This page documents the CI jobs used by the data team in Merge Requests in both the Data Tests and Analytics projects.
This kind of error pops up in the pipeline like KeyError: 'GITLAB_COM_CI_DB_USER'. It means the variable is not defined in the variable section of CI/CD Settings. To resolve this, add the variable name to CI/CD setting i.e. settings –> ci_cd –> variable, also provide the variable value.
Notes:- Turn off the Flags, so the variable is accessible from the CI pipeline.
The same applies to the variable value; if it is incorrect in the job, we can update it in the above link.
CI jobs are grouped by stages.
These jobs are defined in .gitlab-ci.yml.
Run this if you need a clone of any schema available in the prep database. Specify which schema to clone with the SCHEMA_NAME variable. If the clone already exists, this will do nothing.
Run this if you need a clone of any schema available in the prod database. Specify which schema to clone with the SCHEMA_NAME variable. If the clone already exists, this will do nothing.
Runs automatically when the MR opens to be able to run any dbt jobs. Subsequent runs of this job will be fast as it only verifies if the clone exists. This is an empty clone of the prod and prep databases.
Run this if you need to do a real clone of the prod and prep databases. This is a full clone both databases.
Run this if you need to run extract, freshness, or snapshot jobs. Subsequent runs of this job will be fast as it only verifies if the clone exists.
Run this if you only need a clone of the raw tap_postgres schema in order to test changes to postgres pipeline or a manifest file. If the raw clone already exists, this will do nothing.
Run this if you only need a clone of the raw sheetload schema in order to test changes or additions to sheetload. If the raw clone already exists, this will do nothing.
Run this if you need a clone of any other raw schema in order to test changes or additions. Specify which raw schema to clone with the SCHEMA_NAME variable. If the raw clone already exists, this will do nothing.
Run this if you want to force refresh raw, prod, and prep. This does a full clone of raw, but a shallow clone of prep and prod.
These jobs are defined in extract-ci.yml
Run this if you want to test a new boneyard sheetload load. This requires the real prod and prep clones to be available.
Run this if you want to test a new sheetload load. This jobs runs against the clone of RAW. Requires the clone_raw job to have been run.
With the exception of gitlab-ops DB, run this if you're adding or updating a postgres pipeline manifest. Requires a clone of RAW and the MANIFEST_NAME variable, possibly TASK_INSTANCE variable if it's an SCD table.
The MANIFEST_NAME is not the same as the manifest filename. Its the filename except postfix _db_manifest.yaml.
Run this if you're updating the gitlab-ops manifest file. Requires a clone of RAW. Does not require any variables, except possibly a TASK_INSTANCE variable if it's an SCD table. This is separate from the pgp_test job because it requires a CloudSQL Proxy to be running in order to connect to the database.
When making any DBT changes, 🔆⚡️clone_model_dbt_select should be your first pipeline run to set up the environment, followed by either the run_changed or specify_model pipeline (to test & validate).
The following jobs use the same selection syntax as the regular DBT runs, but they use this to begin a SnowFlake cloning operation for the DBT lineage provided. In the case of 🔆⚡️clone_model_dbt_select it is far faster, cheaper, and can handle a much greater data volume than the regular DBT runs, because they do not actually run DBT.
Specify which model to run with the variable DBT_MODELS. Clones all models in the provided selection. Does not run any DBT tests or validation. This job will fail for the same errors as the existing DBT process (i.e. ensure that you have selected the correct lineage above your model).
These jobs are defined in snowflake-dbt-ci.yml
As part of a DBT Model Change MR, you need to trigger a pipeline job to test that your changes won't break anything in production. To trigger these jobs, go to the "Pipelines" tab at the bottom of this MR and click on the appropriate stage (dbt_run or dbt_misc).
These jobs are scoped to the ci target. This target selects a subset of data for the snowplow and version datasets.
Note that job artifacts are available for all dbt run jobs. These include the compiled code and the run results.
These jobs run against the primary RAW database.
Most dbt run jobs can be parameterized with a variable specifying dbt model that requires testing. Watch this intro to see an example of how to set the variable.
The variable DBT_MODELS is a stand-in for any of the examples in the dbt documentation on model selection syntax.
All dbt ci jobs run in --full-refresh. If you'd like to override this and run incremental models as incremental then set the REFRESH variable to a space ` `.
If you are testing changes to tests in the data-tests project, you can pass in DATA_TEST_BRANCH to the manual jobs along with the branch name. This will update the branch in the packages.yml for the data-tests package. This works for any job running dbt test.
You can also add --fail-fast to the end of the model selection to quickly end the dbt call at the first failure. Read the dbt docs for more information.
Specify which model to run with the variable DBT_MODELS
Specify which model to run using an L warehouse with the variable DBT_MODELS
Specify which model to run using an XL warehouse with the variable DBT_MODELS
Specify how to run dbt using the variable DBT_PARAMETERS. This job essentially just puts everything in DBT_PARAMETERS variable into a command after dbt run, and can be used for excluding models.
Specify a dbt model against the clone of the RAW database. This jobs runs against the clone of RAW. Requires the clone_raw job to have been run. This is useful for the following scenarios:
This job tests specific seed file.
Specify seed file with the variable DBT_MODELS.
Specify which snapshot to run with the variable DBT_MODELS.
This jobs runs against the clone of RAW. Requires the clone_raw job to have been run.
Specify which snapshot to run with the variable DBT_MODELS.
This jobs runs against the clone of RAW, using a large SnowFlake warehouse. Requires the clone_raw job to have been run.
Runs all the models in the MR diff whose SQL has been edited. Does not pickup changes to schema.yml / source.yml, only .sql files.
ANCESTOR_TYPE variable along with either the @ or + operator. The operator is inserted before the models.DEPENDENT_TYPE variable along with either the @ or + operator. The operator is inserted after the models.Runs all the models in the MR diff whose SQL has been edited against an L warehouse. Does not pickup changes to schema.yml / source.yml, only .sql files.
ANCESTOR_TYPE variable along with either the @ or + operator. The operator is inserted before the models.DEPENDENT_TYPE variable along with either the @ or + operator. The operator is inserted after the models.Runs all the models in the MR diff whose SQL has been edited against an XL warehouse. Does not pickup changes to schema.yml / source.yml, only .sql files.
ANCESTOR_TYPE variable along with either the @ or + operator. The operator is inserted before the models.DEPENDENT_TYPE variable along with either the @ or + operator. The operator is inserted after the models.If you want to run a dbt job via the specify_*_model or specify_*_exclude, you have the possibility to choose the size of the Snowflake warehouse you want to use in the CI job. Starting with XS, followed by L and last you can select XL size warehouse. This can be done to trigger different CI Jobs.
specify_model is using a XS warehouse.specify_l_model is using a L warehouse.specify_xl_model is using a XL warehouse.Using a bigger warehouse will result in shorter run time (and prevents timing out of large models), but also results in bigger costs for GitLab. You start with the regular specify_model CI Job. If this is not suitable, you can move over to the specify_l_model and alternatively you can use the specify_xl_model. Of course there can be a good reason to use a bigger warehouse, if there are complex transformations or lots of data to be processed more power is required. But always also please check your model. Maybe the model can be adjusted to run more efficiently. Running your test on a bigger warehouse will not trigger instant costs on Snowflake only on this CI Job, but it also could run inefficiently in production and could have a much bigger impact for the long run.
These jobs are defined in snowflake-dbt-ci.yml
Runs all the tests
Runs only data tests
Runs source freshness test. This jobs runs against the clone of RAW. Requires the clone_raw job to have been run.
This job runs automatically and only appears when .sql files are changed. In its simplest form, the job will check to see if any of the currently changed models are queried in Periscope. If they are, the job will fail with a notification to check the relevant dashboard. If it is not queried, the job will succeed.
Current caveats with the job are:
retention_[some_variable])This section explains how the periscope query works.
git clone -b periscope/master --single-branch https://gitlab.com/gitlab-data/periscope.git --depth 1
This clones the periscope project.
git diff origin/$CI_MERGE_REQUEST_TARGET_BRANCH_NAME...HEAD --name-only | grep -iEo "(.*)\.sql" | sed -E 's/\.sql//' | awk -F '/' '{print tolower($NF)}' | sort | uniq > diff.txt
This gets the list of files that have changed from the master branch (i.e. target branch) to the current commit (HEAD). It then finds (grep) only the sql files and substitutes (sed) the .sql with an empty string. Using awk, it then prints the lower-case of the last column of each line in a file (represented by $NF - which is the number of fields), using a slash (/) as a field separator. Since the output is directory/directory/filename and we make the assumption that most dbt models will write to a table named after its file name, this works as expected. It then sorts the results, gets the unique set, and writes it to a file called diff.txt.
periscope_check.py
This recursively searches the entire periscope repo for a string that matches a from|join statement from any of the 3 currently queryable schemas. It does some cleaning on files that match and creates a dictionary of table name mapping to all of the files it is referenced in. It then reads in diff.txt to do a lookup and write to comparison.txt and matches based on the model name.
if (( $(cat comparison.txt | wc -l | tr -d ' ') > 0 )); then echo "Check these!" && cat comparison.txt && exit 1; else echo "All good" && exit 0; fi;
This uses word count (wc) to see how many lines are in the comparison file. If there is more than zero it will print the lines and exit with a failure. If there are no lines it exits with a success.
Runs the SQLFluff linter on all changed sql files within the transform/snowflake-dbt/models directory. This is currently executed manually and is allowed to fail, but we encourage anyone developing dbt models to view the output and format according to the linters specifications as this format will become the standard.
In order to ensure that all SAFE data is being stored in appropriate schemas all models that are downstream of source models with MNPI data must either have an exception tag or be in a restricted schema in PROD. This CI Job checks for compliance with this state. If your MR fails this job it will likely either need to be audited and verified to be without change MNPI data and have the appropriate exception tags added, or models may need to be migrated to the appropriate restricted schema
Runs only schema tests
Runs snapshots. This jobs runs against the clone of RAW. Requires the clone_raw_full job to have been run.
Runs specified model tests with the variable DBT_MODELS
These jobs are defined in .gitlab-ci.yml.
There are several jobs that only appear when .py files have changed. All of them will run automatically on each new commit where .py files are present.
Pipelines running automatically are:
We handle python code formatting using the black library. The pipeline checks the entire /analytics repo (all *.py files).
We use the mypy library to check code correctness. The pipeline checks the entire /analytics repo (all *.py files).
We use the pylint library and check code linting for Python files. The pipeline checks only changed Python files (*.py) in /analytics repo.
We use the flake8 library and check code linting for Python files. The pipeline checks only changed Python files (*.py) in /analytics repo.
We use the vulture library and check unused for Python files. Vulture finds unused classes, functions and variables in your code. This helps you cleanup and find errors in your programs.
The pipeline checks only changed Python files (*.py) in /analytics repo.
We use the xenon library and check code complexity for Python files. The pipeline checks the entire /analytics repo (all *.py files).
We ensure code quality by running the pytest library and test cases in /analytics repo. The pipeline all test files in the entire /analytics repo (all *.py files contains pytest library).
Manually running pipelines are:
Manual job to do a dry run of Permifrost.
Must be run at least once before any changes to permissions/snowflake/roles.yml are merged. Takes around 30 minutes to complete.
Runs the spec-test cli of Permifrost to verify changes have been correctly configured in the database.
Triggered when there is a change to permissions/snowflake/roles.yml. Validates that the YAML is correctly formatted.
These jobs are defined in .gitlab-ci.yml.
Runs automatically when MR is merged or closed. Do not run manually.
All the below run against the Prod DB using the changes provided in the repo. No cloning is needed to run the below.
Runs through all tests in the analytics & data tests repo.
Runs through all the data tests in the analytics & data tests repo's.
Runs through all the schema tests in the analytics & data tests repo's.
Runs specified model tests with the variable DBT_MODELS