Posted on

Clustering Algorithms to Build a Smart Monitoring Tool With Machine Learning K

Utilize clustering algorithms.

Learn how to build your smart system monitoring tool by using Microservices and Machine Learning (k-mean clustering) algorithms to leverage existing server-centric log events system to a predictive smart events system.

Why — Need a Smart Monitoring System

In the modern IT world, technology is evolving rapidly, and businesses need to keep up to stay relevant. More and more applications’ continuous integration and deployment (CI/CD) processes will depend on IT infrastructure and the platform’s stability. Most traditional system monitoring tools are server-centric and not able to handle tends based on application and system runtime experience.

Posted on

Distributed Locks Are Dead, Long Live Distributed Locks

"Distributed locks aren't real," "Anyone who's trying to sell you a distributed lock is selling you sawdust and lies." This may sound rather bleak, but it doesn't say that locking, itself, is impossible in a distributed system: It's just that some like to remind us that all of the system's components must participate in the protocol. This blog post is the story of how we implemented a distributed locking protocol that gives your components a straightforward way of joining in.

The coordinating component of the protocol is an object that extends the semantics of java.util.concurrent.locks.Lock. We call it FencedLock, following the naming used in Martin Kleppmann's 2016 post "How to Do Distributed Locking."

Posted on

Java: How to Become More Productive With Hazelcast in Less Than 5 Minutes

What if you want to use a Hazelcast In-Memory Data Grid (IMDG) to speed up your database applications but you have hundreds of tables to handle? Manually coding all Java POJOs and serialization support would entail weeks of work, and when done, maintaining that domain model by hand would soon turn into a nightmare. Read this article and learn how to save time and do it in 5 minutes.

Now, there is a graceful way to manage these sorts of requirements. The Hazelcast Auto DB Integration Tool allows connection to an existing database, which can generate all these boilerplate classes automatically. We get true POJOs, serialization support, configuration, MapStore/MapLoad, ingest and more without having to write a single line of manual code. As a bonus, we get Java Stream support for Hazelcast distributed maps.

Posted on

How to Use Hazelcast Auto-Discovery With Eureka

Hazelcast IMDG supports auto-discovery for many different environments. Since we introduced the generic discovery SPI, a lot of plugins were developed so you can use Hazelcast seamlessly on Kubernetes, AWS, Azure, GCP, and more. Should you need a custom plugin, you are also able to create your own.

If your infrastructure is not based on any popular Cloud environment but you still want to take advantage of the dynamic discovery rather than static IP configuration, you can set up your service registry. One of the more popular choices, especially in the JVM-based microservice world, is Eureka (initially developed by Netflix and now part of Spring Cloud). Eureka follows the client-server model, and you usually set up a server (or a cluster of servers for high availability) and use clients to register and locate services.