Redis cache-aside with Jedis

Implement a Redis cache-aside layer in Java with Jedis

This guide shows you how to implement a Redis cache-aside layer in Java with the Jedis client library. It includes a small local web server built on the JDK's com.sun.net.httpserver so you can see cache hits, misses, invalidation on write, and stampede protection in action.

Overview

Cache-aside is one of the most common Redis use cases for read-heavy applications. Instead of querying the primary database on every request, the application checks Redis first and only falls back to the primary on a miss. The result is written back to Redis with a TTL so the next read is served from memory.

That gives you:

  • Sub-millisecond reads for the hot working set
  • Bounded staleness — every entry expires within a known window
  • Reduced primary database load proportional to hit rate
  • Field-level updates without re-serializing the full record
  • Protection against cache stampedes when popular keys expire under load

In this example, each cached product is stored as a Redis hash under a key like cache:product:{id}. The hash holds the product fields (id, name, price_cents, stock) and the key has a TTL so stale data is bounded automatically.

How it works

The flow on every read looks like this:

  1. The application calls cache.get(productId, primary::read)
  2. The helper runs HGETALL against cache:product:{id}
  3. On a hit, the cached hash is returned directly
  4. On a miss, the helper acquires a Lua-backed single-flight lock and invokes the loader to fetch from the primary
  5. The helper writes the result back to Redis with HSET plus EXPIRE and releases the lock
  6. Concurrent threads that fail to acquire the lock wait briefly for the cache to populate, then return that value instead of issuing their own primary read

On a write, the application updates the primary and then deletes the cache key, so the next read repopulates from the new source value.

The cache-aside helper

The RedisCache class wraps the cache-aside operations (source):

import redis.clients.jedis.JedisPool;
import redis.clients.jedis.JedisPoolConfig;

JedisPool pool = new JedisPool(new JedisPoolConfig(), "localhost", 6379);
MockPrimaryStore primary = new MockPrimaryStore(150);
RedisCache cache = new RedisCache(pool);

// Read through the cache.
RedisCache.Result result = cache.get("p-001", primary::read);
System.out.printf("hit=%s latency=%.2fms record=%s%n",
    result.hit, result.redisLatencyMs, result.record);

// Update a single field without rewriting the whole record.
cache.updateField("p-001", "stock", "41");

// Invalidate the cache key on a write to the primary.
primary.updateField("p-001", "price_cents", "699");
cache.invalidate("p-001");

Data model

Each cached product is stored in a Redis hash:

cache:product:p-001
  id          = p-001
  name        = Sourdough Loaf
  price_cents = 650
  stock       = 42

The implementation uses:

  • HGETALL to read the cached record
  • HSET plus EXPIRE to repopulate after a miss
  • DEL to invalidate on writes
  • TTL to surface remaining staleness in the demo UI
  • EVAL for the Lua single-flight lock that prevents stampedes
  • WATCH/MULTI/EXEC for the conditional field update path

Cache-aside reads

The get() method runs HGETALL on the cache key first. On a hit it returns the cached hash and increments the in-process hit counter. On a miss, it delegates to a single-flight loader:

public Result get(String entityId, Function<String, Map<String, String>> loader) {
    String cacheKey = cacheKey(entityId);
    Map<String, String> cached;
    long startedNs = System.nanoTime();

    try (Jedis jedis = pool.getResource()) {
        cached = jedis.hgetAll(cacheKey);
    }
    double redisLatencyMs = (System.nanoTime() - startedNs) / 1_000_000.0;

    if (cached != null && !cached.isEmpty()) {
        hits.incrementAndGet();
        return new Result(cached, true, redisLatencyMs);
    }

    misses.incrementAndGet();
    Map<String, String> record = loadWithSingleFlight(entityId, loader);
    return new Result(record, false, redisLatencyMs);
}

The returned Result includes the measured Redis round-trip time so the demo UI can show the latency difference between a hit and a miss.

Stampede protection with a Lua lock

When a popular key expires, every concurrent reader observes the miss at the same instant. Without coordination, all of them would query the primary and overwrite the cache redundantly — a cache stampede.

The helper uses a tiny Lua script to acquire a short-lived lock atomically. Only the thread that wins the SET NX becomes the primary loader; the rest poll the cache briefly and return the value the lock holder writes:

-- Acquire a short-lived lock with SET NX PX. Returns 1 on acquire, 0 otherwise.
if redis.call('SET', KEYS[1], ARGV[1], 'NX', 'PX', ARGV[2]) then
    return 1
end
return 0

A second script releases the lock only if the caller still owns it, so a lock that timed out and was re-acquired by someone else cannot be released by mistake:

if redis.call('GET', KEYS[1]) == ARGV[1] then
    return redis.call('DEL', KEYS[1])
end
return 0

The Java side runs both scripts via eval() on every miss:

private Map<String, String> loadWithSingleFlight(
        String entityId,
        Function<String, Map<String, String>> loader) {
    String cacheKey = cacheKey(entityId);
    String lockKey = lockKey(entityId);
    String token = randomToken();

    try (Jedis jedis = pool.getResource()) {
        Object acquired = jedis.eval(
                ACQUIRE_LOCK_SCRIPT,
                List.of(lockKey),
                List.of(token, Integer.toString(lockTtlMs)));
        if (asInt(acquired) == 1) {
            try {
                Map<String, String> record = loader.apply(entityId);
                if (record == null) return null;
                Transaction tx = jedis.multi();
                tx.del(cacheKey);
                tx.hset(cacheKey, record);
                tx.expire(cacheKey, ttl);
                tx.exec();
                return record;
            } finally {
                jedis.eval(RELEASE_LOCK_SCRIPT, List.of(lockKey), List.of(token));
            }
        }
    }

    stampedesSuppressed.incrementAndGet();
    long deadline = System.nanoTime() + (long) lockTtlMs * 1_000_000L;
    while (System.nanoTime() < deadline) {
        Thread.sleep(waitPollMs);
        try (Jedis jedis = pool.getResource()) {
            Map<String, String> cached = jedis.hgetAll(cacheKey);
            if (cached != null && !cached.isEmpty()) return cached;
        }
    }
    return loader.apply(entityId);
}

The unique token per caller is what makes the release script safe — only the thread that actually holds the lock can release it.

Invalidation on write

When a write hits the primary, the application invalidates the cache key. The next read pulls fresh data from the primary:

public boolean invalidate(String entityId) {
    try (Jedis jedis = pool.getResource()) {
        return jedis.del(cacheKey(entityId)) == 1L;
    }
}

This is the simplest and safest pattern: never try to keep the cache and primary in sync directly, just delete the cache entry and let the next read repopulate it.

Field-level updates

Because each record is stored as a hash, the cache helper can also update a single field in place without re-serializing the full record. The update only writes if the entry is already cached, so a partial record can never appear in Redis:

public boolean updateField(String entityId, String field, String value) {
    String cacheKey = cacheKey(entityId);
    while (true) {
        try (Jedis jedis = pool.getResource()) {
            jedis.watch(cacheKey);
            if (!jedis.exists(cacheKey)) {
                jedis.unwatch();
                return false;
            }
            Transaction tx = jedis.multi();
            tx.hset(cacheKey, field, value);
            tx.expire(cacheKey, ttl);
            List<Object> result = tx.exec();
            if (result == null) continue; // WATCH detected a change — retry.
            return true;
        }
    }
}

This is useful for hot fields that change more often than the rest of the record (a stock counter, a view count) and would otherwise force a full reload.

Hit/miss accounting

The helper keeps in-process counters for hits, misses, and stampedes that were suppressed by the single-flight lock. The demo UI surfaces these so you can see the cache absorbing load:

public Map<String, Object> stats() {
    long h = hits.get();
    long m = misses.get();
    long total = h + m;
    double hitRate = total == 0 ? 0.0 : ((long) (1000.0 * h / total)) / 10.0;
    Map<String, Object> stats = new HashMap<>();
    stats.put("hits", h);
    stats.put("misses", m);
    stats.put("stampedes_suppressed", stampedesSuppressed.get());
    stats.put("hit_rate_pct", hitRate);
    return stats;
}

In production you would emit these as Micrometer counters or push them into your metrics pipeline rather than holding them as AtomicLongs in process memory.

Prerequisites

Before running the demo, make sure that:

  • Redis is running and accessible. By default, the demo connects to localhost:6379.
  • JDK 11 or later is installed.
  • The Jedis JAR is on your classpath. Get it from Maven Central, or via Maven/Gradle in a project setup.

If your Redis server is running elsewhere, start the demo with --redis-host and --redis-port.

Running the demo

A local demo server is included to show the cache-aside layer in action (source):

javac -cp jedis-5.0.0.jar:commons-pool2-2.12.1.jar:slf4j-api-1.7.36.jar:gson-2.9.1.jar:json-20230618.jar \
    RedisCache.java MockPrimaryStore.java DemoServer.java

java -cp .:jedis-5.0.0.jar:commons-pool2-2.12.1.jar:slf4j-api-1.7.36.jar:gson-2.9.1.jar:json-20230618.jar \
    DemoServer --port 8080 --redis-host localhost --redis-port 6379

The demo server uses standard Java libraries for HTTP handling and concurrency:

It exposes a small interactive page where you can:

  • Read a product through the cache and see whether it was a hit or a miss
  • Compare the measured Redis round-trip against the simulated primary read latency
  • Watch the cache TTL count down between requests
  • Update a field on the primary and see the cache invalidate automatically
  • Run a stampede test that fires many concurrent reads at a freshly-invalidated key and confirms only one of them reaches the primary
  • Reset the hit/miss counters at any time

After starting the server, visit http://localhost:8080.

The mock primary store

To make the demo self-contained, the example includes a MockPrimaryStore that stands in for a slow disk-backed database (source):

public Map<String, String> read(String id) {
    try {
        Thread.sleep(readLatencyMs);
    } catch (InterruptedException e) {
        Thread.currentThread().interrupt();
        return null;
    }
    // ...
}

Every call to read() sleeps for readLatencyMs so the difference between a cache hit and a miss is obvious in the UI. The store also tracks the total number of primary reads, which the stampede test uses to confirm that single-flight is working — for N concurrent readers against a cold key, you should see exactly one primary read.

In a real application this would be replaced by a JDBC query, an HTTP call to a downstream service, or any other slow-but-authoritative source.

Production usage

This guide uses a deliberately small local demo so you can focus on the cache-aside pattern. In production, you will usually want to harden several aspects of it.

Choose a TTL that matches your staleness tolerance

The TTL is the upper bound on how long a stale value can be served. Shorter TTLs mean lower hit rates and more primary load; longer TTLs mean higher hit rates and more stale reads between writes. Pick the value that matches your business tolerance for stale data, and combine it with explicit invalidation on writes for the cases where you cannot tolerate any staleness.

Invalidate, don't try to keep the cache in sync

When the underlying record changes, delete the cache key rather than rewriting it. Cache-aside is robust precisely because it never assumes the cache holds the latest value — the next read always re-fetches from the primary on a miss.

Handle missing records explicitly

In this demo, a missing record returns null and nothing is cached. In a real system you may want to cache "not found" sentinels with a short TTL to absorb load from probing for non-existent IDs, while making sure the sentinel TTL is shorter than the positive cache entry so a newly-created record becomes visible quickly.

Tune the single-flight lock TTL

The lock TTL needs to be longer than the worst-case primary read latency so a slow loader does not lose the lock midway. The unique token in RELEASE_LOCK_SCRIPT ensures the original caller does not delete someone else's lock if its lock has expired.

Tune the JedisPool

The demo uses a default JedisPoolConfig. In production, set setMaxTotal() and setMaxIdle() to match your concurrency profile and server-side maxclients. Wrap pool acquisition in try-with-resources so connections are returned even on exceptions.

Namespace cache keys in shared Redis deployments

If multiple applications share a Redis deployment, prefix cache keys with the application name (cache:billing:product:{id}) so different services cannot clobber each other's entries.

Inspect cached entries directly in Redis

When testing or troubleshooting, inspect the stored cache key directly to confirm the application is writing the fields and TTL you expect:

redis-cli HGETALL cache:product:p-001
redis-cli TTL cache:product:p-001

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