大数据平台运维之MapReduce

本文介绍了在大数据平台运维中如何使用MapReduce进行计算任务。通过实例展示了运行MapReduce的PI计算和WordCount程序,以及解决数独问题和统计文件中特定单词出现次数的过程。详细记录了每个步骤的输出结果,包括Map和Reduce任务的进度及完成情况。

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Mapreduce

12.在集群节点中/usr/hdp/2.4.3.0-227/hadoop-mapreduce/目录下,存在一个案例JAR                           包hadoop-mapreduce-examples.jar。运行JAR包中的PI程序来进行计算圆周率π的近似值,要求运行5次Map任务,每个Map任务的投掷次数为5,运行完成后输出结果为。

[root@master ~]# hadoop jar/usr/hdp/2.4.3.0-227/hadoop-mapreduce/hadoop-mapreduce-examples-2.7.1.2.4.3.0-227.jarpi 5 5

WARNING: Use "yarn jar" to launch YARNapplications.

Number of Maps = 5

Samples per Map = 5

Wrote input for Map #0

Wrote input for Map #1

Wrote input for Map #2

Wrote input for Map #3

Wrote input for Map #4

Starting Job

17/05/07 03:25:16 INFO impl.TimelineClientImpl:Timeline service address: http://slaver1:8188/ws/v1/timeline/

17/05/07 03:25:16 INFO client.RMProxy: Connecting toResourceManager at slaver1/10.0.0.15:8050

17/05/07 03:25:17 INFO input.FileInputFormat: Totalinput paths to process : 5

17/05/07 03:25:17 INFO mapreduce.JobSubmitter: numberof splits:5

17/05/07 03:25:18 INFO mapreduce.JobSubmitter:Submitting tokens for job: job_1494125392913_0001

17/05/07 03:25:19 INFO impl.YarnClientImpl: Submittedapplication application_1494125392913_0001

17/05/07 03:25:19 INFO mapreduce.Job: The url to trackthe job: http://slaver1:8088/proxy/application_1494125392913_0001/

17/05/07 03:25:19 INFO mapreduce.Job: Running job:job_1494125392913_0001

17/05/07 03:25:30 INFO mapreduce.Job: Jobjob_1494125392913_0001 running in uber mode : false

17/05/07 03:25:30 INFO mapreduce.Job:  map 0% reduce 0%

17/05/07 03:25:36 INFO mapreduce.Job:  map 40% reduce 0%

17/05/07 03:25:41 INFO mapreduce.Job:  map 60% reduce 0%

17/05/07 03:25:42 INFO mapreduce.Job:  map 80% reduce 0%

17/05/07 03:25:45 INFO mapreduce.Job:  map 100% reduce 0%

17/05/07 03:25:48 INFO mapreduce.Job:  map 100% reduce 100%

17/05/07 03:25:49 INFO mapreduce.Job: Jobjob_1494125392913_0001 completed successfully

17/05/07 03:25:49 INFO mapreduce.Job: Counters: 49

        FileSystem Counters

               FILE: Number of bytes read=116

               FILE: Number of bytes written=819237

               FILE: Number of read operations=0

               FILE: Number of large read operations=0

               FILE: Number of write operations=0

               HDFS: Number of bytes read=1300

               HDFS: Number of bytes written=215

               HDFS: Number of read operations=23

               HDFS: Number of large read operations=0

               HDFS: Number of write operations=3

        JobCounters

               Launched map tasks=5

               Launched reduce tasks=1

               Data-local map tasks=5

               Total time spent by all maps in occupied slots (ms)=50808

               Total time spent by all reduces in occupied slots (ms)=10839

               Total time spent by all map tasks (ms)=16936

               Total time spent by all reduce tasks (ms)=3613

                Total vcore-seconds taken by all maptasks=16936

               Total vcore-seconds taken by all reduce tasks=3613

               Total megabyte-seconds taken by all map tasks=26013696

               Total megabyte-seconds taken by all reduce tasks=5549568

       Map-Reduce Framework

               Map input records=5

               Map output records=10

               Map output bytes=90

               Map output materialized bytes=140

               Input split bytes=710

               Combine input records=0

               Combine output records=0

               Reduce input groups=2

               Reduce shuffle bytes=140

               Reduce input records=10

               Reduce output records=0

               Spilled Records=20

                Shuffled Maps =5

               Failed Shuffles=0

               Merged Map outputs=5

               GC time elapsed (ms)=450

               CPU time spent (ms)=4330

               Physical memory (bytes) snapshot=5840977920

                Virtual memory (bytes) snapshot=19436744704

               Total committed heap usage (bytes)=5483528192

        ShuffleErrors

               BAD_ID=0

### 大数据平台中 Hive 的运维测评与最佳实践 #### 1. Hive 运维的核心关注点 Hive 是一种基于 Hadoop 的分布式数据分析工具,其运维过程中需要重点关注以下几个方面[^1]: - **性能优化**:通过调整查询语句、分区设计以及压缩算法等方式提升查询效率。 - **资源管理**:合理分配 YARN 或 Spark 集群中的计算资源,防止因资源争抢而导致的任务失败。 - **元数据管理**:定期清理无用的表和分区,减少 Metastore 数据库的压力。 #### 2. 性能调优的最佳实践 为了提高 Hive 查询的执行速度,可以采取以下措施[^1]: - 使用合适的文件格式(如 ORC 或 Parquet),这些列式存储格式能够显著降低 I/O 开销。 - 启用谓词下推功能,在读取数据之前就过滤掉不必要的记录。 - 设置合理的并行度参数 `hive.exec.parallel` 和 `mapreduce.job.reduces` 来平衡负载分布。 #### 3. 资源调度策略 在大规模生产环境中,良好的资源调度机制至关重要。推荐采用 Fair Scheduler 或 Capacity Scheduler 对不同优先级的工作流进行隔离处理[^2]: - 定义清晰的服务级别协议(SLA),确保高优先级任务获得足够的计算能力支持。 - 动态扩展节点数量以应对突发流量高峰情况下的需求激增现象。 #### 4. 元数据治理方案 随着时间积累,数据库内的对象数目会快速增长从而影响整体表现效果因此有必要实施有效的元数据管控手段包括但不限于如下几点建议: - 自动化检测长期未被访问过的表格或者分片并且提示管理员考虑删除操作. - 利用 ACID 特性实现事务级别的更新控制使得修改过程更加安全可靠. ```sql -- 示例 SQL: 创建带有分区的外部表 CREATE EXTERNAL TABLE IF NOT EXISTS sales_data ( order_id STRING, product_name STRING, quantity INT, price FLOAT ) PARTITIONED BY (year INT, month INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS TEXTFILE LOCATION '/user/hive/warehouse/sales'; ``` #### 5. 日志监控体系构建 建立健全的日志收集分析框架可以帮助快速定位问题根源所在同时也有助于预防潜在风险的发生概率增加系统稳定性水平达到预期目标值范围内保持正常运转状态不变形不损坏等功能特性得以充分体现出来.[^1] ---
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