Syncthing Amazon S3

This page provides you with instructions on how to extract data from GitLab and load it into Amazon S3. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

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What is GitLab?

GitLab offers a web-based Git repository manager with version control and issue tracking features.

What is S3?

Amazon S3 (Simple Storage Service) provides cloud-based object storage through a web service interface. You can use S3 to store and retrieve any amount of data, at any time, from anywhere on the web. S3 objects, which may be structured in any way, are stored in resources called buckets.

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Getting data out of GitLab

GitLab provides a REST API, but it says, 'Going forward, we will start on moving to GraphQL and deprecate the use of controller-specific endpoints.'

Most of the items stored in GitLab are accessible through the API. Dozens of items are on the list, including merge requests, project milestones, and todos. As an example, to get a list of repository branches for a particular project, you could call GET /projects/[id]/repository/branches.

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Sample GitLab data

GitLab returns information in JSON format. Each JSON object may contain more than a dozen attributes, which you have to parse before loading the data into your data warehouse. Stitch provides documentation on some of the GitLab table schemas. Here's an example of what some of the data for that call to return all tickets might look like:


Preparing GitLab data

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If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. GitLab's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be 'flat' – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Amazon S3

To upload files you must first create an S3 bucket. Once you have a bucket you can add an object to it. An object can be any kind of file: a text file, data file, photo, or anything else. You can optionally compress or encrypt the files before you load them.

Keeping GitLab data up to date

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At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in GitLab.

And remember, as with any code, once you write it, you have to maintain it. If GitLab modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to. If GitLab makes the REST API obsolete and moves ahead solely with GraphQL, you may have to start from scratch.

Other data warehouse options

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S3 is great, but sometimes you want a more structured repository that can serve as a basis for BI reports and data analytics — in short, a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, Microsoft Azure Synapse Analytics, or Panoply, which are RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure Synapse Analytics, and To Panoply.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.


Syncthing Amazon S3 App

Thankfully, products like Stitch were built to move data from GitLab to Amazon S3 automatically. With just a few clicks, Stitch starts extracting your GitLab data, structuring it in a way that's optimized for analysis, and inserting that data into your Amazon S3 data warehouse.

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