Java性能故障排除和优化在阿里巴巴

Fangxi Yin, Denghui Dong, Sanhong Li, Jianmei Guo, K. Chow
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引用次数: 4

摘要

阿里巴巴正朝着全球在线购物最高效的云基础设施之一迈进。在2017年双11全球购物节上,阿里巴巴的云平台实现了超过250亿美元的总销售额,支持的峰值交易量为每秒32.5万笔交易和25.6万笔支付。大多数基于云的电子商务交易都是由数十万个Java应用程序处理的,这些应用程序的代码超过10亿行。为生产中的大规模在线Java应用程序实现全面而有效的性能故障排除和优化是一项挑战。我们为Java性能调优提出了方法分析和代码预热的新方法。我们的细粒度、低开销的方法分析器提高了Java性能故障排除的效率。此外,我们的提前代码预热方法显著降低了即时编译器处理突发流量的运行时开销。我们的方法已经在阿里巴巴JDK (AJDK)中实现,AJDK是OpenJDK的定制版本,并已推广到阿里巴巴的云平台,以支持在线关键业务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Java Performance Troubleshooting and Optimization at Alibaba
Alibaba is moving toward one of the most efficient cloud infrastructures for global online shopping. On the 2017 Double 11 Global Shopping Festival, Alibaba's cloud platform achieved total sales of more than 25 billion dollars and supported peak volumes of 325,000 transactions and 256,000 payments per second. Most of the cloud-based e-commerce transactions were processed by hundreds of thousands of Java applications with above a billion lines of code. It is challenging to achieve comprehensive and efficient performance troubleshooting and optimization for large-scale online Java applications in production. We proposed new approaches to method profiling and code warmup for Java performance tuning. Our fine-grained, low-overhead method profiler improves the efficiency of Java performance troubleshooting. Moreover, our approach to ahead-of-time code warmup significantly reduces the runtime overheads of just-in-time compiler to address the bursty traffic. Our approaches have been implemented in Alibaba JDK (AJDK), a customized version of OpenJDK, and have been rolled out to Alibaba's cloud platform to support online critical business.
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