Redis vs Hazelcast – What’s the Difference ? (Pros and Cons)
Redis vs Hazelcast – What’s the Difference ? (Pros and Cons). Two prominent choices for creating an in-memory database are Redis and Hazelcast. At first glance, they are very similar as they can tackle similar use cases, so it may be hard to decide which is best for your purposes. In this article, we’ll discuss the differences between Redis and Hazelcast. We would also pick out the best features and drawbacks that we can obtain from them.
Shall we start Redis vs Hazelcast – What’s the Difference ? (Pros and Cons). Please read on.
What is Redis?
First of all, Redis, or Remote Dictionary Server, is a quick, free, in-memory key value data store. Salvatore Sanfilippo, the original developer of Redis, created it to increase the scalability of his Italian firm. From there, he created Redis, which now serves as a cache, queue, message broker, and database.
Secondly, Redis has response rates that are less than one millisecond, permitting millions of queries per second for real time applications in sectors including ad-tech, financial services, healthcare, and gaming. As a result, Redis is currently one of the most widely used open source engines.
Moreover, Redis is great for caching, session management, gaming, leaderboards, real time analytics, geospatial, chat/messaging, video streaming, and pub/sub apps due to its speedy responsiveness. In addition, since Redis’ fundamental data types resemble those in programming languages such as strings, dictionaries, or hashes, users often refer to it as a data structure server.
Features of Redis
- Typically utilized as a cache to keep commonly accessed data in memory so that applications can respond quickly to users. Also, Redis offers several smart caching patterns by allowing you to choose how long you want to hold data and which data to remove first. The benefits of intelligent caching are numerous, and it significantly affects user interactions, efficiency, bounce rates, and retail sales.
- Pub/Sub messaging in Redis enables the distribution and delivery of messages to subscribers. Since messages don’t save, this functionality allows information to move swiftly across your infrastructure without taking up database space to update game stats and pass notifications or to inform services of the load on other infrastructure or apps.
- With Redis it also allows for marking data structures with a Time To Live (TTL) set in seconds, after which it will delete them. There are numerous customizable “eviction policies” available. As a result, users can create a tiered structure of memory objects by prioritizing temporary data with a TTL over other data that does not.
- This solution offers many geospatial index data types and functions. Users can search for things within a specified radius of a location or query distances between objects using recorded latitude and longitude coordinates. These instructions can return results in several forms, including feet, kilometers, etc.
- Offers a scripting feature that enables the creation and execution of unique Lua scripts. This enables users to execute programs to add functionality to Redis themselves rapidly. Due to Lua’s lightning quick initialization, scripts can work with the data in various ways without slowing down Redis. Also, atomic operations are ensured by the single threaded nature of the core Redis process.
Pros of Redis
- Redis enables you to create highly available solutions that deliver consistent performance and dependability. It can accomplish this by providing a primary replica architecture in either a single node primary or a clustered topology. There are various options for scaling up, in, or out of cluster size. As a result, your cluster can grow to meet your needs.
- All Redis data stays in memory. Which allows for low latency and fast data access. Since in-memory data storage doesn’t require a trip to the disk like traditional databases do, a few microseconds can be the limit for engine latency. In-memory data stores can therefore accommodate additional operations while also reacting faster. Because of this, read and write operations typically take less than a millisecond, and millions of operations can receive support every second.
- You can use Redis as an in-memory data store to ingest, process, and analyse real time data with a sub millisecond speed in conjunction with streaming technologies like Apache Kafka and Amazon Kinesis. It is the best option for real time analytics use cases like social media analytics, ad targeting, personalization, and IoT.
- Redis has no external dependencies and is written in the ANSI C language. The software runs flawlessly in every POSIX (Portable Operating System Interface) environment. Redis is not officially supported on Windows; however, Microsoft has made an experimental build available for the platform. Redis is also compatible with many other languages, including Java, JavaScript, Python, Ruby, and PHP.
- Also features a primary replica architecture with either a clustered topology or a single node primary. You can produce highly available solutions that deliver dependable performance as a result. Redis’ primary replica design allows for better read performance when read requests are distributed among the servers and quicker recovery when the primary server goes down.
Cons of Redis
- Redis can’t cache static assets for websites, such as photos, CSS, or video files. The ideal way to provide these items is through web server configuration or by employing a content delivery network (CDN).
- All your data must be in memory because Redis is an in-memory store. Redis does not allow you to manage more data than your memory can hold. It can store important data, but it might not be the best data storage option if the data size exceeds the cluster’s memory capacity.
- Redis is a server for data structures. There is no query language, only commands, and no relational algebra support. Ad hoc queries are not supported, and the developer must expect all data access for the correct data access methods to be created. Users compromise a lot of flexibility while using Redis.
- Users cannot scale Redis instances individually. In single-threaded mode, it merely utilizes one CPU core. Users must launch and deploy multiple Redis instances to achieve scalability. Clustering and distribution are handled on the client side; therefore, the developer is responsible for them.
Up next with Redis vs Hazelcast – What’s the Difference ? is to introduce Hazelcast.
What is Hazelcast?
Next we have Hazelcast. Well, it is a Java based distributed In-Memory Data Grid technology. In a clustered context, the architecture supports excellent scalability and data dispersion. Provides node auto discovery and intelligent synchronization and is available in several editions.
Data is dispersed evenly among the nodes of a computer cluster in a Hazelcast grid, enabling horizontal scaling of processing and accessible storage. Backups are also dispersed among nodes to protect against single node failure.
In addition, Hazelcast offers central, predictable scalability of applications through in-memory access to commonly used data and across a fluidly scalable data grid. These methods increase performance while lowering the workload of queries on databases.
So what are the features of Hazelcast? Follow the article Redis vs Hazelcast – What’s the Difference? to find out.
Features of Hazelcast
- The management and observation of running nodes in Hazelcast are made possible through the Hazelcast Management Center. This includes keeping track of the general health of your clusters, as well as thorough analysis and real time browsing of data structures, updating map configurations, and collecting thread dumps from nodes.
- You can store hundreds of gigabytes of data in-memory with Hazelcast High Density Memory Store, which lowers cost by reducing the required CPUs and nodes. Additionally, it improves application performance and uptime by lowering garbage collection overhead.
- With end to end TLS encryption, authorization with X509 certificates, and role based authorization over data structures and actions, the Hazelcast Security Suite provides market leading security. As a result, your Hazelcast IMDG Enterprise application’s security is now a flawlessly integrated part of it.
- You can use the industry standard SQL language to access in-memory data saved as maps with SQL Querying in Hazelcast. With the most recent Hazelcast Platform release, you can insert, update, remove, sort, analyse, and do many other things.
- Whether the restart is a scheduled shutdown or an unexpected cluster wide crash, Hazelcast’s Hot Restart Store enables full recovery to the initial configuration and cluster data state.
- Hazelcast IMDG Striim Hot Cache ensures constant synchronization between the cache and its supporting database, including real time push based dissemination and change data capture features, enabling compatibility with the system of record.
Pros of Hazelcast
- Hazelcast is a Java application with no external dependencies. It offers the same interfaces and API as the well known Java util package. Add “hazelcast.jar” to your code to start using JVM clustering and creating scalable applications immediately.
- Hazelcast is peer to peer, in contrast to many NoSQL alternatives. Each member stores and processes equal amounts of data. You can utilize Hazelcast in client and server mode, making your application a client for Hazelcast users, or you can embed it into an existing application.
- Hazelcast can expand up to tens of thousands of users. Adding more members will gradually increase the cluster’s processing and memory capacity as they naturally discover it. All communication occurs through this layer, where members maintain a TCP connection.
Cons of Hazelcast
- Memory sharing systems can be quite difficult to implement. For instance, the apps require real time, global sharing and access to the data. This can occur with a messaging application just as it does with complicated ERPs or employee timesheets.
- Hazelcast cannot lessen its load when the memory or CPU exceeds specified thresholds. because it shuts things down and sends the user a warning message. This proves that its memory consumption is high.
- Running in virtualization environments comes with potential time adjustments and differences between the CPU and the virtual environment’s adjusted system clock. When the differences go beyond certain thresholds, Hazelcast usually initiates a disconnect.
The next part with Redis vs Hazelcast – What’s the Difference ? is what are their differences?
Differences between Redis and Hazelcast
Threading
Redis
Threading with Redis vs Hazelcast – What’s the Difference? With Redis it uses a high performance core with a small memory footprint despite its single threaded nature. Also, Redis can protect against split brain syndrome during writing operations. This benefit makes it simple to operate numerous Redis instances on a single system while utilizing all CPU cores.
Hazelcast
The split brain problem is a networking issue in which nodes lose communication. Hazelcast’s multi threaded model cannot protect against the split brain problem during writing operations. Each node believes it is the primary node, resulting in data corruption as multiple nodes access the same file or disk.
Caching
Redis
Caching of Redis requires the use of a single caching pattern. Using Redis as a cache instead of another store, such as a database, mandates the usage of the cache-aside pattern, which adds extra network hops. The developer is responsible for writing the cache/database synchronization algorithms and the code to update/read the database. Redis only supports cache-aside when required.
Hazelcast
Hazelcast offers several caching patterns. You can set it up to handle both write-through on updates and read-through on cache misses. Hazelcast needs the update/read logic, making the code base much cleaner and simpler to comprehend. Hazelcast can manage read-through, write-through, and write-behind caching techniques in addition to cache-aside if necessary.
Clustering
Redis
Using several cores on a system for Redis requires clustering. Redis is a single threaded database; therefore, this is encouraged. When you contrast how clustering and sharding are handled in Redis with other distributed data stores, it is clear that Redis was not designed to be an in-memory distributed data store from the start.
Redis Clustering splits bigger datasets across several nodes to offer HA and scalability. The terms “master node” and “backup node” are used with Redis. However, manual reclustering and recovery are necessary when a node departs a Redis cluster.
Hazelcast
Since Hazelcast supports multiple threads, clustering only requires using more RAM. When you see how clustering and partitioning are handled, you will immediately realize that Hazelcast was intended to be an in-memory distributed data store from the onset.
High availability, auto partitioning, and auto discovery are embedded into the core of every Hazelcast member. Each Hazelcast member in a cluster manages a subset of primary and replica entries. There is no concept of a primary process or a backup process. This makes brainstorming about your architecture much easier.
Memory Management
Redis
Terabytes of RAM are no problem for Redis, thanks to the dependable jemalloc memory allocator. Data formats like hashes, lists, and sets utilize memory very well, saving, on average, five times as much memory.
Hazelcast
The Java garbage collector serves as the on-heap memory provider for the entirety of the distributed data in the non-commercial version of Hazelcast. Garbage collection could potentially result in delays in the application’s operation as the volume of data increases. These pauses affect the application’s performance, which may also result in more serious issues and errors.
Compute Functions
Redis
In Redis, Lua scripts can direct compute operations to a node or cluster member holding a particular data set. However, Redis Clustering and Lua Scripting are not free to reach out from the member it is operating on, to obtain data on another member in the cluster.
Hazelcast
Users can direct a Java program in Hazelcast to a cluster member with a specific key. The Hazelcast Java program is also free to reach out to the member it is running on to access data on another member in the cluster.
Querying
Redis
Redis is unable to store complex objects and understand the object graph. The developer must describe the graph as a sequence of key/value entries where each key represents a property and its value in order to reason about graphs. Redis does not allow for the division of data using ideas like tables. All information stays in a single namespace, such as the database. Therefore, developing intricate namespace schemes for keys is necessary.
Hazelcast
Hazelcast can store complex objects and understand the object graph. It provides a predicate API and SQL to query our data. These querying APIs work with JSON and complex objects. Hazelcast also offers a more adaptable namespace, allowing you to create several maps with appropriate names. Then there is no need to pollute the key namespace with these concerns; your keys can describe the actual value saved.
Well, thank you for reading Redis vs Hazelcast – What’s the Difference ? (Pros and Cons). We shall conclude.
Redis vs Hazelcast – What's the Difference ? (Pros and Cons) Conclusion
At first glance, Hazelcast and Redis are very similar. They can tackle similar use cases, so it may be hard to decide which to use. This article has taught you about their pros and cons and individual differences. You have also learned that Redis is a very popular open source project. If your use case is very simple caching and you do not need clustering, querying, or computation, then you should stick with Redis. As a simple single instance cache, it is a great choice.
Likewise, Hazelcast is pretty special as well. It is a feature-rich, enterprise-ready, and developer-friendly in-memory data grid solution. Hazelcast will surely perform well when you implement it in your projects. You’ve seen that, aside from their programming language differences, both Redis and Hazelcast are options for building an in-memory database. They can both direct compute functions directly to a node or member of the cluster that holds specific data.
