Using Redis for Caching: Best Practices and Performance Tuning

Redis is an open source caching tool that stores data in memory and is used primarily as an application cache. Great choice for heavy load applications such as real time analytics, ad-tech, financial applications, gaming, IoT, and more. Since these applications need to access data in time, Redis provides the ultimate in memory storage. Also, Redis delivers responses in sub-millisecond times and is used by many organizations worldwide.

With Redis, you store data in memory rather than in hard disk or SSD. Delivers fast performance and when reading and writing data. It also provides built in replication capabilities to store data close to the user as this reduces latency significantly. In order to benefit from these features, it’s best to utilize Redis optimally.

This article covers the best practices for using Redis as a cache server. It also explores the best strategies to optimize Redis performance. Read on!

Best Practices For Using Redis as a Cache Server

Moreover, Redis helps support highly responsive database constructs. As such, it helps reduce the database access, reducing the instances required and the amount of traffic. Here are some of the best practices for using Redis as a cache server:

Understand the Various Redis Caching Strategies

Caching is the practice of storing data in a fast access medium like RAM, which reduces the time taken to access. Therefore, Redis allows various caching methods depending on the data and its patterns. Choosing optimal caching strategy significantly impacts data retrieval speeds. The most common strategies include:

1. Read-Through Pattern

In this pattern, the cache is responsible for fetching data from the data store in the event of a cache miss. The application interacts with the cache only, and does not handle cache misses.

2. Write-Through Pattern

This pattern updates the cache whenever the primary data store is updated, ensuring the cache is always up to date. Basically, the application writes data to both the primary data store and the cache. Keeping the cache up to date reduces the chances of stale data.

3. Write-Behind Pattern

In this pattern, the application writes data to the cache. It also updates the data store asynchronously. Basically, it’s an optimization of the Write-Through pattern that helps optimize write performance.

The above are the main caching patterns you implement with Redis. 

  • Full Page Caching– cache the entire HTML page. When the user requests the page, Redis delivers it in real time.
  • Partial Page Caching– Redis caches the most accessed elements.
  • Session Caching– caches the session data to simplify user authentication. Mostly, it caches the user’s session ID, and stores the value in the session data.
  • Object Caching– you store some application server objects in Redis instead in the database.

Utilize Hashes to Store Objects

To store objects in Redis use hashes. Convert each field in the object into a set of key value pairs. After converting the objects, you store the key-value pairs in the hash.

Hashing is efficient, as Redis fetches all object fields in a single operation. When you fetch an object from the hash, it returns all fields at a go. 

Monitor Key Performance Indicators (KPIs) When Using Redis Cluster

Redis cluster provides a way to run Redis installation where data is sharded across multiple nodes. It provides high availability, as the application continues to run even when some nodes fail. However, in the event of a large failure, the cluster becomes unavailable. Therefore, it’s essential to monitor the following KPIs:

  • Info.clients.connected_clients- The number of clients connected to a redis instance.
  • Info.memory.used_memory- total number of bytes allocated by memory allocator.
  • Info.memory.maxmemory- maximum memory available for a Redis instance.
  • Info.clients.blocked_clients- number of clients waiting for a blocking request made to redis server.
  • Info.stats.instantaneous_ops_per_sec- number of commands processed by Redis server.

Utilize the Redis’ Built-in Expiration Mechanism

Redis allows you to set expiration time for keys. With the built in expiration, you get to shorten the expiration time for cached items. Once set, these items automatically expire and get evicted from the cache.

Also, this mechanism is important when using Redis to store sensitive info like passwords.  Also leverage the expiration mechanism when storing session information. It helps you set a relatively short expiration time for the keys, if they are not accessed for a certain period of time.

Always Test Redis Keys

Since Redis stores data in memory, you easily lose it, if the server crashes, or in the event of a power outage. Therefore, it’s essential to test your Redis keys before using them in production. Testing keys is the only sure way of ensuring they are durable and don’t get lost in the event something goes wrong.

Store Data in Such a Way That It’s Easy to Query

Basically, Redis returns results as they are ordered when they are stored in memory. If you store data in a way that doesn’t support efficient querying, you most likely end up with inefficient querying.

To avoid inefficient queries, structure your keys depending on how you query data. For instance, if you query data by data range, consider keys that store data using corresponding keys. It makes querying efficient and also easier to write.

Best Practices for Optimizing Redis Performance

1. Tune Persistence Options

Well, Redis has 2 types of persistence options: RDB and AOF. RDB takes snapshots of your dataset at specified intervals, while AOF logs every write operation received by the server. Basically, RDB is faster and consumes less memory, but may result in data loss, if Redis crashes between snapshots. On the other hand, AOF provides better durability, but it’s slower and requires more memory.

Selecting the right persistence option depends on your application’s needs. If data durability is critical, then AOF should be used. But, if you prioritize performance over durability, RDB might be a better choice. There’s also an option to use both methods, where RDB provides fast restarts and AOF ensures that no data is lost.

These options can be further tuned. For instance, configure how often RDB snapshots are taken or rewrite the AOF log to prevent it from growing indefinitely.

2. Manage Memory Efficiently

Redis stores all data in memory, which makes it incredibly fast.  Redis offers a range of memory management strategies, known as eviction policies. These policies determine which data is removed when memory is full.

Different applications may require different eviction policies. Some may benefit from an “allkeys-lru” policy, which evicts less recently used keys first. Others may prefer a “volatile-ttl” policy, which evicts keys with an expired set. Understanding your application and choosing the right eviction policy is key to ensuring optimal Redis performance.

Notably, Redis should not be used as a cache without setting an eviction policy. Without this, when the memory limit is reached, Redis starts throwing errors instead of evicting old data. So, consider your application needs, and set an appropriate eviction policy to handle memory efficiently.

3. Monitor Your Redis Server

Regular monitoring and analysis are key to maintaining optimal performance. There are several commands like INFO and MONITOR, which are used to get real-time information about various aspects of your Redis server.

For instance, INFO provides details about memory usage, CPU usage, and command statistics. MONITOR, on the other hand streams back every command processed by the server, helping you identify inefficient commands or patterns that may be affecting performance.

Moreover, it’s crucial to monitor and measure the latency of your Redis server, as high latency impedes performance. With latency monitoring feature, it provides a human-readable description of latency events and their causes. This information helps identify potential issues or areas for optimization. Additionally, using the intrinsic latency test of redis-cli helps to check the minimum latency that is expected from your runtime environment. Understanding your system’s intrinsic latency provides a baseline for performance expectations.

4. Use Connection Pooling

A connection pool is a collection of reusable connections that is reused when database requests are made. This mechanism pre-establishes a bunch of connections to the Redis server, which are then kept alive, ready to be used by your application whenever needed. This cuts down on the time spent establishing new connections, significantly optimizing the performance of your Redis operations.

Connection pooling reduces the overhead of establishing a new connection every time an application wants to communicate with Redis. It bypasses this by reusing existing connections, making your interaction with Redis faster and more efficient. Additionally, it provides resilience by managing connections, ensuring that your application always has access to a working connection. If a connection fails, the pool establishes a new one, preventing downtime.

Implementing connection pooling is done using libraries such as Jedis in Java. Jedis provides ‘JedisPool’, a robust object which efficiently handles the allocation of connections. By using this, you ensure optimal use of resources while maintaining high performance. Using JedisPool specifies the maximum number of connections that are maintained simultaneously. 

5. Choose the Right Memory Allocators

There are multiple memory allocators to use to compile Redis i.e tcmalloc, jemalloc, libc malloc. These allocators vary in terms of speed and fragmentation. The INFO command is ideal, if you don’t compile memory yourself, as it helps you check the mem-allocator field yourself. 

Since the above supported memory allocators differ when it comes to memory fragmentation outcomes, especially when allocating large blocks of memory. Therefore, it’s best to test your own data and do a benchmark to decide on the most suitable memory allocator to use.

6. Utilize Pipelining

Pipelining is a feature allowing you to send multiple commands to the Redis server at a go. Creates a batch command and runs it on the server simultaneously. Basically, pipelining reduces the Round Trip Time required to send a request to the server and latency as well. 

However, using pipelining requires some proper execution. Too many commands in a pipeline cause longer server side delays, causing other clients’ requests to wait. Hence, it’s crucial to find the right balance. Analyse your application requirements and decide the optimal number of commands to send together without negatively impacting the overall system’s performance.

7. Use Partitioning

Partitioning is splitting data into multiple Redis instances such that each instance contains a subset of your keys. Partitioning allows multiple instances to share the memory and store data, providing more memory space. Also, it allies for much larger databases as it removes the limit of the amount of memory a single computer support.

However, partitioning comes with its own set of challenges like managing multiple connections and keys’ uneven distribution.

There are 2 ways of partitioning:

  • Range and Hash partitioning.

Each type has its own benefits and downsides, but if applied appropriately, it improves performance and scalability. 

8. Choose the Right Data Structures

Redis supports multiple data structures including sets, lists, hashes, and strings. Choose the right data structure in order to optimize performance. Data structures in Redis have their own distinct features and use-cases. For instance, hashes are memory-efficient for storing large objects, while sets efficiently maintain unique items. Choosing an inappropriate data structure significantly hampers the performance and lead to unnecessary memory consumption.

First, analyse the type of data you are working with, as well as the operations to perform on your data. Also, check application requirements, limitations, and characteristics. All these factors help you decide on the right data structures to use.

9. Disable THP

Transparent Huge Pages (THP) is a Linux memory management system designed to reduce the overhead of TLB lookups on machines with huge memory by using larger memory pages. However, when enabled, THP causes database workloads to perform poorly. This creates latency issues and high memory usage. Therefore, you should disable your THP from your kernel using this command:

					echo never > /sys/kernel/mm/transparent_hugepage/enabled

After disabling THP, restart your Redis process to implement the changes.

Using Redis for Caching: Best Practices and Performance Tuning

Optimizing Redis performance is crucial as it ensures your applications access data with incredible speeds. It also ensures that Redis utilizes CPU and memory efficiently. Therefore, it’s crucial to properly configure your Redis cache at all times. By leveraging the above best practices and strategies, you optimize the performance of your Redis and improve your caching speeds. Have in mind that performance tuning is a continuous process, as there are many scenarios that cause performance dips. Always ensure you have proper monitoring in place to alert you of any issues in your database.

Avatar for Dennis Muvaa
Dennis Muvaa

Dennis is an expert content writer and SEO strategist in cloud technologies such as AWS, Azure, and GCP. He's also experienced in cybersecurity, big data, and AI.

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