Monitoring metrics in DynamoDB with Amazon CloudWatch - Amazon DynamoDB
How do I use DynamoDB metrics?Viewing metrics in the CloudWatch consoleViewing metrics in the AWS CLI

Monitoring metrics in DynamoDB with Amazon CloudWatch

You can monitor DynamoDB using CloudWatch, which collects and processes raw data from DynamoDB into readable, near real-time metrics. These statistics are retained for a period of time, so you can access historical information for a better perspective on how your web application or service is performing. By default, DynamoDB metric data is sent to CloudWatch automatically. For more information, see What is Amazon CloudWatch? and Metrics retention in the Amazon CloudWatch User Guide.

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How do I use DynamoDB metrics?

The metrics reported by DynamoDB provide information that you can analyze in different ways. The following list shows some common uses for the metrics. These are suggestions to get you started, not a comprehensive list.

How do I use DynamoDB metrics?

How can I?

Relevant metrics

How can I monitor the rate of TTL deletions on my table?

You can monitor TimeToLiveDeletedItemCount over the specified time period, to track the rate of TTL deletions on your table. For an example of a server-less application using the TimeToLiveDeletedItemCount metric, see Automatically archive items to S3 using DynamoDB time to live (TTL) with AWS Lambda and Amazon Data Firehose.

How can I determine how much of my provisioned throughput is being used?

You can monitor ConsumedReadCapacityUnits or ConsumedWriteCapacityUnits over the specified time period, to track how much of your provisioned throughput is being used.

How can I determine which requests exceed the provisioned throughput quotas of a table?

ThrottledRequests is incremented by one if any event within a request exceeds a provisioned throughput quota. Then, to gain insight into which event is throttling a request, compare ThrottledRequests with the ReadThrottleEvents and WriteThrottleEvents metrics for the table and its indexes.

How can I determine if any system errors occurred?

You can monitor SystemErrors to determine if any requests resulted in an HTTP 500 (server error) code. Typically, this metric should be equal to zero. If it isn't, then you might want to investigate.

How can I monitor the latency value for my table operations?

You can monitor SuccessfulRequestLatency and track the average latency. Occasional spikes in latency aren't a cause for concern. However, if average latency is high, there could be an underlying issue that you must resolve. See Troubleshooting latency issues in Amazon DynamoDB for more information.

Viewing metrics in the CloudWatch console

Metrics are grouped by the service namespace first and then by the various dimension combinations within each namespace.

To view metrics in the CloudWatch console

  1. Open the CloudWatch console at https://console.aws.amazon.com/cloudwatch/.

  2. In the navigation pane, choose Metrics, All metrics.

  3. Select the DynamoDB namespace. You can also select Usage namespace to view DynamoDB usage metrics. For more information about usage metrics, see AWS usage metrics.

  4. The Browse tab displays all metrics in the namespace.

  5. (Optional) To add the metric graph to a CloudWatch dashboard, choose Actions, Add to dashboard.

Viewing metrics in the AWS CLI

To obtain metric information using the AWS CLI, use the CloudWatch command list-metrics. In the following example, you list all metrics in the AWS/DynamoDB namespace.


                aws cloudwatch list-metrics --namespace "AWS/DynamoDB"

To obtain metric statistics, use the command get-metric-statistics. The following command gets ConsumedReadCapacityUnits statistics for the table ProductCatalog over the specific 24-hour period, with a 5-minute granularity.

aws cloudwatch get-metric-statistics —namespace AWS/DynamoDB \
     —metric-name ConsumedReadCapacityUnits \
     —start-time 2023-11-01T00:00:00Z \
     —end-time 2023-11-02T00:00:00Z \
     —period 360 \
     —statistics Average \
     —dimensions Name=TableName,Value=ProductCatalog

Sample output appears as follows:

{
    "Datapoints": [
        {
            "Timestamp": "2023-11-01T 09:18:00+00:00", 
            "Average": 20, 
            "Unit": "Count"
        }, 
        {
            "Timestamp": "2023-11-01T 04:36:00+00:00", 
            "Average": 22.5, 
            "Unit": "Count"
        }, 
        {
            "Timestamp": "2023-11-01T 15:12:00+00:00", 
            "Average": 20, 
            "Unit": "Count"
        }, ...
        {
            "Timestamp": "2023-11-01T 17:30:00+00:00", 
            "Average": 25, 
            "Unit": "Count"
        }
    ], 
    "Label": " ConsumedReadCapacityUnits "
}