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Dsql

AWS LabsOfficialPublished by AWS Labs

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Load before answering DSQL limit or error-code questions; it has limits docs omit. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.
— the description by AWS Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The dsql skill provides a structured framework for interacting with Amazon Aurora DSQL. It encompasses schema management, migration strategies from MySQL and PostgreSQL, performance diagnostics, and bulk data loading. The skill integrates with MCP tools for direct database operations and provides extensive documentation on IAM authentication, multi-tenant patterns, and transaction management to ensure safe and efficient database operations.

What it does

  • Executes SQL queries and DDL operations via MCP tools.
  • Provides migration paths and type mapping for MySQL and PostgreSQL to DSQL.
  • Offers diagnostic workflows for cluster performance and query plan explainability.
  • Facilitates bulk data loading using specialized loaders.
  • Enforces best practices for IAM authentication and scoped database roles.
  • Validates SQL compatibility and provides linting for DSQL-specific constraints.

When to use it

  • When building or migrating applications to Aurora DSQL.
  • When performing schema changes or complex DDL migrations.
  • When troubleshooting performance issues or query plan inefficiencies.
  • When setting up multi-tenant database architectures.
  • When bulk loading data into a DSQL cluster.

Advantages

  • Ensures adherence to DSQL-specific transaction and concurrency limits.
  • Promotes secure access patterns through IAM and scoped roles.
  • Provides clear, phased workflows for complex tasks like performance tuning.
  • Reduces migration friction with automated linting and type mapping guidance.
  • Standardizes diagnostic reporting for query plan analysis.

Usage examples

“How do I migrate my existing PostgreSQL schema to Aurora DSQL?”
The agent will guide you through pg-migrations/type-mapping.md and schema-objects.md, providing steps to convert indexes, enums, and data types while ensuring compatibility.
“My query is running slowly on DSQL, can you help?”
The agent will initiate Workflow 9 (Query Plan Explainability) to analyze the query plan, check for full scans, and provide a structured diagnostic report.
“How do I set up a multi-tenant table in DSQL?”
The agent will provide instructions on using tenant_id in your schema design, enforcing it in WHERE clauses, and using separate transact calls for DDL.
“What are the transaction limits for DSQL?”
The agent will consult the limits-and-error-codes.md or awsknowledge tool to provide the exact limits for row modifications, data size, and duration.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/awslabs/dsql/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip dsql.zip -d .agents/skills

Documentation by AWS Labs

Amazon Aurora DSQL Skill

Aurora DSQL is a serverless, PostgreSQL-compatible distributed SQL database. This skill covers direct query execution via MCP tools, schema management, migrations, multi-tenant isolation, IAM auth, and bulk data loading via aurora-dsql-loader.


Reference Files

Load these files as needed for detailed guidance:

Core:
ReferenceWhen to LoadContains
development-guide.mdALWAYS before schema changes or DB operationsBest practices, DDL rules, transaction limits, foreign key constraints
foreign-keys.mdMUST load for foreign key operations or migrationsFK syntax, actions, validation, tenant keys
language.mdMUST load for language-specific choicesDriver selection, DSQL Connectors, connection code
access-control.mdMUST load for roles, grants, or sensitive dataScoped role setup, IAM-to-database role mapping
troubleshooting.mdSHOULD load for errors or unexpected behaviorOCC and 23503, FK, connections, cluster state
limits-and-error-codes.mdSHOULD load before sizing batches, keys or indexes, or on 54000/0A000/54011Every limit, rejection and quota with SQLSTATE and exact message
dsql-examples.mdLoad for implementation examplesMulti-tenant access, batch operations, identity and sequences, connection pooling
onboarding.mdUser requests "Get started with DSQL"Interactive step-by-step guide
occ-retry-patterns.mdMUST load for OCC retry code or conflict mitigationConnectors, 40001 retry, non-retryable 23503, FK read conflicts, idempotent design
MCP:
ReferenceWhen to LoadContains
mcp-setup.mdAlways for MCP server guidanceSetup instructions, 2 configuration options
mcp-tools.mdFor MCP tool syntax and examplesTool parameters, input validation
dsql-lint.mdMUST load before running dsql_lint or processing external SQLTool reference, fix statuses, unfixable error resolution
DDL Migrations:
ReferenceWhen to LoadContains
ddl-migrations/overview.mdMUST load for ALTER TYPE, SET NOT NULL, MODIFY PRIMARY KEYDirect ALTER coverage and last-resort table recreation
ddl-migrations/column-operations.mdDROP COLUMN, ALTER TYPE, SET/DROP NOT NULL/DEFAULTColumn-level migration patterns
ddl-migrations/constraint-operations.mdADD/DROP CONSTRAINT, VALIDATE CONSTRAINT, MODIFY PRIMARY KEYConstraint and structural changes
ddl-migrations/batched-migration.mdTables exceeding 3,000 rowsBatching patterns, progress tracking
MySQL Migrations:
ReferenceWhen to LoadContains
mysql-migrations/type-mapping.mdMUST load for MySQL → DSQL migrationData type mappings, feature alternatives
mysql-migrations/ddl-operations.mdTranslating MySQL DDL to DSQLAUTO_INCREMENT, ENUM, SET, FK patterns
mysql-migrations/full-example.mdComplete MySQL table migrationEnd-to-end example with decision summary
PostgreSQL Migrations:
ReferenceWhen to LoadContains
pg-migrations/type-mapping.mdMUST load for DSQL NUMERIC or PG type questionsC collation rules, NUMERIC(p,s), JSON/JSONB
pg-migrations/index-conversion.mdMUST load for any index conversion or index support questionGIN/GiST/BRIN → btree, operator class removal, partial + expression indexes, sort order
pg-migrations/schema-objects.mdMUST load for ENUM, materialized views, extensions, multi-schema, CREATE FUNCTIONENUM → CHECK, views, role/IAM mapping, LANGUAGE sql functions
pg-migrations/multi-region.mdMulti-region, active-active, or HA questionsArchitecture, geographic partitioning
ORM Guides:
ReferenceWhen to LoadContains
orm-guides/overview.mdMigrating an ORM or using ORM locking with DSQLAdapter names, locking, and key gotchas for Django/EF Core/Hibernate/Rails/SQLAlchemy
Data Loading:
ReferenceWhen to LoadContains
data-loading.mdPlanning or running bulk loads with aurora-dsql-loaderFresh-vs-warm partitions, resume/retry, --on-conflict semantics, throughput diagnostics
System Diagnostics:
ReferenceWhen to LoadContains
system-diagnostics/workflow.mdMUST load at Workflow 12 entry — cluster performance diagnosticsPrerequisites, 5 diagnostic phases, temporal comparison, handoff
system-diagnostics/wait-events.mdALWAYS load when interpreting AAS resultsCanonical DSQL wait event descriptions and investigation guidance
system-diagnostics/promql-patterns.mdLoad when constructing PromQL queriesReusable query templates for AAS breakdown, top-SQL, temporal compare
Query Plan Explainability:
ReferenceWhen to LoadContains
query-plan/workflow.mdMUST load at Workflow 9 entry — gates all other filesTrigger criteria, context disambiguation, routing, phased workflow
query-plan/plan-interpretation.mdMUST load at Workflow 9 Phase 0DSQL node types, Node Duration math, estimation-error bands
query-plan/catalog-queries.mdMUST load at Workflow 9 Phase 0pg_class/pg_stats/pg_indexes SQL, correlated-predicate verification
query-plan/guc-experiments.mdMUST load at Workflow 9 Phase 0GUC experiment procedures, 30-second skip protocol
query-plan/report-format.mdMUST load at Workflow 9 Phase 0Required report structure, element checklist, support request template
query-plan/query-rewrites-generic.mdSHOULD load at Phase 0; sub-files on-demandIndex of 10 generic rewrite patterns
query-plan/query-rewrites-dsql-specific.mdSHOULD load at Phase 0; sub-files on-demandIndex of DSQL-specific rewrite patterns

Choosing How to Connect: MCP vs CLI/psql

The aurora-dsql MCP server binds a single cluster at startup (--cluster_endpoint), so using it for another cluster means editing .mcp.json and restarting the session.

  • Use the aurora-dsql MCP tools (readonly_query, transact, get_schema) ONLY when the server already targets the cluster you need.

  • Otherwise — unconfigured, disabled, or bound to a different cluster — do NOT reconfigure it. Use the CLI + psql path instead: scripts/psql-connect.sh <cluster-id> --region <region> --command "SELECT ..." (mints an IAM token and runs via psql).

  • If you cannot confirm which cluster the MCP targets, confirm first or use the CLI/psql path — running against the wrong cluster is worse than the check.

The doc-only MCP tools (dsql_lint, dsql_*_documentation, dsql_recommend) need no cluster. The CloudWatch MCP (Workflow 12) takes region/cluster_id per call, so one running server can query clusters in any PromQL-enabled region (pass each cluster's region on the call). Details: connectivity-tools.md.

MCP Tools Available

The aurora-dsql MCP server provides these tools:

Database Operations:

  1. readonly_query - Execute SELECT queries (returns list of dicts)

  2. transact - Execute DDL/DML statements in transaction (takes list of SQL statements)

  3. get_schema - Get table structure for a specific table

SQL Validation:

  1. dsql_lint - Validate SQL for DSQL compatibility and optionally auto-fix issues. Use before executing externally-sourced SQL.

Documentation & Knowledge:

  1. dsql_search_documentation - Search Aurora DSQL documentation

  2. dsql_read_documentation - Read specific documentation pages

  3. dsql_recommend - Get DSQL best practice recommendations

Note: There is no list_tables tool. Use readonly_query with information_schema.

See mcp-setup.md for detailed setup instructions. See mcp-tools.md for detailed usage and examples.

AWS Knowledge MCP (awsknowledge)

Consult for verifying DSQL service limits before advising users. The numeric limits below are defaults that may change — when a user's decision depends on an exact limit, verify it first:

LimitDefaultVerify query
Max row modifications per transaction3,000aurora dsql transaction limits
Max data size per transaction10 MiBaurora dsql transaction limits
Max transaction duration5 minutesaurora dsql transaction limits
Max connections per cluster10,000aurora dsql connection limits
Auth token expiry15 minutesaurora dsql authentication token
Max connection duration60 minutesaurora dsql connection limits
Max indexes per table24 incl. PKaurora dsql index limits
Max columns per index8aurora dsql index limits
IDENTITY/SEQUENCE CACHE values1 or >= 65536aurora dsql sequence cache
Supported column data typesSee docsaurora dsql supported data types

When to verify: Before recommending batch sizes, connection pool settings, or schema designs where hitting a limit would cause failures; any time the exact number can affect user decision. For the full limit set with each SQLSTATE and its exact error message, see limits-and-error-codes.md; where it marks a value observed and the docs differ, use the observed value.

Fallback: If awsknowledge is unavailable, use the defaults above and flag that limits should be verified against DSQL documentation.

CLI Scripts Available

Bash scripts in scripts/ for cluster management (create, delete, list, cluster info), psql connection, and bulk data loading from local/s3 csv/tsv/parquet files. See scripts/README.md for usage and hook configuration.


Quick Start

  1. Pick a connection path: confirm the aurora-dsql MCP targets your cluster; if not, use the CLI/psql path instead — see Choosing How to Connect. The steps below name MCP tools; the equivalent SQL runs the same way through psql-connect.sh --command "...".

  2. Explore: Use readonly_query with information_schema to list tables. Use get_schema for table structure.

  3. Query: Use readonly_query for SELECT queries. MUST include tenant_id in WHERE for multi-tenant apps. MUST build SQL with safe_query.build().

  4. Schema changes: Use transact with one DDL per transaction. MUST batch DML under 3,000 rows. MUST use CREATE INDEX ASYNC in a separate call. Use dsql_lint to validate first.

  5. Bulk load data: Use aurora-dsql-loader for CSV/TSV/Parquet. Load data-loading.md for details. Use --dry-run first.


Performance Routing

When the user reports a performance problem, use this table to select the correct workflow:

User signalRoute to
General performance complaint, "cluster is slow", "something changed", latency regression, no specific query identifiedWorkflow 12 (System Diagnostics) — observe via CloudWatch first
Specific query or query_id to investigate, "explain this plan", "why is this query slow"Workflow 9 (Query Plan Explainability) — direct EXPLAIN analysis
OCC conflicts, commit errors, retry stormsWorkflow 12 (System Diagnostics) — confirm via CW metrics before investigating
Cost optimization, "where is compute time spent"Workflow 12 (System Diagnostics) — identify top contributors first

Rule: When in doubt, start with Workflow 12. It identifies specific queries to investigate and routes to Workflow 9 with context.


Common Workflows

Workflow 1: Create Multi-Tenant Schema
  1. Create tenant-owned tables with tenant_id using separate transact calls

  2. Use composite tenant FKs for tenant-owned parents and ordinary FKs for shared parents

  3. Create tenant and query-pattern indexes with separate CREATE INDEX ASYNC calls

  4. Verify with get_schema

  • MUST issue each DDL in its own transact call: transact(["CREATE TABLE ..."])

  • MUST serialize arrays into a single-column representation — DSQL has no array column type; PREFER JSONB (operators work directly); MAY use TEXT when the column is opaque to the database; ASK the user. For JSONB arrays, expand at query time with jsonb_array_elements_text(data)

Workflow 2: Safe Data Migration

MUST validate every DDL with dsql_lint(fix=true) before executing. DML does not require linting.

  1. Validate DDL with dsql_lint(sql=..., fix=true) — handle diagnostics per dsql-lint.md

  2. Execute the reviewed fixed_sql when present, otherwise the reviewed source statement

  3. Add column in its own transact call

  4. Populate existing rows with UPDATE (batched under 3,000 rows)

  5. Verify with readonly_query COUNT

  6. Create an index if needed: validate then execute the reviewed DDL in its own transact call

  • MUST issue each ALTER TABLE in its own transact call — DSQL rejects multi-DDL transactions with multiple ddl statements not supported in a transaction

  • MUST add column with only name and type; apply DEFAULT via separate UPDATE

  • MUST batch updates under 3,000 rows in separate transact calls

Recovery: Resume failed batches by filtering WHERE new_column IS NULL.

Workflow 3: Bulk Data Loading

Use aurora-dsql-loader for CSV, TSV, or Parquet loads. MUST load data-loading.md before advising on throughput or diagnosing slow loads.

  1. Validate with --dry-run first

  2. Run with --manifest-dir on persistent storage (not /tmp — tmpfs on AL2023, lost on crash) and --header if file has a header row

  3. On failure: resume with --resume-job-id; for duplicates use --on-conflict do-nothing

  4. For large tables: create secondary indexes after load using CREATE INDEX ASYNC

Workflow 4: Foreign Key Constraints

MUST load and follow foreign-keys.md before creating, altering, dropping, or migrating foreign keys.

Workflow 5: Query with Tenant Isolation
  1. MUST authorize the caller against the tenant — format validation does not establish authorization

  2. MUST build SQL with safe_query.build() — use allow()/regex() for values (emits 'v'), ident() for table/column names (emits "v"). See input-validation.md

  3. MUST include tenant_id in the WHERE clause; reject cross-tenant access at the application layer

Workflow 6: Set Up Scoped Database Roles

MUST load access-control.md for role setup, IAM mapping, and schema permissions.

Workflow 7: Table Recreation DDL Migration

For ALTER COLUMN TYPE, SET NOT NULL, or MODIFY PRIMARY KEY, MUST load ddl-migrations/overview.md. Use a direct ALTER form when supported; otherwise, present a user-approved table-recreation plan.

Workflow 8: Validate and Migrate to DSQL

MUST load dsql-lint.md before running dsql_lint. Run dsql_lint(sql=source_sql, fix=true) to validate and auto-convert. For MySQL-origin SQL, MUST cross-check against mysql-migrations/type-mapping.md even when lint returns clean. On parse_error, fall back to manual conversion then re-lint.

Workflow 9: Query Plan Explainability

Explains why the DSQL optimizer chose a particular plan. Triggered by slow queries, high DPU, unexpected Full Scans, or plans the user doesn't understand. REQUIRES a structured Markdown diagnostic report as the deliverable.

MUST load query-plan/workflow.md at entry — it defines trigger criteria, context disambiguation, routing, and the full phased workflow (Phase 0–4). Workflow.md specifies which reference files to load at each phase.

Safety. Plan capture uses readonly_query exclusively. Rewrite DML to SELECT for plan capture. MUST NOT use transact --allow-writes for plan capture.

Workflow 10: Full PostgreSQL → DSQL Schema Migration

MUST load pg-migrations/type-mapping.md, pg-migrations/index-conversion.md, pg-migrations/schema-objects.md, and foreign-keys.md. Run dsql_lint(fix=true) first for mechanical fixes, preserve foreign-key relationships, translate unsupported source syntax or options, then apply semantic conversions from the pg-migrations references for unfixable diagnostics and patterns the linter cannot handle. Re-lint the final output before deploying.

Workflow 11: ORM Migration and Locking (Django/EF Core/Hibernate/Rails/SQLAlchemy)

Load orm-guides/overview.md for adapter names, locking guidance, and framework-specific gotchas.

Workflow 12: System Diagnostics (CloudWatch AAS)

Diagnose cluster performance by querying db.active_sessions.avg via PromQL. Detects temporal anomalies in wait event distribution, identifies regressed queries, and routes to Workflow 9 for per-query investigation.

Requires: CloudWatch MCP server (awslabs.cloudwatch-mcp-server) enabled and configured with PromQL access in the same region as the cluster — see mcp/mcp-setup.md for enabling it, region requirements, and the session restart needed for its tools to register.

MUST load system-diagnostics/workflow.md at entry — it defines prerequisites, 5 diagnostic phases, temporal baselines, and the routing to Workflow 9 for identified queries.

Error Scenarios

  • awsknowledge returns no results: Use the default limits in the table above and note that limits should be verified against DSQL documentation.

  • dsql_lint unavailable or timing out: See the Error Handling section of dsql-lint.md. Do not silently skip validation — inform the user and require explicit confirmation before proceeding with manual rules from development-guide.md.

  • OCC serialization error (40001): Retry with backoff, and keep (OC001) in the loop too — see occ-retry-patterns.md.

  • Foreign key violation (23503): Correct the relationship or referential action; MUST NOT send it through the 40001 retry loop — see troubleshooting.md.

  • Transaction exceeds limits: Split batches under 3,000 rows and 10 MiB; size is checked at COMMIT — see limits-and-error-codes.md to size them and batched-migration.md for the pattern.

  • Token expiration mid-operation: Generate a fresh IAM token — see authentication-guide.md. See troubleshooting.md for other issues.

Additional Resources

Files

references/pg-migrations/index-conversion.md

Index Conversion for DSQL

Run dsql_lint(fix=true) first — it handles most index conversions automatically (ASYNC, USING gin/gist/brin/hash → btree, CONCURRENTLY removal, INCLUDE preservation, and stripping ASC/DESC from index keys — DSQL rejects sort direction outright, see Index Key Sort Order).

This file covers the cases a mechanical fix cannot resolve:

  • GIN, GiST, and BRIN indexes — dsql_lint strips the USING clause, but the resulting btree index does not serve the same queries. These need a redesign.

  • Operator classes such as text_pattern_ops — dsql_lint leaves these in place with no diagnostic, so strip them by hand. DSQL rejects them with ERROR: 0A000: opclass not supported for index keys

  • Sort direction — index_sort_direction; auto-fixed, but the fix changes which ORDER BY the index serves, and for ASC it is reported as neither an error nor a warning. See Index Key Sort Order. NULLS placement is not part of this rule and is never stripped — see NULLS placement is retained.

Partial indexes (WHERE) and expression indexes are supported — add ASYNC and keep them as written. No redesign is needed.

Sources:

Table of Contents

  1. GIN Index Conversion

  2. GiST Index Conversion

  3. BRIN Index Conversion

  4. Partial Indexes

  5. Expression Indexes

  6. Index Limits

  7. Index Key Sort Order

  8. Monitoring Async Index Status

  9. Conversion Decision Flowchart


GIN Index Conversion

GIN indexes are used for full-text search, JSONB containment, and array operations. DSQL uses btree indexes exclusively — convert GIN to btree where possible.

JSONB GIN → Expression Index on Extracted Key
-- PostgreSQL: GIN index on JSONB column
CREATE INDEX idx_users_prefs ON users USING gin (preferences);
-- Used for: preferences @> '{"theme":"dark"}'

-- DSQL: No equivalent index. JSONB operators work at runtime without index.
-- The query still works, just without index acceleration:
SELECT * FROM users WHERE preferences @> '{"theme":"dark"}';

-- If you need indexed lookup on a specific JSON key, index the extraction expression.
CREATE INDEX ASYNC idx_users_pref_theme ON users ((preferences->>'theme'));
-- Query: SELECT * FROM users WHERE preferences->>'theme' = 'dark';
Array GIN → Join Table
-- PostgreSQL: GIN index on array column
CREATE INDEX idx_posts_tags ON posts USING gin (tags);
-- Used for: tags @> ARRAY['database']

-- DSQL: Array column types not supported. Normalize tags into a join table for
-- indexed lookup, or store as jsonb if indexed lookup isn't needed.
CREATE TABLE post_tags (
  post_id uuid NOT NULL,
  tag text NOT NULL
);
CREATE INDEX ASYNC idx_post_tags_tag ON post_tags (tag);
CREATE INDEX ASYNC idx_post_tags_post ON post_tags (post_id);
-- Query: SELECT DISTINCT post_id FROM post_tags WHERE tag = 'database';
Full-Text Search GIN → External Service
-- PostgreSQL: GIN index for full-text search
CREATE INDEX idx_articles_search ON articles USING gin (to_tsvector('english', title || ' ' || body));

-- DSQL: No equivalent. Use OpenSearch/Elasticsearch for full-text search.
-- Store the text in DSQL, index in OpenSearch, query OpenSearch for IDs, then fetch from DSQL.
-- Remove the index entirely from the DSQL schema.
Trigram GIN (pg_trgm) → Application Layer
-- PostgreSQL: Trigram index for LIKE '%pattern%'
CREATE INDEX idx_users_name_trgm ON users USING gin (name gin_trgm_ops);

-- DSQL: No equivalent. Options:
-- 1. Use prefix matching (LIKE 'pattern%') with a btree index
CREATE INDEX ASYNC idx_users_name ON users (name);
-- 2. Use OpenSearch for fuzzy/substring matching
-- 3. Accept full scan for infrequent LIKE '%pattern%' queries

GiST Index Conversion

GiST indexes are used for geometric data, range types, and exclusion constraints.

Geometric GiST → No Index
-- PostgreSQL: GiST index on point column
CREATE INDEX idx_locations_coords ON locations USING gist (coords);

-- DSQL: Geometric types are not supported — ERROR: 0A000: datatype point not supported
-- Option 1: Store lat/lng as separate numeric columns, index those
ALTER TABLE locations ADD COLUMN lat double precision;
ALTER TABLE locations ADD COLUMN lng double precision;
CREATE INDEX ASYNC idx_locations_lat ON locations (lat);
CREATE INDEX ASYNC idx_locations_lng ON locations (lng);
-- Bounding box queries: WHERE lat BETWEEN x1 AND x2 AND lng BETWEEN y1 AND y2

-- Option 2: Use a geohash text column for proximity queries
ALTER TABLE locations ADD COLUMN geohash text;
CREATE INDEX ASYNC idx_locations_geohash ON locations (geohash);
-- Prefix matching: WHERE geohash LIKE 'dr5ru%'
Range GiST → Separate Columns
-- PostgreSQL: GiST index on range type
CREATE INDEX idx_events_during ON events USING gist (during);
-- Used for: during && '[2024-01-01, 2024-02-01)'

-- DSQL: Store range as two columns
CREATE TABLE events (
  id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
  start_time timestamptz NOT NULL,
  end_time timestamptz NOT NULL
);
CREATE INDEX ASYNC idx_events_start ON events (start_time);
CREATE INDEX ASYNC idx_events_end ON events (end_time);
-- Overlap query: WHERE start_time < '2024-02-01' AND end_time > '2024-01-01'

BRIN Index Conversion

BRIN indexes are used for large, naturally-ordered tables (time-series data).

-- PostgreSQL: BRIN index on timestamp column
CREATE INDEX idx_logs_created ON logs USING brin (created_at);

-- DSQL: Use btree. DSQL's PK-ordered storage provides similar benefits
-- if created_at correlates with PK order.
CREATE INDEX ASYNC idx_logs_created ON logs (created_at);

-- If the table is very large and you need to limit index size,
-- use a composite index with the most selective column first.
-- No ASC/DESC on index keys — see "Index Key Sort Order" below.
CREATE INDEX ASYNC idx_logs_tenant_created ON logs (tenant_id, created_at);

Partial Indexes

DSQL supports partial indexes. Keep the WHERE clause — only add ASYNC.

-- PostgreSQL: Partial index
CREATE INDEX idx_orders_pending ON orders (customer_id, created_at)
  WHERE status = 'pending';

-- DSQL: WHERE clause preserved
CREATE INDEX ASYNC idx_orders_pending ON orders (customer_id, created_at)
  WHERE status = 'pending';

DSQL uses a partial index only when it can prove the query's WHERE conditions imply the index predicate. Queries that do not mention status will not use the index above.

-- Eligible to use idx_orders_pending (status = 'pending' implies the predicate)
SELECT * FROM orders WHERE customer_id = $1 AND status = 'pending';

-- Does NOT use it — the predicate is not implied
SELECT * FROM orders WHERE customer_id = $1;

Expression Indexes

DSQL supports expression indexes. Keep the expression — only add ASYNC.

-- PostgreSQL: Expression index
CREATE INDEX idx_users_email_lower ON users (lower(email));

-- DSQL: expression preserved
CREATE INDEX ASYNC idx_users_email_lower ON users ((lower(email)));
-- Query: WHERE lower(email) = lower($1)
-- PostgreSQL: Expression index on JSON field
CREATE INDEX idx_users_city ON users ((preferences->>'city'));

-- DSQL: expression preserved
CREATE INDEX ASYNC idx_users_city ON users ((preferences->>'city'));
-- Query: WHERE preferences->>'city' = 'Seattle'

All functions and operators in the expression must be IMMUTABLE. Mark user-defined functions IMMUTABLE, and rebuild the index if the function definition changes.

If the expression is not IMMUTABLE, make it immutable rather than replacing the index:

-- Rejected: extract on timestamptz depends on the session time zone
CREATE INDEX ASYNC idx_orders_year ON orders ((extract(year FROM created_at)));
-- ERROR: 42P17: functions in index expression must be marked IMMUTABLE

-- Pin the time zone
CREATE INDEX ASYNC idx_orders_year
  ON orders ((extract(year FROM created_at AT TIME ZONE 'UTC')));
-- Query: WHERE extract(year FROM created_at AT TIME ZONE 'UTC') = 2024
--        must repeat the pinned expression, or the index is not used

A bare created_at::timestamp cast does NOT work — it depends on the session time zone too.

Do not reach for a computed column instead: ALTER TABLE ... ADD COLUMN ... GENERATED fails with ALTER TABLE ADD COLUMN with constraint not supported, and the generation expression would have to be IMMUTABLE anyway.


Index Limits

LimitValueSQLSTATEMessage when exceeded
Max indexes per table24, incl. the PK54000more than 24 indexes per table are not allowed
Max columns per index854011more than 8 column keys in an index are not supported
Max PK/index key size~1,981 bytes observed; docs say 1 KiB54000key size too large

For what takes one of the 24 slots, the key-size budget and the rest of the limit set, see limits-and-error-codes.md.

Strategy when approaching 24 index limit:

  • Use composite indexes instead of multiple single-column indexes

  • Use INCLUDE columns for covering indexes (avoids storage round-trips)

  • Remove indexes for rarely-used query patterns

  • Consider if the query can use an existing composite index with a prefix match


Index Key Sort Order

DSQL rejects ASC/DESC on index keys — both of them, with ERROR: 0A000: specifying sort order not supported for index keys. dsql_lint reports this as rule index_sort_direction and fix=true strips the direction, but the severity depends on which direction you wrote:

Inputfix_result.statussummary
created_at DESCfixed_with_warning{errors: 0, warnings: 1, fixed: 0}
created_at ASCfixed{errors: 0, warnings: 0, fixed: 1}

So a pipeline that gates on errors == 0, or that consumes only fixed_sql, proceeds without surfacing that the statement changed — and for ASC even a stricter pipeline gating on warnings == 0 proceeds, because ASC raises no warning at all. Review every diagnostic rather than either counter, and see dsql-lint.md for what zero diagnostics does and does not mean.

-- PostgreSQL
CREATE INDEX idx_logs_tenant_created ON logs (tenant_id, created_at DESC);

-- DSQL: ERROR: 0A000: specifying sort order not supported for index keys
-- Drop the direction:
CREATE INDEX ASYNC idx_logs_tenant_created ON logs (tenant_id, created_at);

This costs no capability for a uniformly-ordered ORDER BY. The planner reads an index backwards, so a single direction-free index serves both directions. Against (tenant_id, created_at), with a projection the index covers:

ORDER BYPlan
tenant_id, created_atIndex Only Scan
tenant_id DESC, created_at DESCIndex Only Scan Backward
tenant_id, created_at DESCIncremental Sort above the scan, Presorted Key: tenant_id
tenant_id DESC, created_atIncremental Sort above a backward scan

The projection matters as much as the direction. The plans above hold for SELECT tenant_id, created_at. A secondary index is not covering, so SELECT * adds a Sort above a primary-key scan in all four cases — the ordering is lost to the column lookup, not to the direction. Use INCLUDE to cover the projection, or accept the sort.

The rule is direction uniformity over a leading prefix — uniformity alone is not enough. An ORDER BY is served by a scan when its keys are a leading prefix of the index keys and all point the same way. ORDER BY created_at is perfectly uniform and still gets a full Sort against (tenant_id, created_at), because created_at is not a prefix. A mixed-direction ORDER BY over a prefix keeps the Incremental Sort, and no DSQL index can remove it, because index keys carry no direction to match against. If a mixed ordering is on a hot path, either sort in the application or store an inverted column (for example a negated numeric, or a computed descending rank) and order on it uniformly.

NULLS placement is retained

NULLS FIRST / NULLS LAST is accepted on an index key, and dsql_lint keeps it — only ASC/DESC are removed. A backward scan flips the NULLS placement along with the direction, so an index serves exactly two orderings: its own placement ascending, and the opposite placement descending. A default index (NULLS LAST) therefore covers the two default forms:

IndexServed by forward scanServed by backward scan
(a)ORDER BY aORDER BY a DESC
(a NULLS FIRST)ORDER BY a NULLS FIRSTORDER BY a DESC NULLS LAST

Only add NULLS FIRST to serve a query that explicitly asks for it. An index declared NULLS FIRST no longer satisfies the plain ORDER BY a or ORDER BY a DESC — both fall back to a Sort — so adding one to fix a single query can cost the default ordering on every other. Nullable columns need no special handling otherwise: the default index covers both directions.


Monitoring Async Index Status

Indexes created with ASYNC are not immediately usable. Monitor:

-- Check for indexes still being built
SELECT indexrelid::regclass AS index_name, indisvalid AS is_ready
FROM pg_index
WHERE NOT indisvalid;

-- If this returns rows, those indexes are still building.
-- Queries work but won't use the index until indisvalid = true.

Do NOT rely on index performance until indisvalid = true.


Conversion Decision Flowchart

Is it a btree index?
├── Yes → CREATE INDEX ASYNC (preserve columns, INCLUDE, WHERE, IMMUTABLE expressions;
│         drop ASC/DESC — a backward scan serves uniformly-descending ORDER BY)
│
├── Is it GIN?
│   ├── For JSONB containment → unindexed at runtime; expression index for key equality
│   ├── For array ops → normalize to join table + btree
│   ├── For FTS → remove (use OpenSearch)
│   └── For trigram → remove or use prefix btree
│
├── Is it GiST?
│   ├── For geometry → separate lat/lng columns + btree
│   ├── For ranges → separate start/end columns + btree
│   └── For exclusion → remove (enforce in application)
│
├── Is it BRIN?
│   └── Convert to btree (DSQL PK-order gives similar benefit)
│
└── Is it CONCURRENTLY?
    └── Remove CONCURRENTLY, use ASYNC

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python, Needs Node.js

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Publisher
AWS Labs (Official)
Source
awslabs/agent-plugins · plugins/databases-on-aws/skills/dsql · commit fbefb37
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