Category ‘Research’

G1 Garbage Collector is mature in Java 9, finally

The G1 garbage collector is the default collector in Java 9. So it is time to reevaluate its performance which in 2013 I had criticized in a previous blog article that compared G1’s performance in late Java 7 and early Java 8 to the traditional collectors. The improvements achieved in the meantime indeed are very impressive as I will show in this article.

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Test Management with JIRA

In this first blog post of our series on Atlassian tools and 3rd-party plugins for Atlassian tools, we’ll have a close look on how to do test management in JIRA. You will get an insight in first thoughts and ideas when organizing tests and which main requirements are the bottom-line for test management in JIRA. After this we will show which types of test management plugins are available for JIRA. We will have a closer look on the specific functions of two representative examples and compare them with each other. And finally we will give you some hints on how to find the right plugin for your tests.

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Geomesa vs. GeoWave: A Benchmark for Geotemporal Point Data

With Geomesa and GeoWave two technologies based on Hadoop will be compared which are specialized in the efficient storage and retrieval of geotemporal data. Both technologies use Apache Accumulo as backend — a key-value store following the BigTable Design (PDF) — and GeoTools for handling geodata.

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Big Data Benchmark: Geotemporal Clustering

The Big Data technology landscape is expanding. Choosing the right frameworks from a growing range of technologies is increasingly becoming a challenge in projects. Helpful for the selection process are performance comparisons of frequently used operations such as cluster analyses. This is exactly what a cooperation between mgm and the University of Leipzig has been about. In the context of the master thesis “Skalierbares Clustering geotemporaler Daten auf verteilten Systemen” (“Scalable clustering of geotemporal data in distributed systems”) of Paul Röwer at the chair of Prof. Dr. Martin Middendorf for parallel processing and complex systems, the k-means algorithm has been implemented for four open source technologies of the Apache Software Foundation — Hadoop, Mahout, Spark, and Flink. Benchmarks have been carried out which compare runtime and scalability.

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Introducing Big Data with Apache Hadoop

The revenue gained with Big Data solutions rose by 66% up to 73.5 billion euro world-wide and 59% up to 6.1 billion in Germany over the past year. One of the core technologies used is Hadoop which creates the base for a broad and rich eco-system containing distributed databases, data and graph processing libraries, query and workflow engines and much more. In one of our former blog posts, we have described how we use Hadoop for storing log messages. Since then, a lot has happened in the Hadoop universe and ecosystem. With the start of our new Big Data series, we want to cover those changes and show best practices in the Big Data world.

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Controlling GC pauses with the GarbageFirst Collector

In the previous post I have shown that the GarbageFirst (G1) collector in Java 7 (and also 8ea) does a reasonable job but cannot reach the GC throughput of the “classic” collectors as soon as old generation collections come about. This article focuses on G1’s ability to control the duration of GC pauses. To this end, I refined my benchmark from the previous tests and also ran it with a huge heap size of 50 GB for which G1 was designed. I learnt that G1’s control of GC pauses is not only costly but, unfortunately, also weaker than expected.

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Benchmarking G1 and other Java 7 Garbage Collectors

As mentioned in a first post of this series, Oracle’s GarbageFirst (G1) collector has been a supported option in Java 7 for some time. This post examines in more detail the performance of the G1 garbage collector compared to the other collectors available in the Hotspot JVM. I used benchmark tests for this purpose instead of a real application because they can be executed and modified more easily. I found surprising strengths and weaknesses in several of Hotspot’s garbage collectors and even disclose a fully-fledged bug.

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Searching OpenStreetMap Geospatial Data with Solr

We are currently experiencing a Geospatial Revolution that changes in how we navigate from A to B and how we search for locations like a specific sight or restaurants nearby. Geospatial search technology provides such information. This article shows how commercial applications can utilize geospatial search, e.g. for real estate search (qualifing real estates by their distance to the nearest kindergartens, schools, doctors, etc.), calculating building density in cities and so on.

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