Hadoop作业优化：微软技术白皮书

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“微软Hadoop优化文档 - Hadoop Job Optimization” 本文档由微软ITSESE企业数据架构团队于2014年5月30日发布，主要关注Hadoop作业优化，提供了解决性能瓶颈的策略和建议，以提高整体Hadoop作业的执行效率。在“HadoopJobOptimization”中，作者首先对MapReduce的内部工作原理进行了简短的介绍，这对于理解后续的优化策略至关重要。MapReduce是Hadoop的核心组件，通过将大型任务分解为小的并行任务，使得分布式处理成为可能。第二部分定义了一个通用的性能调优框架，用于指导可重复的优化过程。这个框架帮助识别基于性能指标的资源瓶颈，例如CPU利用率、内存使用、磁盘I/O和网络带宽等。了解这些指标对于定位问题至关重要。在第三部分，文档详细阐述了各种调优技术，包括但不限于： 1. **Mapper和Reducer数量的调整**：适当增加Mapper和Reducer的数量可以提高并行度，但过多可能导致资源竞争和调度开销。 2. **内存管理优化**：设置合适的堆内存大小，避免溢出错误，同时优化数据序列化和反序列化过程。 3. **数据本地性**：尽量使数据与处理节点位于同一集群，减少数据传输的延迟和带宽消耗。 4. **Shuffle阶段优化**：调整Shuffle阶段的缓冲区大小、合并策略和压缩选项，以降低网络传输负担和内存压力。 5. **作业调度策略**：选择合适的作业调度器（如FIFO、CapacityScheduler或FairScheduler），根据作业优先级和资源需求进行合理调度。 6. **HDFS参数调整**：优化副本数量、块大小、预读取策略等，提高数据读取速度。此外，文档还提供了针对不同性能问题的技术选择矩阵，帮助读者根据具体问题选择最适合的解决方案。在结论部分，作者总结了整个调优流程，并强调持续监控和调整的重要性，因为Hadoop环境中的工作负载可能会随时间变化。附录A专注于Hive的Join操作优化，给出了在Hive中执行Join操作时的性能考虑和最佳实践。附录B则列举了Shuffle阶段的相关调优属性，这有助于更深入地理解Shuffle过程并对其进行优化。这份文档为Hadoop管理员和开发者提供了一份详实的性能调优指南，涵盖了从基础概念到高级策略的全面内容，是优化Hadoop作业性能的重要参考资料。

Microsoft IT SES Enterprise Data Architect Team 2014-05-30

Hadoop Job Optimization

pipeline, the CPU is waiting for other resources to feed in data before it can proceed to the actual

computation.

Low CPU utilization can also be caused by inappropriate values in the job configuration. For example, if you

set the number of Map or Reduce tasks to a value that is too conservative, the job might under-utilize the

computational capacity of the cluster.

RAM

The amount of RAM available on the task tracker nodes is another potential bottleneck that can have

significant effects. If you have memory-intensive workloads and/or the memory settings are not properly

configured, the Map and Reduce tasks might be initiated but immediately fail. Depending on whether or

not this is a transient issue, Hadoop might eventually succeed in running the task, but the retry operation

itself imposes overhead, and in many cases the job will simply fail to execute. You must be careful to

configure memory usage based on the number of tasks and the physical memory you have on each nodes.

Network Bandwidth

High network utilization occurs when large amounts of data travel among nodes. Most often, this happens

when Reduce tasks pull data from Map tasks in the Shuffle phase, and also when the job outputs the final

results into HDFS.

To ensure that network utilization does not become a bottleneck, you need to be aware of the current

network bandwidth used in a particular job, as well as the maximum network bandwidth available. This

number can be determined by performing a stress test of the Hadoop cluster. By constantly monitoring

MapReduce network utilization, you will be able to figure out if the cluster has sufficient network bandwidth

to move data efficiently.

Storage I/O

Storage I/O is probably the most important resource for running a MapReduce job. In our experience, I/O

throughput is also one of the most common bottlenecks. It can decrease MapReduce job performance

across the board, and become a bottleneck at every stage of the execution pipeline.

Storage I/O utilization can be monitored by using Windows performance counters. We recommend that

you learn your cluster’s maximum I/O throughput by running a stress test beforehand, so that you can

determine when your job has encountered a bottleneck. Storage I/O utilization heavily depends on the

volume of input, intermediate data, and final output data. If the bottleneck is in storage I/O, almost all

techniques for reducing data size will help.

Microsoft IT SES Enterprise Data Architect Team 2014-05-30

Hadoop Job Optimization

Identifying Bottlenecks

In this section, we will describe some of the common issues in Hadoop jobs, and explain the methods that

we use to monitor a job and to identify bottlenecks as they occur.

In the following section, Removing Bottlenecks, we will provide ways to resolve these common issues.

Overview of the Performance Tuning Cycle

When you consider optimizing a MapReduce job, you probably already feel that the job is taking too long,

and are looking for changes that will make the job finish faster. However, before you put any effort into

solving individual bottlenecks, you must always take into account the overall configuration of the cluster,

and the type of job. Having a good balance of resources across your cluster is the real key to achieving

high job performance.

Hadoop resources can be classified into computation, memory, network bandwidth, and storage I/O. If you

notice that a job runs for an unreasonably long time, very likely the cause is resource limits at some phase

of the job, which prevents the job from running faster. Such resources limitations are the principal

bottlenecks that will be described in the rest of this section.

The first step towards job optimization is identifying bottlenecks and finding resource limitations. The next

step is to remove bottlenecks by applying the tuning techniques described in this paper, to change the way

that resources are used in the context of a particular job.

Typically, optimization is a repeatable process that you have to run for many cycles, comparing the

benchmarks for each run, until you are certain that you have located the bottlenecks and found the most

effective tuning techniques.

Massive I/O Caused by Large Input Data in Map Input Stage

In the Map input phase (Stage 1), source data is read into the Map tasks and used for calculation. This

process of reading data consumes disk I/O. If disk I/O is not fast enough, computation resources will be idle

and spend most of the job time waiting for the incoming data.

Therefore, performance can be constrained by disk I/O. This problem happens most often on jobs with light

computation and large volumes of source data.

Indicators

The following indicators can be used to look for I/O issues:

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