Redis Consulting

Redis is an open source in-memory storage that can be used as a cache, database and message broker. It not just provides an in-memory store but it also offers features that can be used to store data on disc.
Redis Enterprise offers loads of additional features like linear scale to hundreds of millions of operations per second, flexible deployment options (cloud, on-premises, hybrid), multi-layer security and compliance, backup, cluster recovery, and disaster recovery capabilities and more.
Our experts have used Redis in projects with petabytes of data. Our expertise in polyglot persistence helps us create a successful strategy tailored to customer needs.
At Cazton, we help Fortune 500, large and mid-size companies with Redis best practices, development, performance tuning, consulting, recruiting services and hands-on training services.

Do you have a good caching strategy for your applications? Have you felt the pain of sticky sessions? Have you had a caching strategy that didn't work for you? Do you need a caching strategy that scales seamlessly with the least amount of effort? If the answer to any of these questions is a "yes," the good news is that you are in the right place.

Did you know that Redis is an auto-scale caching engine that also persists data to the disk? How is it possible to have a fast persistence mechanism? Which technologies provide fast storage? The simple answer is in-memory databases that backup data to the master as well as multiple secondary disks in real time. In our experience, working with companies all over the world, we have found that not only companies fail to take advantage of different data stores for different kinds of data, polyglot persistence, unfortunately, the workarounds cost them millions of dollars in the long run.

Traditionally, companies have stored caching data in databases, which eventually gets stored on the disk. Retrieval from the disk is always slower than retrieval for the memory except in some exceptional cases including non-contiguous data storage. Some of these databases do offer in-memory storage mechanisms, but that is not their default behavior. An in-memory database, on the other hand, can simply be defined as a database management system that uses RAM for data retrieval and disk for persistence. This approach leads to the best of both worlds - performance and reliability.

When you think of in-memory storage, the very first question that may raise concern is... should they be used as a source of record? The short answer is - "No"! You should never use in-memory databases as the default persistent storage mechanism for your applications. Though there are some disadvantages, there are many cases where in-memory storage can be extremely useful. We will look at some use cases later in this article to better understand the importance of in-memory databases.

There are many in-memory databases available today, and each of these offer several benefits over the other. Some of them are Redis, Memcached, MemSQL, SQLLite, Apache Ignite and many more. At Cazton, we have used many of these in-memory databases for different use-cases and we fully understand the capabilities of these technologies. One such technology that has often suited our customer requirements is Redis. Continue reading to learn more about redis, its use cases, top features and more.

Redis is an open source in-memory storage that can be used as a cache, database and message broker. Redis stores data as key-value pairs and offers a wide variety of data structures that can be used for different purposes. Some of these data structures include strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries, streams and more. Redis not just provides an in-memory store but it also offers features that can be used to store data on disc.

Redis offers an optional disc persistence where data can either be stored to the disk every few seconds or by appending each command to a log file. To leverage the capabilities of Redis, we can also disable the optional disc persistence (not recommended in most cases) and use its feature-rich, networked, ultra-fast in-memory cache. Redis in-memory storage is super fast, but there's a disadvantage to this feature. There's a big chance of data loss if the machine that is storing data in memory is shut down or loses power. Hence using disc persistence as a backup is always the best option. So even if the server loses power and the current data is lost, we can read the last persisted data from the disc and replenish the Redis in-memory storage. Let's learn more about the persistence mechanisms in the next section.

Redis has two persistence mechanisms:

Redis Database Backup (RDB): This mechanism is also termed as snapshotting and it is the default persistence mechanism in Redis. RDB typically takes snapshots of your data and stores them in a single file called dump.rdb. This mechanism is great for backups and disaster recovery as there's only one single file to persist data from. This file can also be used to restore data to different data centers. Data backups are taken at a specific interval using RDBs. You can configure a specific time interval (for ex: 5 or 10 mins) and Redis starts taking backups at the specified interval. RDBs do not affect performance as it uses a child process to take those frequent backups. In such cases the parent instance is not responsible for any disc/IO operations.
Append Only File (AOF): This is the most durable persistence mechanism that Redis offers. Under this mechanism, every write operation that the Redis instances recieve is logged in a file in an append-only fashion. Redis keeps taking snapshots of your data and appends it to the log file with every single request. This mechanism is safe as you will always recover the most recent data in case of data loss, but it is also a time consuming operation as the server remains busy taking backups with every request. This mechanism is not recommended if you expect a high performance output from Redis.

Choosing the correct persistence mechanism for your application depends on your use case. If you think that the data you are storing in memory is not important and losing it does not affect your users, then choosing RDB suits your requirements. But if you really need to guarantee no data loss then AOF suits best for such scenarios. It is also possible to enable both RDB and AOF persistence to have a clean backup of your data. In such cases, if there is a data loss then Redis chooses AOF as the preferred persistence mechanism for reloading data into its memory, since it is guaranteed to be the most complete.

Now that you have learned the different persistence mechanisms that Redis offers, let's take a quick look at its top features and general use cases.

Scalability & High Availability: Redis offers data partitioning features that let you split data among multiple Redis instances. Through data partitioning, you can achieve scalability and the power to store more data and achieve more network bandwidth, more computational power and the ability to make your application fault tolerant. With distributing redis storage to multiple computers, you achieve the ability to make your data highly available.
Pub/Sub Model: Publisher-Subscriber pattern is one of the most well messaging patterns in the industry. It allows publishers to supply data/information to a channel irrespective of knowing the consumer of that information. This decoupling of publishers and subscribers allows for greater scalability and a more dynamic network topology. Redis offers very easy to implement commands like the PUBLISH, SUBSCRIBE and UNSUBSCRIBE to implement pub/sub models in your application.
Pipelining: Redis offers a Request/Response server where clients and servers are connected via a networking link. Through Redis pipelining, it is possible for the client to send multiple new requests without waiting to read the old response. It offers the ability to the client to read all server responses in one single step. Redis offers pipelining features since its early stage of development, so no matter which version you are using, you can easily implement Redis pipelining in your application.
Memory Optimization: With Redis v2.2 many of its data types are optimized to use less memory space. Some of its data types like Hashes, Sets, Lists are optimized in such a way that it can use upto 10 times less memory being extremely efficient and fast in processing.
Atomic Transactions: Redis offers commands, which form the basis of transactions. These transactions are atomic in nature, meaning either all commands will be executed or none. Commands in a transaction are serialized and executed sequentially. So if multiple clients issue requests when a redis transaction is in process, then those requests will not be executed immediately. Those requests will have to wait till the transaction has finished processing. All commands in a transaction are executed as a single isolated operation.
Bulk Data Insertion: There are cases where you may want to insert a bulk of data together into the data store. In such cases, Redis offers a user friendly protocol that enables inserting huge amounts of data into Redis instances really easily.
Fast Performance: As we have learned so far, Redis offers an in-memory data storage mechanism. Since the data is stored in memory, it offers an ultra fast performance as compared to disc storage based databases. Redis is one of the best options when persistent storage is not a requirement. Its optional disc persistence also makes it a great choice for many use cases.
Versatile Data Structures: Data structures for a basis of any storage technology. It defines the way and type in which data will be stored by the underlying technology. With redis comes a wide range of data structures that can be used to store different types of data for different purposes. It also offers Redis Modules that can be used to implement new data structures that feel like Redis native data types.
Extensible: Redis is an open source (BSD licensed), in-memory data structure store and one of the most preferred in-memory data storage for a wide variety of applications. With libraries and plugins available for almost 50 programming languages it makes redis a developer friendly data storage technology.
Easy to Use: Redis offers a huge list of easy to use commands that can be used to perform different types of operations like pipelining, LUA scripting, mass insertion of data, performing transactions, partitioning, distributed locks and more. It has a great documentation for developers explaining each and every command and its usage.

Caching: Caching is a common technique used in applications to reduce response times by saving information in cache that is being requested frequently. Redis, being an in-memory data storage, can also be used as a cache to store frequently requested information. With its ability to persist data on disc, Redis becomes a perfect fit for caching data, which can later be retrieved if connection is lost.
Real-time Analytics: Redis offers data structures that can be used for analytical processing. It also offers machine learning commands that can be used for serving recommendations and predictions. Using Redis data structures, it is possible to perform cardinality for fraud detection, location based searches, real-time score analysis, etc. You can extend its analytical capabilities by incorporating modules RediSearch, Redis Graph, Redbloom, Redis-cell and more.
Full-Text Search: Using open source modules like RediSearch, it is now possible to implement full-text search feature with Redis. It now becomes easy to implement search engines on top of redis using RediSearch. This module offers fast indexing and search capabilities with inverted indexes, which are stored as a special compressed data type. It enhances the traditional Redis search capabilities by adding features like exact phrase matching and numeric filtering for text queries.
Streaming: It is now possible to implement streaming using Redis as a new data type called Redis Streams is introduced with its new version - v5.0. It is primarily an append-only data structure; however, it offers additional features like consumer groups, which was initially introduced with Kafka. Redis implements consumer groups in a different way to allow a group of clients to cooperate consuming a different portion of the same stream of messages.
Messaging: Redis offers many commands that enable the publisher-subscriber model. These commands can be used by developers to create chat applications, applications that send notifications and more. Redis pub/sub feature is one of the most used features by developers.

The Cazton team has helped many Fortune 500 clients, mid-size companies and startups create a fool-proof polyglot persistence strategy. A caching strategy is an integral component of polyglot persistence. It's usually deceptively simple. The Cazton experts have learned a lot from their experiences to create a caching strategy that is extremely cost-effective, futurist, scales out seamlessly and also prevents a degraded user experience in terms of an absolute collapse of the underlying Redis architecture. Caching is a science as well as an art. The Cazton team has been fortunate to create successful caching strategies for applications and organizations that have different scalability needs as well as budgetary constraints. Redis is an essential component of the overall data strategy because it enhances the user experience multi-fold given the fast retrieval of data.

One of the major problems in the tech industry is to use the same tool or technology for pretty much every other use case. This is common in the data world. Redis is really great as a caching engine, has a very good pub-sub mechanism and makes sense for streaming and messaging. Unfortunately, it gets used for a lot of other use cases that it may not be a good fit for. For example, using Redis for full-text search might not be a great idea. That's simple because there are other tools and technologies (ElasticSearch, Solr with Fusion) that are much better suited to that particular use case.

The Cazton team is highly adept to work in all major business domains including financial, tech, airlines, manufacturing, health care, insurance, fintech etc. We provide different case studies and comparative reports before we suggest technologies that will be the best for a particular client or project. There is no, 'one shoe fits all' strategy in the data world. Our team is also very flexible, especially on brown field projects where the tech stack is already determined and there is not much room to make major changes. We specialize in making your legacy stack modern with the least amount of effort.

Due to the great popularity of Redis, it is also made available as an enterprise platform. Redis Enterprise or Redis Labs is a robust in-memory database platform built by the people who develop open source Redis. Redis Enterprise offer loads of additional features like linear scale to hundreds of millions of operations per second, Active-Active global distribution with local latency, true multi-model with dedicated modules, flexible deployment options (cloud, on-premises, hybrid), multi-layer security and compliance, backup, cluster recovery, and disaster recovery capabilities and more.

With full support to all the data types offered by Redis, you can also extend Redis data structures by using ready to use modules like RediSearch, RedisGraph, RedisJSON, RedisTimeSeries, and RedisAI that allows operations to be executed across and between modules and core. Redis enterprise offers these features without affecting database performance. Application performance is fast as the database latency remains under one millisecond.

At the time of writing this article, the Redis enterprise website claims to have more than 7,400 enterprise customers with 1 million plus databases already created. If you have a lot of data to manage, then Redis enterprise can be a great option.

If you are using Redis to deal with a huge amount of data, it is typically advised to optimize Redis instances and increase its performance and high availability by adding more machines. Scaling Redis can be time consuming and expensive if you plan to do it on your own. But with cloud computing reaching its peak, there are many options available for doing cloud deployments. With cloud deployment, you leverage full capabilities of Redis with options to pay as per your usage. Many companies including IBM, Microsoft, Amazon and Google offer cloud services for Redis at cost effective rates. At Cazton, we have used Redis with many different platforms and we have the experience to deploy it on prem and many different cloud services. Contact us if you are looking for a cloud deployment or need help using redis on your preferred cloud service.

We have experts who have used Redis in many multi-million dollar projects with data up to a petabyte scale. We have real world experience using this technology and leverage its overall capabilities in many different scenarios. We offer the following services at the Enterprise level, team level and/or the project level:

Polyglot persistence strategy tailored to the customer needs.
Caching best practices.
Redis best practices.
Redis consulting, development and training services.
Redis deployment on-prem and cloud.
Redis monitoring, telemetry, troubleshooting and reporting.
Redis security implementation and support.
And any other related services including (Big Data, Search, NoSQL and RDBMS)

At Cazton, our team of expert developers, consultants, architects, data analysts, data scientists, DBAs, awarded Microsoft Most Valuable Professionals and Google Developer Experts understand the changing requirements and demands of the industry. We help you make the right decision to achieve your business goals. We have the expertise to understand your requirements and tackle your data problems. Learn more about database, cloud, big data, artificial intelligence and other consulting and training services.