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Linux环境下Hadoop-2.7.2与HBase集成Jar包压缩文件解析

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标题中的知识点主要包括了Hadoop和HBase这两个分布式系统领域的开源框架,以及压缩包文件的相关信息。 Hadoop是一个由Apache基金会开发的开源框架,它是分布式系统的基础架构,可以部署在廉价的硬件上,实现大规模数据的存储和处理。Hadoop的核心是HDFS(Hadoop Distributed File System)和MapReduce计算模型。HDFS负责数据的存储,具有高容错性的特点,适合于大数据集的存储。MapReduce负责数据处理,通过将任务分解为多个小任务,实现并行处理,提高数据处理的效率。Hadoop还拥有一个生态系统,里面包括了各种工具和库,例如Pig, Hive, HBase等,用于扩展Hadoop的功能。 HBase是建立在Hadoop之上的分布式、可扩展的非关系型数据库,它利用Hadoop的HDFS作为其文件存储系统,利用MapReduce进行数据处理。HBase特别适合于需要快速读写访问的场景,它的数据模型非常简单,是稀疏的、多维的、排序的映射,其表可以非常大,能够存储数十亿行乘以数百万列。HBase在大规模数据集上提供随机实时的读/写访问。HBase基于列而不是基于行的模式,每行中的数据都是以列族的形式存储的,能够动态地对列进行管理,非常灵活。 压缩包文件的名称为"hadoop_hadoop-2.7.2-hbase-jar.rar",这说明了压缩包中包含了Hadoop版本2.7.2和HBase相关的jar包文件。在Linux环境下解压和使用这些文件可以构建起Hadoop和HBase的运行环境。具体操作通常包括下载压缩包,使用Linux命令解压(比如使用命令RAR x hadoop_hadoop-2.7.2-hbase-jar.rar),解压后得到jar包和配置文件。然后需要对Hadoop和HBase进行配置,设置好环境变量,确保Hadoop的各个组件能够正常工作,比如HDFS和MapReduce任务的运行。在HBase部分,需要对它的配置文件进行编辑,确保它能够正确连接到Hadoop的HDFS,并设置好ZooKeeper等。完成配置后,就可以启动Hadoop和HBase集群,进行数据存储和处理工作。 在Linux下操作Hadoop和HBase需要具备一定的系统管理知识,熟悉命令行操作,以及对Hadoop和HBase的架构和运行机制有所了解。此外,还需要掌握一些基本的Java知识,因为Hadoop和HBase都是基于Java开发的,许多配置文件和任务都是以Java相关的代码或命令来进行操作的。对于集群的管理和维护,则需要了解网络配置,性能调优以及故障排查等高级技能。 这个压缩包文件对于需要搭建和使用Hadoop及HBase环境的开发者和管理员来说是一个非常重要的资源,因为它提供了解决方案中必需的组件。对于希望学习和实践大数据技术的人员来说,它是学习和实现大数据应用的良好起点。

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java.lang.Exception: org.apache.hadoop.hbase.io.hfile.CorruptHFileException: Problem reading HFile Trailer from file hdfs://192.168.8.201:8020/apps/hbase/data/data/gt_dw/profile_gid_lbs_locvalue/7ef0422f73082b2d140d755a08ab6904/lbs/75c83b238e0b4be496eecf33eed5e5c3     at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462) ~[hadoop-mapreduce-client-common-2.7.2.jar:na]     at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:522) ~[hadoop-mapreduce-client-common-2.7.2.jar:na] Caused by: org.apache.hadoop.hbase.io.hfile.CorruptHFileException: Problem reading HFile Trailer from file hdfs://192.168.8.201:8020/apps/hbase/data/data/gt_dw/profile_gid_lbs_locvalue/7ef0422f73082b2d140d755a08ab6904/lbs/75c83b238e0b4be496eecf33eed5e5c3     at org.apache.hadoop.hbase.io.hfile.HFile.pickReaderVersion(HFile.java:463) ~[hbase-server-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.hfile.HFile.createReader(HFile.java:506) ~[hbase-server-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at com.glab.fz.etl.hfile.util.HFileInputFormat$HFileRecordReader.initialize(HFileInputFormat.java:60) ~[classes/:na]     at org.apache.hadoop.mapred.MapTask$NewTrackingRecordReader.initialize(MapTask.java:548) ~[hadoop-mapreduce-client-core-2.7.2.jar:na]     at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:786) ~[hadoop-mapreduce-client-core-2.7.2.jar:na]     at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341) ~[hadoop-mapreduce-client-core-2.7.2.jar:na]     at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:243) ~[hadoop-mapreduce-client-common-2.7.2.jar:na]     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) ~[na:1.8.0_431]     at java.util.concurrent.FutureTask.run(FutureTask.java:266) ~[na:1.8.0_431]     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) ~[na:1.8.0_431]     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) ~[na:1.8.0_431]     at java.lang.Thread.run(Thread.java:750) ~[na:1.8.0_431] Caused by: java.lang.RuntimeException: native snappy library not available: this version of libhadoop was built without snappy support.     at org.apache.hadoop.io.compress.SnappyCodec.checkNativeCodeLoaded(SnappyCodec.java:65) ~[hadoop-common-2.7.2.jar:na]     at org.apache.hadoop.io.compress.SnappyCodec.getDecompressorType(SnappyCodec.java:193) ~[hadoop-common-2.7.2.jar:na]     at org.apache.hadoop.io.compress.CodecPool.getDecompressor(CodecPool.java:178) ~[hadoop-common-2.7.2.jar:na]     at org.apache.hadoop.hbase.io.compress.Compression$Algorithm.getDecompressor(Compression.java:327) ~[hbase-common-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.compress.Compression.decompress(Compression.java:422) ~[hbase-common-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.encoding.HFileBlockDefaultDecodingContext.prepareDecoding(HFileBlockDefaultDecodingContext.java:91) ~[hbase-common-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.hfile.HFileBlock.unpack(HFileBlock.java:507) ~[hbase-server-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.hfile.HFileBlock$AbstractFSReader$1.nextBlock(HFileBlock.java:1255) ~[hbase-server-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.hfile.HFileBlock$AbstractFSReader$1.nextBlockWithBlockType(HFileBlock.java:1261) ~[hbase-server-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.hfile.HFileReaderV2.<init>(HFileReaderV2.java:147) ~[hbase-server-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.hfile.HFileReaderV3.<init>(HFileReaderV3.java:73) ~[hbase-server-0.98.13-hadoop2.jar:0.98.13-hadoop2]     at org.apache.hadoop.hbase.io.hfile.HFile.pickReaderVersion(HFile.java:453) ~[hbase-server-0.98.13-hadoop2.jar:0.98.13-hadoop2]

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SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found binding in [jar:file:/opt/spark/spark-2.4.0-bin-hadoop2.7/jars/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/opt/Hadoop/hadoop-2.7.2/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/opt/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation. SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory] 25/03/21 15:50:53 WARN Utils: Your hostname, master resolves to a loopback address: 127.0.0.1; using 192.168.180.130 instead (on interface ens33) 25/03/21 15:50:53 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address 25/03/21 15:50:53 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 25/03/21 15:50:56 WARN FileStreamSink: Error while looking for metadata directory. Traceback (most recent call last): File "/home/abz/PycharmProjects/untitled1/2024.10.22.test.py", line 6, in <module> df = spark.read.csv("传媒综合.csv", header=True, inferSchema=True, sep="|", encoding='utf-8') File "/opt/spark/spark-2.4.0-bin-hadoop2.7/python/pyspark/sql/readwriter.py", line 472, in csv return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path))) File "/opt/spark/spark-2.4.0-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__ File "/opt/spark/spark-2.4.0-bin-hadoop2.7/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/opt/spark/spark-2.4.0-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value py4j.protocol.Py4JJavaError: An erro

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