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Setup and run SQL Runner

SQL Runner enables you to execute SQL scripts against the Snowplow data in your data warehouse. Specifically, it allows you to organize your SQL scripts in templatable playbooks, and execute them in series or in parallel on Snowflake, Amazon Redshift, GCP BigQuery and PostgreSQL.

To set up SQL Runner, Snowplow open source users can start from the User guide .

For Snowplow Insights customers, SQL Runner is already setup as part of your pipeline, so you can get started with configuring and deploying your SQL data models immediately.

A SQL Runner data model consists of:

  • SQL files (containing one or more SQL statements)
  • Playbooks (YAML files organizing the SQL into steps)


A playbook consists of one of more steps, each of which consists of one or more queries. Steps are run in series, queries are run in parallel within the step.

Each query contains the path to a query file

All steps are applied against all targets. All targets are processed in parallel.

In the following example, a.sql, b.sql and c.sql are run in parallel.

:steps: - :name: "Run a,b and c in parallel" :queries: - :name: a :file: a.sql - :name: b :file: b.sql - :name: c :file: c.sql
Code language: YAML (yaml)

By contrast, in the example below, the three SQL files are executed in sequence.

:steps: - :name: "Run a..." :queries: - :name: a :file: a.sql - :name: "...then run b..." :queries: - :name: b :file: b.sql - :name: "...then run c..." :queries: - :name: c :file: c.sql
Code language: YAML (yaml)

Playbooks can be templated, and corresponding variables can be passed in with the var flag like this:

sql-runner -var host=value,username=value2,password=value3
Code language: Bash (bash)

Here is the corresponding playbook template:

:targets: - :name: "My Postgres database 1" :type: postgres :host: {{.host}} :database: sql_runner_tests_1 :port: 5432 :username: {{.username}} :password: {{.password}} :ssl: false # SSL disabled by default :variables: :test_schema: sql_runner_tests :timeFormat: "2006_01_02" :steps: - :name: Create schema and table :queries: - :name: Create schema and table :file: postgres-sql/good/1.sql :template: true
Code language: YAML (yaml)

SQL files

A query file contains one or more SQL statements. These are executed “raw” (i.e. not in a transaction) in series by SQL Runner. If the query file is flagged as a template in the playbook, then the file is pre-processed as a template before being executed.

Note: If your query is a template that requires pre-processing, you must add template: true to the query definition in the playbook yml file, for example:

:name: "Run a.." :queries: - :name: a :file: a.sql :template: true
Code language: YAML (yaml)


Templates are run through Golang’s text template processor. The template processor can access all variables defined in the playbook.

The following custom functions are also supported:

  • nowWithFormat [timeFormat]: where timeFormat is a valid Golang time format
  • systemEnv "ENV_VAR": where ENV_VAR is a key for a valid environment variable
  • awsEnvCredentials: supports passing credentials through environment variables, such as AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
  • awsProfileCredentials: supports getting credentials from a credentials file, also used by boto/awscli
  • awsEC2RoleCredentials: supports getting role-based credentials, i.e. getting the automatically generated credentials in EC2 instances
  • awsChainCredentials: tries to get credentials from each of the three methods above in order, using the first one returned
  • randomInt: will return a random integer

Note: All AWS functions output strings in the Redshift credentials format (CREDENTIALS 'aws_access_key_id=%s;aws_secret_access_key=%s').

For an example query file using templating see: integration/resources/postgres-sql/good/3.sql

Failure modes

If a statement fails in a query file, the query will terminate and report failure.

If a query fails, its sibling queries will continue running, but no further steps will run.

Failures in one target do not affect other targets in any way.

Return codes

- 0 for no errors - 5 for target initialization errors - 6 for query errors - 7 for both types of error - 8 for no queries run

Target configuration


If your storage target is Amazon Redshift, then the target configuration in the playbook is:

targets: - name: "My Redshift database" type: redshift host: ADD HERE # The endpoint as shown in the Redshift console database: ADD HERE # Name of database port: 5439 # Default Redshift port username: ADD HERE password: ADD HERE ssl: false # SSL disabled by default variables: ...
Code language: YAML (yaml)


To access a BigQuery project, sql-runner will need some Google credentials. These can be set up by creating a new service account in the GCP console, then providing its private key to the application via a GOOGLE_APPLICATION_CREDENTIALS environment variable – a detailed walkthrough of this process is available on the GCP documentation website.

After the credentials are set up, simply create a playbook with the following BigQuery-specific target configuration:

targets: - name: "My BigQuery database" type: bigquery project: ADD HERE # Project ID as shown in the GCP console's front page variables: ...
Code language: YAML (yaml)


If your data warehouse is Snowflake, then the SQL Runner playbooks will have a target configuration as:

targets: - name: "My Snowflake database" type: snowflake account: ADD HERE # Your Snowflake account name database: ADD HERE # Name of database warehouse: ADD HERE # Name of warehouse to run the queries username: ADD HERE password: ADD HERE host: # Leave blank port: # Leave blank ssl: true # Snowflake connection is always secured by TLS variables: ...
Code language: YAML (yaml)


Finally, if your storage target is PostgreSQL, then can be configured as:

targets: - name: "My Postgres database" type: postgres host: ADD HERE database: ADD HERE # Name of database port: 5432 # Default Postgres port username: ADD HERE password: ADD HERE ssl: false # SSL disabled by default variables:
Code language: YAML (yaml)

That’s it – you’re now ready to start running SQL against your data warehouse!

User guide

SQL Runner is a zero-dependency binary and can be found as a release asset for:

CLI Arguments

./sql-runner --help sql-runner version: 0.9.3 Run playbooks of SQL scripts in series and parallel on Redshift and Postgres Usage: -checkLock string Checks whether the lockfile already exists -consul string The address of a consul server with playbooks and SQL files stored in KV pairs -consulOnlyForLock Will read playbooks locally, but use Consul for locking. -deleteLock string Will attempt to delete a lockfile if it exists -dryRun Runs through a playbook without executing any of the SQL -fillTemplates Will print all queries after templates are filled -fromStep string Starts from a given step defined in your playbook -help Shows this message -lock string Optional argument which checks and sets a lockfile to ensure this run is a singleton. Deletes lock on run completing successfully -playbook string Playbook of SQL scripts to execute -runQuery string Will run a single query in the playbook -showQueryOutput Will print all output from queries -softLock string Optional argument, like '-lock' but the lockfile will be deleted even if the run fails -sqlroot string Absolute path to SQL scripts. Use PLAYBOOK, BINARY and PLAYBOOK_CHILD for those respective paths (default "PLAYBOOK") -var value Variables to be passed to the playbook, in the key=value format -version Shows the program version
Code language: AsciiDoc (asciidoc)

More on Consul

Using the -consul argument results in the following changes:

  • The -playbook argument becomes the key that is used to look for the playbook in Consul.
  • The -sqlroot argument also becomes a key argument for Consul.
  • The -lock argument creates a lock as a Consul key value pair
  • The -softLock argument creates a lock as a Consul key value pair
  • The -checkLock argument searches in Consul for a lock
  • The -deleteLock argument searches in Consul for a lock

If you pass in the default:

  • ./sql-runner -consul "localhost:8500" -playbook "sql-runner/playbook/1"

This results in:

  • Looking for your playbook file at this key sql-runner/playbook/1
  • Expecting all your SQL file keys to begin with sql-runner/playbook/<SQL path from playbook>

However as the key here can be used as a both a data and folder node we have added a new sqlroot option:

  • ./sql-runner -consul "localhost:8500" -playbook "sql-runner/playbook/1" -sqlroot PLAYBOOK_CHILD

This results in:

  • Looking for your playbook file at this key sql-runner/playbook/1
  • Expecting all your SQL file keys to begin with sql-runner/playbook/1/<SQL path from playbook>
    • The data node is used as a folder node as well.

If you’d like to learn more about Snowplow Insights you can book a demo with our team, or if you’d prefer, you can try Snowplow technology for yourself quickly and easily.