“Why is my database slow?” If requests repeatedly fetch the same data, a missing cache may be part of the answer. But that is a hypothesis to test, not a diagnosis: a cache can reduce repeated database reads when some staleness is acceptable; it will not repair every slow query, write bottleneck, poor index, or unsuitable data model. Before asking “Should I add Redis?” or “Do I need a cache?”, find out which reads repeat, how much they cost, and how fresh their results must be.
How to tell whether repeated reads are the problem
Start with the workload rather than choosing a caching product. Look for frequently repeated reads, a high ratio of reads to writes, expensive queries whose results change infrequently, or read demand that is costly to scale. These are the kinds of workloads AWS identifies as potential cache candidates (AWS Well-Architected: Caching).
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- Measure which queries and keys account for the most database work, and how often the same results are requested.
- Record database query volume and CPU alongside application P95 and P99 latency. Compare these before and after any change.
- Ask whether each result can safely be a little out of date. If it cannot, caching may be the wrong fix for that read.
- Investigate slow individual queries, missing or unsuitable indexes, write pressure, and data-model issues separately. A cache can hide symptoms without correcting those causes.
A cache holds data that can be reconstructed from an origin or an earlier computation. Its value depends on repeated demand and an acceptable freshness trade-off—not simply on whether a database is present.
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Local or client-side cache
A local cache avoids a remote cache network hop and can keep some reads available during a backend disruption. The trade-offs are duplicated data across clients and the possibility that clients see different versions or freshness.
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Remote, shared cache
A remote cache can share entries among clients and scale storage separately from them, but every cache access adds a network hop. A local tier in front of a remote cache is possible, at the cost of more moving parts and freshness complexity. Compare end-to-end latency, not just the cache lookup in isolation.
Query-result caching
If a small set of repeated, expensive SQL queries is the bottleneck, caching query results may target the work directly. AWS documents a JDBC caching plugin for selected Java queries against PostgreSQL, MySQL, or MariaDB. It requires an ElastiCache for Valkey or Redis OSS cache and the plugin’s documented dependencies; it is not a general-purpose setting for every database client. See the AWS query caching documentation.
Pick a read and write pattern
Cache-aside (lazy loading)
- On a read, check the cache for the requested key.
- If it is present, return the cached value.
- If it is absent, read from the primary database, place the result in the cache, and return it.
This pattern focuses cache storage on data actually requested. The first read after a miss must do the cache lookup and database fetch, so cold misses add a round trip and work. In cache-aside, the database remains the source of truth; the cache is an acceleration layer (AWS caching strategies).
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Write-through
With write-through, a write updates the primary and the cache. Updating a known-hot entry can make later reads more likely to hit, but writes may also fill the cache with data nobody reads and create extra churn. AWS describes combining write-through with lazy loading where appropriate. Every path that changes the data must be accounted for, or the cache may retain an outdated value (AWS caching strategies; AWS Database Caching Strategies Using Redis).
| Pattern or location | Potential benefit | Main trade-off |
|---|---|---|
| Cache-aside | Stores data as it is requested | A miss adds a database fetch and cache-fill work |
| Write-through | Can keep cache entries current when application writes succeed | May store cold data and increase write churn |
| Local or client-side | Avoids a remote lookup hop | Clients can duplicate data and disagree about freshness |
| Remote or shared | Shares entries among clients and scales storage separately | Adds a network hop |
| Query-result caching | Targets repeated, expensive query results | Requires a compatible implementation and careful consistency choices |
Set freshness rules before adding entries
There is no universally correct time-to-live (TTL). Set expiration according to how quickly the source data changes and the harm an outdated result could cause. Static or reference data may tolerate a longer lifetime than rapidly changing fields. Explicit invalidation or write-through may suit application-updated data, but only if all write paths are covered; a TTL can also limit how long an overlooked invalidation leaves a stale entry in place. AWS recommends TTLs for cache keys except those maintained through write-through (AWS Database Caching Strategies Using Redis).
For some reads, staleness is not an acceptable trade-off. AWS cautions against query caching where strong consistency is required or a multi-statement transaction needs read-after-write consistency: “Query caching is not recommended for queries where strong consistency is required, or for queries inside multi-statement transactions that require read-after-write consistency.” (AWS ElastiCache documentation). Keep those reads on a path that meets their consistency requirements.
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Plan for expiration, misses, and outages
Prevent synchronized expiration
If many popular keys expire together, concurrent requests can all miss and refill from the database at once—a cache stampede. Add random jitter to expiration times so keys do not all expire on the same schedule. For a hot key, consider single-flight coordination or locking so concurrent callers share one refill, or refresh it early. Redis documents Lua-based locking and probabilistic early refresh approaches (Redis client-side caching documentation).
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Make the origin able to recover
Plan what happens when entries are evicted, the cache restarts, or the cache service is unavailable. Misses and recovery traffic still reach the backing database, so a cache should not be treated as the only copy of data in a cache-aside design. AWS recommends monitoring misses as well as hit rate and accounting for failure behavior in cache design (AWS Well-Architected: Caching).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Judge results by more than hit rate
AWS Well-Architected guidance gives 80% or higher as a cache-hit-rate goal. Treat that as a starting benchmark in that guidance, not a pass/fail rule for every application: low hit rate can point to an undersized cache, but it can also reveal that the workload is not a good fit (AWS Well-Architected: Caching).
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Evaluate a candidate design against these practical criteria:
- Reuse: Are the same keys or query results requested often enough to produce useful hits?
- Correctness: What staleness is acceptable, and which reads require strong or read-after-write consistency?
- Latency: Does a hit save more time than the cache’s network hop costs, and how do cold misses affect tail latency?
- Capacity and cost: How much memory is needed, what will eviction remove, and what does running the cache service add?
- Operations: Can the team maintain invalidation rules, handle stampedes, and recover when the cache is cold or unavailable?
- Measured effect: Do database query volume or CPU and application P95/P99 latency improve on the real workload?
Do not assume a universal speedup: the result depends on the workload, placement, network, freshness policy, and failure handling. Application-level caching also requires application-specific choices; a qualitative study of ten software projects found that teams often handled it ad hoc and that it could be error-prone. That is a warning about implementation complexity, not evidence that caching fails at a particular rate (qualitative study of application-level caching).
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