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A real-time database is a traditional database management system that uses near real-time processing to handle workloads whose state is continuously changing. The real-time database construct is in direct juxtaposition to the traditional RDBMS (Relational Database Management System), where the data persists, unaffected by time.
Real-time databases are primarily used to process transactions fast enough for the processed results to return in time to act on them instantly or as close as possible to the original transaction load time. Real-time databases are mainly used for stock market data analysis, scientific data analysis, and process control transaction analysis.
By way of expanding on the real-time database construct, let’s consider two real-time databases, Redis and Apache Cassandra.
1. Redis (Remote Dictionary Server)
Redis is a “fast, open-source, in-memory, data structure store” used as a “database, cache, message broker, and queue.”
It is a real-time database first developed by Salvatore Sanfilippo when he tried to scale up his Italian startup. Consequently, Redis delivers precision transactional response times less than one millisecond, facilitating hundreds of thousands of requests per second. As a result, its best use case scenarios include real-time applications in social networking, gaming, financial services, healthcare, and IoT. Finally, Redis is a popular choice for session-management, real-time analytics, media streaming, ride-hailing, and geospatial applications.
How is Redis able to process data transactions at the speed it does?
Succinctly stated, all Redis data remains in memory. This is in direct contrast to persistent data stores or databases that store the data on disk. By eliminating the need for continuous read-write transactions between the disk (or SSD) and memory, Redis avoids seek-time delays and can access data in microseconds. Additionally, Redis employs “versatile data structures, high-availability, geospatial, Lua scripting, transactions, on-disk persistence, and cluster support, making it simpler to build real-time,” scalable applications.
Apart from transaction processing speed, Redis offers a wide variety of other features. Let’s consider several Redis highlights to complete our understanding of the product.
Redis offers a wide variety of flexible data structures covering a whole range of data types, including strings, lists, sets, sorted sets, hashes, bitmaps, and HyperLogLogs (a probabilistic data structure used to estimate the unique items in a dataset), reducing the complexities of converting from one data type to another when the data is loaded into Redis.
Ease of use
Not only is Redis easy to use, but it simplifies your code by reducing the number of lines of code you have to write to store and access this data. For instance, if the source data is in a hashmap, if you were using a database with no native support for hashes, you would typically have to convert the data from the hashmap to a type that the data store supported. However, because Redis has built-in native support for hashes, it is quicker and easier to manipulate and interact with the data stored in the hashmap.
Replication and persistence
The Amazon AWS web page describes Redis’s replication and persistence features as follows.
“Redis employs a primary-replica architecture and supports asynchronous replication where data can be replicated to multiple replica servers.”
As a result, Redis’s read performance is enhanced because the read requests are split amongst the multiple replica servers. Secondly, the use of multiple servers improves the recovery rate should the primary server go down. Finally, Redis supports point-in-time backups to persist the data by copying its dataset to disk.
Availability and scalability
Redis has a primary-replica architecture structured as a single node primary or a clustered domain of servers. This allows for the construction of highly available solutions that are designed to provide consistent performance and reliability. Also, there are many options available to scale up, down, in, and out, should you need to adjust your cluster size.
Lastly, on its own, there is no cost to using Redis as it is open-source software. Should you wish to, you can download and install Redis on a local server. However, the best way to use Redis is as part of a cloud-based stack like AWS or Google Cloud. For instance, a fully managed Amazon AWS Redis service and Amazon ElastiCache for Redis, available for trial with the AWS free tier, costs are usage-based.
2. Apache Cassandra
Apache Cassandra is an open-source, distributed, wide-column, NoSQL database management system designed to store and process voluminous data across a distributed server architecture, providing high-availability, high-performance, linear scalability, and no single point of failure.
Cassandra had its origins at Facebook, where it was released as open-source software in 2008. It was initially designed to power Facebook’s inbox search function, assisting users in finding conversations and content they were looking for. The architecture is based on a distributed model, allowing horizontal scaling across multiple nodes, resulting in a highly available, highly scalable database designed to solve the most data-rich and performance-intensive use cases. Cassandra is an ideal mechanism for processing server logs, social media posts, PDF documents, and emails.
Cassandra’s NoSQL feature is worth discussing. In summary, a NoSQL database stores, distributes, and accesses data using methods dissimilar to traditional RDBMS. The NoSQL database construct was initially created so that tech giants like Facebook, Amazon, and Google required massively scalable databases that could deliver read-write performance and availability to millions of users worldwide.
Ease of use
Cassandra is relatively easy to use because its processing language, CQL (Cassandra Query Language), is similar to SQL. Hence, SQL developers won’t have a problem familiarizing themselves with the Cassandra user interface and query language.
Availability and scalability
Apache Cassandra has a high-performance, high-availability, high-scalability architecture. Compared to most RDBMS that feature a primary/secondary structure where the primary replica performs read-write operations and the secondary image only performs read operations, Cassandra’s architecture is designed that every node is capable of performing read-write operations. No single node is responsible for replicating data across the distributed cluster. This improves the performance, increases the database’s robustness and resilience, and allows for greater scalability by adding more nodes to the cluster.
As with Redis, because Apache Cassandra is open source, there is no cost to downloading and installing Cassandra. However, it is best to sign up for a managed service such as the Amazon Managed Apache Cassandra Service to reduce operations costs. This pricing is based on what you use. There is no minimum cost; you only pay for the read-write throughput, storage, and networking resources you use.
3. Redis versus Cassandra
After studying both Redis and Cassandra, the final question remains: Which platform should you use?
The straightforward answer to this question is that it depends on the use case.
While both are NoSQL databases, Redis is an in-memory data store that supports many different data types, used as a database, cache, and message broker. On the other hand, Cassandra is a distributed key-value store. Because Redis stores voluminous data in memory, its transactional response times are much faster than Cassandra that persists data to disk by performing traditional read-write transactions, albeit much quicker than a conventional RDBMS.
Therefore, Cassandra is the preferred option for use cases that require writing data to disk. It has a better Fault Tolerance than Redis because it is based on the original Hadoop architecture.
Juxtapositionally, Redis is preferred for use cases where massive amounts of rapidly changing data are processed in-memory before persisting to disk.
Real-time database processing is required when there are workloads whose state is constantly changing. There is a need for near real-time data processing in the post-modern digital age or Fourth Industrial Revolution, where massive amounts of data are generated in log files from use cases such as IoT applications. Consequently, to store and process these transactions, the NoSQL databases, Redis and Apache Cassandra, are the options to choose from. And the decision between the two rests on the individual use case. Both have merits when considered within the parameters of a use case. On the other hand, both have disadvantages when viewed outside of the applicable use case. As a result, the best decision relies on an in-depth study of the use case before diving in and selecting either the Redis or the Cassandra option.