This page describes importing from versions >= v0.24.0. For ealier versions, see mvcc import

Import data into lakeFS

Table of contents

1. Copying using external tools
2. Limitations
3. Importing data from an object store without actually copying it
   1. Running lakectl ingest with S3 as the source
   2. Running lakectl ingest with Azure Blob storage as the source
   3. Running lakectl ingest with Google Cloud storage as the source
4. Very large buckets: Using lakeFS S3 inventory import tool
   1. Prerequisites
   2. Using the import tool
      1. Merging imported data to the main branch
   3. Gradual Import
   4. Limitations

Copying using external tools

In order to import existing data to lakeFS, you may choose to copy it using S3 CLI or using tools like Apache DistCp. This is the most straightforward way, and we recommend it if it’s applicable for you.

Limitations

Unfortunately, copying data is not always feasible for the following reasons:

1. Some data is just too big to copy.
2. It requires you to stop making changes to the data before starting to copy.
3. It requires you to switch to using the lakeFS endpoint in all places at once.

Importing data from an object store without actually copying it

The lakectl command supports ingesting objects from a source object store without actually copying the data itself. This is done by listing the source bucket (and optional prefix), and creating pointers to the returned objects in lakeFS.

By doing this, it’s possible to take even large sets of objects, and have them appear as objects in a lakeFS branch, as if they were written directly to it.

For this to work, we’d need to ensure 2 things first:

1. The user calling lakectl ingest must have permissions to list the object at the source object store
2. The lakeFS installation must have read permissions to the objects being ingested

Running lakectl ingest with S3 as the source

lakectl ingest \
  --from s3://bucket/optional/prefix/ \
  --to lakefs://my-repo/ingest-branch/optional/path/

The lakectl ingest command will attempt to use the current user’s existing credentials and will respect instance profiles, environment variables and credential files in the same way that the AWS cli does

Running lakectl ingest with Azure Blob storage as the source

export AZURE_STORAGE_ACCOUNT="storageAccountName"
export AZURE_STORAGE_ACCESS_KEY="EXAMPLEroozoo2gaec9fooTieWah6Oshai5Sheofievohthapob0aidee5Shaekahw7loo1aishoonuuquahr3=="
lakectl ingest \
   --from https://storageAccountName.blob.core.windows.net/container/optional/prefix/ \
   --to lakefs://my-repo/ingest-branch/optional/path/

The lakectl ingest command currently supports storage accounts configured through environment variables as shown above.

Note: Currently lakectl import supports the http:// and https:// schemes for Azure storage URIs. wasb, abfs or adls are currently not supported.

Running lakectl ingest with Google Cloud storage as the source

export GOOGLE_APPLICATION_CREDENTIALS="$HOME/.gcs_credentials.json"  # Optional, will fallback to the default configured credentials
lakectl ingest \
   --from gs://bucket/optional/prefix/ \
   --to lakefs://my-repo/ingest-branch/optional/path/

The lakectl ingest command currently supports the standard GOOGLE_APPLICATION_CREDENTIALS environment variable as described in Google Cloud’s documentation.

Very large buckets: Using lakeFS S3 inventory import tool

Importing a very large amount of objects (> ~250M) might take some time using lakectl ingest as described above, since it has to paginate through all the objects in the source using API calls.

For S3, we provide a utility as part of the lakefs binary, called lakefs import.

The lakeFS import tool will use the S3 Inventory feature to create lakeFS metadata. In case the repository is empty, the imported metadata will be committed directly to the main branch. In all other cases, it will be committed to a special branch, called import-from-inventory. You should not make any changes or commit anything to branch import-from-inventory: it will be operated on only by lakeFS. After importing, you will be able to merge this branch into your main branch.

The imported data is not copied to the repository’s dedicated bucket. Rather, it will be read directly from your existing bucket when you access it through lakeFS. Files created or replaced through lakeFS will then be stored in the repository’s dedicated bucket.

It is important to note that due to the deduplication feature of lakeFS, data will stay in your original bucket even when accessing it through other branches. In a sense, your original bucket becomes an initial snapshot of your data.

Note: lakeFS will never make any changes to the import source bucket.

Prerequisites

• Your bucket should have S3 Inventory enabled.
• The inventory should be in Parquet or ORC format.
• The inventory must contain (at least) the size, last-modified-at, and e-tag columns.
• The S3 credentials you provided to lakeFS should have GetObject permissions on the source bucket and on the bucket where the inventory is stored.
• If you want to use the tool for gradual import, you should not delete the data for the most recently imported inventory, until a more recent inventory is successfully imported.

Using the import tool

Import is performed by lakefs ‘s import command.

Assuming your manifest.json is at s3://example-bucket/path/to/inventory/YYYY-MM-DDT00-00Z/manifest.json, and your lakeFS configuration yaml is at config.yaml (see notes below), run the following command to start the import:

lakefs import lakefs://example-repo -m s3://example-bucket/path/to/inventory/YYYY-MM-DDT00-00Z/manifest.json --config config.yaml

You will see the progress of your import as it is performed. After the import is finished, a summary will be printed along with suggestions for commands to access your data.

Added or changed objects: 565000
Deleted objects: 0
Commit ref: cf349ded0a0e65e20bd3b25ea8d9b656c2870b7f1f32f60eb1d90ca5873b6c03

Import to branch import-from-inventory finished successfully.
To list imported objects, run:
	$ lakectl fs ls lakefs://example-repo/cf349ded0a0e65e20bd3b25ea8d9b656c2870b7f1f32f60eb1d90ca5873b6c03/
To merge the changes to your main branch, run:
	$ lakectl merge lakefs://example-repo/import-from-inventory lakefs://goo/main

Merging imported data to the main branch

As previously mentioned, the above command imports data to the dedicated import-from-inventory branch. By adding the --with-merge flag to the import command, this branch will be automatically merged to your main branch immediately after the import.

lakefs import --with-merge lakefs://example-repo -m s3://example-bucket/path/to/inventory/YYYY-MM-DDT00-00Z/manifest.json --config config.yaml

Notes

1. Perform the import from a machine with access to your database, and on the same region of your destination bucket.

2. You can download the lakefs binary from here. Make sure you choose one compatible with your installation of lakeFS.

3. Use a configuration file like the one used to start your lakeFS installation. This will be used to access your database. An example can be found here.

Warning: the import-from-inventory branch should only be used by lakeFS. You should not make any operations on it.

Gradual Import

Once you switch to using the lakeFS S3-compatible endpoint in all places, you can stop making changes to your original bucket. However, if your operation still requires that you work on the original bucket, you can repeat using the import API with up-to-date inventories every day, until you complete the onboarding process. You can specify only the prefixes that require import. lakeFS will merge those prefixes with the previous imported inventory. For example, a prefixes-file that contains only the prefix new/data/. The new commit to import-from-inventory branch will include all objects from the HEAD of that branch, except for objects with prefix new/data/ that is imported from the inventory.

Limitations

Note that lakeFS cannot manage your metadata if you make changes to data in the original bucket. The following table describes the results of making changes in the original bucket, without importing it to lakeFS: