Pipeline data partitioning design

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What problem are we trying to solve?

We want to partition the CI/CD dataset, because some of the database tables are extremely large, which might be challenging in terms of scaling single node reads, even after we ship the CI/CD database decomposition.

We want to reduce the risk of database performance degradation by transforming a few of the largest database tables into smaller ones using PostgreSQL declarative partitioning.

See more details about this effort in the parent blueprint.

pipeline data time decay

CI/CD decomposition is an extraction of a CI/CD database cluster out of the “main” database cluster, to make it possible to have a different primary database receiving writes. The main benefit is doubling the capacity for writes and data storage. The new database cluster will not have to serve reads / writes for non-CI/CD database tables, so this offers some additional capacity for reads too.

CI/CD partitioning is dividing large CI/CD database tables into smaller ones. This will improve reads capacity on every CI/CD database node, because it is much less expensive to read data from small tables, than from large multi-terabytes tables. We can add more CI/CD database replicas to better handle the increase in the number of SQL queries that are reading data, but we need partitioning to perform a single read more efficiently. Performance in other aspects will improve too, because PostgreSQL will be more efficient in maintaining multiple small tables than in maintaining a very large database table.

CI/CD time-decay allows us to benefit from the strong time-decay characteristics of pipeline data. It can be implemented in many different ways, but using partitioning to implement time-decay might be especially beneficial. When implementing a time decay we usually mark data as archived, and migrate it out of a database to a different place when data is no longer relevant or needed. Our dataset is extremely large (tens of terabytes), so moving such a high volume of data is challenging. When time-decay is implemented using partitioning, we can archive the entire partition (or set of partitions) by simply updating a single record in one of our database tables. It is one of the least expensive ways to implement time-decay patterns at a database level.


Why do we need to partition CI/CD data?

We need to partition CI/CD data because our database tables storing pipelines, builds, and artifacts are too large. The ci_builds database table size is currently around 2.5 TB with an index of around 1.4 GB. This is too much and violates our principle of 100 GB max size. We also want to build alerting to notify us when this number is exceeded.

We’ve seen numerous S1 and S2 database-related production environment incidents, over the last couple of months, for example:

We have approximately 50 ci_* prefixed database tables, and some of them would benefit from partitioning.

A simple SQL query to get this data:

WITH tables AS (SELECT table_name FROM information_schema.tables WHERE table_name LIKE 'ci_%')
  SELECT table_name,
    pg_size_pretty(pg_total_relation_size(quote_ident(table_name))) AS total_size,
    pg_size_pretty(pg_relation_size(quote_ident(table_name))) AS table_size,
    pg_size_pretty(pg_indexes_size(quote_ident(table_name))) AS index_size,
    pg_total_relation_size(quote_ident(table_name)) AS total_size_bytes
  FROM tables ORDER BY total_size_bytes DESC;

See data from March 2022:

Table name Total size Index size
ci_builds 3.5 TB 1 TB
ci_builds_metadata 1.8 TB 150 GB
ci_job_artifacts 600 GB 300 GB
ci_pipelines 400 GB 300 GB
ci_stages 200 GB 120 GB
ci_pipeline_variables 100 GB 20 GB
(…around 40 more)    

Based on the table above, it is clear that there are tables with a lot of stored data.

While we have almost 50 CI/CD-related database tables, we are initially interested in partitioning only 6 of them. We can start by partitioning the most interesting tables in an iterative way, but we also should have a strategy for partitioning the remaining ones if needed. This document is an attempt to capture this strategy, describe as many details as possible, to share this knowledge among engineering teams.

How do we want to partition CI/CD data?

We want to partition the CI/CD tables in iterations. It might not be feasible to partition all of the 6 initial tables at once, so an iterative strategy might be necessary. We also want to have a strategy for partitioning the remaining database tables when it becomes necessary.

It is also important to avoid large data migrations. We store almost 6 terabytes of data in the biggest CI/CD tables, in many different columns and indexes. Migrating this amount of data might be challenging and could cause instability in the production environment. Due to this concern, we’ve developed a way to attach an existing database table as a partition zero without downtime and excessive database locking, what has been demonstrated in one of the first proofs of concept. This makes creation of a partitioned schema possible without a downtime (for example using a routing table p_ci_pipelines), by attaching an existing ci_pipelines table as partition zero without exclusive locking. It will be possible to use the legacy table as usual, but we can create the next partition when needed and the p_ci_pipelines table will be used for routing queries. To use the routing table we need to find a good partitioning key.

Our plan is to use logical partition IDs. We want to start with the ci_pipelines table and create a partition_id column with a DEFAULT value of 100 or 1000. Using a DEFAULT value avoids the challenge of backfilling this value for every row. Adding a CHECK constraint prior to attaching the first partition tells PostgreSQL that we’ve already ensured consistency and there is no need to check it while holding an exclusive table lock when attaching this table as a partition to the routing table (partitioned schema definition). We will increment this value every time we create a new partition for p_ci_pipelines, and the partitioning strategy will be LIST partitioning.

We will also create a partition_id column in the other initial 6 database tables we want to iteratively partition. After a new pipeline is created, it will get a partition_id assigned, and all the related resources, like builds and artifacts, will share the same value. We want to add the partition_id column into all 6 problematic tables because we can avoid backfilling this data when we decide it is time to start partitioning them.

We want to partition CI/CD data iteratively, so we will start with the pipelines table, and create at least one, but likely two, partitions. The pipelines table will be partitioned using the LIST partitioning strategy. It is possible that, after some time, p_ci_pipelines will store data in two partitions with IDs of 100 and 101. Then we will try partitioning ci_builds. Therefore we might want to use RANGE partitioning in p_ci_builds with IDs 100 and 101, because builds for the two logical partitions used will still be stored in a single table.

Physical partitioning and logical partitioning will be separated, and a strategy will be determined when we implement partitioning for the respective database tables. Using RANGE partitioning works similarly to using LIST partitioning in database tables other than ci_pipelines, but because we can guarantee continuity of partition_id values, using RANGE partitioning might be a better strategy.

Why do we want to use explicit logical partition ids?

Partitioning CI/CD data using a logical partition_id has several benefits. We could partition by a primary key, but this would introduce much more complexity and additional cognitive load required to understand how the data is being structured and stored in partitions.

CI/CD data is hierarchical data. Stages belong to pipelines, builds belong to stages, artifacts belong to builds (with rare exceptions). We are designing a partitioning strategy that reflects this hierarchy, to reduce the complexity and therefore cognitive load for contributors. With an explicit partition_id associated with a pipeline, we can cascade the partition ID number when trying to retrieve all resources associated with a pipeline. We know that for a pipeline 12345 with a partition_id of 102, we are always able to find associated resources in logical partitions with number 102 in other routing tables, and PostgreSQL will know in which partitions these records are being stored in for every table.

Another interesting benefit for using a single and incremental latest partition_id number, associated with pipelines, is that in theory we can cache it in Redis or in memory to avoid excessive reads from the database to find this number, though we might not need to do this.

The single and uniform partition_id value for pipeline data gives us more choices later on than primary-keys-based partitioning.

Splitting large partitions into smaller ones

We want to start with the initial pipeline_id number 100 (or higher, like 1000, depending on our calculations and estimations). We do not want to start from 1, because existing tables are also large already, and we might want to split them into smaller partitions. If we start with 100, we will be able to create partitions for partition_id of 1, 20, 45, and move existing records there by updating partition_id from 100 to a smaller number.

PostgreSQL will move these records into their respective partitions in a consistent way, provided that we do it in a transaction for all pipeline resources at the same time. If we ever decide to split large partitions into smaller ones (it’s not yet clear if we will need to do this), we might be able to just use background migrations to update partition IDs, and PostgreSQL is smart enough to move rows between partitions on its own.

Storing partitions metadata in the database

In order to build an efficient mechanism that will be responsible for creating new partitions, and to implement time decay we want to introduce a partitioning metadata table, called ci_partitions. In that table we would store metadata about all the logical partitions, with many pipelines per partition. We may need to store a range of pipeline ids per logical partition. Using it we will be able to find the partition_id number for a given pipeline ID and we will also find information about which logical partitions are “active” or “archived”, which will help us to implement a time-decay pattern using database declarative partitioning.

ci_partitions table will store information about a partition identifier, pipeline ids range it is valid for and whether the partitions have been archived or not. Additional columns with timestamps may be helpful too.

Implementing a time-decay pattern using partitioning

We can use ci_partitions to implement a time-decay pattern using declarative partitioning. By telling PostgreSQL which logical partitions are archived we can stop reading from these partitions using a SQL query like the one below.

SELECT * FROM ci_builds WHERE partition_id IN (
  SELECT id FROM ci_partitions WHERE active = true

This query will make it possible to limit the number of partitions we will read from, and therefore will cut access to “archived” pipeline data, using our data retention policy for CI/CD data. Ideally we do not want to read from more than two partitions at once, so we need to align the automatic partitioning mechanisms with the time-decay policy. We will still need to implement new access patterns for the archived data, presumably through the API, but the cost of storing archived data in PostgreSQL will be reduced significantly this way.

There are some technical details here that are out of the scope of this description, but by using this strategy we can “archive” data, and make it much less expensive to reside in our PostgreSQL cluster by simply toggling a boolean column value.

Accessing partitioned data

It will be possible to access partitioned data whether it has been archived or not, in most places in GitLab. On a merge request page, we will always show pipeline details even if the merge request was created years ago. We can do that because