读写hive_重要|mr使用hcatalog读写hive表

企业中&＃xff0c;由于领导们的要求&＃xff0c;hive中有数据存储格式很多时候是会变的&＃xff0c;比如为了优化将tsv&＃xff0c;csv格式改为了parquet或者orcfile。那么这个时候假如是mr作业读取hive的表数据的话&＃xff0c;我们又要重新去写mr并且重新部署。这个时候就很蛋疼。hcatalog帮我们解决了这个问题&＃xff0c;有了它我们不用关心hive中数据的存储格式。详细信息请仔细阅读本文。本文主要是讲mapreduce使用HCatalog读写hive表。hcatalog使得hive的元数据可以很好的被其它hadoop工具使用&＃xff0c;比如pig&＃xff0c;mr和hive。HCatalog的表为用户提供了(HDFS)中数据的关系视图&＃xff0c;并确保用户不必担心他们的数据存储在何处或采用何种格式&＃xff0c;因此用户无需知道数据是否以RCFile格式存储&＃xff0c; 文本文件或sequence 文件。它还提供通知服务&＃xff0c;以便在仓库中有新数据可用时通知工作流工具(如Oozie)。HCatalog提供HCatInputFormat / HCatOutputFormat&＃xff0c;使MapReduce用户能够在Hive的数据仓库中读/写数据。它允许用户只读取他们需要的表和列的分区。返回的记录格式是方便的列表格式&＃xff0c;用户无需解析它们。下面我们举个简单的例子。在mapper类中&＃xff0c;我们获取表schema并使用此schema信息来获取所需的列及其值。下面是map类。

public class onTimeMapper extends Mapper { &＃64;Override protected void map(WritableComparable key, HCatRecord value, org.apache.hadoop.mapreduce.Mapper.Context context) throws IOException, InterruptedException { // Get table schema HCatSchema schema &＃61; HCatBaseInputFormat.getTableSchema(context); Integer year &＃61; new Integer(value.getString("year", schema)); Integer month &＃61; new Integer(value.getString("month", schema)); Integer DayofMonth &＃61; value.getInteger("dayofmonth", schema); context.write(new IntPair(year, month), new IntWritable(DayofMonth)); }}

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在reduce类中&＃xff0c;会为将要写入hive表中的数据创建一个schema。

public class onTimeReducer extends Reducer {public void reduce (IntPair key, Iterable value, Context context) throws IOException, InterruptedException{ int count &＃61; 0; // records counter for particular year-month for (IntWritable s:value) { count&＃43;&＃43;; } // define output record schema List columns &＃61; new ArrayList(3); columns.add(new HCatFieldSchema("year", HCatFieldSchema.Type.INT, "")); columns.add(new HCatFieldSchema("month", HCatFieldSchema.Type.INT, "")); columns.add(new HCatFieldSchema("flightCount", HCatFieldSchema.Type.INT,"")); HCatSchema schema &＃61; new HCatSchema(columns); HCatRecord record &＃61; new DefaultHCatRecord(3); record.setInteger("year", schema, key.getFirstInt()); record.set("month", schema, key.getSecondInt()); record.set("flightCount", schema, count); context.write(null, record);}}最后&＃xff0c;创建driver类&＃xff0c;并且表明输入输出schema和表信息。

public class onTimeDriver extends Configured implements Tool{ private static final Log log &＃61; LogFactory.getLog( onTimeDriver.class ); public int run( String[] args ) throws Exception{ Configuration conf &＃61; new Configuration(); Job job &＃61; new Job(conf, "OnTimeCount"); job.setJarByClass(onTimeDriver.class); job.setMapperClass(onTimeMapper.class); job.setReducerClass(onTimeReducer.class); HCatInputFormat.setInput(job, "airline", "ontimeperf"); job.setInputFormatClass(HCatInputFormat.class); job.setMapOutputKeyClass(IntPair.class); job.setMapOutputValueClass(IntWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(DefaultHCatRecord.class); job.setOutputFormatClass(HCatOutputFormat.class); HCatOutputFormat.setOutput(job, OutputJobInfo.create("airline", "flight_count", null)); HCatSchema s &＃61; HCatOutputFormat.getTableSchema(job); HCatOutputFormat.setSchema(job, s); return (job.waitForCompletion(true)? 0:1); } public static void main(String[] args) throws Exception{ int exitCode &＃61; ToolRunner.run(new onTimeDriver(), args); System.exit(exitCode);}}

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当然&＃xff0c;在跑上面写的代码之前&＃xff0c;应该先在hive中创建输出表。

create table airline.flight_count(Year INT ,Month INT ,flightCount INT)ROW FORMAT DELIMITED FIELDS TERMINATED BY &＃39;,&＃39;STORED AS TEXTFILE;可能会引起错误的地方是没有设置$HIVE_HOME.推荐阅读&＃xff1a;Hive性能优化(全面)Hive鲜为人知的宝石-Hooks浪尖以案例聊聊spark 3.0 sql的动态分区裁剪