using the S3 Gateway.
- By using the Pachyderm dashboard. The Pachyderm Enterprise dashboard provides a convenient way to upload data right from the UI.
In the Pachyderm UI, you can only specify an S3 data source. Uploading data from your local device is not supported.
Load Your Data by Using
pachctl put file command enables you to do everything from loading local files into Pachyderm to pulling data from an existing object store bucket and extracting data from a website. With
pachctl put file, you can append new data to the existing data or overwrite the existing data. All these options can be configured by using the flags available with this command. Run
pachctl put file --help to view the complete list of flags that you can specify.
To load your data into Pachyderm by using
pachctl, you first need to create one or more data repositories. Then, you can use the
pachctl put file command to put your data into the created repository.
In Pachyderm, you can start and finish commits. If you just run
pachctl put file and no open commit exists, Pachyderm starts a new commit, adds the data to which you specified the path in your command, and finishes the commit. This is called an atomic commit.
Alternatively, you can run
pachctl start commit to start a new commit. Then, add your data in multiple
put file calls, and finally, when ready, close the commit by running
pachctl finish commit.
To load your data into a repository, complete the following steps:
Create a Pachyderm repository:
$ pachctl create repo <repo name>
Select from the following options:
To start and finish an atomic commit, run:
$ pachctl put file <repo>@<branch>:</path/to/file1> -f <file1>
To start a commit and add data in iterations:
Start a commit:1. Put your data:
$ pachctl start commit <repo>@<branch>
$ pachctl put file <repo>@<branch>:</path/to/file1> -f <file1>
Work on your changes, and when ready, put more data:
$ pachctl put file <repo>@<branch>:</path/to/file2> -f <file2>
Close the commit:
$ pachctl finish commit <repo>@<branch>
In Pachyderm, you specify the path to file by using the
-f option. A path to file can be a local path or a URL to an external resource. You can add multiple files or directories by using the
-i option. To add contents of a directory, use the
The following table provides examples of
pachctl put file commands with various filepaths and data sources:
Put data from a URL:
$ pachctl put file <repo>@<branch>:</path/to/file> -f http://url_path
Put data from an object store. You can use
as://in your filepath:
$ pachctl put file <repo>@<branch>:</path/to/file> -f s3://object_store_url
If you are configuring a local cluster to access an S3 bucket, you need to first deploy a Kubernetes
Secret for the selected object store.
Add multiple files at once by using the
-ioption or multiple
-fflags. In the case of
-i, the target file must be a list of files, paths, or URLs that you want to input all at once:
$ pachctl put file <repo>@<branch> -i <file containing list of files, paths, or URLs>
Input data from stdin into a data repository by using a pipe:
$ echo "data" | pachctl put file <repo>@<branch> -f </path/to/file>
Add an entire directory or all of the contents at a particular URL, either HTTP(S) or object store URL,
as://, by using the recursive flag,
$ pachctl put file <repo>@<branch> -r -f <dir>
Loading Your Data Partially¶
Depending on your use case, you might decide not to import all of your data into Pachyderm but only store and apply version control to some of it. For example, if you have a 10 PB dataset, loading the whole dataset into Pachyderm is a costly operation that takes a lot of time and resources. To optimize performance and the use of resources, you might decide to load some of this data into Pachyderm, leaving the rest of it in its original source.
One possible way of doing this is by adding a metadata file with a URL to the specific file or directory in your dataset to a Pachyderm repository and refer to that file in your pipeline. Your pipeline code would read the URL or path in the external data source and retrieve that data as needed for processing instead of needing to preload it all into a Pachyderm repo. This method works particularly well for mostly immutable data because in this case, Pachyderm will not keep versions of the source file, but it will keep track and provenance of the resulting output commits in its version-control system.