Compute system tables reference

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This article provides you with an overview of the compute system tables, including the schemas and example queries. There are two cluster system tables available now: clusters and node_types.

Cluster table schema

The cluster table is a slow-changing dimension table that contains the full history of cluster configurations over time for all-purpose and jobs clusters.

The clusters system table is located at system.compute.clusters and has the following schema:

Column name

Data type

Description

Example

account_id

string

ID of the account where this cluster was created.

23e22ba4-87b9-4cc2-9770-d10b894b7118

workspace_id

string

ID of the workspace where this cluster was created.

1234567890123456

cluster_id

string

ID of the cluster for which this record is associated.

0000-123456-xxxxxxxx

cluster_name

string

User defined name for the cluster.

My cluster

owned_by

string

Username of the cluster owner. Defaults to the cluster creator, but can be changed through the Clusters API.

sample_user@email.com

create_time

timestamp

Timestamp of the change to this compute definition.

2023-01-09 11:00:00.000

delete_time

timestamp

Timestamp of when the cluster was deleted. The value is null if the cluster is not deleted.

2023-01-09 11:00:00.000

driver_node_type

string

Driver node type name. This matches the instance type name from the cloud provider.

i3.xlarge

worker_node_type

string

Worker node type name. This matches the instance type name from the cloud provider.

i3.xlarge

worker_count

bigint

Number of workers. Defined for fixed-size clusters only.

4

min_autoscale_workers

bigint

The set minimum number of workers. This field is valid only for autoscaling clusters.

1

max_autoscale_workers

bigint

The set maximum number of workers. This field is valid only for autoscaling clusters.

1

auto_termination_minutes

bigint

The configured autotermination duration.

120

enable_elastic_disk

boolean

Autoscaling disk enablement status.

true

tags

map

User-defined tags for the cluster (does not include default tags).

{"ResourceClass":"SingleNode"}

cluster_source

string

Indicates the creator for the cluster: UI, API, DLT, JOB, etc.

UI

init_scripts

array

Set of paths for init scripts.

"/Users/example@email.com/files/scripts/install-python-pacakges.sh"

aws_attributes

struct

AWS specific settings.

{<br>"ebs_volume_count": null<br>"availability": "SPOT_WITH_FALLBACK",<br>"first_on_demand": "0",<br>"spot_bid_price_percent": "100"<br>}

azure_attributes

struct

Azure specific settings.

empty

gcp_attributes

struct

GCP specific settings. This field will be empty.

empty

driver_instance_pool_id

string

Instance pool ID if the driver is configured on top of an instance pool.

1107-555555-crhod16-pool-DIdnjazB

worker_instance_pool_id

string

Instance Pool ID if the worker is configured on top of an instance pool.

1107-555555-crhod16-pool-DIdnjazB

dbr_version

string

The Databricks Runtime of the cluster.

14.x-snapshot-scala2.12

change_time

timestamp

Timestamp of change to the compute definition.

2023-01-09 11:00:00.000

change_date

date

Change date. Used for retention.

2023-01-09

Node types table schema

The node type table captures the currently available node types with their basic hardware information. The node type system table is located at system.compute.node_types and has the following schema:

Column name

Data type

Description

Example

account_id

string

ID of the account where this cluster was created.

23e22ba4-87b9-4cc2-9770-d10b894b7118

node_type_name

string

Unique identifier for node type.

i3.xlarge

core_count

double

Number of vCPUs for the instance.

48.0

memory_mb

long

Total memory for the instance.

393216

gpu_count

long

Number of GPUs for the instance.

0

Known limitations

  • Clusters that were marked deleted before October 23, 2023 do not appear in the clusters table. This might result in joins from the system.billing.usage table not matching cluster records in the clusters table. All active clusters have been backfilled.

  • The clusters table only includes records for all-purpose and jobs clusters. It does not contain Delta Live Tables clusters or SQL warehouses.

Sample queries

You can use the following sample queries to answer common questions about clusters:

Note

These examples join the cluster table with the system.billing.usage table. Since billing records are cross-regional and cluster records region-sepcific, billing records only match cluster records for the region in which you are querying. To see records from another region, please execute the query in that region.

Join cluster records with the most recent billing records

This query can help you understand spending over time. Once you update the usage_start_time to the most current billing period, it grabs the most recent updates to the billing records to join into clusters data.

Each record is associated with the cluster owner during that particular run. So, if the cluster owner changes, costs will roll up to the correct owner based on when the cluster was used.

SELECT
  u.record_id,
  c.cluster_id,
  c.owned_by,
  c.change_time,
  u.usage_start_time,
  u.usage_quantity
FROM
  system.billing.usage u
  JOIN system.compute.clusters c
  JOIN (SELECT u.record_id, c.cluster_id, max(c.change_time) change_time
    FROM system.billing.usage u
    JOIN system.compute.clusters c
    WHERE
      u.usage_metadata.cluster_id is not null
      and u.usage_start_time >= '2023-01-01'
      and u.usage_metadata.cluster_id = c.cluster_id
      and date_trunc('HOUR', c.change_time) <= date_trunc('HOUR', u.usage_start_time)
    GROUP BY all) config
WHERE
  u.usage_metadata.cluster_id is not null
  and u.usage_start_time >= '2023-01-01'
  and u.usage_metadata.cluster_id = c.cluster_id
  and u.record_id = config.record_id
  and c.cluster_id = config.cluster_id
  and c.change_time = config.change_time
ORDER BY cluster_id, usage_start_time desc;

Attribute costs for a cluster to the cluster owner

If you are looking to reduce compute costs, you can use this query to find out which cluster owners in your account are using the most DBUs.

SELECT
  u.record_id record_id,
  c.cluster_id cluster_id,
  max_by(c.owned_by, c.change_time) owned_by,
  max(c.change_time) change_time,
  any_value(u.usage_start_time) usage_start_time,
  any_value(u.usage_quantity) usage_quantity
FROM
  system.billing.usage u
  JOIN system.compute.clusters c
WHERE
  u.usage_metadata.cluster_id is not null
  and u.usage_start_time >= '2023-01-01'
  and u.usage_metadata.cluster_id = c.cluster_id
  and c.change_time <= u.usage_start_time
GROUP BY 1, 2
ORDER BY cluster_id, usage_start_time desc;