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These tables are created and maintained by the Data Team and scheduled to get updated periodically via Airflow. - - [Process to create an aggregate table in BigQuery](https://mozilla.github.io/bigquery-etl/cookbooks/common_workflows/#adding-a-new-scheduled-query). - - -1. **[BigQuery Materialized Views](https://cloud.google.com/bigquery/docs/materialized-views-intro).** These are views defined by the developer using only SELECT and created, managed and scheduled by BigQuery. Materialized views are periodically updated reading only changes from the based table to compute results. Materialized view definitions [*do not support* certain BigQuery features and expressions](https://cloud.google.com/bigquery/docs/materialized-views-intro#limitations), such as UDFs, certain aggregate functions, backfilling or nesting. - - - Example to create a Materialized view in BigQuery. - ``` - CREATE MATERIALIZED VIEW `moz-fx-data-shared-prod.monitoring_derived.suggest_click_rate_live_v1` - OPTIONS - (enable_refresh = TRUE, refresh_interval_minutes = 5) - AS - SELECT - TIMESTAMP_TRUNC(submission_timestamp, minute) AS submission_minute, - COUNT(*) AS n, - COUNTIF(release_channel = "release") AS n_release, - COUNTIF(release_channel = "beta") AS n_beta, - COUNTIF(release_channel = "nightly") AS n_nightly, - COUNT(request_id) AS n_merino, - COUNTIF(request_id IS NOT NULL AND release_channel = "release") AS n_merino_release, - COUNTIF(request_id IS NOT NULL AND release_channel = "beta") AS n_merino_beta, - COUNTIF(request_id IS NOT NULL AND release_channel = "nightly") AS n_merino_nightly, - FROM - `moz-fx-data-shared-prod.contextual_services_live.quicksuggest_click_v1` - WHERE - DATE(submission_timestamp) > '2010-01-01' - GROUP BY - 1 - ``` +- [Process to create an aggregate table in BigQuery](https://mozilla.github.io/bigquery-etl/cookbooks/common_workflows/#adding-a-new-scheduled-query). + +1. **[BigQuery Materialized Views](https://cloud.google.com/bigquery/docs/materialized-views-intro).** These are views defined by the developer using only SELECT and created, managed and scheduled by BigQuery. Materialized views are periodically updated reading only changes from the based table to compute results. Materialized view definitions [_do not support_ certain BigQuery features and expressions](https://cloud.google.com/bigquery/docs/materialized-views-intro#limitations), such as UDFs, certain aggregate functions, backfilling or nesting. + + - Example to create a Materialized view in BigQuery. + ``` + CREATE MATERIALIZED VIEW `moz-fx-data-shared-prod.monitoring_derived.suggest_click_rate_live_v1` + OPTIONS + (enable_refresh = TRUE, refresh_interval_minutes = 5) + AS + SELECT + TIMESTAMP_TRUNC(submission_timestamp, minute) AS submission_minute, + COUNT(*) AS n, + COUNTIF(release_channel = "release") AS n_release, + COUNTIF(release_channel = "beta") AS n_beta, + COUNTIF(release_channel = "nightly") AS n_nightly, + COUNT(request_id) AS n_merino, + COUNTIF(request_id IS NOT NULL AND release_channel = "release") AS n_merino_release, + COUNTIF(request_id IS NOT NULL AND release_channel = "beta") AS n_merino_beta, + COUNTIF(request_id IS NOT NULL AND release_channel = "nightly") AS n_merino_nightly, + FROM + `moz-fx-data-shared-prod.contextual_services_live.quicksuggest_click_v1` + WHERE + DATE(submission_timestamp) > '2010-01-01' + GROUP BY + 1 + ``` 1. **[Looker PDTs & aggregate awareness](https://cloud.google.com/looker/docs/aggregate_awareness)** These are aggregations that a developer defines in an Explore file (`explore.lkml`). From this definition, Looker creates a table in BigQuery's `mozdata.tmp` using the naming convention`scratch schema + table status code + hash value + view name` and runs the scheduled update of the data. - Looker's PDTs and aggregate awareness are **only** referenced in Looker when at least one of the columns is used in a Looker object. These aggregates can be particularly beneficial to avoid having to rebuild dashboards after a schema change. - + Looker's PDTs and aggregate awareness are **only** referenced in Looker when at least one of the columns is used in a Looker object. These aggregates can be particularly beneficial to avoid having to rebuild dashboards after a schema change. + - Template to create aggregate awareness in a Looker Explore: - - ``` - aggregate_table: { - query: { - dimensions: [.] - measures: [
.] - filters: [
.: ""] - } - materialization: { - sql_trigger_value: SELECT CURRENT_DATE() ;; - increment_key:
. - increment_offset: } - } - ``` - - An [Example of aggregate awareness in a Looker Explore](https://mozilla.cloud.looker.com/projects/spoke-default/files/combined_browser_metrics/explores/active_users_aggregates.explore.lkml) + ``` + aggregate_table: { + query: { + dimensions: [
.] + measures: [
.] + filters: [
.: ""] + } + materialization: { + sql_trigger_value: SELECT CURRENT_DATE() ;; + increment_key:
. + increment_offset: } + } + ``` + + - An [Example of aggregate awareness in a Looker Explore](https://mozilla.cloud.looker.com/projects/spoke-default/files/combined_browser_metrics/explores/active_users_aggregates.explore.lkml) ## Important considerations: @@ -72,6 +71,7 @@ This doc is about when to use different options to aggregate data, their limitat ## When to use each of these aggregates? #### A BigQuery aggregate table is used when: + - The results should not be unexpectedly affected by Shredder. - The metric requires strict change control. - A visible alert is required in case of failure or data out of sync. Airflow sends emails and alerts on failure for BigQuery aggregate tables, which are addressed daily by the Data Engineering team during the Airflow Triage. @@ -79,6 +79,7 @@ This doc is about when to use different options to aggregate data, their limitat - The query requires full flexibility to use DML, data types, aggregation functions and different types of JOIN. #### A Materialized View is used when: + - To aggregate data in real-time (for example, for implementing real-time monitoring of certain metrics) - The results can be based on shredded data (tables with client_id). - The view will be queried directly or used as a source for other analysis. @@ -90,6 +91,7 @@ This doc is about when to use different options to aggregate data, their limitat - The data does not need to be backfilled. #### A Looker PDT is used when: + - The results can be based on shredded data (tables with client_id). - Change control is not required or is already implemented in the base table. This can be verified by looking for the label `change_controlled: true` in the metadata. - An explicit alert on failure is **not** required. Failures are monitored in the Dashboard [Errors and Broken Content Dashboard](https://mozilla.cloud.looker.com/dashboards/system__activity::errors_and_broken_content) or in the [PDTs Dashboard](https://mozilla.cloud.looker.com/admin/pdts).