Compute system tables reference

Article
10/24/2024

Important

This system table is in Public Preview. To access the table, the schema must be enabled in your system catalog. For more information, see Enable system table schemas.

This article provides you with a reference guide for the compute system tables. You can use these tables to monitor the activity and metrics of all-purpose and jobs compute in your account:

clusters: Records compute configurations in your account.
node_types: Includes a single record for each of the currently available node types, including hardware information.
node_timeline: Includes minute-by-minute records of your compute’s utilization metrics.

Cluster table schema

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

Table path: This system table is located at system.compute.clusters

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-crmpt124`
`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.	`Standard_D16s_v3`
`worker_node_type`	string	Worker node type name. This matches the instance type name from the cloud provider.	`Standard_D16s_v3`
`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`, `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.	`null`
`azure_attributes`	struct	Azure specific settings.	`{` `"first_on_demand": "0",` `"availability": "ON_DEMAND_AZURE",` `"spot_bid_max_price": "—1"` `}`
`gcp_attributes`	struct	GCP specific settings. This field will be empty.	`null`
`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.

Table path: This system table is located at system.compute.node_types.

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`	string	Unique identifier for node type.	`Standard_D16s_v3`
`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`

Node timeline table schema

The node timeline table captures node-level resource utilization data at minute granularity. Each record contains data for a given minute of time per instance.

Table path: This system table is located at system.compute.node_timeline.

Column name	Data type	Description	Example
`account_id`	string	ID of the account where this compute resource is running.	`23e22ba4-87b9-4cc2-9770-d10b894b7118`
`workspace_id`	string	ID of the workspace where this compute resource is running.	`1234567890123456`
`cluster_id`	string	ID of the compute resource.	`0000-123456-crmpt124`
`instance_id`	string	ID for the specific instance.	`i-1234a6c12a2681234`
`start_time`	timestamp	Start time for the record in UTC.	`2024-07-16T12:00:00Z`
`end_time`	timestamp	End time for the record in UTC.	`2024-07-16T13:00:00Z`
`driver`	boolean	Whether the instance is a driver or worker node.	`true`
`cpu_user_percent`	double	Percentage of time the CPU spent in userland.	`34.76163817234407`
`cpu_system_percent`	double	Percentage of time the CPU spent in the kernel.	`1.0895310279488264`
`cpu_wait_percent`	double	Percentage of time the CPU spent waiting for I/O.	`0.03445157400629276`
`mem_used_percent`	double	Percentage of the compute’s memory that was used during the time period (including memory used by background processes running on the compute).	`45.34858216779041`
`mem_swap_percent`	double	Percentage of memory usage attributed to memory swap.	`0.014648443087939`
`network_sent_bytes`	bigint	The number of bytes sent out in network traffic.	`517376`
`network_received_bytes`	bigint	The number of received bytes from network traffic.	`179234`
`disk_free_bytes_per_mount_point`	map	The disk utilization grouped by mount point. This is ephemeral storage provisioned only while the compute is running.	`{"/var/lib/lxc":123455551234,"/":` `123456789123,"/local_disk0":123412341234}`
`node_type`	string	The name of the node type. This will match the instance type name from the cloud provider.	`Standard_D16s_v3`

Known limitations

Compute resources 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 records in the clusters table. All active compute resources have been backfilled.
These tables only includes records for all-purpose and jobs compute. They do not contain records for serverless compute, Delta Live Tables compute, or SQL warehouses.
Nodes that ran for less than 10 minutes might not appear in the node_timeline table.

Sample queries

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

Join cluster records with the most recent billing records
Attribute costs to the cluster owner
Identify the compute resources with the highest average utilization and peak utilization

Note

Some of 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 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;

Identify the compute resources with the highest average utilization and peak utilization

Identify the all-purpose and jobs compute that have the highest average CPU utilization and the highest peak CPU utilization.

SELECT
        distinct cluster_id,
driver,
avg(cpu_user_percent + cpu_system_percent) as `Avg CPU Utilization`,
max(cpu_user_percent + cpu_system_percent) as `Peak CPU Utilization`,
        avg(cpu_wait_percent) as `Avg CPU Wait`,
        max(cpu_wait_percent) as `Max CPU Wait`,
        avg(mem_used_percent) as `Avg Memory Utilization`,
        max(mem_used_percent) as `Max Memory Utilization`,
avg(network_received_bytes)/(1024^2) as `Avg Network MB Received per Minute`,
avg(network_sent_bytes)/(1024^2) as `Avg Network MB Sent per Minute`
FROM
        node_timeline
WHERE
        start_time >= date_add(now(), -1)
GROUP BY
        cluster_id,
        driver
ORDER BY
        3 desc;

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