compute_usage system table-Data Lake Formation(DLF)-阿里云帮助中心

The compute_usage system table records per-table CU-Hours consumed by table optimization, aggregated by hour. Query it with Flink or another supported engine to analyze costs and optimize resource usage.

Note

compute_usage retains only the last 30 days of data. Partitions outside this window are automatically expired and cleaned up. To keep data longer, archive it periodically to a table you manage.
The CU-Hours in this table are intended for consumption analysis and trend monitoring. Refer to your bill for the final charges.
A table appears in compute_usage only when Storage optimization is enabled on it (enabled by default).

Field reference

Note

The following is the built-in schema definition. It is shown for reference only and does not need to be executed manually.

CREATE TABLE `compute_usage` (
  `database_name` STRING  COMMENT 'Database name',
  `table_id`      STRING  COMMENT 'Table ID',
  `table_name`    STRING  COMMENT 'Table name',
  `cu`            DOUBLE  COMMENT 'CU * Hours consumed',
  `start_time`    BIGINT  COMMENT 'Usage period start time (epoch milliseconds)',
  `end_time`      BIGINT  COMMENT 'Usage period end time (epoch milliseconds)',
  `dt`            STRING  COMMENT 'Date (yyyyMMdd)'
) COMMENT 'Hourly table-level compute resource consumption'
PARTITIONED BY (dt) WITH (
   'file.format' = 'avro',
   'partition.expiration-time' = '30 d',
   'partition.timestamp-formatter' = 'yyyyMMdd'
);

The fields are defined as follows:

Field	Description
database_name	The database name.
table_id	The table ID. For multi-table operations, database-level operations, and similar scenarios, the value is `multi-table-job-default-table-id`.
table_name	The table name. For multi-table operations, database-level operations, and similar scenarios, the value is `multi-table-job-default-table-name`.
cu	The CU-Hours consumed during the hour. This is the total across all lakehouse table optimization jobs, including intelligent compaction and lifecycle management.
start_time	The start time of the metering period, as an epoch millisecond timestamp. Typically aligned to the top of the hour.
end_time	The end time of the metering period, as an epoch millisecond timestamp. Typically aligned to the next top of the hour.
dt	The partition field. The date format is `yyyyMMdd`. Data is retained for the last 30 days.

Query examples

Use DLF or Flink SQL to query DLF system tables:

DLF data preview

You can run SQL queries directly using the DLF data preview and discovery features without setting up a separate compute engine. For details and pricing, see Data preview.

Flink SQL

To run the SQL examples using Flink, a DLF catalog must be configured in Flink. For detailed configuration steps, see Access DLF with Flink SQL.

Rank per-table compute consumption for a given day

SELECT
    database_name,
    table_name,
    table_id,
    SUM(cu) AS total_cu
FROM `<yourCatalogName>`.`system`.`compute_usage`
WHERE dt = '<date>'  -- Replace with the actual query date in yyyyMMdd format
GROUP BY database_name, table_name, table_id
ORDER BY total_cu DESC;

Track total compute consumption for a catalog

By day

SELECT
    dt,
    SUM(cu) AS daily_cu,
    min(start_time) AS min_start_time,
    max(start_time) AS max_start_time
FROM `<yourCatalogName>`.`system`.`compute_usage`
WHERE dt >= '<startDate>'  -- Replace with the actual start date
    AND dt <= '<endDate>'  -- Replace with the actual end date
GROUP BY dt
ORDER BY dt;

By hour

SELECT
    start_time,
    end_time,
    SUM(cu) AS hourly_cu
FROM `<yourCatalogName>`.`system`.`compute_usage`
WHERE dt = '<date>'  -- Replace with the actual query date
GROUP BY start_time, end_time
ORDER BY start_time;

Track compute consumption for a single table

By day

SELECT
    dt,
    SUM(cu) AS daily_cu,
    min(start_time) AS min_start_time,
    max(start_time) AS max_start_time
FROM `<yourCatalogName>`.`system`.`compute_usage`
WHERE table_id = '<yourTableId>'
    AND dt >= '<startDate>'  -- Replace with the actual start date
    AND dt <= '<endDate>'  -- Replace with the actual end date
GROUP BY dt
ORDER BY dt;

By hour

SELECT
    start_time,
    end_time,
    SUM(cu) AS hourly_cu
FROM `<yourCatalogName>`.`system`.`compute_usage`
WHERE table_id = '<yourTableId>'
    AND dt = '<date>'  -- Replace with the actual query date
GROUP BY start_time, end_time
ORDER BY start_time;