Visualize Exadata Warehouse Metrics

Exadata Warehouse provides multiple options for visualizing historical and forecast metric data for capacity planning, forecasting, and resource analysis.

Visualize Exadata Warehouse metric data using one of the following methods:

Use the Metrics Dashboard to visualize historical and forecasted capacity utilization and resource usage. For information, see Metrics Dashboard.
Create a custom dashboard in Enterprise Manager to visualize Exadata Warehouse metric data. Add a custom query-based widget to the dashboard and, when configuring the widget using custom SQL, select EM Target Database as the data source and select Exadata Warehouse. For information, see Using Dashboards in Oracle Enterprise Manager Monitoring Guide.

Exadata Warehouse Metrics

Exadata Warehouse collects a curated set of metrics from supported targets for capacity planning, forecasting, and resource analysis.

Exadata Warehouse collects metrics from ASM cluster, Exadata Storage Server, Exadata compute node, and ZDLRA targets and provides coverage for the following areas:

CPU/vCPU and memory consumption
Storage utilization
I/O capacity, utilization, and performance
Network performance

The following table lists the default metrics collected by Exadata Warehouse for each supported target type.

ASM Cluster	Exadata Storage Server	Exadata Compute Node	ZDLRA
Disk Group Physical Free (MB) Disk Group Free (MB) Disk Group Used % Used % of Safely Usable Physical Size (MB) Size (MB) Disk Group Usable Free (MB) Disk Group Usable (MB)	Large Read/Write Throughput Small Read/Write Throughput Large Write Throughput Small Write Throughput I/O Load Large Read Requests Small Read Requests Large Write Requests Small Write Requests Average Large Read Response Time Average Small Read Response Time Average Large Write Response Time Average Small Write Response Time I/O Utilization Flash Cache Size Flash Cache Used Read Throughput for Scan Read Throughput Redirected to Disk for Scan Read Throughput Redirected to Disk Read IOPS for Random I/O	CPU Utilization Memory Utilization Network Read Rate Network Write Rate	Storage Locations Unused Space (GB) Storage Locations Size (GB) Storage Locations Recovery Window Space (GB) Number of Protected Databases

Exadata Warehouse Schema

Exadata Warehouse schema provides views for accessing target, historical metric, and forecast data stored in Exadata Warehouse.

Oracle provides the following views to access data in Exadata Warehouse:

XAWH_TARGETS

The XAWH_TARGETS view contains information about the targets for which data is collected in Exadata Warehouse.

Column	Data type
`EM_ID`	`VARCHAR2(32 BYTE)`
`TARGET_GUID`	`VARCHAR2(32 BYTE)`
`PARENT_GUID`	`VARCHAR2(32 BYTE)`
`TARGET_NAME`	`VARCHAR2(256 BYTE)`
`TARGET_TYPE`	`VARCHAR2(64 BYTE)`
`TIMEZONE`	`VARCHAR2(64 BYTE)`
`EM_URL`	`VARCHAR2(256 BYTE)`
`LAST_DATA_COLLECTION_TIME`	`DATE`
`LATEST_UPLOAD_TO_WAREHOUSE`	`DATE`

XAWH_MINUTES_METRIC_DATA

The XAWH_MINUTES_METRIC_DATA view stores historical metric data with per-minute granularity.

Column	Data type
`EM_ID`	`VARCHAR2(32 BYTE)`
`TARGET_NAME`	`VARCHAR2(256 BYTE)`
`TARGET_TYPE`	`VARCHAR2(64 BYTE)`
`METRIC_NAME`	`VARCHAR2(64 BYTE)`
`METRIC_COLUMN`	`VARCHAR2(64 BYTE)`
`KEY_PART_1`	`VARCHAR2(256 BYTE)`
`KEY_PART_2`	`VARCHAR2(256 BYTE)`
`KEY_PART_3`	`VARCHAR2(256 BYTE)`
`KEY_PART_4`	`VARCHAR2(256 BYTE)`
`KEY_PART_5`	`VARCHAR2(256 BYTE)`
`KEY_PART_6`	`VARCHAR2(256 BYTE)`
`KEY_PART_7`	`VARCHAR2(256 BYTE)`
`METRIC_UNIT`	`VARCHAR2(32 BYTE)`
`METRIC_TIME`	`DATE`
`METRIC_VALUE`	`NUMBER`

XAWH_HOURLY_METRIC_DATA

The XAWH_HOURLY_METRIC_DATA view stores historical metric data with per-hour granularity.

Column	Data type
`EM_ID`	`VARCHAR2(32 BYTE)`
`TARGET_NAME`	`VARCHAR2(256 BYTE)`
`TARGET_TYPE`	`VARCHAR2(64 BYTE)`
`METRIC_NAME`	`VARCHAR2(64 BYTE)`
`METRIC_COLUMN`	`VARCHAR2(64 BYTE)`
`KEY_PART_1`	`VARCHAR2(256 BYTE)`
`KEY_PART_2`	`VARCHAR2(256 BYTE)`
`KEY_PART_3`	`VARCHAR2(256 BYTE)`
`KEY_PART_4`	`VARCHAR2(256 BYTE)`
`KEY_PART_5`	`VARCHAR2(256 BYTE)`
`KEY_PART_6`	`VARCHAR2(256 BYTE)`
`KEY_PART_7`	`VARCHAR2(256 BYTE)`
`METRIC_UNIT`	`VARCHAR2(32 BYTE)`
`METRIC_TIME`	`DATE`
`MAX_VALUE`	`NUMBER`
`MIN_VALUE`	`NUMBER`
`MEAN_VALUE`	`NUMBER`
`NUM_SAMPLES`	`NUMBER`
`PERCENTILE_5`	`NUMBER`
`PERCENTILE_10`	`NUMBER`
`PERCENTILE_15`	`NUMBER`
`PERCENTILE_20`	`NUMBER`
`PERCENTILE_25`	`NUMBER`
`PERCENTILE_30`	`NUMBER`
`PERCENTILE_35`	`NUMBER`
`PERCENTILE_40`	`NUMBER`
`PERCENTILE_45`	`NUMBER`
`PERCENTILE_50`	`NUMBER`
`PERCENTILE_55`	`NUMBER`
`PERCENTILE_60`	`NUMBER`
`PERCENTILE_65`	`NUMBER`
`PERCENTILE_70`	`NUMBER`
`PERCENTILE_75`	`NUMBER`
`PERCENTILE_80`	`NUMBER`
`PERCENTILE_85`	`NUMBER`
`PERCENTILE_90`	`NUMBER`
`PERCENTILE_95`	`NUMBER`
`PERCENTILE_97`	`NUMBER`
`PERCENTILE_99`	`NUMBER`
`STDDEV_VALUE`	`NUMBER`

XAWH_DAILY_METRIC_DATA

The XAWH_DAILY_METRIC_DATA view stores historical metric data with per-day granularity.

Column	Data type
`EM_ID`	`VARCHAR2(32 BYTE)`
`TARGET_NAME`	`VARCHAR2(256 BYTE)`
`TARGET_TYPE`	`VARCHAR2(64 BYTE)`
`METRIC_NAME`	`VARCHAR2(64 BYTE)`
`METRIC_COLUMN`	`VARCHAR2(64 BYTE)`
`KEY_PART_1`	`VARCHAR2(256 BYTE)`
`KEY_PART_2`	`VARCHAR2(256 BYTE)`
`KEY_PART_3`	`VARCHAR2(256 BYTE)`
`KEY_PART_4`	`VARCHAR2(256 BYTE)`
`KEY_PART_5`	`VARCHAR2(256 BYTE)`
`KEY_PART_6`	`VARCHAR2(256 BYTE)`
`KEY_PART_7`	`VARCHAR2(256 BYTE)`
`METRIC_UNIT`	`VARCHAR2(32 BYTE)`
`METRIC_TIME`	`DATE`
`MAX_VALUE`	`NUMBER`
`MIN_VALUE`	`NUMBER`
`MEAN_VALUE`	`NUMBER`
`NUM_SAMPLES`	`NUMBER`
`PERCENTILE_5`	`NUMBER`
`PERCENTILE_10`	`NUMBER`
`PERCENTILE_15`	`NUMBER`
`PERCENTILE_20`	`NUMBER`
`PERCENTILE_25`	`NUMBER`
`PERCENTILE_30`	`NUMBER`
`PERCENTILE_35`	`NUMBER`
`PERCENTILE_40`	`NUMBER`
`PERCENTILE_45`	`NUMBER`
`PERCENTILE_50`	`NUMBER`
`PERCENTILE_55`	`NUMBER`
`PERCENTILE_60`	`NUMBER`
`PERCENTILE_65`	`NUMBER`
`PERCENTILE_70`	`NUMBER`
`PERCENTILE_75`	`NUMBER`
`PERCENTILE_80`	`NUMBER`
`PERCENTILE_85`	`NUMBER`
`PERCENTILE_90`	`NUMBER`
`PERCENTILE_95`	`NUMBER`
`PERCENTILE_97`	`NUMBER`
`PERCENTILE_99`	`NUMBER`
`STDDEV_VALUE`	`NUMBER`

XAWH_FORECASTS

The XAWH_FORECASTS view stores metadata regarding each forecast that has been performed, including information about targets, metrics, model period, forecast steps, and forecast algorithms.

Column	Data type
`FORECAST_NAME`	`VARCHAR2(256 BYTE)`
`EM_ID`	`VARCHAR2(32 BYTE)`
`TARGET_NAME`	`VARCHAR2(256 BYTE)`
`TARGET_TYPE`	`VARCHAR2(64 BYTE)`
`METRIC_NAME`	`VARCHAR2(64 BYTE)`
`METRIC_COLUMN`	`VARCHAR2(64 BYTE)`
`KEY_PART_1`	`VARCHAR2(256 BYTE)`
`KEY_PART_2`	`VARCHAR2(256 BYTE)`
`KEY_PART_3`	`VARCHAR2(256 BYTE)`
`KEY_PART_4`	`VARCHAR2(256 BYTE)`
`KEY_PART_5`	`VARCHAR2(256 BYTE)`
`KEY_PART_6`	`VARCHAR2(256 BYTE)`
`KEY_PART_7`	`VARCHAR2(256 BYTE)`
`MODEL_PERIOD`	`NUMBER`
`FORECAST_PERIOD`	`NUMBER`
`FORECAST_INTERVAL`	`VARCHAR2(64 BYTE)`
`FORECAST_TYPE`	`NUMBER`
`ALGORITHM`	`VARCHAR2(64 BYTE)`
`FORECAST_START_TIME`	`DATE`
`FORECAST_COMPLETION_TIME`	`DATE`
`FORECAST_TIME_SEC`	`NUMBER`
`STATUS`	`NUMBER`
`MODEL_PERIOD_START_TIME`	`DATE`
`MODEL_PERIOD_END_TIME`	`DATE`
`MODEL_PERIOD_DATA_COUNT`	`NUMBER`
`FORECAST_PERIOD_START_TIME`	`DATE`
`FORECAST_PERIOD_END_TIME`	`DATE`
`FORECAST_PERIOD_DATA_COUNT`	`NUMBER`
`LABEL`	`VARCHAR2(256 BYTE)`
`COMMENTS`	`VARCHAR2(256 BYTE)`

XAWH_FORECAST_METRIC_DATA

The XAWH_FORECAST_METRIC_DATA view stores forecasted data for each metric.

Column	Data type
`FORECAST_NAME`	`VARCHAR2(256 BYTE)`
`EM_ID`	`VARCHAR2(32 BYTE)`
`TARGET_NAME`	`VARCHAR2(256 BYTE)`
`TARGET_TYPE`	`VARCHAR2(64 BYTE)`
`METRIC_NAME`	`VARCHAR2(64 BYTE)`
`METRIC_COLUMN`	`VARCHAR2(64 BYTE)`
`KEY_PART_1`	`VARCHAR2(256 BYTE)`
`KEY_PART_2`	`VARCHAR2(256 BYTE)`
`KEY_PART_3`	`VARCHAR2(256 BYTE)`
`KEY_PART_4`	`VARCHAR2(256 BYTE)`
`KEY_PART_5`	`VARCHAR2(256 BYTE)`
`KEY_PART_6`	`VARCHAR2(256 BYTE)`
`KEY_PART_7`	`VARCHAR2(256 BYTE)`
`METRIC_UNIT`	`VARCHAR2(32 BYTE)`
`METRIC_TIME`	`DATE`
`METRIC_VALUE`	`NUMBER`
`METRIC_VALUE_LOW`	`NUMBER`
`METRIC_VALUE_HIGH`	`NUMBER`

Exadata Warehouse Sample Queries

The following examples use historical Exadata Warehouse data to help identify systems for capacity planning.

Top 10 Exadata Targets by Average CPU Usage: This query returns up to 10 Exadata Database Machines, ranked by average CPU usage over the selected historical period. It uses daily historical data. The output includes the target name and average CPU usage. For information, see Top 10 Exadata Targets Based on Average CPU Usage Over a Chosen Time Period in Oracle Enterprise Manager Oracle Exadata Database Machine Getting Started Guide.
Exadata Targets with Underutilized IOPS: This query identifies Exadata targets with low IOPS usage over the selected historical period. It uses cell disk read and write request metrics for small and large blocks. The output includes the Exadata target, storage server, disk type, and total IOPS. For information, see Exadata Targets with Under-utilized IOPS in Oracle Enterprise Manager Oracle Exadata Database Machine Getting Started Guide.