Next CI/CD scale target: 20M builds per day by 2024


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 2 million builds each day on, we are reaching database limits that are slowing our development velocity down.

On February 1st, 2021, a billionth CI/CD job was created and the number of builds is growing exponentially. We will run out of the available primary keys for builds before December 2021 unless we improve the database model used to store CI/CD data.

We expect to see 20M builds created daily on in the first half of 2024.

ci_builds cumulative with forecast


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


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

We are running out of the capacity to store primary keys

The primary key in ci_builds table is an integer 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.

We will run out of the capacity of the integer type to store primary keys in ci_builds table before December 2021. When it happens without a viable workaround or an emergency plan, will go down.

ci_builds is just one of the tables that are running out of the primary keys available in Int4 sequence. There are multiple other tables storing CI/CD data that have the same problem.

Primary keys problem will be tackled by our Database Team.

The table is too large

There is more than a billion rows in ci_builds table. We store more than 2 terabytes of data in that table, and the total size of indexes is more than 1 terabyte (as of February 2021).

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

Most of the problem 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 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 database depends on.

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

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 each day. In comparison to around 2M we see created today, this is 10x growth our product might need to sustain in upcoming years.

ci_builds daily forecast

Queuing mechanisms are using the large table

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

This mechanism is very inefficient, and it has been causing problems on the production environment frequently. This usually results in a significant drop of the CI/CD apdex score, and sometimes even causes a significant performance degradation in the production environment.

There are multiple other strategies that can improve performance and reliability. We can use Redis queuing, or a separate table that will accelerate SQL queries used to build queues and we want to explore them.

Moving big amounts of data is challenging

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.

Development velocity is negatively affected

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.


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 situation around bottlenecks that are known already, like queuing mechanisms using the large table and 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.

As we work on first iterations we expect our Database Sharding team and Database Scalability Working Group to make progress on patterns we will be able to use to partition the large CI/CD dataset. We consider the strong time-decay effect, related to the diminishing importance of pipelines with time, as an opportunity we might want to seize.


Work required to achieve our next CI/CD scaling target is tracked in the GitLab CI/CD 20M builds per day scaling target epic.


In progress.



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


Role Who
Leadership Darby Frey
Product Jackie Porter
Engineering Grzegorz Bizon

Domain experts:

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