These are my notes from the DevOps Stuttgart Meetup: CI/CD for relational databases with Jasmin Fluri

The slides for this talk can be found here.

A short story about a DB Change

A team with

  • 1 DB engineer
  • rest: application engineer / developer
  • lots of turnover in the team

Tech stack:

  • Spring boot app / relational DB
  • CI/CD in place for the app, but not for the DB


  • avoid DB changes whenever possible
  • if totally can’t avoid, someone needs to write a migration script, send to DBA via mail, executed manually


  • scripts were not in VCS
  • it was never clear, when the DBA applies the change
  • no automated (regression) testing

What happens if DB changes are avoided

see Database decay and how to avoid it (Stonebraker et al 2016 )

What we know about releasing SW

  • DORA report
  • CI/CD profits from losely coupled architecture
  • without proper architecture, you don’t benefit from CI/CD

4 (5) key metrics

  • lead for changes (starts with implementing a feature, ends with feature in prod)
  • MTTR (time needed to fix a failure in production)
  • change failure rate (how often does a deployment fail)
  • deployment frequency (the more, the better because features will be slow, low risk)
  • deployment reliability is opposite of change failure rate (as a corollary to change failure rate)

High performing teams and delivery capabilities

High performing teams are able to ship their changes in smaller increments:

  • small change, small impact, lower risk, faster feedback
  • small changes, easier to undo

Goal of SW release

  • ship small features continuously
  • but this is not very common in database development
  • DB teams are used to ship large changes, because the automation does not support a process with smaller changes
  • that is where CI/CD comes to help

DB development in numbers

  • 50% of DB applications have no QA for database testing (data quality / application testing)
  • < 50% have no automated development workflows
  • lack of DB automation is one of the most common bottlenecks in releasing changes
  • < 30% of DB dev have automated testing and static code analysis

CI/CD for apps

  • dev make change
  • dev pushes to VCS
  • triggers pipeline
  • CI server executes CI pipeline
  • pipeline generates new version, replaces an old version
  • no state in building the application
    • it is always build from scratch
    • application deployment is usually immutable

CI/CD for DB

  • dev makes change in file, generated code (migration script), exported code (diffs, statements…) - many methods to be considered here
  • all the DB changes in VCS have a defined order (in which they later on need to be executed)
  • builds always run against a physical instance of a DB
  • there is usually / always state involved - a lot of state!!!
    • you need a database ready with a state to test your changes

5 pre conditions for DB CI/CD

1. Version control

  • w/o VCS there is no single source of truth
  • dir structure of repo depends on migration tool used
  • DDLs for “target state structure”
  • usually delta files are used to transition from one DB state to the other

  • how do you keep DDLs and migration scripts in sync?

  • liquibase only DDLs?
    • renaming is an operation where you need a delta script (renaming can’t be derived from DDL only)
  • test code and test data (also belongs in repo)

BUT: pure file-based dev is uncommon in DB development

  • choice of migration tool will affect how you store and order your sourc code
  • “passive version control”: because active part is in the database
    • risk to forget exporting things that we changed inside the DB
    • changes made to dev environment are also exported correctly
  • often, scripts for DB changes are generated from changes made manually to the DB

Repeatability of migration scripts

  • most of all: atomicity / idempotency of migration scripts
  • if not repeatable: if fails midway, we only want to apply the changes that have not yet been applied before sth failed
  • if it fails, we want no new migration scripts, but want the

Branching strategy

  • DB changes are related to infra changes, build on each other, only make sense in in sequence
  • testing on feature branches becomes useless
    • WHY? because the underlying DB state might long have changed
  • trunk-based development to the rescue
    • forces people to do small changes
    • testing on main makes sure people know what happens previously
    • only one path to reach “current state”
  • access source code in Delivery pipeline
    • breaks (least privilege) single responsibility for CD pipeline
  • code reviews with tbd
    • async code review takes lots of time
  • long lead time for merge requests

2. Static code analysis

  • before linting & SCA: you need coding guidelines
    • save time discussing coding guidelines in MRs (let the linter do the work)
  • not much adoption of linters for SQL / DDL
    • assumption: too big of a rule set for linters

3. Database migration tools

  • initial state: applying initial DDL
  • after that: applying DDL & DML
  • database schema evolution
  • 2 tools: flyway and liquibase
  • don’t write a migration tool yourself
  • if you use Liquibase: don’t use XML (or JSON or YAML)

4. Automated DB tests

  • projects without automated testing spends 25% of team capacity for manual testing
  • possibility 1: app code for DB testing (junit and dbunit)
  • possibility 2: unit testing in the database (pgTAP, utplsql)

5. Decoupled codebase

  • between app and DB
  • coupled code bases require organised releases across teams
    • every release becomes a big issue
    • this is not CD!
    • people have to wait for others to release their changes
  • why is it important to deploy changes at any time?
    • we don’t wanna work outside business hours
    • reduce stress
  • abstraction layer
    • between app and DB - DB API
    • needs to be versioned
    • no direct access to a table
  • see DB schema as an API, changes to it can be breaking for consumers
    • if DB changes, the app has to adopt the changes
  • decoupling means: provide a versioned access layer
    • app A can access version 1 of my DB API
    • can switch from version 1 to version 2 of DB API when ready
  • API could be views and procedures that can be versioned

CI/CD pipelines for databases

  • development environment by the click of a button
    • if this deviates a lot from the target, production DB, causes pain –> “DB version drift”
    • no infra resemblance necessary, schem resemblance is sufficient (eg. Docker container)
  • check code, before you deploy it
    • applies to coding guidelines
  • store artefacts in artefact storage after CI
    • no more repo access in CD
  • do a backup of your environment before you deploy a change
    • with a DB you can’t “deploy” a previous artefact - your data will be gone without a backup
  • deploy SQL code

Effects of CI/CD in database development

  • not automating integration and deployment is a change preventer
    • releases tend to get bigger
  • with automation, have independency to deploy changes
    • without relying on other people
  • number of deployments increased significantly after introducing automated pipelines
    • with more releases, changesets got smaller
  • Change failure rate decreased by 75% with automation pipelines
  • Cognitive load of developers decreased with automation pipelines
    • interviews
    • less stress


  • code examples for DB tests
    • is there a reason to test, if the DB is rather simple?
    • there is more than tables in a DB, that requires testing
      • eg. assure that the procedures I write do what I expect
  • indication for DB abstraction layer
  • differences between nosql vs relational DBs when it comes to CI/CD and migration
    • most nosql don’t allow for much functionality to be developed in the DB
  • what is a good boundary between DB config (eg. managed in Terraform) / and DB schema (managed in DDL / SQL)
    • TF everything that’s not in the schema eg. users