Query AWS Redshift System Tables
Overview
Works best with the AWS MCP server for sandboxed execution and audit logging. All commands below use the AWS CLI and work in any environment with configured AWS credentials. Use IAM roles or temporary credentials; avoid long-lived access keys.
Redshift can publish system tables — the SYS_* monitoring data such as sys_query_history, sys_query_detail, and sys_connection_log — to S3 Tables as continuously-updated Apache Iceberg tables.
Terminology used throughout: system table refers to a SYS_* dataset generally, and each one maps 1:1 to a published Iceberg table. Where this skill says SYS_ view, it means specifically the live in-cluster object you query on the cluster itself — that is a view, and it is a different thing from the published S3 Tables copy. This applies to both Provisioned clusters and Serverless namespaces. It is an opt-in extension of the existing logging APIs. Published tables are read-only, stored in the AWS-managed aws-redshift table bucket, and queryable via any Iceberg-compatible engine including Amazon Athena and Amazon Redshift itself.
Querying the S3 Tables copy is preferred over the live in-cluster SYS_ views when analyzing historical or high-volume system-table data because:
- The in-cluster
SYS_ views have a limited retention window; S3 Tables retains history well beyond it.
- Querying S3 Tables adds no load to the running Redshift cluster.
- The logs are Iceberg tables, so they can be queried at scale from any Iceberg-compatible engine and joined with other lake data.
Decision Tree
| User intent | Use this skill? | Alternative |
|---|
| Turn on S3 Tables log publishing for a cluster or namespace | Yes | — |
| Confirm a cluster/namespace is publishing / find its S3 Tables namespace | Yes | — |
| Querying non-realtime data from Redshift system tables | Yes | — |
| Build daily/weekly/monthly dashboard for Redshift monitoring and auditing | Yes | — |
| Selectively stop S3 Tables publishing | Yes | — |
| Query published system tables from Redshift (cross-database) | Yes | — |
| Query published system tables from Athena | Yes | — |
Inspect the current, real-time SYS_ state on a live cluster | No | Query the SYS_ view on the cluster directly |
| Query data inside customer tables | No | Direct Redshift SQL on the cluster |
Supported Data Sources
| Compute type | Enable / disable API | Status API | Granularity options |
|---|
| Redshift Provisioned cluster | redshift enable-logging / redshift disable-logging | redshift describe-logging-status | cluster (default), account |
| Redshift Serverless namespace | redshift-serverless update-namespace with --s3-table-action Enable/Disable | redshift-serverless get-namespace | namespace (default), account |
Both compute types publish into the same AWS-managed aws-redshift table bucket and are queried identically once published. They differ only in the enable/disable API surface and in the casing of the status response — see the flag and field tables in Common Tasks.
Not covered by this skill: Redshift audit logs delivered to S3 or CloudWatch (useractivitylog, userlog, connectionlog), which use the separate --log-exports mechanism on Serverless and are not SYS_* system tables.
Common Tasks
1. Check If Configured
Before querying, confirm the cluster or namespace is publishing to S3 Tables.
# Provisioned
aws redshift describe-logging-status --region <REGION> --cluster-identifier <CLUSTER_ID>
# Serverless
aws redshift-serverless get-namespace --region <REGION> --namespace-name <NAMESPACE_NAME>
Interpret the response. The two compute types return the same information under different field names and casing — Provisioned uses PascalCase under S3Tables, Serverless uses camelCase under namespace.s3TablePublishStatus:
| Meaning | Provisioned (describe-logging-status) | Serverless (get-namespace) |
|---|
| Not enabled | LoggingEnabled: false or no S3Tables block | no s3TablePublishStatus block |
| Destination includes S3 Tables | LogDestinationType contains s3table | logDestinationType contains s3table |
List of published SYS_* tables | S3Tables.S3Tables | namespace.s3TablePublishStatus.s3Tables |
| The exact S3 Tables namespace (required for querying) | S3Tables.S3TableNamespace | namespace.s3TablePublishStatus.s3TableNamespace |
| Granularity | S3Tables.S3TableGranularity (cluster/account) | namespace.s3TablePublishStatus.s3TableGranularity (namespace/account) |
| Per-table last ingest time | S3Tables.LastIngestionTimes | namespace.s3TablePublishStatus.lastIngestionTimes |
| All available system tables published | S3Tables.EnabledAll | namespace.s3TablePublishStatus.enabledAll |
Notes:
LogDestinationType is a comma-joined list when more than one destination is active — e.g. "cloudwatch,s3table". Test with a substring/contains check, not equality against s3table.- An empty
LastIngestionTimes / lastIngestionTimes map, or a table listed as published but absent from the map, means data for that table may still be in flight. Compare successive values to confirm new data is landing.
- On Serverless, do not read the top-level
logExports field for this feature — that field carries the CloudWatch/S3 audit logs (useractivitylog, userlog, connectionlog) and is unrelated to SYS_* S3 Tables publishing.
2. Enable (if not configured)
# Provisioned
aws redshift enable-logging --region <REGION> --cluster-identifier <CLUSTER_ID> --log-destination-type s3table --log-exports <SYS_TABLE>... --s3-table-granularity <cluster|account> --s3-table-kms-key-id <KMS_KEY_ARN>
# Serverless
aws redshift-serverless update-namespace --region <REGION> --namespace-name <NAMESPACE_NAME> --log-destination-type s3table --s3-table-names <SYS_TABLE>... --s3-table-action Enable --s3-table-granularity <namespace|account> --s3-table-kms-key-id <KMS_KEY_ARN>
--s3-table-kms-key-id is part of both commands deliberately, not an optional add-on. Omitting it does not fail — the tables fall back to an AWS-owned key you cannot audit, restrict by policy, or revoke. Because SYS_* tables carry query_text, user_name, and remote_host, treat the customer-managed key as the default and drop the flag only for throwaway environments.
Enable from AWS console Amazon Redshift Console > Clusters > select your cluster > Tabs > Integrations / System table integration
The two compute types take different flags for the same feature. Do not carry Provisioned flag names over to Serverless:
| Purpose | Provisioned (enable-logging) | Serverless (update-namespace) |
|---|
| Which system tables to publish | --log-exports | --s3-table-names |
| Enable vs disable | separate enable-logging / disable-logging operations | --s3-table-action Enable | Disable |
| Granularity | --s3-table-granularity cluster | account | --s3-table-granularity namespace | account |
| Customer-managed KMS key | --s3-table-kms-key-id | --s3-table-kms-key-id |
| Validate without applying | --dry-run | --dry-run |
Notes:
- Granularity: Provisioned supports
cluster (default) or account; Serverless supports namespace (default) or account.
cluster/namespace granularity → one S3 table per cluster/namespace; account → one shared table for all clusters/namespaces per account per region.- Use
all to publish all available SYS_* tables — --log-exports all on Provisioned, --s3-table-names all on Serverless.
- Encryption at rest is strongly recommended for production. Without
--s3-table-kms-key-id the published tables are encrypted with an AWS-owned key, which you cannot audit, restrict by policy, or revoke. SYS_* tables carry query_text, user_name, and remote_host (see Security Considerations), so pass a customer-managed key. Grant key access using the complete key policy in ${SKILL_DIR}/references/security.md rather than an abbreviated action list — it needs two service principals (systemtables.redshift.amazonaws.com for publishing and maintenance.s3tables.amazonaws.com for table maintenance/compaction). Provisioning only the publishing principal lets writes succeed while compaction silently fails.
- Both operations accept
--dry-run to validate the request without changing anything. Provisioned returns a DryRunOperation error on success ("Request would have succeeded, but DryRun flag is set"); Serverless returns an empty body and exit code 0. Note that the Serverless dry-run validates request shape only, not parameter values, so a successful dry-run there does not guarantee the values are accepted.
Disable selectively:
# Provisioned
aws redshift disable-logging --region <REGION> --cluster-identifier <CLUSTER_ID> --log-destination-type s3table --log-exports <SYS_TABLE>...
# Serverless
aws redshift-serverless update-namespace --region <REGION> --namespace-name <NAMESPACE_NAME> --log-destination-type s3table --s3-table-names <SYS_TABLE>... --s3-table-action Disable
3. Verify Permissions
Full setup commands for both paths: ${SKILL_DIR}/references/permissions-setup.md. Load it before creating roles or registering resources.
Athena path — needs the s3tablescatalog/aws-redshift catalog registered in Glue, a workgroup with an output location, and S3 Tables read permissions. Confirm the catalog is queryable:
aws glue get-databases --region <REGION> \
--catalog-id "<ACCOUNT>:s3tablescatalog/aws-redshift"
Namespaces returned → registered and queryable. EntityNotFoundException / CATALOG_NOT_FOUND → the S3 Tables integration is not enabled (S3 console > Table buckets > Enable integration). Encrypt the workgroup output location — Athena writes full result sets, including query_text and user_name, to S3.
Redshift auto-mount path — needs a Provisioned RA3 cluster and a four-step setup: create the query_s3_tables role (trust policy must name both redshift.amazonaws.com and lakeformation.amazonaws.com, the latter with all four of sts:AssumeRole, sts:SetContext, sts:SetSourceIdentity, sts:TagSession), attach it to the cluster, register the table bucket with Lake Formation, and add the Redshift service-linked roles to ReadOnlyAdmins. Constraints that cause most failures:
- Condition both trust statements on
aws:SourceAccount — a bare service principal is a confused-deputy risk.
- Do not attach
AWSLakeFormationDataAdmin to the cluster's query role. It is needed only by the principal performing setup, and only during setup. The cluster's role needs read access alone.
- Auto-mount is a poll, not a callback — the catalog can take up to 300 seconds to appear in
pg_database. A cluster reboot forces immediate discovery.
4. Identify the Target Table
Namespace — resolve it from the API, do not construct it:
- Read
S3Tables.S3TableNamespace from describe-logging-status (Provisioned) or s3TablePublishStatus.s3TableNamespace from get-namespace (Serverless) and use it verbatim.
- Optional sanity check only: the API value typically follows
<namespace_arn_id>_sys for cluster/namespace granularity and <account>_sys for account granularity. Use this only to verify the value looks right — never to generate the namespace when the API response is unavailable.
Table — each publishable system table maps 1:1 to a table in the aws-redshift table bucket. Do not work from a memorized list — resolve it at runtime, in this order:
- The published set for this cluster/namespace —
S3Tables.S3Tables (Provisioned) or s3TablePublishStatus.s3Tables (Serverless) from the status call above, e.g. sys_query_history. This is the only authoritative answer to "what can I query right now".
- The set this API accepts —
aws redshift enable-logging help (accepted --log-exports values) or aws redshift-serverless update-namespace help (accepted --s3-table-names values).
- What each table contains — the public Redshift SYS monitoring views reference, which documents every
SYS_* view and its columns. AWS adds views over time, so treat the docs as the current list rather than hardcoding one.
Column names and types come from the same public reference, or from the live table:
aws glue get-table --region <REGION> \
--catalog-id "<ACCOUNT>:s3tablescatalog/aws-redshift" \
--database-name "<NAMESPACE>" --name "<SYS_TABLE>"
Two caveats when reading the public docs against a published table: enum-valued columns (query_type, status, event) gain values over time, so confirm with SELECT DISTINCT rather than filtering on an assumed set; and the published Iceberg table prepends warehouse-identity columns (warehouse_name, warehouse_namespace_arn, and peers) that the in-cluster SYS_ view does not have — they are how you tell apart multiple clusters publishing at account granularity.
5. Query
Query from Athena
Query syntax:
"s3tablescatalog/aws-redshift"."<NAMESPACE>"."<SYS_TABLE>"
Query from Redshift (Auto-Mounted Catalog)
Once the auto-mounted catalog is set up (see ${SKILL_DIR}/references/permissions-setup.md), query using cross-database notation:
"aws-redshift@s3tablescatalog"."<NAMESPACE>".<SYS_TABLE>
Query from Redshift (External Schema)
Alternatively, create an external schema pointing to the S3 Tables catalog:
CREATE EXTERNAL SCHEMA <schema_name>
FROM DATA CATALOG
DATABASE '<NAMESPACE>'
CATALOG_ID '<ACCOUNT>:s3tablescatalog/aws-redshift'
IAM_ROLE 'arn:aws:iam::<ACCOUNT>:role/query_s3_tables'
REGION '<REGION>';
SELECT * FROM <schema_name>.<SYS_TABLE> LIMIT 10;
Constraints
- You MUST run
describe-logging-status or get-namespace to get the namespace before writing any SQL query — never construct it manually
- For Athena queries, you MUST confirm workgroup and output location before executing
- Timing columns are in microseconds. Divide by
1000000.0 for seconds
- Tables are read-only — no
INSERT/UPDATE/DELETE
- Always add a
LIMIT when the user doesn't specify one; filter on start_time/record_time where possible
Examples
Worked SQL for the common asks — longest-running queries, error analysis, connection auditing, queue-time trends, cross-table joins — is in ${SKILL_DIR}/references/example-queries.md. Two rules that apply to every one of them:
- Timing columns are microseconds. Divide by 1,000,000 for seconds. Reporting
elapsed_time as-is overstates durations by 10^6.
- Filter on the Iceberg partition columns (
year/month/day or the table's own partitioning) in addition to any timestamp predicate, or the engine scans the full history.
Routing: Athena vs Redshift vs Direct SYS_ Access
| Scenario | Use |
|---|
| Historical/high-volume log analysis, no cluster load | Athena or Redshift on S3 Tables |
| Already connected to a Redshift cluster, want to query S3 Tables logs | Redshift cross-database or external schema |
| Join system table logs with other lake data | Athena or Redshift Spectrum |
| Real-time current state of the cluster | Direct SYS_ view on the cluster |
| Quick ad-hoc query without Redshift cluster access | Athena |
Key Behaviors
- No backfill — only events recorded after enabling are delivered to S3 Tables
- Namespace from the API — always read the namespace from
describe-logging-status (S3Tables.S3TableNamespace) or get-namespace (s3TablePublishStatus.s3TableNamespace); never construct it manually
- Microsecond timing — all duration columns are in microseconds; divide by 1000000.0 for seconds
- Read-only — published tables cannot be written to
- Both Provisioned and Serverless — same table bucket (
aws-redshift), different enable APIs
- Any Iceberg-compatible engine — query from Athena, Redshift, or any engine that reads Iceberg
Troubleshooting
| Error | Cause | Fix |
|---|
aws-redshift bucket not found | S3 Tables integration not enabled or logging not started | Run enable-logging (Provisioned) or update-namespace (Serverless) with --log-destination-type s3table |
CATALOG_NOT_FOUND in Athena | S3 Tables not registered in Glue | Enable integration: S3 console > Table buckets > Enable integration |
| Athena table empty after enabling | Ingestion still in flight | Check LastIngestionTimes (Provisioned) / lastIngestionTimes (Serverless); wait and re-query |
SYS_* table missing from the namespace | System table not included when enabling | Re-run enable with that table included, or use all — --log-exports (Provisioned), --s3-table-names (Serverless) |
| Wrong / empty namespace | Namespace constructed instead of read from API | Use the namespace from the describe/get response — S3Tables.S3TableNamespace (Provisioned) or s3TablePublishStatus.s3TableNamespace (Serverless) |
Status response has no S3Tables / s3TablePublishStatus field at all, even though publishing is on | Outdated AWS CLI / SDK. The field is silently omitted rather than raising an error, so this looks identical to the feature being disabled | Upgrade the CLI/SDK, then re-run. Confirm publishing is actually off before acting on the absence — check the aws-redshift table bucket for the namespace, or that LogDestinationType includes s3table |
Unknown options: --log-exports, --log-export-action on Serverless | Provisioned flag names used against update-namespace | Use --s3-table-names and --s3-table-action — see the flag table in the Enable section |
AccessDenied querying the table | Missing s3tables:GetTable or GetTableData | See references/security.md |
Empty results from sys_connection_log | Querying identity lacks visibility | Use an identity with superuser-level access |
Catalog doesn't appear in pg_database | LF resource not registered, or SLRs not ReadOnlyAdmins | Complete the Lake Formation steps in references/permissions-setup.md, wait 5 min or reboot |
| "Unable to assume role" from Glue | Missing sts:SetContext/sts:SetSourceIdentity in trust policy, or missing AWSLakeFormationDataAdmin | Fix trust policy and attach AWSLakeFormationDataAdmin |
VerificationStatus: NOT_VERIFIED | Normal after Lake Formation registration | No action needed if queries work |
| Query fails with "does not exist" in Redshift | Catalog not yet auto-mounted (poll delay) | Wait up to 300s or reboot cluster |
Security Considerations
Full policies, key policy, and detection setup: ${SKILL_DIR}/references/security.md. Read it before granting access. The non-negotiables:
- Scope IAM to the S3 Tables catalog, not wildcards. Glue database/table ARNs nest under
s3tablescatalog/aws-redshift — the bare database/* form grants metadata read on the whole account. lakeformation:GetDataAccess is the one action that must use "Resource": "*"; constrain it with an aws:ResourceAccount StringEquals condition.
- The KMS key policy needs two principals, not one:
systemtables.redshift.amazonaws.com (publisher) and maintenance.s3tables.amazonaws.com (compaction). Granting only the publisher lets writes succeed while compaction silently fails.
query_text can contain credentials, not just schema — interpolated SQL and CREATE USER ... PASSWORD land verbatim in sys_query_history. Treat broad access to that table as a secrets-exposure decision; restrict the column with Lake Formation.- Publishing is itself auditable and worth alarming on.
s3tables.amazonaws.com AccessDenied spikes and sys_connection_log failed-auth counts are the two signals to alert on; encrypt the alarm topic with a customer-managed key.
Reference Files
${SKILL_DIR} is the absolute path of the directory containing this SKILL.md. Load these on demand; do not read them all up front.
| File | What it covers | When to load |
|---|
${SKILL_DIR}/references/permissions-setup.md | Athena prerequisites and workgroup encryption; the full Redshift auto-mount path — IAM role trust/inline policies, Lake Formation register-resource, put-data-lake-settings, the SLR ReadOnlyAdmins step, and the 300s auto-mount poll | Before running any IAM or Lake Formation setup |
${SKILL_DIR}/references/example-queries.md | Worked SQL for longest-running queries, error analysis, connection auditing, queue-time trends, and joins across sys_* tables | When writing queries against the published tables |
${SKILL_DIR}/references/security.md | Full least-privilege policy, KMS key policy with both service principals, query_text sensitivity, CloudTrail/metric-filter detection | Before granting access, or when hardening an existing setup |
Additional Resources
Security best practices: