On July 21, Redis Labs announced the finalization of $14M in Series C funding led by Bain Capital Ventures and Carmel Ventures. The Series C funding raise builds upon 350% year over year revenue growth for Redis Labs, the creators of Redis and distributors of an enterprise-grade version of the Redis NoSQL database platform. In the first two quarters of 2016, Redis Labs notched up over 600 new enterprise customers including the likes of TD Bank, Groupon, Verizon and Twitch. Redis Labs now claims over 6,200 enterprise customers and more than 55,000 Redis Cloud accounts. The announcement of over 350% YOY revenue growth in conjunction with details of a roster of new enterprise customers in verticals that include finance, media and retail emphatically illustrate the increasing penetration of Redis in the contemporary, enterprise NoSQL database space. Fueled by the proliferation of applications marked by high transactional volume and data throughput, the impressive growth of Redis amongst enterprise customers in the first half of 2016 testifies to the importance of its in-memory, data structure store database technology whose optimization of data structures and commands delivers enhanced execution response times and application performance. With an extra $14M in funding, the industry should expect even more innovation from Redis Labs that builds upon the announcement of Redis Modules in May.
At RedisConf2016, Redis, the open source, in-memory data structure store, announced the introduction of a new functionality called Redis Modules that allows developers to extend Redis to cover an expanded set of use cases. Redis Modules enable developers to create new database functionality in Redis by means of a Modules API. The modules render Redis extensible by allowing developers to create applications that have access to the Redis core without requiring that the module be rewritten in conjunction with updates to the Redis core. The Modules API for Redis allows Redis Modules to co-exist independently of the evolution of the Redis core such that a module will continue to function irrespective of updates to either the Redis core or the module itself. As noted in a blog post by Salvatore Sanfilippo, the creator of Redis, the vision that gave birth to the evolution of the Redis Module was marked by a desire for sustainable compatibility between the Redis Module and the Redis Core:
What I wanted was an extreme level of API compatibility for the future, so that a module wrote today could work in 4 years from now with the same API, regardless of the changes to the Redis core. I also wanted binary compatibility so that the 4 years old module could even *load* in the new Redis versions and work as expected, without even the need to be recompiled.
Sanfilippo goes on to note that the nature of the API in question required a low level API in contrast to Lua’s high level scripting capabilities:
What we wanted to accomplish was to allow Redis developers to create commands that were as capable as the Redis native commands, and also as fast as the native commands. This cannot be accomplished just with a high level API that calls Redis commands, it’s too slow and limited. There is no point in having a Redis modules system that can just do what Lua can already do. You need to be able to say, get me the value associated with this key, what type is it? Do this low level operation on the value. Given me a cursor into the sorted set at this position, go to the next element, and so forth. To create an API that works as an intermediate layer for such low level access is tricky, but definitely possible.
Here, Sanfllippo remarks on the differentiation of the Redis Module API from the Lua scripting language for Redis by commenting on the need for a fast, low level API that can access the Redis core. Examples of Redis modules that are preliminarily available include an image processing Module and a full text search Module. That said, the code for the Redis Modules API remains unstable and awaits incorporation into an official release of the open source Redis software platform. Nevertheless, Redis Labs, home of Redis and provider of a commercial, enterprise-grade Redis solution, recently announced the release of Modules Hub, a marketplace for Redis Modules that renders available battle-tested, production-grade Redis modules for Redis users. Judging by comments to Salvatore Sanfllippo’s blog post, however, Redis Modules have sparked enthusiasm galore from users as they experiment with the API and mull over its various possibilities.
Learn more about Redis Modules via their API reference manual here.
Redis Labs today announced the release of a Spark-Redis connector that accelerates the performance of Spark in comparison to other database infrastructures. The Spark-Redis connector allows Redis users to leverage the power of Spark to perform analytics on streaming data in real-time on large datasets. The open source Spark-Redis connector boasts the capability to read and write to Redis clusters while preserving Redis data structures. The integration of Redis and Spark results in Spark performance acceleration by a factor of 135 when compared to HDFS and a 45 fold acceleration when compared to Spark using Tachyon. Yiftach Shoolman, co-founder and CTO of Redis Labs, remarked on the significance of the acceleration of Spark on Redis data stores as follows:
Big data is coming of age and customers are demanding that big data insights are extracted in real-time. This is where Redis Labs fills the gap by delivering both the right performance and optimized distributed memory infrastructure to accelerate Spark. Our goal is to make Redis the de-facto data store for any Spark deployment.
Here, Shoolman comments on how the integration between Redis and Spark enhances the derivation of analytic insights from big datasets. The increase in Spark’s ability to perform on Redis allows users to conduct analyses in real-time while subsequently enjoying the performance the “optimized distributed memory infrastructure” that Redis delivers. In addition to benefits in speed, one of the key advantages of using Spark with Redis consists of the latter’s ability to allow Spark to access to individual data elements in ways that avoid the operational overhead associated with transferring or running analytics on large batches of data. Today’s announcement features news of a Spark-Redis connector, support for Spark SQL and the capability to use Redis as a distributed memory database for Spark. The Spark-Redis connector’s acceleration of Redis to blistering speeds promises to catapult the positioning of Redis within the NoSQL database landscape and the database infrastructure space more generally. By accelerating Spark to speeds over 100 times faster than its performance on HDFS, Redis gives customers faster access to data analytics on real-time in ways that can be crucial for use cases that demand split second analytic transactions on massive datasets. Going forward, Redis plans to collaborate with Spark to enable the use of Spark’s functionality for machine learning and graph database use cases as well. The graphic below illustrates the acceleration in speed enabled by the Spark-Redis connector as compared to other database infrastructures:
On June 25, Redis Labs announced the finalization of $15M in a Series B funding round led by Bain Capital Ventures and Carmel Ventures. The funding raise validates the traction of Redis Labs and its cloud platform, the Redis Cloud, a fully managed cloud platform for running Redis databases. An open source, in-memory, key value data store, Redis boasts the capability to facilitate a high volume of read and write requests with sub-millisecond latency. The single threaded event-driven architecture of Redis in conjunction with its other protocols allows Redis to claim speeds that exceed its in-memory database competitors by 5-10x. Popular use cases for Redis involve leaderboards, session management and player profiles with reference to gaming applications. Other use cases include message queues, search engines and caching in highly interactive web applications that need to scale to tens of millions of users, each of which may have a unique user profile and representation in dynamically updated, application-related lists that represent how many “objects” are being “followed” or “saved” by a user. Last week’s funding raise brings the total capital raised by Redis Labs to $28M. With paying customers that include Hotel Tonight, Bleacher Report and Docker and a healthy infusion of Series B funding, Redis Labs stands poised to capitalize on its meteoric growth since the company was founded in 2011. Within a broader landscape of growing adoption of NoSQL databases and a proliferation of datacentric, mobile and web-based applications that require real-time updates that cascade across a constellation of database objects, expect Redis Labs to continue to drive innovation with respect to enterprise-grade Redis deployments and to spearhead Redis adoption more generally.
Garantia Data, distributor of cloud-based, enterprise-grade solutions for Redis and Memcached, recently changed its name to Redis Labs. The name change is intended to more accurately illustrate the company’s commitment to providing Redis-based solutions for the enterprise. News of Garantia Data’s name change to Redis Labs comes hot on the heels of its recent announcement of the availability of the Redis Cloud on IBM’s SoftLayer Dallas platform in addition to its pre-existing partnerships with Amazon Web Services and Windows Azure. In an interview with Cloud Computing Today, Redis Labs CEO Ofer Bengal remarked on the uniqueness of the Redis database platform as follows:
NoSQL databases like Redis are becoming increasingly popular. According to a 451 Research report, Redis adoption is projected to increase from 11.3 percent today to 15.9 percent in 2015. Redis in particular will become a preferred database technology because it is faster than any other database and it has rich data structures – which are very similar to those of today’s high level programming languages. Leading companies like Twitter and Pinterest use Redis, which shows it is highly useful for companies with rapidly growing datasets.
The Redis Cloud delivered by Redis Labs represents a fully managed service that boasts infinite, automated scalability, high availability, integrated data backups and high performance. Redis Labs also offers a managed service for the Memcached Cloud built on Redis technology.
Garantia Data’s (Garantia) Redis Cloud and Memcached Cloud products are now generally available on the IBM SoftLayer cloud by means of its Dallas region. As a result, Garantia Data’s Redis Cloud is now available on Amazon Web Services, Windows Azure and IBM SoftLayer Dallas. Redis is an open source, in-memory, key value data store that differs from other NoSQL databases by way of its ability to “serve a very high volume of write and read requests…at sub millisecond latency” as noted by CEO Ofer Bengal in an interview with Cloud Computing Today. The partnership between Garantia Data and IBM means that IBM benefits from the feather in its cap marked by the addition of Redis to its IaaS platform, whereas Garantia Data cements yet another high profile partnership with a major public cloud platform that promises to attract even more developers into using Garantia’s distribution of Redis. The Redis Cloud offers developers a fully managed service for development within an infrastructure that includes “automated clustering, scaling, data persistence, performance optimization, and failure recovery from a single console” according to Garantia Data’s Itamar Haber. IBM is pricing Redis on SoftLayer Dallas aggressively at $79/month for 1 GB of storage in contrast to Azure, which charges $108/month for the same. In comparison, AWS prices the Redis Cloud competitively at either $79/month or $89/month for 1 GB, depending on the region.