C4: Continuously Concurrent Compacting Collector

C4: Continuously Concurrent Compacting Collector

Written for architects and Java experts, this paper, originally presented to the International Symposium on Memory Management (ISMM), describes how C4 differentiates itself from other generational garbage collectors by supporting simultaneous – generational concurrency: the different generations are collected using concurrent (non stop-the-world) mechanisms that can be simultaneously and independently active. The C4 Continuously Concurrent Compacting Collector, is an updated generational form of the Azul Pauseless GC Algorithm. This paper was originally published in the proceedings of the ISMM 2011, copyright ACM, 2011. Available here under license.

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JVMパフォーマンス研究 ― Apache Cassandra™を使ったOracle HotSpot<sup>®</sup>とAzul Zing<sup>®</sup>の比較

JVMパフォーマンス研究 ― Apache Cassandraを使ったOracle HotSpot®とAzul Zing®の比較

エグゼクティブサマリ

Apache Cassandra は、Java で開発されています。そのため、従来の Java 仮想マシン(JVM)の制約や一時的に処理を中断するという動作の影響を受けます。予想外の遅延や、頻繁なガーベジコレクション、メモリ不足、システムの停止などがよくみられる症状です。Cassandra を配備する際には、これらを回避するために、長い時間をかけて JVM のチューニングと調整を行い、その後、負荷の状況が変わるたびに(あるいは、Cassandra にアクセスするアプリケーションに変更があるたびに)、再チューニングする必要があります。このホワイトペーパーでは、Zing の利用が、JVM のチューニング回避と、Apache Cassandra の短期間での配備にどのように寄与するかを示します。

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Benchmark Report: Comparing Oracle HotSpot® and Azul Zing® Using Apache Cassandra™

Benchmark Report: Comparing Oracle HotSpot® and Azul Zing® Using Apache Cassandra

This benchmark study compares the response time performance of two different Java Virtual Machines (JVMs), namely Azul Zing® and Oracle HotSpot®, while running Apache Cassandra at different throughput levels.

The focus is technical.

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Consistent Cassandra Performance with Zing®

Consistent Cassandra Performance with Zing®

Apache Cassandra is built in Java, which makes it subject to the limitations and performance hiccups associated with traditional Java Virtual Machines (JVMs). Common issues include response time outliers, frequent Java garbage collections, out of memory errors and system hangs. Companies deploying Cassandra spend extensive time tuning and tweaking the JVM to avoid these issues, then need to re-tune each time load (or the application accessing Cassandra) changes.

This white paper describes how Zing allows you to avoid JVM tuning and get your Apache Cassandra deployment launched faster.

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451 Research Report: Azul pitches Docker as alternative to virtualization for heavy-duty Java applications

451 Research Report: Azul pitches Docker as alternative to virtualization for heavy-duty Java applications

In this white paper published by 451 Research, Analyst John Abbott discusses Docker and Java, “For enterprise customers with heavy-duty Java applications wanting the flexibility of virtualization without the performance penalty and unpredictability, Docker could be something of a savior.”

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Java for the Real Time Business

Java for the Real Time Business

In this white paper, learn how new Java innovations can transform existing applications to business real time, opening up new opportunities, freeing resources and creating a competitive advantage.

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Azul Disrupts the ROI Equation for High Performance Applications

Azul Disrupts the ROI Equation for High Performance Applications

When evaluating innovative new technologies like Zing, delivering a compelling return on investment (ROI) is essential. Written for business people with direct or indirect P&L responsibility, this white paper discusses how Azul’s Zing technology enables firms to leverage the power of Java across the enterprise – even previously sacred high performance/low-latency areas – by dramatically improving performance, resource leverage and above all, ROI to deliver enhanced revenue opportunities and lower costs.

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Introduction to Innovative Azul Zing

Introduction to Innovative Azul Zing

Written for a non-technical audience, This short paper provides an introduction to what makes Zing uniquely able to support applications that require large memory, high transaction rates, consistent response times and high sustained throughput.

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Java Without the Jitter

Java Without the Jitter

Written for IT professionals, architects and developers who work on applications that require low latency, this white paper discusses the historic pitfalls of Java application latency and jitter behavior and explains how modern Java applications can achieve the long-sought-after goal of continuously consistent latency at high sustainable throughputs. The paper then describes how to use new JVM technology to achieve worst case platform latencies of 10 sec or less “out of the box” and maximum platform latencies of 1-2 milliseconds with very little tuning effort.

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Java at Scale

Java at Scale

In this paper we discuss the ways Java gets used, why it has such wide appeal, and where it has limitations that either prevent its use in specific domains or require a great deal of effort to make it fit. We also describe Azul Systems’ answer to Java’s limitations.

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Supercharging the Java Runtime

Supercharging the Java Runtime

Written for application and line of business owners, this paper reviews the current limitations of enterprise Java applications, challenges of modern deployment topologies such as virtualization and Clouds and the advantages of a highly innovative and elastic JVM that can provide guaranteed predictability, even under load. Azul Systems has adapted award-winning technology from its line of Java accelerator appliances to create the Zing® JVM, a unique, software-based solution. Zing complies with the Java SE standard and is a JVM designed for enterprise applications and workloads that require large memory, high transaction rates, consistent response times and high sustained throughput.

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Understanding Java Garbage Collection

Understanding Java Garbage Collection

Written for IT professionals, architects and developers, this white paper reviews and classifies the various garbage collectors and collection techniques available in JVMs today. It provides an overview of common garbage collection techniques, algorithms and defines terms and metrics common to all collectors including generational, parallel, stop-the-world, incremental, concurrent, and mostly-concurrent. It further classifies each major JVM collector’s mechanisms & characteristics and discusses the tradeoffs involved in balancing requirements for responsiveness, throughput, space, and available memory across varying scale levels. The paper concludes with some pitfalls, common misconceptions, and “myths” around garbage collection behavior, as well as examples of how certain choices can result in impressive application behavior.

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Azul Pauseless Garbage Collection

Azul Pauseless Garbage Collection

Written for IT and developers, this white paper provides technical detail on the operation of Zing’s garbage collector to explain how it achieves pauseless operation. Based on the Azul C4 (Continuously Concurrent Compacting Collector) garbage collection algorithm the Zing collector improves application performance and removes barriers to Java scalability by eliminating pause times even at very large heap sizes. Using a combination of software techniques and hardware emulation features, the Azul garbage collector uses ‘concurrent compaction’ to allow applications to continue processing while remapping memory.

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451 Group Impact Report on Azul Systems

451 Group Impact Report on Azul Systems

In this report by the 451 Group, John Abbott discusses Azul Systems’ shift from Java acceleration appliances to a software-based Java runtime.

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Zing: The Industrial Strength JVM

Zing: The Industrial Strength JVM

An Industrial Strength JVM contributes both business and technology benefits to your company.

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Push Technology Diffusion™ benchmark on Zing

Push Technology Diffusion™ benchmark on Zing

Benchmark comparing the performance of Push Technology’s Java based data distribution solution, Diffusion™, running on Azul Systems Zing® versus Oracle’s HotSpot™ JVM.

 

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PontusVision benchmark on Red Hat MRG Realtime and Azul Zing®

PontusVision benchmark on Red Hat MRG Realtime and Azul Zing®

Pontus Networks recently performed this benchmark for an investment bank. They were able to show the bank how to improve the latency of an FX pricing system without changing a single line of code or any of the target hardware. The system’s peak latency (from receiving a raw market data tick to sending a FIX market data snapshot to 25 clients) was decreased by over 20 times.

PontusVision benchmark on Red Hat MRG Realtime and Azul Zing®

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Zing vs. Java HotSpot running an in-memory data grid

Zing vs. Java HotSpot running an in-memory data grid

Benchmark comparing the performance of Zing and Java HotSpot™ running an in-memory data grid.

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