Querying a database using the query editor - Amazon Redshift

Querying a database using the query editor

Using the query editor is an easy way to run queries on databases hosted by your Amazon Redshift cluster. After creating your cluster, you can immediately run queries by using the query editor on the Amazon Redshift console.


You can't query data in Amazon Redshift Serverless using this original query editor. Use Amazon Redshift query editor v2 instead.

In February 2021, an updated query editor was deployed and authorization permissions to use the query editor changed. The new query editor uses the Amazon Redshift Data API to run queries. The AmazonRedshiftQueryEditor policy, which is an AWS managed AWS Identity and Access Management (IAM) policy, was updated to include the necessary permissions. If you have a custom IAM policy, make sure that you update it. Use AmazonRedshiftQueryEditor as a guide. The changes to AmazonRedshiftQueryEditor include the following:

  • Permission to manage query editor statement results requires the statement owner user.

  • Permission to use Secrets Manager to connect to a database has been added.

For more information, see Permissions required to use the Amazon Redshift console query editor.

When you connect to your cluster from the new query editor, you can use one of two authentication methods, as described in Connecting with the query editor.

Using the query editor, you can do the following:

  • Run single SQL statement queries.

  • Download result sets as large as 100 MB to a comma-separated value (CSV) file.

  • Save queries for reuse. You can't save queries in the Europe (Paris) Region, the Asia Pacific (Osaka) Region, the Asia Pacific (Hong Kong) Region, or the Middle East (Bahrain) Region.

  • View query runtime details for user-defined tables.

  • Schedule queries to run at a future time.

  • View a history of queries that you created in the query editor.

  • Run queries against clusters using enhanced VPC routing.

Query editor considerations

Consider the following about working with queries when you use the query editor:

  • The maximum duration of a query is 24 hours.

  • The maximum query result size is 100 MB. If a call returns more than 100 MB of response data, the call is terminated.

  • The maximum retention time for query results is 24 hours.

  • The maximum query statement size is 100 KB.

  • The cluster must be in a virtual private cloud (VPC) based on the Amazon VPC service.

  • You can't use transactions in the query editor. For more information about transactions, see BEGIN in the Amazon Redshift Database Developer Guide.

  • You can save a query up to 3,000 characters long.

Enabling access to the query editor

To access the query editor, you need permission. To enable access, we recommend that you attach the AmazonRedshiftQueryEditor and AmazonRedshiftReadOnlyAccess AWS managed policies for IAM permissions to the IAM role that you use to access your cluster. Then, you can assign the role to a user. You can use the IAM console (https://console.aws.amazon.com/iam/) to attach IAM policies. For more information, see Using identity-based policies (IAM policies) for Amazon Redshift.

If you have already created a user to access Amazon Redshift, you can attach the AmazonRedshiftQueryEditor and AmazonRedshiftReadOnlyAccess AWS managed policies to that user by means of an assigned role. If you haven't created a user yet, create one and attach the policy to the IAM role and assign the role to the user.

The AWS managed policy AmazonRedshiftQueryEditor allows the action redshift:GetClusterCredentials, which by default gives superuser access to the database. To restrict access, you can do one of the following:

  • Create a custom policy that allows calling redshift:GetClusterCredentials and restricts the resource to a given value for DbUser.

  • Add a policy that denies permission to redshift:GetClusterCredentials. Any user assigned a role with this permission attached must sign in the query editor with temporary credentials. This denial policy illustrates the example.

    { "Version": "2012-10-17", "Statement": { "Effect": "Deny", "Action": "redshift:GetClusterCredentials", "Resource": "*" } }

For more information about creating a role with the required permissions, see Create an IAM role with permissions to call GetClusterCredentials.

Any user granted access to Amazon Redshift query editor by means of the AWS managed policy AmazonRedshiftQueryEditor can list all secrets. However, this policy allows creation and retrieval of only secrets tagged with the key RedshiftQueryOwner and the value ${aws:userid}. If you create the key from the Amazon Redshift query editor, the key is automatically tagged. To use a secret that wasn't created with the Amazon Redshift query editor, confirm that the secret is tagged with the key RedshiftQueryOwner and a value of your unique IAM user identifier, for example AIDACKCEVSQ6C2EXAMPLE.

The required permissions to use the Amazon Redshift query editor are AmazonRedshiftQueryEditor and AmazonRedshiftReadOnlyAccess.

To provide access, add permissions to your users, groups, or roles:

Connecting with the query editor

When you connect to a cluster with the query editor, you use one of the following authentication methods. Each method requires a different combination of input from the Amazon Redshift console.

AWS Secrets Manager

With this method, provide a secret value for secret-arn that is stored in AWS Secrets Manager. This secret contains credentials to connect to your database.

Temporary credentials

With this method, provide your database and db-user values.

Storing database credentials in AWS Secrets Manager

When you call the query editor, you can pass credentials for the cluster by using a secret in AWS Secrets Manager. To pass credentials in this way, you specify the name or the Amazon Resource Name (ARN) of the secret.

For more information about the minimum permissions, see Creating and Managing Secrets with AWS Secrets Manager in the AWS Secrets Manager User Guide.

To store your credentials in a secret for an Amazon Redshift cluster
  1. Use AWS Secrets Manager to create a secret that contains credentials for the cluster. When you choose Store a new secret, choose Credentials for Redshift cluster. Store a value for User name (the database user), Password, and DB cluster (cluster identifier) in your secret.

    For instructions, see Creating a Basic Secret in the AWS Secrets Manager User Guide.

  2. Use the AWS Secrets Manager console to view the details for the secret you created, or run the aws secretsmanager describe-secret AWS CLI command.

If you chose to your cluster's admin credentials using AWS Secrets Manager, you can connect to the database using your Secrets Manager stored admin credentials.

Using the query editor

In the following example, you use the query editor to perform the following tasks:

  • Run SQL commands.

  • View query execution details.

  • Save a query.

  • Download a query result set.

To complete the following example, you need an existing Amazon Redshift cluster. If you don't have a cluster, create one by following the procedure described in Creating a cluster.

To use the query editor on the Amazon Redshift console
  1. Sign in to the AWS Management Console and open the Amazon Redshift console at https://console.aws.amazon.com/redshiftv2/.

  2. On the navigation menu, choose Query editor, then connect to a database in your cluster.

  3. For Schema, choose public to create a new table based on that schema.

  4. Enter the following in the query editor window and choose Run to create a new table.

    create table shoes( shoetype varchar (10), color varchar(10));
  5. Choose Clear.

  6. Enter the following command in the query editor window, and choose Run to add rows to the table.

    insert into shoes values ('loafers', 'brown'), ('sandals', 'black');
  7. Choose Clear.

  8. Enter the following command in the query editor window and choose Run to query the new table.

    select * from shoes;

    The Query results displays the results.

    Shoe type Color





  9. Choose Execution to view the run details.

  10. Choose Data, then Export to download the query results as a file.