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README.md

Google Cloud Bigquery Module

This module allows managing a single BigQuery dataset, including access configuration, tables and views.

Simple dataset with access configuration

Access configuration defaults to using the separate google_bigquery_dataset_access resource, so as to leave the default dataset access rules untouched.

You can choose to manage the google_bigquery_dataset access rules instead via the dataset_access variable, but be sure to always have at least one OWNER access and to avoid duplicating accesses, or terraform apply will fail.

The access variables are split into access and access_identities variables, so that dynamic values can be passed in for identities (eg a service account email generated by a different module or resource).

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  access = {
    reader-group   = { role = "READER", type = "group" }
    owner          = { role = "OWNER", type = "user" }
    project_owners = { role = "OWNER", type = "special_group" }
    view_1         = { role = "READER", type = "view" }
  }
  access_identities = {
    reader-group   = "[email protected]"
    owner          = "[email protected]"
    project_owners = "projectOwners"
    view_1         = "my-project|my_dataset|my-table"
  }
}
# tftest modules=1 resources=5 inventory=simple.yaml

IAM roles

Access configuration can also be specified via IAM instead of basic roles via the iam variable. When using IAM, basic roles cannot be used via the access family variables.

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  iam = {
    "roles/bigquery.dataOwner" = ["user:[email protected]"]
  }
}
# tftest modules=1 resources=2 inventory=iam.yaml

Authorized Views, Datasets, and Routines

You can specify authorized views, datasets, and routines via the authorized_views, authorized_datasets and authorized_routines variables, respectively.

// Create private BigQuery dataset that will not be publicly accessible, except via the authorized BigQuery resources
module "bigquery-dataset-private" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "private_project"
  id         = "private_dataset"
  authorized_views = [
    {
      project_id = "auth_view_project"
      dataset_id = "auth_view_dataset"
      table_id   = "auth_view"
    }
  ]
  authorized_datasets = [
    {
      project_id = "auth_dataset_project"
      dataset_id = "auth_dataset"
    }
  ]
  authorized_routines = [
    {
      project_id = "auth_routine_project"
      dataset_id = "auth_routine_dataset"
      routine_id = "auth_routine"
    }
  ]
}

// Create authorized view in a public dataset
module "bigquery-authorized-views-dataset-public" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "auth_view_project"
  id         = "auth_view_dataset"
  views = {
    auth_view = {
      friendly_name       = "Public"
      labels              = {}
      query               = "SELECT * FROM `private_project.private_dataset.private_table`"
      use_legacy_sql      = false
      deletion_protection = true
    }
  }
}

// Create public authorized dataset
module "bigquery-authorized-dataset-public" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "auth_dataset_project"
  id         = "auth_dataset"
}

// Create public authorized routine
module "bigquery-authorized-authorized-routine-dataset-public" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "auth_routine_project"
  id         = "auth_routine_dataset"
}

resource "google_bigquery_routine" "public-routine" {
  project         = "private_project"
  dataset_id      = module.bigquery-authorized-authorized-routine-dataset-public.dataset_id
  routine_id      = "auth_routine"
  routine_type    = "TABLE_VALUED_FUNCTION"
  language        = "SQL"
  definition_body = <<-EOS
    SELECT 1 + value AS value
  EOS
  arguments {
    name          = "value"
    argument_kind = "FIXED_TYPE"
    data_type     = jsonencode({ "typeKind" = "INT64" })
  }
  return_table_type = jsonencode({ "columns" = [
    { "name" = "value", "type" = { "typeKind" = "INT64" } },
  ] })
}
# tftest modules=4 resources=9 inventory=authorized_resources.yaml

Authorized views can be specified both using the standard access options and the authorized_views blocks. The example configuration below uses both blocks, and will create a dataset with three authorized views view_id_1, view_id_2, and view_id_3.

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  authorized_views = [
    {
      project_id = "view_project"
      dataset_id = "view_dataset"
      table_id   = "view_id_1"
    },
    {
      project_id = "view_project"
      dataset_id = "view_dataset"
      table_id   = "view_id_2"
    }
  ]
  access = {
    view_2 = { role = "READER", type = "view" }
    view_3 = { role = "READER", type = "view" }
  }
  access_identities = {
    view_2 = "view_project|view_dataset|view_id_2"
    view_3 = "view_project|view_dataset|view_id_3"
  }
}
# tftest modules=1 resources=4 inventory=authorized_resources_views.yaml

Dataset options

Dataset options are set via the options variable. all options must be specified, but a null value can be set to options that need to use defaults.

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  options = {
    default_table_expiration_ms     = 3600000
    default_partition_expiration_ms = null
    delete_contents_on_destroy      = false
    max_time_travel_hours           = 168
  }
}
# tftest modules=1 resources=1 inventory=options.yaml

Tables, views and routines

Tables are created via the tables variable. Support for external tables will be added in a future release.

locals {
  countries_schema = jsonencode([
    { name = "country", type = "STRING" },
    { name = "population", type = "INT64" },
  ])
}

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  tables = {
    countries = {
      friendly_name       = "Countries"
      schema              = local.countries_schema
      deletion_protection = true
    }
  }
}
# tftest modules=1 resources=2 inventory=tables.yaml

If partitioning is needed, populate the partitioning variable using either the time or range attribute.

locals {
  countries_schema = jsonencode([
    { name = "country", type = "STRING" },
    { name = "population", type = "INT64" },
  ])
}

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  tables = {
    table_a = {
      deletion_protection = true
      friendly_name       = "Table a"
      schema              = local.countries_schema
      partitioning = {
        time = { type = "DAY", expiration_ms = null }
      }
    }
  }
}
# tftest modules=1 resources=2 inventory=partitioning.yaml

To create views use the views variable. If you're querying a table created by the same module terraform apply will initially fail and eventually succeed once the underlying table has been created. You can probably also use the module's output in the view's query to create a dependency on the table.

locals {
  countries_schema = jsonencode([
    { name = "country", type = "STRING" },
    { name = "population", type = "INT64" },
  ])
}

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  tables = {
    countries = {
      friendly_name       = "Countries"
      schema              = local.countries_schema
      deletion_protection = true
    }
  }
  views = {
    population = {
      friendly_name       = "Population"
      query               = "SELECT SUM(population) FROM my_dataset.countries"
      use_legacy_sql      = false
      deletion_protection = true
    }
  }
}
# tftest modules=1 resources=3 inventory=views.yaml

To create routines use the routines variable.

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  routines = {
    custom_masking_routine = {
      routine_type         = "SCALAR_FUNCTION"
      language             = "SQL"
      data_governance_type = "DATA_MASKING"
      definition_body      = "SAFE.REGEXP_REPLACE(ssn, '[0-9]', 'X')"
      return_type          = "{\"typeKind\" :  \"STRING\"}"
      arguments = {
        ssn = {
          data_type = "{\"typeKind\" :  \"STRING\"}"
        }
      }
    }
  }
}
# tftest modules=1 resources=2 inventory=routines.yaml

Tag bindings

Refer to the Creating and managing tags documentation for details on usage.

module "org" {
  source          = "./fabric/modules/organization"
  organization_id = var.organization_id
  tags = {
    environment = {
      description = "Environment specification."
      values = {
        dev     = {}
        prod    = {}
        sandbox = {}
      }
    }
  }
}

module "bigquery-dataset" {
  source     = "./fabric/modules/bigquery-dataset"
  project_id = "my-project"
  id         = "my_dataset"
  tag_bindings = {
    env-sandbox = module.org.tag_values["environment/sandbox"].id
  }
}
# tftest modules=2 resources=6

TODO

  • check for dynamic values in tables and views
  • add support for external tables

Variables

name description type required default
id Dataset id. string ✓
project_id Id of the project where datasets will be created. string ✓
access Map of access rules with role and identity type. Keys are arbitrary and must match those in the access_identities variable, types are domain, group, special_group, user, view. map(object({…})) {}
access_identities Map of access identities used for basic access roles. View identities have the format 'project_id|dataset_id|table_id'. map(string) {}
authorized_datasets An array of datasets to be authorized on the dataset. list(object({…})) []
authorized_routines An array of routines to be authorized on the dataset. list(object({…})) []
authorized_views An array of views to be authorized on the dataset. list(object({…})) []
dataset_access Set access in the dataset resource instead of using separate resources. bool false
description Optional description. string "Terraform managed."
encryption_key Self link of the KMS key that will be used to protect destination table. string null
friendly_name Dataset friendly name. string null
iam IAM bindings in {ROLE => [MEMBERS]} format. Mutually exclusive with the access_* variables used for basic roles. map(list(string)) {}
labels Dataset labels. map(string) {}
location Dataset location. string "EU"
materialized_views Materialized views definitions. map(object({…})) {}
options Dataset options. object({…}) {}
routines Routine definitions. map(object({…})) {}
tables Table definitions. Options and partitioning default to null. Partitioning can only use range or time, set the unused one to null. map(object({…})) {}
tag_bindings Tag bindings for this dataset, in key => tag value id format. map(string) {}
views View definitions. map(object({…})) {}

Outputs

name description sensitive
dataset Dataset resource.
dataset_id Dataset id.
id Fully qualified dataset id.
materialized_view_ids Map of fully qualified materialized view ids keyed by view ids.
materialized_views Materialized view resources.
routine_ids Map of fully qualified routine ids keyed by routine ids.
routines Routine resources.
self_link Dataset self link.
table_ids Map of fully qualified table ids keyed by table ids.
tables Table resources.
view_ids Map of fully qualified view ids keyed by view ids.
views View resources.