View partition and cluster recommendations

Preview

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This document describes how the partition and cluster recommender works, and how to view your recommendations and insights.

The BigQuery partitioning and clustering recommender generates partition or cluster recommendations to optimize your BigQuery tables. The recommender analyzes workflows on your BigQuery tables and offers recommendations to better optimize your workflows and query costs using either table partitioning or table clustering.

For more information about the Recommender service, see the Recommender overview.

How the recommender works

To generate a recommendation, the partitioning and clustering recommender uses the project's workload execution data from the past 30 days to analyze each BigQuery table for suboptimal partitioning and clustering configurations. The recommender also uses machine learning to predict how much the workload execution could be optimized with different partitioning or clustering configurations. If the recommender finds that partitioning or clustering a table yields significant savings, the recommender generates a recommendation. The partitioning and clustering recommender generates the following types of recommendations:

Existing table type	Recommendation subtype	Recommendation example
Non-partitioned, non-clustered	Partition	"Save about 64 slot hours per month by partitioning on column_C by DAY"
Non-partitioned, non-clustered	Cluster	"Save about 64 slot hours per month by clustering on column_C"
Partitioned, non-clustered	Cluster	"Save about 64 slot hours per month by clustering on column_C"

Each recommendation consists of three parts:

• Guidance to either partition or cluster a specific table
• The specific column in a table to partition or cluster
• Estimated monthly savings for applying the recommendation

To calculate potential workload savings, the recommender assumes that the historical execution workload data from the past 30 days represents the future workload.

The recommender API also returns table workload information in the form of insights. Insights are findings that help you understand your project's workload, providing more context on how a partition or cluster recommendation might improve workload costs.

Limitations

The partitioning and clustering recommender excludes legacy SQL queries in its analysis.

The partitioning and clustering recommender does not support resources that are stored in the following regions:

• europe-central2, europe-west8, europe-west9, europe-west12, europe-north1, europe-southwest1
• us-east1, us-east5, us-south1
• me-central1, me-central2, me-west1
• australia-southeast2
• southamerica-west1

Before you begin

Before you can view partition and cluster recommendations, you need to enable the Recommender API.

Required permissions

To get the permissions that you need to access partition and cluster recommendations, ask your administrator to grant you the BigQuery Partitioning Clustering Recommender Viewer (roles/recommender.bigqueryPartitionClusterViewer) IAM role. For more information about granting roles, see Manage access.

This predefined role contains the permissions required to access partition and cluster recommendations. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to access partition and cluster recommendations:

• recommender.bigqueryPartitionClusterRecommendations.get
• recommender.bigqueryPartitionClusterRecommendations.list

You might also be able to get these permissions with custom roles or other predefined roles.

For more information about IAM roles and permissions in BigQuery, see Introduction to IAM.

View recommendations

This section describes how to view partition and cluster recommendations and insights using the Google Cloud console, the Google Cloud CLI or the Recommender API.

Note: You can also export recommendations to BigQuery using the BigQuery Data Transfer Service. For more information, see Export recommendations to BigQuery.

Select one of the following options:

Console

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. To open the recommendations tab, click Recommendations > View all recommendations.

   

   The recommendations tab lists all recommendations available to your project.

3. In the Optimize BigQuery workload cost panel, click View all.

   The cost recommendation table lists all recommendations generated for the current project. For example, the following screenshot shows that the recommender analyzed the example_table table, and then recommended clustering the example_column column to save an approximate amount of bytes and slots.

   

4. To see more information about the table insight and recommendation, click on a recommendation.

gcloud

To view partition or cluster recommendations for a specific project, use the gcloud recommender recommendations list command:

gcloud recommender recommendations list \
    --project=PROJECT_NAME \
    --location=REGION_NAME \
    --recommender=google.bigquery.table.PartitionClusterRecommender \
    --format=FORMAT_TYPE \

Replace the following:

• PROJECT_NAME: the name of the project that contains your BigQuery table
• REGION_NAME: the region that your project is in
• FORMAT_TYPE: a supported gcloud CLI output format—for example, JSON

The following table describes the important fields from the recommender API response:

Property	Relevant for subtype	Description
recommenderSubtype	Partition or cluster	Indicates the type of recommendation.
content.overview.partitionColumn	Partition	Recommended partitioning column name.
content.overview.partitionTimeUnit	Partition	Recommended partitioning time unit. For example, DAY means the recommendation is to have daily partitions on the recommended column.
content.overview.clusterColumns	Cluster	Recommended clustering column names.

• For more information about other fields in the recommender response, see REST Resource: projects.locations.recommendersrecommendation.
• For more information about using the Recommender API, see Using the API - Recommendations.

To view table insights using the gcloud CLI, use the gcloud recommender insights list command:

gcloud recommender insights list \
    --project=PROJECT_NAME \
    --location=REGION_NAME \
    --insight-type=google.bigquery.table.StatsInsight \
    --format=FORMAT_TYPE \

Replace the following:

• PROJECT_NAME: the name of the project that contains your BigQuery table
• REGION_NAME: the region that your project is in
• FORMAT_TYPE: a supported gcloud CLI output format—for example, JSON

The following table describes the important fields from the insights API response:

Property	Relevant for subtype	Description
content.existingPartitionColumn	Cluster	Existing partitioning column, if any
content.tableSizeTb	All	Size of the table in terabytes
content.bytesReadMonthly	All	Monthly bytes read from the table
content.slotMsConsumedMonthly	All	Monthly slot milliseconds consumed by the workload running on the table
content.queryJobsCountMonthly	All	Monthly count of jobs running on the table

• For more information about other fields in the insights response, see REST Resource: projects.locations.insightTypes.insights.
• For more information about using insights, see Using the API - Insights.

REST API

To view partition or cluster recommendations for a specific project, use the REST API. With each command, you must provide an authentication token, which you can get using the gcloud CLI. For more information about getting an authentication token, see Methods for getting an ID token.

You can use the curl list request to view all recommendations for a specific project:

curl
    -H "Authorization: Bearer $GCLOUD_AUTH_TOKEN"
    -H "x-goog-user-project: PROJECT_NAME" https://recommender.googleapis.com/v1/projects/my-project/locations/us/recommenders/google.bigquery.table.PartitionClusterRecommender/recommendations

Replace the following:

• GCLOUD_AUTH_TOKEN: the name of a valid gcloud CLI access token
• PROJECT_NAME: the name of the project containing your BigQuery table

The following table describes the important fields from the recommender API response:

Property	Relevant for subtype	Description
recommenderSubtype	Partition or cluster	Indicates the type of recommendation.
content.overview.partitionColumn	Partition	Recommended partitioning column name.
content.overview.partitionTimeUnit	Partition	Recommended partitioning time unit. For example, DAY means the recommendation is to have daily partitions on the recommended column.
content.overview.clusterColumns	Cluster	Recommended clustering column names.

• For more information about other fields in the recommender response, see REST Resource: projects.locations.recommendersrecommendation.
• For more information about using the Recommender API, see Using the API - Recommendations.

To view table insights using the REST API, run the following command:

curl
-H "Authorization: Bearer $GCLOUD_AUTH_TOKEN"
-H "x-goog-user-project: PROJECT_NAME" https://recommender.googleapis.com/v1/projects/my-project/locations/us/insightTypes/google.bigquery.table.StatsInsight/insights

Replace the following:

• GCLOUD_AUTH_TOKEN: the name of a valid gcloud CLI access token
• PROJECT_NAME: the name of the project containing your BigQuery table

The following table describes the important fields from the insights API response:

Property	Relevant for subtype	Description
content.existingPartitionColumn	Cluster	Existing partitioning column, if any
content.tableSizeTb	All	Size of the table in terabytes
content.bytesReadMonthly	All	Monthly bytes read from the table
content.slotMsConsumedMonthly	All	Monthly slot milliseconds consumed by the workload running on the table
content.queryJobsCountMonthly	All	Monthly count of jobs running on the table

• For more information about other fields in the insights response, see REST Resource: projects.locations.insightTypes.insights.
• For more information about using insights, see Using the API - Insights.

Troubleshooting

Issue: No recommendations appear for a specific table.

Partition and cluster recommendations might not appear under the following circumstances:

• The table is less than 10GB.
• The table has a high write cost from data manipulation language (DML) operations.
• The table was not read in the past 30 days.
• The estimated monthly savings is too insignificant (less than 1 slot hour of savings).
• The table is already clustered.

Note: Tables that are both unclustered and unpartitioned can receive both cluster and partition recommendations. Tables that are unclustered but partitioned can only receive cluster recommendations. Clustered tables don't receive any recommendations.

Pricing

There is no cost or adverse impact on workload performance when you view recommendations.

What's next

• Apply cluster and partition recommendations.