This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
Store your data in RAM, spread and replicate it across a cluster of machines, and perform data-local computation on it. Replication gives you resilience to failures of cluster nodes.
Hazelcast is an open-source distributed In-memory object store supporting a wide variety of data structures such as Map, Set, List, MultiMap, RingBuffer, HyperLogLog. Cloud and Kubernetes friendly.
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Build data pipelines processing streams of events such as from message queues and database changelogs. The processing state is replicated, allowing you to scale up and down the computation without any loss of data.
Hazelcast enables open-source distributed stream and batch processing with embedded in-memory storage and a variety of connectors such as Kafka, Amazon S3, Hadoop, JMS and JDBC.
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10-15 min bite-sized tutorials.
// Start the Embedded Hazelcast Cluster Member.
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
// Get the Distributed Map from Cluster.
IMap map = hz.getMap("my-distributed-map");
//Standard Put and Get.
//Concurrent Map methods, optimistic updating
map.replace("key", "value", "newvalue");
// Shutdown the Hazelcast Cluster Member
Distributed Map is the most widely use data structure within Hazelcast. You can store objects directly from your application and get them back using the key or via SQL like queries. Everything is stored in memory with replicas spread across the cluster. Adding cluster members expands the space available for data. The example you can see here is in Java, but the API is similar across other languages. The Distributed Map can also recognise JSON values and allows querying on its elements.
IMap<String,Double> averagePrices = jet.getMap("current-avg-trade-price");
Pipeline p = Pipeline.create();
// Stream (trade symbol, price) records from Kafka.
.filter(trade -> STOCKS.contains(trade.getSymbol()))
// 10 second sliding window, updated every 100ms
// write results to a distributed map.
.map(window -> Util.entry(window.getKey(), window.getValue()))
Hazelcast can apply continuous transforms to a stream of data, such as filtering, mapping or aggregation over windows or joining multiple data sources. It can deal with events arriving out of order or can be used for detecting patterns in an event stream. Jet supports many different data sources and sinks, such as Apache Kafka, message brokers, relational databases, Amazon S3, Hadoop and its own built in distributed map structure.
<map-store enabled="true" initial-mode="LAZY">
Use Hazelcast IMDG to speed-up applications that read and write to disks, such as relational databases and NoSQL stores. Hazelcast IMDG supports several cache patterns such as Read-Through, Write-Through, Write-Behind & Cache-Aside. Using the first three patterns the application need not know anything about the backing stores, they just deal with data structure APIs such as Map. Write-Behind solves the problem of slow data stores where the application would usually wait for an acknowledgment. The following example shows cluster configuration for a Write-Behind store.
IMap<Integer, Product> productMap = jet.getMap(PRODUCTS);
Pipeline p = Pipeline.create();
// read a list of trades, join with the product map and then write back to files
trade -> trade.productId(),
(t, product) -> tuple2(t, product.name())
Use Hazelcast to speed up your MapReduce, Spark, or custom Java data processing jobs. Load data sets to a cluster cache and perform compute jobs on top of the cached data. You get significant performance gains by combining an in-memory approach and co-location of jobs and data with parallel execution.
Hazelcast provides tools for building distributed applications. Use Hazelcast for distributed coordination and in-memory data storage and Hazelcast Jet for building streaming data pipelines. Using Hazelcast allows developers to focus on solving problems rather than data plumbing.
It’s easy to get started with Hazelcast. The nodes automatically discover each other to form a cluster, both in a cloud environment and on your laptop. This is great for quick testing and simplifies deployment and maintenance. No additional dependencies.
Hazelcast automatically partitions and replicates data in the cluster and tolerates node failures. You can add new nodes to increase storage capacity immediately. You can use it as a cache or to store transactional state and perform data-local computations or queries. As all data is stored in memory, you can access it in sub-millisecond latencies. Clients for Java, Python, .NET, C++ and Go are available.
Get Started with In-Memory Computing
Use Hazelcast to build massively parallel data pipelines. You can process data using a rich library of transforms such as windowing, joins and aggregations. Hazelcast keeps processing data without loss even when a node fails, and as soon as you add another node, it starts sharing the computation load. First-class support for Apache Kafka, Hadoop and many other data sources and sinks.
Get Started with Stream Processing
Hazelcast has a full implementation of Raft, allowing a simple API for building linearizable distributed systems. Use tools like FencedLock, Semaphore and AtomicReference to simplify coordination between distributed applications.
Hazelcast is a single Java archive (JAR) less than 15MB. It’s lightweight enough to run on small devices, you can embed it into your application as just another dependency or deploy it as a standalone cluster. First-class support for Kubernetes is included.
Redis and Hazelcast solve many similar use cases, most commonly caching. They are quite different in how they approach things such as cache patterns, clustering & querying.
Set up a Hazelcast cluster in Kubernetes, and make use of Hazelcast storage and messaging capabilities in your microservices architectures.
Use Hazelcast Jet to build a data processing pipeline that will process events from Apache Kafka as they arrive.
Use Hazelcast IMDG for storing and retrieving data from distributed in-memory data structures. You can store your data from one machine and access it from another or perform queries on it.
This post was co-authored with Michal Biesek, a software engineer at Intel. It was originally published on the pmem.io blog as well. If interested in Persistent Memory development or PMDK in particular, check the other posts there, too. The mission of the PMDK team has always been and will always be to make programming persistent […]
I’m pleased to announce Management Center v4.2021.04 has been released as of today! Let’s see what this new release brings. Before we start, please note that the following features require you to have an enterprise license. You can request a free enterprise trial license here. Configuration Check Improvements Management Center has a very useful feature […]
“Which serialization option is best?“. In this post we’ll explore some of the most common serialization options for Hazelcast, which includes standard coding, and the external libraries Avro, Kryo and Protobuf. Following our previous posts on how much memory you will need (here and here) which looked at object sizing in Java, we need to […]
Learn how the cloud-native architecture of Hazelcast works with Kubernetes when deploying fast cloud applications.
This whitepaper discusses how an in-memory computing platform is used in the healthcare industry to help improve patient care.
BNP Paribas Bank Polska increases revenue through real-time offers driven by specific customer needs.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered for FREE!