You might as yourself: Why is there another java redis client? Until now, there are tons available.
True.
Let me tell you about my motivation.
You might as yourself: Why is there another java redis client? Until now, there are tons available.
True.
Let me tell you about my motivation.
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I’ve released today logstash-gelf 1.4.1, I’ll be available until this evening on Maven Central.
Logstash is one of the hottest tools when it comes to log management. There are lots of integration possibilities in standalone or web applications. But what if you want to capture the whole log output of your JBoss server? Take a look at the snippets.
visualizr – Show your data Once in a while I get in touch with lots of time based data, such as monitoring or profiling values.
Calls per minute, actions per time unit and so on. Having such data is a great value but having it visualized gives a different impression instead looking at numbers. I’ve created visualizr. It’s a small Single-Page-Application with a Java-based backend. Visualizr provides support for multiple data sources (a data source can contain multiple charts), auto-reloading and a great user experience.
Give it a try, you can find the code at https://github.com/mp911de/visualizr or as a maven artifact (SNAPSHOT-version until now)
<dependency>
<groupId>biz.paluch.visualizr</groupId>
<artifactId>visualizr</artifactId>
<version>1.0-SNAPSHOT</version>
</dependency>
Sonatype OSS Snapshot Repository: https://oss.sonatype.org/content/repositories/snapshots
It happens currently all the time. I’m always coming back to projects I worked on. Over and over I’m meeting old coworkers and colleagues. Same faces, sometimes a bit older than I left them. This raises mixed feelings. It’s sort of coming home, thus it is different.
Traditional applications progress sequentially. This was ok for a certain time, multi-threading, concurrency and parallelism are hard to code right. With growing requirements to performance, large scale data and user base, application have to progress concurrently and even parallelize tasks. Using Akka for event distribution and processing enables applications to work on different tasks in parallel in a very simple and configurative way.
Operating a Hadoop cluster means lots of daemons running on lots of machines. When it comes to the logs, and you’re searching for something, it can get nasty, since you do not know, where to search.
Stateless services are the easiest to scale out. Just add more machines without worrying about maintaining a state. Stateful applications are harder to scale. So are web applications with user front ends and sessions.
Today I’ve released the patch 1.2.6 of logstash-gelf. This release contains a performance bugfix which caused an incident in the past.
The java.util.logging hoghandler’s publish method was synchronized. This caused stalling of different threads which ended up in a denial of service. Usually a restart of the application is sufficient to solve that problem, but that’s only a temporary solution. Other implementations of logstash-gelf (Log4j, Log4j2 and Logback) are not affected.
Links
Any feedback is appreciated or create issues on GitHub.