debian-mirror-gitlab/doc/architecture/blueprints/ci_scale/index.md

stage	group	comments	description
none	unassigned	false	Improve scalability of GitLab CI/CD

CI/CD Scaling

Summary

GitLab CI/CD is one of the most data and compute intensive components of GitLab. Since its initial release in November 2012, the CI/CD subsystem has evolved significantly. It was integrated into GitLab in September 2015 and has become one of the most beloved CI/CD solutions.

GitLab CI/CD has come a long way since the initial release, but the design of the data storage for pipeline builds remains almost the same since 2012. We store all the builds in PostgreSQL in ci_builds table, and because we are creating more than 5 million builds each day on GitLab.com we are reaching database limits that are slowing our development velocity down.

On February 1st, 2021, GitLab.com surpassed 1 billion CI/CD builds created. In February 2022 we reached 2 billion of CI/CD build stored in the database. The number of builds continues to grow exponentially.

The screenshot below shows our forecast created at the beginning of 2021, that turned out to be quite accurate.

Goals

Enable future growth by making processing 20M builds in a day possible.

Challenges

The current state of CI/CD product architecture needs to be updated if we want to sustain future growth.

We were running out of the capacity to store primary keys: DONE

The primary key in ci_builds table is an integer value, generated in a sequence. Historically, Rails used to use integer type when creating primary keys for a table. We did use the default when we created the ci_builds table in 2012. The behavior of Rails has changed since the release of Rails 5. The framework is now using bigint type that is 8 bytes long, however we have not migrated primary keys for ci_builds table to bigint yet.

In early 2021 we had estimated that would run out of the capacity of the integer type to store primary keys in ci_builds table before December 2021. If it had happened without a viable workaround or an emergency plan, GitLab.com would go down. ci_builds was just one of many tables that were running out of the primary keys available in Int4 sequence.

Before October 2021, our Database team had managed to migrate all the risky tables' primary keys to big integers.

See the related Epic for more details.

Some CI/CD database tables are too large: IN PROGRESS

There is more than two billion rows in ci_builds table. We store many terabytes of data in that table, and the total size of indexes is measured in terabytes as well.

This amount of data contributes to a significant number of performance problems we experience on our CI PostgreSQL database.

Most of the problems are related to how PostgreSQL database works internally, and how it is making use of resources on a node the database runs on. We are at the limits of vertical scaling of the CI primary database nodes and we frequently see a negative impact of the ci_builds table on the overall performance, stability, scalability and predictability of the CI database GitLab.com depends on.

The size of the table also hinders development velocity because queries that seem fine in the development environment may not work on GitLab.com. The difference in the dataset size between the environments makes it difficult to predict the performance of even the most simple queries.

Team members and the wider community members are struggling to contribute the Verify area, because we restricted the possibility of extending ci_builds even further. Our static analysis tools prevent adding more columns to this table. Adding new queries is unpredictable because of the size of the dataset and the amount of queries executed using the table. This significantly hinders the development velocity and contributes to incidents on the production environment.

We also expect a significant, exponential growth in the upcoming years.

One of the forecasts done using Facebook's Prophet shows that in the first half of 2024 we expect seeing 20M builds created on GitLab.com each day. In comparison to around 5M we see created today. This is 10x growth from numbers we saw in 2021.

Status: As of October 2021 we reduced the growth rate of ci_builds table by writing build options and variables to ci_builds_metadata table. We are also working on partitioning the largest CI/CD database tables using time decay pattern.

Queuing mechanisms were using the large table: DONE

Because of how large the table is, mechanisms that we used to build queues of pending builds (there is more than one queue), were not very efficient. Pending builds represented a small fraction of what we store in the ci_builds table, yet we needed to find them in this big dataset to determine an order in which we wanted to process them.

This mechanism was very inefficient, and it had been causing problems on the production environment frequently. This usually resulted in a significant drop of the CI/CD Apdex score, and sometimes even caused a significant performance degradation in the production environment.

There were multiple other strategies that we considered to improve performance and reliability. We evaluated using Redis queuing, or a separate table that would accelerate SQL queries used to build queues. We decided to proceed with the latter.

In October 2021 we finished shipping the new architecture of builds queuing on GitLab.com. We then made the new architecture generally available.

Moving big amounts of data is challenging: IN PROGRESS

We store a significant amount of data in ci_builds table. Some of the columns in that table store a serialized user-provided data. Column ci_builds.options stores more than 600 gigabytes of data, and ci_builds.yaml_variables more than 300 gigabytes (as of February 2021).

It is a lot of data that needs to be reliably moved to a different place. Unfortunately, right now, our background migrations are not reliable enough to migrate this amount of data at scale. We need to build mechanisms that will give us confidence in moving this data between columns, tables, partitions or database shards.

Effort to improve background migrations will be owned by our Database Team.

Status: In progress. We plan to ship further improvements that will be described in a separate architectural blueprint.

Proposal

Below you can find the original proposal made in early 2021 about how we want to move forward with CI Scaling effort:

Making GitLab CI/CD product ready for the scale we expect to see in the upcoming years is a multi-phase effort.

First, we want to focus on things that are urgently needed right now. We need to fix primary keys overflow risk and unblock other teams that are working on database partitioning and sharding.

We want to improve known bottlenecks, like builds queuing mechanisms that is using the large table, and other things that are holding other teams back.

Extending CI/CD metrics is important to get a better sense of how the system performs and to what growth should we expect. This will make it easier for us to identify bottlenecks and perform more advanced capacity planning.

Next step is to better understand how we can leverage strong time-decay characteristic of CI/CD data. This might help us to partition CI/CD dataset to reduce the size of CI/CD database tables.

Iterations

Work required to achieve our next CI/CD scaling target is tracked in the CI/CD Scaling epic.

1. ✓ Migrate primary keys to big integers on GitLab.com.
2. ✓ Implement the new architecture of builds queuing on GitLab.com.
3. ✓ Make the new builds queuing architecture generally available.
4. Partition CI/CD data using time-decay pattern.

Status

Created at 21.01.2021, approved at 26.04.2021.

Status: In progress.

Who

Proposal:

Role	Who
Author	Grzegorz Bizon
Architecture Evolution Coach	Kamil Trzciński
Engineering Leader	Cheryl Li
Product Manager	Jackie Porter
Domain Expert / Verify	Fabio Pitino
Domain Expert / Database	Jose Finotto
Domain Expert / PostgreSQL	Nikolay Samokhvalov

DRIs:

Role	Who
Leadership	Cheryl Li
Product	Jackie Porter
Engineering	Grzegorz Bizon

Domain experts:

Area	Who
Domain Expert / Verify	Fabio Pitino
Domain Expert / Verify	Marius Bobin
Domain Expert / Database	Jose Finotto
Domain Expert / PostgreSQL	Nikolay Samokhvalov