Using Amazon Redshift with Lookout for Metrics - Amazon Lookout for Metrics

Using Amazon Redshift with Lookout for Metrics

You can use Amazon Redshift as a datasource for an Amazon Lookout for Metrics detector. With Amazon Redshift, you can choose columns to monitor (measures) and columns that segment measure values (dimensions). The detector monitors the values in these columns to find anomalies in your data.


Lookout for Metrics can only connect to databases in a subset of Availability Zones in some Regions. The following Availability Zones are supported.

  • US East (N. Virginia)use1-az1,use1-az4, use1-az6

  • US West (Oregon)usw2-az1, usw2-az2, usw2-az3

  • Asia Pacific (Tokyo)apne1-az1, apne1-az2, apne1-az4

  • Other Regions – All Availability Zones.

Availability Zone names such as us-west-2a are aliases for zone IDs that vary by account. To see which names map to which IDs in your account, visit the EC2 dashboard in the AWS Management Console.

To use an Amazon Redshift data warehouse with Lookout for Metrics, the table must have a timestamp column that is defined as the sort key. Amazon Redshift uses the sort key to store data on disk and construct plans that exploit the way that the data is stored. For more information, see Working with sort keys in the Amazon Redshift Database Developer Guide.

You also need an AWS Secrets Manager secret for the detector. The secret must have the database password and have a name that starts with AmazonLookoutMetrics-.

The detector imports data at the end of each interval. You configure an offset to allow time after an interval ends for all data to be written. For example, if you choose an offset of 30 seconds, the detector waits 30 seconds after the end of each interval before reading data for that interval.

Before you configure the dataset, you need to know the following information.

  • DB identifier – The unique identifier of the DB instance or cluster. For example, mysql-dbi or ld1xmplvzghgn47.

  • Database name – The software-level database name. For example, mydb.

  • Table name – The name of the table. For example, events.

  • Column names – The names of columns that contain timestamps, measures, and dimensions.

  • Subnets – The virtual private cloud (VPC) subnets where the detector creates network interfaces to connect to the database. For example, subnet-0752xmpl92bf2e4b7.

  • Security group – A VPC security group that allows traffic to the database. For example, sg-0f92xmplfbad0bc95.

  • Secret name – The name of an AWS Secrets Manager secret that the detector uses to retrieve the database password. For example, AmazonLookoutMetrics-mysqldbi.

  • Secret ID – The ID of the secret, for generating a service role that can access it. For example, AmazonLookoutMetrics-mysqldbi-Nxmplo.

To create an Amazon Redshift dataset
  1. Open the Lookout for Metrics console Detectors page.

  2. Choose a detector.

  3. Choose Add dataset.

  4. Choose Amazon Redshift.

  5. Follow the instructions to create the datasource.

To configure metrics in Lookout for Metrics, you choose columns to be measures and dimensions. Each measure is a column with a numerical value that you want to monitor for anomalies. Each dimension is a column with a string value that segments the measure(s). A metric in Lookout for Metrics is a combination of a measure value and a dimension value, aggregated within an interval. For example, average availability in Colorado, or maximum temperature in furnace 17.

The detector reads new data from Amazon Redshift periodically, by querying records with timestamps in the most recently completed interval. If it detects any anomalies in the metrics for the interval, it records an anomaly and sends anomaly alerts, if configured.

When you activate the detector, it uses data from several intervals to learn, before attempting to find anomalies. For a five minute interval, the training process takes approximately one day. Training time varies depending on the detector's interval.


When you add an Amazon Redshift dataset to your detector, the Lookout for Metrics console creates a service role with permission to use the database secret and monitor Amazon Redshift resources. Lookout for Metrics also creates up to two elastic network interfaces, which allow it to connect to your VPC to access your database. When you delete the detector, Lookout for Metrics deletes the network interfaces.

For more information about Amazon Redshift, see Getting started with Amazon Redshift in the Amazon Redshift Getting Started Guide.

Sample IAM policies

The GitHub repository for this guide provides sample IAM policies that you can use as reference for developing service roles. You can use a single role that grants permission for both importing data and sending alerts by combining the applicable policies.

Example datasource-redshift.json – Monitor and access an Amazon Redshift cluster
{ "Version": "2012-10-17", "Statement": [ { "Action": [ "redshift:DescribeClusters", "redshift:DescribeClusterSubnetGroups" ], "Resource": "*", "Effect": "Allow" }, { "Action": [ "secretsmanager:GetSecretValue" ], "Resource": [ "arn:aws:secretsmanager:${Region}:${Account}:secret:${SecretId}" ], "Effect": "Allow", "Condition": { "ForAllValues:StringEquals": { "secretsmanager:VersionStage": "AWSCURRENT" } } }, ...

The second sample policy shows how to grant the detector permission to connect to a cluster across accounts. The account with the cluster (Account B) must be in the same organization and share its subnet with the account that contains the detector (AccountA).

Example datasource-redshift-xaccount.json – Cross-account access
... { "Action": [ "ec2:CreateNetworkInterface" ], "Resource": [ "arn:aws:ec2:${Region}:${AccountA}:network-interface/*", "arn:aws:ec2:${Region}:${AccountA}:security-group/*", "arn:aws:ec2:${Region}:${AccountB}:subnet/${SubnetId}" ], "Effect": "Allow" }, ...

For more information, see Working with shared VPCs in the Amazon VPC User Guide.