What Is Database Optimization?

Databases are like the foundation of an enterprise system architecture. Developers today use databases not only to serve records but also to store application configurations, session states, personalization options, and more. Therefore, it's no surprise that enterprise teams do all they can to optimize database performance for speed and efficiency. Database optimization becomes even more important as the architecture scales to support multi-node distributed applications. In fact, when users say an app is slow, the database is often the primary culprit.

However, there's no single trick to maximize database performance. While there are general best practices, how you fine-tune the service can depend greatly on the particular database you use, the problem you're experiencing, and other infrastructure components. Optimization can mean redesigning a schema, adding read replicas and caches, or simply rewriting a single slow query. This guide will walk you through what database optimization is, how to find bottlenecks, and the techniques engineers use to achieve maximum performance. We'll also show you how a Valkey or Redis cache layer takes optimization to the next level when building Java apps with Redisson.

Understanding Database Performance and Optimization

The general-purpose definition of database optimization is the practice of maximizing a database's speed, efficiency, and scalability. Said another way, it's the steps you take to get the most performance out of your database with the fewest resources. But how do you measure performance?

Objective metrics such as lower query latency, higher throughput, and shorter read/write times under load are the best indicators of database performance. You can also look at the host system to measure performance, as a database that uses inordinate amounts of memory and CPU time, or has poor I/O figures, is not optimized. These are all useful high-level statistics, but to truly understand performance and develop an optimization plan, think of it in terms of time.

Every read or write your application performs costs database time. Optimization is the process of either making each operation cheaper or avoiding the operation altogether. For example, an improved query plan or adding the right index can make operations cheaper, and caching a result removes the operation because the database is never touched. The best optimization plans typically include both approaches.

The Top Database Optimization Techniques

As an application grows and incurs higher database costs, optimization becomes more important. A query that runs in two milliseconds against ten thousand rows can take seconds against ten million. A connection pattern that works for a hundred users can collapse at ten thousand. Maximizing performance in these and other situations starts by figuring out where database time actually goes.

Most, if not all, enterprise databases offer runtime statistics, performance monitoring tools, and a slow query log. Often, you will find the specific queries and endpoints responsible for the most expensive operations. After you have identified the problem, you can use the following database optimization techniques:

Query Optimization and Execution Plans

Inefficient queries often cause slowness, as they incur considerable costs. Most database systems provide a way to view a query's execution plan — typically via a command such as EXPLAIN or EXPLAIN ANALYZE — that shows how it will process the request. These plans show which indexes it uses, where it performs full table scans, how it joins tables, and where it estimates the most rows will flow. The ability to read and comprehend these plans may be the single most valuable skill in database optimization.

Common query optimization techniques include eliminating full table scans on large tables, rewriting correlated subqueries as joins, removing SELECT * in favor of only the columns you need, and avoiding functions on indexed columns in WHERE clauses (which prevent the index from being used). A query that touches fewer rows and fewer pages will always be faster, without exception.

Indexing

The most direct way to speed up read performance is with indexes. An index lets the database jump straight to the rows that match a predicate rather than scanning the whole table. Adding the right index to a column used in frequent WHERE, JOIN, or ORDER BY clauses can turn a multi-second query into a sub-millisecond one.

The trade-off is that every index must be maintained on writes, so each INSERT, UPDATE, and DELETE becomes slightly more expensive and consumes more storage. Effective indexing is therefore a balancing act. Follow these rules to achieve the best balance:

  • Index the columns your hot queries filter and sort on.
  • Use composite indexes that match your query patterns.
  • Remove unused indexes that only add write overhead.

Keep in mind that sometimes even well-indexed queries can incur significant read load, which is when adding a caching layer often makes the most sense, as it can eliminate the database operation.

Schema Design and Denormalization

How you structure data plays a large part in how hard the database has to work to respond to queries. Normalization, the process of structuring data in various degrees of the normal form, minimizes duplication and writes. For reads, however, normalization can force expensive multi-table joins. Denormalization is the process of intentionally abandoning the normal form, trading some redundancy and potential inconsistency in exchange for cheaper reads.

For apps that read significantly more than they write to the database, this trade-off is worth it. Caching can be seen as taking denormalization to its logical conclusion, as it keeps a ready-to-serve copy of frequently used data outside the database.

Connection Pooling

Simply opening a database connection is expensive, given the network handshake, authentication, and session setup involved. Under heavy load, creating a new connection per request quickly exhausts the database's connection limit and adds latency to every call. A connection pool keeps a set of established connections open and hands them out to requests as needed, dramatically reducing overhead and protecting the database from connection storms.

Finding the right pool size is critical. If it's too small, the request queue waits for a connection; if it's too large, the database is overwhelmed by concurrency. Connection pooling doesn't make individual queries faster, but it does remove a major source of latency and instability.

Caching: The Best Optimization Tool for Read-Heavy Workloads

You might have noticed caching come up several times in the discussion of optimization techniques. That's because caching is the most cost-efficient method available. Think of it this way: the fastest database query is the one you never have to run, and that's what caching provides.

A cache stores the results of expensive reads in fast in-memory storage (such as Valkey or Redis), so repeated requests for the same data are served in microseconds without ever touching the database. The most common pattern is cache-aside: the application checks the cache first, and only on a cache miss does it query the database and populate the cache for future requests.

When done right, Valkey/Redis caching can absorb most read traffic, leaving the database free to handle writes and the long tail of uncached queries. Because cached data can go stale, you have to deploy cache invalidation and time-to-live (TTL) values carefully, but the performance payoff for read-heavy systems is hard to beat.

Database Optimization With Valkey/Redis and Redisson

So far, we've established that when the database is the bottleneck in a read-heavy system, the most effective single change is often to move hot reads from the database to an in-memory cache. However, you don't want to simply shift the burden from the database to your development team, as they manually manage TTLs and write their own cache invalidation code. Instead, look into Redisson, which makes it easy for Java developers to manage a Valkey or Redis cache.

Redisson is a Valkey/Redis Java client that implements caching through familiar Java structures, including distributed implementations of Map, so that adding a cache layer requires very little overhead. Below are examples that show how Redisson seamlessly integrates into Java code via three complementary techniques: offloading hot reads with a TTL-backed map, serving the hottest keys from a near-cache in local JVM memory, and collapsing many reads into a single network round trip with a batch.

Offloading Hot Reads with a Cache-Aside Map

RMapCache is a distributed map with per-entry TTLs, making it a natural cache for database rows. The following implements the cache-aside pattern: check the cache, fall back to the database on a miss, then populate the cache with a TTL so entries expire on their own:

RedissonClient redisson = Redisson.create();
// Distributed cache with per-entry TTL — ideal for hot database rows
RMapCache<Long, Product> cache = redisson.getMapCache("products");

public Product getProduct(long id) {
    // 1. Look in the cache first
    Product product = cache.get(id);
    if (product != null) {
        return product;                       // cache hit — no database round trip
    }

    // 2. Cache miss — fall back to the database
    product = productRepository.findById(id);

    // 3. Store the result with a 10-minute TTL so it expires automatically
    if (product != null) {
        cache.put(id, product, 10, TimeUnit.MINUTES);
    }
    return product;
}

With modern versions of Valkey (9.0 or higher) and Redis (7.4 or higher), you can let the server handle per-entry expiration natively with RMapCacheNative, which uses native hash-field TTLs instead of a Redisson-side eviction task:

// Per-entry TTL enforced by Redis/Valkey itself (requires Redis 7.4+ or Valkey 9.0+)
RMapCacheNative<Long, Product> cache = redisson.getMapCacheNative("products");
cache.put(id, product, 10, TimeUnit.MINUTES);

This single layer typically deflects most read traffic away from the database, thereby directly relieving the bottleneck.

Serving the Hottest Keys from a Local Near-Cache

For the small set of keys that are read constantly, even a microsecond network hop to Redis adds up. RLocalCachedMap keeps a near cache of entries in local JVM memory and serves reads from there, falling back to Redis only when needed. Local cache reads are dramatically faster than a standard network round trip. Update and invalidation events are propagated between instances over PubSub, so the local copies stay updated:

LocalCachedMapOptions<Long, Product> options = LocalCachedMapOptions
    .<Long, Product>defaults()
    .evictionPolicy(EvictionPolicy.LRU)
    .cacheSize(10_000)                    // entries kept in JVM memory
    .timeToLive(10, TimeUnit.MINUTES);

RLocalCachedMap<Long, Product> products =
    redisson.getLocalCachedMap("products", options);

Product p = products.get(id);   // hot reads served from local memory — zero network hops

This is effectively a two-tier cache: local memory in front of Valkey/Redis, in front of the database.

Cutting Round Trips with RBatch

When you look for the cause of latency, you'll often find excessive network round trips. A perfect latency-causing example is fetching many keys at once, sending each read as a separate command. Redisson's RBatch pipelines multiple commands into a single network round trip, which can sharply reduce the time required for bulk reads and writes:

RBatch batch = redisson.createBatch(BatchOptions.defaults());
RMapAsync<Long, Product> products = batch.getMap("products");

// Queue several reads — nothing is sent yet
products.getAsync(101L);
products.getAsync(102L);
products.getAsync(103L);

// One network round trip for the entire batch
BatchResult<?> result = batch.execute();
List<?> responses = result.getResponses();   // results in the order they were queued

For higher-level integrations, Redisson also implements standard caching APIs such as JCache, Spring Cache, and the Hibernate second-level cache. Thanks to these integrations, an existing application can adopt a Valkey/Redis cache layer with minimal code changes.

Is Caching in Your Database Optimization Strategy?

While caching is an excellent way to reduce database costs, it should be just one part of an optimization strategy. Query tuning, indexing, schema design, connection pooling, and read replicas all have their place in ensuring that queries reaching your database are fast and cost-efficient.

Java developers can easily implement a Valkey or Redis cache layer with Redisson's familiar Java structures. Explore the feature sets of Redisson and Redisson PRO to learn more.