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Amazon Elasticache

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Medium risk
  • Has scripts
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From the author

Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.
— the description by AWS

AutoGineer summary

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

The Amazon ElastiCache skill provides a structured, modular approach to managing in-memory data stores on AWS. It guides developers through the entire lifecycle of ElastiCache and Valkey, from initial requirements gathering and provisioning to complex data modeling, AI-driven vector search, and operational monitoring. The skill enforces best practices for connection safety, security, and infrastructure-as-code, ensuring reliable performance for caching, session management, and GenAI workloads.

What it does

  • Assesses workload requirements to recommend optimal ElastiCache configurations.
  • Automates provisioning and connectivity setup for serverless and node-based clusters.
  • Provides implementation patterns for common use cases like cache-aside, session stores, and rate limiting.
  • Supports advanced AI workloads including semantic caching, RAG, and vector similarity search.
  • Delivers operational diagnostics for performance tuning, cost optimization, and troubleshooting.
  • Manages migration paths from self-managed Redis or between different deployment models.

When to use it

  • When you need to reduce database load or improve API response times with an in-memory cache.
  • When implementing AI agent memory, RAG retrieval, or semantic caching for LLM applications.
  • When migrating existing Redis workloads to a managed AWS environment.
  • When troubleshooting performance issues like high latency, replication lag, or memory pressure.
  • When setting up secure, production-ready caching infrastructure using IaC.

Advantages

  • Standardizes deployment and operational patterns across development teams.
  • Ensures compliance with security best practices like TLS and IAM authentication.
  • Reduces configuration errors through automated input validation and tagging.
  • Provides clear guidance on engine selection and version-specific feature availability.
  • Simplifies complex tasks like cross-region migration and vector search setup.

Usage examples

“I need to speed up my RDS database. Should I use ElastiCache?”
The agent will initiate the requirements sub-skill to analyze your workload, stack, and latency needs, then provide a recommendation on whether ElastiCache is the right fit.
“How do I set up a Valkey cluster for vector search?”
The agent will route to the genai sub-skill, verify you are using a node-based Valkey 8.2+ cluster, and provide the necessary configuration and code to enable vector similarity search.
“My cache hit rate is low and latency is spiking. Can you help me troubleshoot?”
The agent will activate the monitoring sub-skill to analyze your metrics, identify potential hot keys or memory imbalance, and suggest specific remediation steps.
“Help me migrate my self-managed Redis to ElastiCache.”
The agent will guide you through the migration sub-skill, performing preflight checks and validating your source configuration for a zero-downtime cutover.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/aws/amazon-elasticache/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip amazon-elasticache.zip -d .agents/skills

Documentation by AWS

ElastiCache

A modular ElastiCache toolkit organized as a registry of sub-skills. Each sub-skill handles one domain of ElastiCache work. The router below matches user intent to the right sub-skill, then loads only the references needed for that sub-skill.

How this skill works

  1. Match the user's request against the semantic categories in the registry below. Match on meaning, not exact wording ("help me figure out which data structures to use" matches data-modeling even without the word "pattern").

  2. Disambiguation: If the user's intent matches multiple sub-skills, apply these rules in order:

    • If .elasticache/requirements.json exists with infrastructure.endpoint set, prefer monitoring or data-modeling (the user has an existing cache).

    • If no cache exists (no requirements.json or no endpoint), prefer requirements.

    • If still ambiguous, ask one clarifying question: "Are you looking to set up something new, or troubleshoot something existing?"

  3. Check the Guardrails section before recommending an engine or deployment model.

  4. Read references/{sub-skill-id}/instructions.md for the matched sub-skill. If the file is not found at a relative path, check your prompt or environment for the skill directory absolute path and retry with {skill-directory}/references/{sub-skill-id}/instructions.md.

  5. If the request spans multiple sub-skills, execute them in pipeline order.

  6. If a sub-skill requires upstream context (engine, deployment model, endpoint) not yet in session memory, route to the upstream sub-skill first.

  7. If no sub-skill matches, activate requirements first.

  8. If a script or CLI call fails, show the error to the user and suggest a specific fix before retrying.

Sub-skill registry

Each entry has: an ID (directory name under references/), a domain description, semantic categories for matching, and upstream/downstream dependencies.

IDNameDomainSemantic CategoriesUpstreamDownstream
requirementsSolution FitGathers workload, stack, scale, latency, persistence, and budget through workspace scan + structured interview. Decides whether ElastiCache is the right service and hands off with a routing recommendation.I need a cache, speed up my app, reduce database load, lower Bedrock cost, should I use ElastiCache, what's best for my workload, evaluating cache options, ElastiCache vs X, Valkey vs X, vague new workload—setup, data-modeling, genai, monitoring, migration
setupCreate and ConnectProvisioning, connectivity, security, authentication, IaC, deployment choice. Gets the user to a working cache with least friction. Covers engine selection, serverless vs node-based, VPC, TLS, RBAC/IAM, jump-host/SSM tunnels, CLI/SDK/CFN/CDK/Terraform starters.create a cache, set up ElastiCache, provision, Valkey cluster, connect Lambda/ECS/EKS/EC2, VPC, security groups, TLS, RBAC, IAM auth, jump host, SSM tunnel, CloudFormation, CDK, Terraform, engine selection, serverless vs node-based, backup, snapshot, restore, exportrequirements (optional)data-modeling, genai, monitoring
data-modelingApplication PatternsPicks data structures, key schema, TTL strategy, invalidation approach, and client code for non-AI patterns: cache-aside, session store, rate limiting, leaderboards, counters, pub/sub, streams, shopping carts, job queues, activity feeds.session store, rate limiting, leaderboard, cache-aside, query caching, counters, streams, pub/sub, shopping cart, job queue, activity feed, key schema, TTL, invalidation, data structuressetup (cache must exist)monitoring
genaiAI and Vector WorkloadsClassifies request into Mode 1 (plain cache), Mode 2 (semantic response cache), or Mode 3 (full vector search). Selects Valkey and forces node-based Valkey 8.2 or above (recommend 9.0) when server-side vector similarity is needed. Covers semantic caching, agent memory, RAG retrieval, recommendation, personalization, conversation/session persistence for AI agents, and framework wiring (Strands, mem0, LangChain).semantic cache, RAG, agent memory, conversational memory, vector search, embeddings, recommendation, personalization, Bedrock latency, Bedrock cost, LLM caching, Strands, mem0, LangChain, conversation history, AI session store, embedding provider, framework integrationsetup (cache must exist)monitoring
monitoringOperate and ObserveDiagnoses performance, cost, and reliability using metrics first, then recommends the smallest change. Covers dashboards, alarms, log delivery, cost reporting, event routing, troubleshooting high CPU / memory / replication lag / connection spikes / low hit rate / hot keys / big keys / slot imbalance / latency spike root cause.cache is slow, cost too high, hit rate low, high CPU, memory pressure, replication lag, connection spikes, dashboards, alarms, CloudWatch, cost comparison, troubleshoot, hot key, uneven shard load, one node pinned, big key, memory bloat, which key is biggest, keyspace distribution, prefix analysis, cost attribution by tenant, memory imbalance, one shard full, slot memory skew, latency spike, slow command incident, root cause for latency bump—setup, migration
migrationEngine and Platform MigrationSelects the migration path and sequences preflight, validation, cutover, and rollback. Covers self-managed Redis → ElastiCache, Redis OSS → Valkey, node-based ↔ serverless, version upgrades. Hard validate-before-migrate gate.migrate, Redis OSS to Valkey, self-managed to ElastiCache, node-based to serverless, serverless to node-based, engine upgrade, version upgrade, zero-downtime cutover, rollback—setup, monitoring

Pipeline order

Sub-skills run independently, but common multi-step journeys follow these pipelines:

  • requirements → setup → (data-modeling | genai) → monitoring

  • migration → setup → monitoring

  • monitoring → setup | migration (if metrics indicate)

State handoff: requirements.json

.elasticache/requirements.json is the single source of truth for cross-sub-skill state. Each sub-skill reads it at start and writes its section after completing work. Read before writing; merge, do not overwrite.

SectionOwnerKey fields
top-levelrequirementsengine, deployment_model, region, runtime, patterns, use_case, vpc_id, subnet_ids, security_group_ids
infrastructuresetupcache_name, resource_id, engine_version, topology, endpoint, port, auth_model, tls, client_library, execution_path, access_mode, tunnel_instance_id, embedding_provider, embedding_model, embedding_dim, embedding_module
genaigenaimode, mode_2_path, framework
migrationmigrationsource_type, source_host, migration_path, cutover_status

Ownership note: deployment_model is set by requirements during initial interview. migration may update it after an engine or deployment model switch (e.g., node-based to serverless).

requirements.json should include "schema_version": 1 and "last_updated": "<ISO timestamp>" at the top level. Every sub-skill that writes to requirements.json must update last_updated. If last_updated is older than 7 days, warn the user that cached state may be stale.

requirements.json tracks one active cache. If the user works with multiple caches in the same project, confirm which cache is active before reading or writing state.

When a sub-skill needs upstream context (engine, endpoint, auth model), check requirements.json first. If the field is null or the file does not exist, route to the upstream sub-skill.

Global rules (apply to every sub-skill)

  1. Execution path. Use AWS CLI, SDK (boto3), CloudFormation, or CDK as the primary path for control-plane work. Use valkey-py as the primary path for data-plane work.

  2. Response depth. Summary (2-3 sentences) for "should I" or "which" questions. Standard (recommendation + config + code + next steps) by default. Expert (full decision matrix with alternatives, cost, security caveats) for "why" or "compare all" questions. Escalate on user request; never downgrade unprompted.

  3. Session memory. Track region, VPC, engine, deployment model, auth model, compute runtime, and language. Carry forward across sub-skills. Do not re-ask. If the user overrides a value, update it everywhere. Inferred values (from workspace scan or IaC) must be re-confirmed before high-risk decisions (engine, deployment model, security posture); low-risk inferences (language, framework, region) can be used as defaults silently.

  4. Source priority. Always answer from skill-local files first (sub-skill references, then scripts/). Do not fetch external documentation or web search unless the local files cannot answer the query. When local files are insufficient, fall back to official AWS docs: https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/ for features and https://aws.amazon.com/elasticache/pricing/ for pricing. Never invent price points or version constraints. If the user references a Valkey or Redis version, feature, or pricing tier not covered in local files, fall back to https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/ before answering. Do not extrapolate from local content that may be outdated.

  5. Freshness disclaimer. When outputting pricing, version constraints, or feature availability, include a one-line disclaimer: "For current pricing see https://aws.amazon.com/elasticache/pricing/. For current feature availability see https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/."

  6. Connection safety. Never create a Valkey/Redis client at module level (top of file, import time). Initialize connections inside a function or on first use. Module-level connections crash applications that import the module before the cache is reachable.

  7. Code environment. Use python3 (not python) in all commands and shebangs. Do not generate pip install or pytest commands in application code. Prefer the agent's file-writing tool over shell redirection for writing files.

  8. Input validation (write operations only). Before running any create, modify, or delete CLI command or SDK call that uses user-provided values (cache name, region, subnet IDs, security group IDs, VPC ID, KMS key, snapshot name), validate them with scripts/input_validator.py. Run python3 scripts/input_validator.py cache_name=<val> region=<val> ... or import validate_all as a library. Skip validation for read-only operations (describe, list) where bad input is harmless.

  9. Resource tagging (always apply on resource creation). When creating any cache (create-serverless-cache, create-replication-group, create-cache-cluster), ALWAYS include these tags:

    --tags Key=managed_by,Value=aws-skills Key=skill,Value=elasticache Key=skill_version,Value=1.0.0 Key=created_by,Value=elasticache-skill Key=generation_model,Value={your-model-id}

    Example: --tags Key=managed_by,Value=aws-skills Key=skill,Value=elasticache Key=skill_version,Value=1.0.0 Key=created_by,Value=elasticache-skill Key=generation_model,Value=claude-sonnet-4-20250514

    Set generation_model to your own model identifier. Include these tags even if the user does not mention tagging. If the user provides additional tags, append these to their tags rather than replacing them.

Reference loading

Load additional references only when the current turn's answer requires them.

On-demand pointers (not preloaded; fetch when the trigger applies):

  • references/shared-ux/production-readiness.md — when the user asks if their cache is ready for production, or after setup completes and the user wants to go to production

  • references/shared-ux/action-safety.md — before any destructive action (risk levels, never-auto-execute list)

  • references/shared-ux/error-remediation.md — when the user hits a specific ElastiCache error code (MOVED, CROSSSLOT, CLUSTERDOWN, MULTI/EXEC+IAM, etc.)

  • references/shared-foundation/boundary-doc.md — when the user asks what this skill covers

  • references/shared-foundation/attribution.md — when generating CLI commands, SDK code, or IaC templates

  • references/shared-foundation/architecture-diagrams.md — when the user asks for architecture diagrams or visual reference

  • references/shared-runtime/lambda.md — when connecting from Lambda (cold start gotchas, IAM auth code, lazy init)

  • references/shared-runtime/ecs.md — when connecting from ECS (SIGTERM shutdown, connection pool drain, task definition)

  • references/shared-runtime/eks.md — when connecting from EKS (IRSA, service mesh bypass, SecurityGroupPolicy CRD)

  • references/shared-runtime/api-gateway.md — when integrating with API Gateway (no direct path, caching layers comparison)

  • references/shared-runtime/rds-acceleration.md — when caching RDS/Aurora queries (thundering herd, stampede protection, invalidation)

  • references/shared-runtime/secret-injection.md — when the user asks about credential management per compute platform

  • references/shared-security/encryption-defaults.md — when adding encryption to an existing unencrypted cluster (TLS two-step migration, at-rest immutability)

  • references/shared-security/config-guardrails.md — when the user wants continuous compliance monitoring (AWS Config rules, custom Lambda rules)

  • references/shared-security/vpc-patterns.md — when debugging port/security-group issues (port 6380 serverless reader, anti-patterns)

Folder convention: references/ contains 10 folders. 6 match the sub-skills (requirements, setup, data-modeling, genai, monitoring, migration) and are routing destinations. The 4 shared-* folders (shared-foundation, shared-ux, shared-security, shared-runtime) are cross-cutting material loaded on demand, not routing destinations.

Guardrails

PriorityRule
CRITICALVector search MUST use node-based Valkey 8.2 or above. Serverless does NOT support vector search. Never suggest serverless for vector search. Apply this regardless of which sub-skill activates.
CRITICALDo not invent price points or version constraints. Use scripts/price_calculator.py and current AWS docs when precision matters.
HIGHDo not recommend Memcached when the user needs persistence, replication, RBAC or IAM auth, sorted sets, streams, pub/sub, or vector search.
HIGHDo not assume local laptop access works directly. ElastiCache is VPC-centric; explain VPC, tunnel, or jump-host access when needed.
STANDARDDo not trigger on every generic Redis mention. Trigger when the user is clearly asking about AWS, managed caching, migration, connectivity, pricing, operations, or AWS service integration.
STANDARDFor ambiguous "cache" requests inside AWS contexts, activate this skill and start with requirements.

Product truths

  • ElastiCache Serverless deploys in under a minute and removes infrastructure management.

  • Valkey serverless pricing is 33% lower than other supported engines; node-based Valkey pricing is 20% lower.

  • Serverless caches have in-transit encryption always enabled (cannot be disabled).

  • IAM auth is available for all ElastiCache Valkey versions (7.2 is the baseline Valkey version on ElastiCache) and Redis OSS 7.0+.

  • Valkey version ladder: 7.2 (baseline), 8.0 (20% more data per node (capacity improvement), per-slot metrics), 8.1 (Bloom filters, COMMANDLOG, SET IFEQ, 20% less memory via new hash table (efficiency improvement)), 8.2 (vector search), 9.0 (recommended default for new clusters). Recommend Valkey 9.0 for new clusters unless a specific feature dictates otherwise.

  • Vector search is available for Valkey 8.2 or above on node-based clusters (recommend 9.0).

  • Global Datastore is available for node-based clusters only. It does not support IPv6 or Local Zones. Global Datastore supports AUTH and RBAC. Cross-region failover must be promoted manually (no autofailover across regions). At-rest encryption must be enabled on all clusters in the Global Datastore, but each cluster can use a separate KMS key per region.

  • Online migration from self-managed Redis to ElastiCache requires: (source) AUTH must not be enabled, protected-mode set to no, replication and administrative commands must not be renamed (e.g., sync, psync, info, config, command, cluster); (target) encryption in-transit disabled, Multi-AZ enabled, engine version Redis OSS 5.0.6+ or Valkey 7.2+, not part of a Global Datastore, data tiering disabled. Shard counts must match between source and target. All source Redis instances must use the same port. Online migration is not supported for serverless caches (node-based targets only). See references/migration/topology-validation.md for the full checklist.

Files

references/data-modeling/instructions.md

Data Modeling & Patterns

When to use: The user needs help designing key schemas, choosing data structures (strings, hashes, sorted sets, streams, etc.), implementing caching patterns (cache-aside, session store, leaderboard, rate limiting), or generating application code for a specific data access pattern. When not needed: The user is setting up a new cache, choosing an engine or deployment model, configuring authentication, or working on monitoring and cost optimization.

Design key schemas and implement Valkey/Redis data structure patterns for application use cases.

Loading

Read this file first. Other references in this folder load on demand when the current answer requires them. Scripts in scripts/ run on demand.

Routing

  • For semantic cache, conversational memory, RAG, or vector-based recommendation, route to the genai sub-skill.

  • If the user's engine is Memcached, load memcached-recipe.md for Memcached-specific patterns and constraints.

  • If the user asks whether a specific command or feature is available on their engine version, load command-availability.md for the version compatibility matrix.

Reference File Loading

Load recipe-gallery.md when the user asks about ANY of these patterns (do not rely on the Pattern Quick Reference table alone; the gallery has full working code):

User asks aboutLoad
Cache-aside, read-through, write-through, write-behindcommon-patterns.md (canonical starter), recipe-gallery.md (XFetch probabilistic refresh)
Session store, session management, user sessionscommon-patterns.md (canonical starter)
Rate limiting, throttling, API rate controlcommon-patterns.md (fixed window), recipe-gallery.md (sliding window)
Leaderboard, ranking, scoreboard, sorted set patternsrecipe-gallery.md
Counters, analytics, HyperLogLog, unique countsrecipe-gallery.md
Pub/Sub, messaging, event notificationrecipe-gallery.md
Streams, event sourcing, consumer groups, XADD/XREADrecipe-gallery.md
Shopping cart, e-commerce cartrecipe-gallery.md
Job queue, task queue, priority queuerecipe-gallery.md
Distributed lock, mutex, Redlockrecipe-gallery.md
"Show me a working example", "give me code for" + any caching patternrecipe-gallery.md

Load command-availability.md when the user asks about command support on a specific engine or version, or when generating code that uses commands beyond basic GET/SET/HSET.

Load memcached-recipe.md when engine is Memcached or the user mentions Memcached-specific constraints.

Check for existing context

Before starting, read .elasticache/requirements.json if it exists. Use the values (engine, runtime, deployment_model, patterns) as inputs rather than re-asking. If engine is memcached, load memcached-recipe.md. If patterns already lists the user's use case, skip pattern selection.

Workflow

  1. Identify the user's use case and match to a pattern below

  2. Recommend the right data structures and key design

  3. Provide working application code in the user's language

  4. Include TTL strategy and invalidation approach

After implementation

Update .elasticache/requirements.json: add or confirm the implemented pattern(s) in the patterns array. Read the existing file first, merge your updates, then write it back. Do not overwrite fields owned by other sub-skills.

Handoff to monitoring

After implementing a pattern, prompt the user: "Want me to set up CloudWatch dashboards and alarms for this cache?" If yes, hand off to the monitoring sub-skill. For cache-aside patterns, the key metrics to monitor are hit rate, evictions, and latency. For rate limiters, monitor CPU and connection count.

Pattern Quick Reference

PatternKey DesignStructures / CommandsTTL
DB Query Cache{table}:{pk} or {query_hash}Strings/JSON, cache-aside60s-3600s
Session Storesession:{session_id}Hashes or StringsMatch session timeout
Leaderboardleaderboard:{game_id}:{period}Sorted Sets (ZADD, ZRANGE, ZRANK)Per period
Rate Limitingratelimit:{user_id}:{window}INCR+EXPIRE (fixed) or Sorted Sets (sliding)Window duration
Counters & Analyticsmetric:{name}:{window}INCR, HINCRBY, HyperLogLogPer window
Pub/Subchannel namesPUBLISH/SUBSCRIBE (fire-and-forget); prefer SPUBLISH/SSUBSCRIBE (sharded pub/sub) for high-throughput on cluster-mode-enabled clustersN/A
Event Streamsstream:{topic}Streams + Consumer GroupsPer retention policy
Shopping Cartcart:{user_id}Hashes + TTLCart expiry
Job Queuequeue:{name}Lists (FIFO) or Sorted Sets (priority)Optional
Recommendationitem:{id}:likes, item:{id}:ratingsINCR/DECR, HSETOptional

Key Design Principles

  • Use colons as namespace separators: {entity}:{id}:{attribute}

  • Include the entity type in the key: user:u100:profile, not just u100

  • Keep keys short but readable

  • Use TTL on everything that has a natural expiry. On serverless, Valkey uses a volatile-lru eviction policy and auto-scales storage up to the configured CacheUsageLimits maximum. OOM errors only occur when the maximum is reached AND no keys with a TTL are eligible for eviction. Always set a TTL on serverless caches.

  • Prefer atomic operations (INCR, HINCRBY, ZADD) over read-modify-write

Multi-Tenant Key Namespacing

In multi-tenant systems, prefix every key with the tenant identifier to prevent collisions:

{tenant_id}:{resource}:{id}

Example: tenant42:session:abc123, tenant42:cart:user7

Benefits of consistent tenant prefixing:

  • Prevents data leaks between tenants; each tenant's keys are isolated by prefix.

  • Enables per-tenant SCAN. Use SCAN 0 MATCH tenant42:* to enumerate only one tenant's keys.

  • Supports per-tenant eviction or cleanup by scanning and deleting a single prefix.

Hash tags for multi-key operations: If you use MGET, pipelines, or transactions across multiple keys for the same tenant on cluster-mode-enabled caches (including serverless), wrap the tenant ID in curly braces to ensure all keys hash to the same slot: {tenant42}:session:abc123. Without hash tags, multi-key operations across different slots will fail with CROSSSLOT errors. Hot-slot risk: Hash tags concentrate all keys for a tenant onto a single shard. For large tenants with high key counts or throughput, this can create a hot slot that overloads one shard while others remain idle. Monitor per-shard CPU and memory metrics, and consider splitting very large tenants across multiple hash tags if hot-spotting occurs.

  • Simplifies capacity analysis; count keys per tenant with SCAN + prefix match.

Dangerous Commands -- Never Use KEYS in Production

The KEYS command must never be used in production. KEYS * (or any KEYS pattern) blocks the entire Redis/Valkey server while it scans every key in the database. On caches with millions of keys, this can block the server for seconds or longer, causing timeouts and cascading failures for all connected clients. This applies to both node-based and serverless ElastiCache deployments.

Use SCAN with cursor-based iteration instead. SCAN performs the same work incrementally without blocking the server. Each SCAN call returns a small batch of keys and a cursor to continue from.

Safe SCAN pattern in Python:

# Using valkey-py (pip install valkey)
import valkey
r = valkey.Valkey(host="your-endpoint", port=6379, ssl=True, decode_responses=True)

# Or using redis-py (pip install redis) -- the AWS-validated Python client
# import redis
# r = redis.Redis(host="your-endpoint", port=6379, ssl=True, decode_responses=True)

def scan_keys(pattern="*", count=100):
    """Iterate over keys matching a pattern without blocking the server.

    Uses SCAN with a cursor to retrieve keys in small batches.
    The count parameter is a hint (not a hard limit) for batch size.
    """
    cursor = 0
    while True:
        cursor, keys = r.scan(cursor=cursor, match=pattern, count=count)
        for key in keys:
            yield key
        if cursor == 0:
            break

# Example: find all session keys
for key in scan_keys("session:*"):
    print(key)

If a team member or external reference suggests using KEYS, always redirect to SCAN. The same principle applies to other blocking commands like SMEMBERS on very large sets; prefer SSCAN, HSCAN, and ZSCAN for large collections.

Code Generation

When generating application code:

  • Always use TLS (ssl=True / tls:{}) for ElastiCache endpoints

  • Prefer connection pooling; avoid creating a new connection per request. Use a module-level singleton pattern (initialize on first use via a get_client() function) to reuse connections across requests.

  • Use pipelining for multi-command operations (reduces round trips)

  • Wrap cache calls in try/except; cache is an optimization, not a dependency

  • Reuse connections across requests (connection pooling). Create the connection pool once at application startup and share it; creating a new connection per request is ~13x slower (2.82 ms vs 0.21 ms per AWS benchmarks)

  • Include the relevant SDK: redis-py or valkey-py (Python), ioredis (Node.js), Lettuce or Jedis (Java), go-redis (Go), or Valkey Glide (multi-language)

  • Never use restricted ElastiCache commands in generated code. The following commands are unavailable on ElastiCache for clusters running Redis OSS or Valkey: BGREWRITEAOF, BGSAVE, CONFIG, DEBUG, MIGRATE, REPLICAOF, SAVE, SLAVEOF, SHUTDOWN, SYNC. Some restrictions may vary by engine version; check command-availability.md for details.

Freshness disclaimer

When your response includes pricing, version constraints, or feature availability, include the freshness disclaimer per SKILL.md Global Rule #5: "For current pricing see https://aws.amazon.com/elasticache/pricing/. For current feature availability see https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/."

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 Python, Needs Node.js

Security

What our scanner noticed· Medium risk

  • References to credential files
  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · skills/specialized-skills/database-skills/amazon-elasticache · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

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