Hazelcast announces new unified platform with version 5.0
Hazelcast, the distributed compute and storage platform, has announced the release of the Hazelcast platform version 5.0. This new platform unifies the existing Hazelcast IMDG and Hazelcast Jet products, the former having provided rapid means of storing, retrieving and modifying data while the latter providing rapid data processing.
The new version 5.0 also introduces new features such as extended SQL support, streaming capabilities (provided by the Jet module), a redesigned UI console, and a new serialization format (as preview feature).
The Hazelcast Platform SQL Engine now supports basic Data Manipulation Language (DML) functionality to enable INSERT, UPDATE, and DELETE on data in Hazelcast. The platform also adds sorts, aggregations and new SQL expressions.
The streaming capabilities provided with the Hazelcast Jet merge in Hazelcast Platform 5.0, enable stateful and fault-tolerant data processing and querying on data streams and data at rest using SQL or the ‘Data flow API.
In addition, Hazelcast Platform provides a comprehensive library of connectors such as Kafka, Hadoop, S3, RDBMS, JMS and many more, messages delivered through queues or pub / sub topics and cloud native architecture.
The use cases offered by Hazelcast can vary between caching, fraud detection, real-time streaming analysis or used in a microservice architecture for fast data searches. Their documentation states that their unique data processing architecture results in 99.99% less than 10ms latency for streaming requests with millions of events per second.
John DesJardins, Chief Technology Officer at Hazelcast, spoke to InfoQ about this new platform.
InfoQ: What are the motivations and benefits of joining Hazelcast IMDG and Hazelcast Jet in the recent version 5.0?
Gardens : Unifying these products is very powerful in several ways.
Simplicity First – That simplicity starts with developers, who can now harness the full power of these capabilities by simply adding a library to their project and having a streamlined developer experience. It goes on to architectural simplicity – a unified runtime environment is very powerful when deploying distributed applications. This means deployment, scaling, and management are simplified. This also translates into operational simplicity. And all of this, combined, leads to streamlined DevOps processes and greater agility. This simplicity also means simplified scaling of data and compute, and simplified resiliency.
Second, performance – Unifying compute and data, when combined with a distributed architecture, data-aware computation, and memory-optimized architecture, it’s very powerful. The data does not need to be moved to calculation and analysis, as this greatly reduces overhead due to the location of the data. This alone can reduce huge impacts on latency and throughput compared to the well-known “Lambda architecture” or what has been recently discussed, a “delta architecture”, both of which involve quite a bit of ping-pong. data between computation and storage. When this is combined with optimized computation and data in memory, these benefits are greatly amplified. This can mean that processing can take anywhere from minutes to seconds, or seconds to milliseconds, or even milliseconds to microseconds.
Third, scaling and resiliency – because compute AND storage are distributed together, that means you can scale performance in a very linear fashion while still providing resiliency. This is demonstrated, for example, in our ability to easily scale to over a billion events per second, as discussed in this blog post.
Finally, unifying these products and their Open Source projects allows us faster release cycles, allowing us to innovate faster and bring more value to our ecosystem of Open Source and Enterprise users.
InfoQ: What are the community’s contributions?
Gardens : Our community actively contributes in a variety of areas, including support for many languages, connectors to data sources, and support for other frameworks or projects. Community contributions come from many fields, including other independent software companies, small and large companies in all industries, as well as systems integrators and all regions of the world.
InfoQ: What’s on the horizon for Hazelcast?
Gardens : Hazelcast has huge plans for 2022, including expanding our storage, SQL and analytics capabilities, as well as connectivity to other data platforms and open source projects, building on on our DNA expressed by our vision: to empower the world to act instantly on data everywhere.
Instant compute and data processing is in our DNA, as is delivering a resilient product that enables zero downtime architectures.
We will develop this to add more capabilities around streaming, machine learning, more advanced connectors, as well as innovations around microservices and the cloud. We are also working to develop our technological partnerships in these areas. There are more exciting announcements to come in these areas and on how we are working to co-innovate with this ecosystem. More to come soon, including news around our 5.1 release in Q1.
InfoQ: What has been the response from the Java community?
Gardens : As a Java-based technology, the Java community has always been a strong ecosystem for us. We have seen this continue with our version 5.0. Unifying our capabilities means Java developers can take advantage of streaming and data grid capabilities with a row added to their Maven or Gradle project. We have also seen strong interest from Java ISV partners. I won’t name names here, as I don’t want to speak for them, but Hazelcast has been incorporated into over a dozen other projects.
An Enterprise edition is also available which adds new persistence functionality and an increase in the high density memory limit to 200 GB per member.