AWS Big Data Blog

AI-powered performance recommendations for Amazon Redshift

Data platform teams running Amazon Redshift collect performance telemetry across system views like SYS_QUERY_HISTORY, SVV_TABLE_INFO, and SVV_ALTER_TABLE_RECOMMENDATIONS, plus Amazon CloudWatch metrics for capacity, query execution, and storage. The challenge is interpretation. Correlating a spike in QueryRuntimeBreakdown commit time with hundreds of small INSERT statements, or connecting high disk spill with undersized compute, takes deep expertise and hours of manual analysis.

In this post, you learn how to build an AI-powered solution that collects the telemetry, pre-computes performance signals, correlates them with CloudWatch, and uses Amazon Bedrock to generate prioritized recommendations. The source code is in the accompanying GitHub repository: sample-ai-performance-advisor-for-amazon-redshift.

The signal-based design is what makes this solution produce precise recommendations rather than generic advice. Instead of dumping raw system view output into the large language model (LLM) prompt, the collector pre-computes boolean and threshold-based findings, pairs them with CloudWatch correlations, and hands the model a structured context. The model then cross-references specific query IDs, table names, and metric values in its output.

Solution overview

Two AWS Lambda functions run on a 24-hour Amazon EventBridge schedule:

  • The collector Lambda runs 13 diagnostic SQL queries against Amazon Redshift Serverless and reads the workgroup’s Workload Management (WLM) configuration. It also collects CloudWatch metrics across capacity, query execution, WLM, connections, and storage. From these inputs, it computes the performance signals. Finally, it writes a telemetry JSON file to Amazon Simple Storage Service (Amazon S3).
  • The analyzer Lambda reads the telemetry from Amazon S3, builds a structured prompt with inline CloudWatch-to-signal correlations. Using the correlations, the analyzer calls Amazon Bedrock (Anthropic Claude Sonnet 4.6), and writes the resulting recommendations JSON back to Amazon S3.
  • An Amazon Simple Notification Service (Amazon SNS) topic sends an email summary of the top recommendations to subscribers.
AWS architecture diagram showing an automated Redshift analysis pipeline within the AWS Cloud. Amazon EventBridge triggers a “Collector” AWS Lambda function, which interacts bidirectionally with AWS Secrets Manager, Amazon Redshift, and Amazon CloudWatch to gather data. The Collector passes results to an “Analyzer” AWS Lambda function, which exchanges data with Amazon Bedrock and reads/writes to Amazon S3. The Analyzer then publishes to Amazon Simple Notification Service (SNS), which delivers an email notification.

Figure 1 – Architecture diagram

Prerequisites

Before deploying the solution, make sure the following are in place.

  • An Amazon Redshift Serverless workgroup with a database and query history.
  • An Amazon Redshift database administrator user (superuser). The collector reads views that only a superuser can query (SVV_TABLE_INFO, SVV_ALTER_TABLE_RECOMMENDATIONS, SVV_MV_INFO, SYS_SERVERLESS_USAGE, SYS_AUTO_TABLE_OPTIMIZATION).
    Store the admin credentials in AWS Secrets Manager and pass the secret ARN to the collector.
    Alternatively, have an existing superuser run ALTER USER "IAMR:redshift-performance-recommendations-role" CREATEUSER;
    once to grant the Lambda role superuser privileges.
  • Amazon Bedrock model access for the model of choice. For this solution, a us.anthropic.claude-* model is recommended for multi-region inference. The solution doesn’t depend on a single model.
  • The AWS Command Line Interface (AWS CLI) installed and configured, and a clone of the GitHub repository.

Create the supporting resources

You need an Amazon S3 bucket, an Amazon SNS topic, an AWS Secrets Manager secret, and an AWS Identity and Access Management (IAM) role before the Lambda functions can run.

Create the Amazon S3 bucket

The Amazon S3 bucket will host the output report.

  • Open the Amazon S3 console and choose Create bucket.
  • Enter a globally unique name (for example, amzn-s3-demo-bucket), keep the default settings, and choose Create bucket.

The collector writes telemetry JSON under the telemetry/ prefix and the analyzer writes recommendations under the recommendations/ prefix.

Create the Amazon SNS topic and subscription

Use Amazon SNS to generate notifications once reports are created.

  • Open the Amazon SNS console and choose Topics, Create topic.
  • Select Standard, and enter the name redshift-performance-recommendations.
  • Choose Create topic.
  • On the topic detail page, choose Create subscription.
  • Select Email as the protocol, enter your email address, and choose Create subscription.
  • Open the confirmation email from AWS Notifications and choose Confirm subscription.
Amazon SNS “Create topic” console page. The Type is set to Standard (selected over FIFO), and the Name field contains “redshift-performance-recommendations.” Annotation arrows highlight the Topics nav item, the Standard topic type, the entered name, and the “Create topic” button in the lower right. Optional sections for Encryption, Access policy, Delivery policy, Message delivery status logging, Tags, and Active tracing are collapsed below.

Figure 2 – Create SNS Topic

Store the admin credentials in AWS Secrets Manager

To avoid using hard-coded credentials, create an AWS Secrets Manager secret to connect to Amazon Redshift.

  • Open the AWS Secrets Manager console and choose Store a new secret.
  • Select Other type of secret, choose the Plaintext tab, and paste the following, replacing <ADMIN_PASSWORD> with the workgroup’s admin password: 
    {"username":"admin","password":"<ADMIN_PASSWORD>"}
  • Choose Next, enter redshift-performance-admin as the secret name, then choose Next, Next, and Store.
  • Copy the secret Amazon Resource Name (ARN) from the secret detail page. You pass it to the collector in a later step.
AWS Secrets Manager “Store a new secret” page, Step 1: Choose secret type. “Other type of secret” is selected, and the Plaintext tab shows the key-value pair {“username”:“admin”,“password”:“”}. The encryption key is set to aws/secretsmanager. Annotation arrows highlight the secret type selection, the plaintext credentials, and the “Next” button in the lower right.

Figure 3 – Create secret

Create the IAM role and attach the policy

The repository includes a trust policy in iam/trust-policy.json (allowing lambda.amazonaws.com to assume the role) and the least-privilege permission policy in iam/lambda-role-policy.json. Replace the <ACCOUNT_ID>, <REGION>, <YOUR_BUCKET>, and SNS topic ARN placeholders in the permission policy with your values, then create the role in the AWS Management Console or with this AWS CLI command:

aws iam create-role --role-name redshift-performance-recommendations-role \
    --assume-role-policy-document file://iam/trust-policy.json

aws iam put-role-policy --role-name redshift-performance-recommendations-role \
    --policy-name redshift-performance-policy \
    --policy-document file://iam/lambda-role-policy.json

The permission policy grants the Amazon Redshift Data API, Amazon S3, Amazon SNS, Amazon Bedrock, AWS Lambda invoke, AWS Secrets Manager, and Amazon CloudWatch Logs permissions that both Lambda functions require.

Deploy the Lambda functions

The collector source is in lambda/collector.py and it loads the SQL files in sql/ at runtime. The deployment package must contain both.

Package the collector

Open a terminal or shell window and execute a command to copy the collector code, supporting SQL into a folder and archive.

mkdir -p build/collector/sql
cp lambda/collector.py build/collector/
cp sql/*.sql build/collector/sql/
(cd build/collector && zip -qr ../collector.zip .)

Create the collector function

Using the AWS Management Console, navigate to AWS Lambda.

  • Choose Create function.

    AWS Lambda “Create function” console page with the “Configure custom execution role” panel open on the right. “Author from scratch” is selected, the function name is “redshift-performance-collector,” and the runtime is Python 3.14. Under Additional settings, the “Custom execution role” toggle is enabled, and the execution role list is set to “redshift-performance-recommendations-role.” Annotation highlights mark the Author from scratch option, function name, runtime, custom execution role toggle, the selected role, the Save button, and the “Create function” button.

    Figure 4 – Create AWS Lambda function

  • Select Author from scratch, enter redshift-performance-collector as the name, and select Python 3.14.
  • Expand Custom settings, toggle Custom execution role, choose an existing role, select redshift-performance-recommendations-role, and choose Save.
  • On the function page, choose Upload from, .zip file, and upload build/collector.zip.
  • In Runtime settings, select Edit, and set the Handler to collector.lambda_handler.

    Lambda console for the “redshift-performance-collector” function, Code tab. The code editor shows collector.py — a Python file that runs diagnostic SQL queries against Amazon Redshift Serverless, collects CloudWatch metrics, writes telemetry to Amazon S3, and invokes the analyzer Lambda. The Runtime settings section below shows the Handler highlighted as “lambda_function.lambda_handler,” with an arrow pointing to the Edit button and the “Upload from .zip file” option highlighted.

    Figure 5 – Set AWS Lambda handler

  • Choose Configuration, Edit, set timeout to 5 minutes, and memory to 256 MB.

    Lambda console for “redshift-performance-collector,” Configuration tab with “General configuration” selected. The panel shows Memory 128 MB, Ephemeral storage 512 MB, and Timeout 0 min 3 sec, with SnapStart set to None. Annotation arrows point to the General configuration menu item and the Edit button.

    Figure 6 – Set AWS Lambda timeout and memory

  • Under Configuration, select Environment variables, and add the following keys:
    • WORKGROUP: your Amazon Redshift Serverless workgroup name.
    • NAMESPACE_NAME: the namespace the workgroup belongs to.
    • DATABASE: dev (or your target database).
    • BUCKET: the Amazon S3 bucket name you created earlier.
    • SECRET_ARN: the AWS Secrets Manager secret ARN you copied earlier.
    • ANALYZER_FN: redshift-performance-analyzer.

Package and create the analyzer

Repeat the same steps for the analyzer, using lambda/analyzer.py with a 15-minute timeout:

(cd lambda && zip -q ../build/analyzer.zip analyzer.py)

Use the Lambda console to create redshift-performance-analyzer with handler analyzer.lambda_handler, timeout 15 minutes, memory 256 MB, the same execution role, and these environment variables:

  • BUCKET: the same Amazon S3 bucket.
  • SNS_TOPIC: the SNS topic ARN.
  • MODEL_ID: us.anthropic.claude-sonnet-4-6.

The analyzer creates the Amazon Bedrock client with read_timeout=600 and max_tokens=16384 to handle large prompts and long responses. Anthropic Claude inference on a full telemetry payload typically takes 2–4 minutes.

How the signals and the prompt work

You don’t write any custom code for signal computation or prompt construction. Both computation and construction live in the repository.

The compute_signals() function in lambda/collector.py scans the telemetry for Boolean and threshold-based anti-patterns. At the table level, it looks for row skew, ghost rows, stale statistics, unsorted data, sub-optimal sort or distribution keys, and oversized VARCHAR columns. It also flags runtime and workload issues such as disk spill, small-insert bursts, high Data Definition Language (DDL) executions, and unoptimized COPY file size. Beyond that, it catches Amazon Redshift Spectrum queries that fail to prune partitions and data sharing materialized views doing full refresh. It also flags WLM configurations that lack Query Monitoring Rules (QMR), such as limits on blocks spilled to disk and query execution time. The full set of signals and thresholds is defined inline in the function. To tune a threshold or add a custom signal, edit this function and redeploy.

The build_prompt() function in lambda/analyzer.py constructs the Amazon Bedrock prompt in four sections. The first section lists the triggered signals. The second adds CloudWatch metrics, annotated with >> CORRELATION lines that pair each signal with its supporting metric. The third includes the filtered supporting data, limited to the table and query rows that triggered a signal. The fourth gives explicit instructions to return a pipe delimited text where every recommendation references specific table names, query IDs, and metric values. This structure is why the model produces targeted output rather than generic best-practice advice.

Schedule daily runs

Use the Amazon EventBridge console to trigger the collector every 24 hours.

  • Open the EventBridge console and choose Schedules under Scheduler, Create schedule.
  • Enter the name redshift-performance-daily for Schedule name, toggle Recurring schedule and Rate-based schedule.
  • Under Rate expression, enter 24 and select hours.
  • For Flexible time window, choose Off, and select Next.
    Amazon EventBridge Scheduler “Create schedule” page, Step 1: Specify schedule detail. The schedule name is “redshift-performance-daily.” Under Schedule pattern, “Recurring schedule” and “Rate-based schedule” are selected, with a rate expression of 24 hours, and the time zone set to (UTC-06:00) America/Denver. Annotation highlights mark the Schedules nav item, the recurring/rate-based selections, the rate expression, and the Next button.

    Figure 7 – Create Amazon EventBridge schedule

     

  • On the Select target page, choose AWS Lambda, select the redshift-performance-collector function, and choose Next.

    EventBridge Scheduler “Create schedule” page, Step 2: Select target. “Templated targets” is selected and the AWS Lambda “Invoke” target is chosen from the grid of target options. In the Invoke section, the Lambda function list is set to “redshift-performance-collector” with an empty JSON payload. Annotation highlights mark the Templated targets toggle, the AWS Lambda Invoke target, the selected function, and the Next button.

    Figure 8 – Select Amazon EventBridge schedule target

  • Accept the defaults for Settings and select Next. EventBridge automatically adds a resource-based permission on the Lambda function so the rule can invoke it.
  • Choose Create schedule.

Run it once and review the output

Invoke the collector manually to confirm the pipeline works end-to-end.

  • In the Lambda console, open the redshift-performance-collector function and choose Test. Create a test event named manual with the body {} and choose Test.

    Lambda console for “redshift-performance-collector,” Test tab. A new test event named “manual” is being configured with Invocation type set to Synchronous, event sharing set to Private, the “Hello World” template selected, and an empty {} Event JSON body. Annotation arrows point to the function in the left nav, the Synchronous option, the event name, the Event JSON field, and the Test button.

    Figure 9 – Test end-to-end workflow

  • The function completes in under a minute. Check the Monitor tab for the invocation log via the CloudWatch live logs link.
  • In the Amazon S3 console, open your bucket. Confirm that the telemetry/ prefix contains a JSON file with the current timestamp.
  • Within 2–4 minutes, the analyzer publishes a message to the SNS topic. Check the email address you subscribed for the summary with the top 10 recommendations. Confirm that the recommendations/ prefix in Amazon S3 contains the full JSON.

Each recommendation has a priority (critical, high, medium, low) and a category (query_optimization, table_design, capacity, wlm, maintenance, or ingestion). It also includes a signal_source that names the signals and CloudWatch metrics that triggered it, a plain-language explanation, a specific SQL or configuration action, and an expected impact estimate.

Email notification from AWS Notifications with the subject “Redshift performance: 3 critical, 5 high, 4 medium, 2 low (8 signals)” highlighted. The body is a plain-text “Amazon Redshift Performance Recommendations” report listing workgroup, namespace, database, analysis time, and 14 recommendations. Two critical items are shown for the game_events table: fixing extreme row-skew via DISTSTYLE ALL, and eliminating non-encoded columns with column compression, each with a category, source, explanation, SQL action, and expected impact.

Figure 10 – Sample analyzer emailed output

Best practices

  • Tune thresholds to your workload. The default thresholds in compute_signals() come from the Amazon Redshift operational review playbook. For high-velocity ingestion or small-cluster environments, consider lowering the small-insert threshold, widening the stale-statistics window, or adding custom signals for your own tables.
  • Keep the signal-to-metric correlations current. When you add a signal, also add a matching correlation in build_correlations(). The inline >> CORRELATION lines are what make the model connect an infrastructure metric to an application-level symptom.
  • Review recommendations before you act. The analyzer produces prioritized suggestions, but VACUUM, ANALYZE, and ALTER TABLE actions change table state. Read the explanation and action on each recommendation, validate the SQL against your schema, and run it during a maintenance window.

Cleaning up

To avoid ongoing charges, delete the resources you created for this solution:

  • The two AWS Lambda functions: redshift-performance-collector and redshift-performance-analyzer.
  • The Amazon EventBridge rule: redshift-performance-daily.
  • The Amazon SNS topic and its email subscription: redshift-performance-recommendations.
  • The Amazon S3 bucket, including the telemetry/ and recommendations/ objects.
  • The AWS Secrets Manager secret: redshift-performance-admin.
  • The IAM role and its inline policy: redshift-performance-recommendations-role.

Conclusion

You now have a daily performance review for Amazon Redshift Serverless that runs entirely on AWS Lambda, stores every run in Amazon S3, and delivers prioritized recommendations by email. The signal-based prompt pattern keeps the Amazon Bedrock cost low and the recommendations specific to your workload.

To learn more, see the following resources:

About the authors

Steve Phillips

Steve Phillips

Steve is a Principal Technical Account Manager and Analytics specialist at AWS in the North America region. Steve currently focuses on data warehouse architectural design, AI/ML data foundations, data lakes, data ingestion pipelines, and cloud distributed architectures.

Richard Raseley

Richard Raseley

Richard is a Senior Technical Account Manager in North America who works with Games customers. He is passionate about applying his background in automation, cloud computing, networking, and storage to help customers build AI solutions.