Monitoring a Cloud Bigtable Instance

You can monitor your Cloud Bigtable instance using the graphs that are available in the Google Cloud Platform Console, or by using Stackdriver Monitoring.

Monitoring an instance with the GCP Console

The Google Cloud Platform Console provides several ways to monitor your Cloud Bigtable instance. You can view information about any of the following metrics:

  • Data stored: The amount of data stored in the instance's cluster. This metric reflects the fact that Cloud Bigtable compresses your data when it is stored.
  • Read requests: The number of random reads and scan requests per second.
  • Write requests: The number of write requests per second.
  • Rows read: The number of rows read per second. This metric provides a more useful view of Cloud Bigtable's overall throughput than the number of read requests, because a single request can read a large number of rows.
  • Rows written: The number of rows written per second. This metric provides a more useful view of Cloud Bigtable's overall throughput than the number of write requests, because a single request can write a large number of rows.
  • Read throughput: The number of uncompressed bytes per second that were read.
  • Write throughput: The number of uncompressed bytes per second that were written.
  • Error rate: The percentage of all requests that failed on the Cloud Bigtable server side.
  • Disk load: The total utilization of HDD disks across all of the nodes in the instance's cluster. Available only for HDD clusters.
  • Node count: The number of nodes in the instance's cluster.
  • CPU utilization: The average CPU utilization across all nodes in the instance's cluster.
  • CPU utilization of hottest node: The CPU utilization of the busiest node in the instance's cluster (the node with the highest CPU utilization). Exceeding the recommended maximum for your busiest node can cause latency and other issues for your cluster, even if the average CPU usage across your nodes is under the maximum. If this graph shows significantly higher usage than your average CPU utilization, it may indicate that a process is generating a relatively large number of requests on one or more rows on a single node. Cloud Bigtable typically redistributes these requests across your nodes within a few minutes, but it cannot do so if the requests are all on a single row—in that case, your graph may remain high for more than a few minutes.

You can view each of these metrics over a period ranging from the past 1 hour to the past 30 days.

To view metrics for a Cloud Bigtable instance:

  1. In the GCP Console, open the list of your existing instances.

    Open the list of instances

  2. Click the name of the instance you want to view.

    The GCP Console displays two charts, one showing CPU utilization over the past four days and another showing the error rate over the past four days. If there is no data available for this time period, the charts will be empty.

    To view a different metric, click CPU utilization or Error rate and choose the metric you want to view.

    To view a different time period, click one of the time periods above the top of the chart.

Monitoring an instance with Stackdriver Monitoring

Cloud Bigtable exports usage metrics that can be monitored programmatically using Stackdriver Monitoring. You can use the Stackdriver Monitoring API or the Metrics Explorer to track Cloud Bigtable usage metrics. In addition, you can set up alerting policies based on usage metrics, and you can create custom dashboards that include Cloud Bigtable usage metrics.

To view usage metrics in the Metrics Explorer:

  1. In the GCP Console, open the Monitoring page.

    Open the Monitoring page

    If you are prompted to choose an account, choose the Google account that you use to access Google Cloud Platform.

  2. Click Resources, then click Metrics Explorer.

  3. Under Find resource type and metric, type bigtable. A list of Cloud Bigtable resources and metrics appears.
  4. Click a metric to view a graph of that metric.

You can also use a graphing library, such as Matplotlib for Python, to plot and analyze the usage metrics for Cloud Bigtable. To learn more, see the tutorial on using Matplotlib with Stackdriver Monitoring and Cloud Bigtable.

For additional information about using Stackdriver Monitoring, see the Stackdriver Monitoring documentation.

What's next

Send feedback about...

Cloud Bigtable Documentation