hcatalog简介和使用

Hcatalog是apache开源的对于表和底层数据管理统一服务平台，目前最新release版本是0.5，不过需要hive 0.10支持，由于我们hive集群版本是0.9.0，所以只能降级使用hcatalog 0.4，由于hcatalog中所有的底层数据信息都是保存在hive metastore里，所以hive版本升级后schema变动或者api变动会对hacatalog产生影响，因此在hive 0.11中已经集成了了hcatalog，以后也会成为hive的一部分，而不是独立的项目。

 

HCatalog底层依赖于Hive Metastore，执行过程中会创建一个HiveMetaStoreClient，通过这个instance提供的api来获取表结构数据，如果是local metastore mode的话，会直接返回一个HiveMetaStore.HMSHandler，如果是remote mode的话（hive.metastore.local设置为false），会依据hive.metastore.uris（比如thrift://10.1.8.42:9083, thrift://10.1.8.51:9083）中设定的一串uri逐一顺序建立连接。只要有一个链接建立就可以了，同时为了避免所有client都和第一个uri建立连接，导致负载过大，我加了点小trick，对这串uris随机shuffle来做load balance

 

由于我们的集群开启了kerberos security，需要获取DelegationToken，但是local mode是不支持的，所以只用能remote mode

HiveMetaStoreClient.Java

 

public String getDelegationToken(String owner, String renewerKerberosPrincipalName) throws
    MetaException, TException {
  if (localMetaStore) {
    throw new UnsupportedOperationException("getDelegationToken() can be " +
        "called only in thrift (non local) mode");
  }
  return client.get_delegation_token(owner, renewerKerberosPrincipalName);
}

 

HCatInputFormat和HCatOutputFormat提供一些mapreduce api来读取表和写入表

HCatInputFormat API:

 

public static void setInput(Job job,
    InputJobInfo inputJobInfo) throws IOException;

 

先实例化一个InputJobInfo对象，该对象包含三个参数dbname，tablename，filter，然后传给setInput函数，来读取相应的数据

 

 

public static HCatSchema getTableSchema(JobContext context)
    throws IOException;

在运行时(比如mapper阶段的setup函数中)，可以传进去JobContext，调用静态getTableSchema来获取先前setInput时设置的table schema信息

 

 

HCatOutputFormat API:

 

public static void setOutput(Job job, OutputJobInfo outputJobInfo) throws IOException;

OutPutJobInfo接受三个参数databaseName, tableName, partitionValues，其中第三个参数类型是Map<String, String>，partition key放在map key里，partition value放在对应map key的value中，该参数可传入null或空map，如果指定的partition存在的话，会抛org.apache.hcatalog.common.HCatException : 2002 : Partition already present with given partition key values

比如要要写入指定的partition（dt='2013-06-13',country='china' ）,可以这样写

 

Map<String, String> partitionValues = new HashMap<String, String>();
partitionValues.put("dt", "2013-06-13");
partitionValues.put("country", "china");
HCatTableInfo info = HCatTableInfo.getOutputTableInfo(dbName, tblName, partitionValues);
HCatOutputFormat.setOutput(job, info);

 

public static HCatSchema getTableSchema(JobContext context) throws IOException;

获取之前HCatOutputFormat.setOutput指定的table schema信息

 

 

public static void setSchema(final Job job, final HCatSchema schema) throws IOException;

设置最终写入数据的schema信息，若不调用这个方法，则默认会使用table schema信息

 

 

下面提供一个完整mapreduce例子计算一天每个guid访问页面次数，map阶段从表中读取guid字段，reduce阶段统计该guid对应pageview的总数，然后写回另外一张带有guid和count字段的表中

 

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hcatalog.data.DefaultHCatRecord;
import org.apache.hcatalog.data.HCatRecord;
import org.apache.hcatalog.data.schema.HCatSchema;
import org.apache.hcatalog.mapreduce.HCatInputFormat;
import org.apache.hcatalog.mapreduce.HCatOutputFormat;
import org.apache.hcatalog.mapreduce.InputJobInfo;
import org.apache.hcatalog.mapreduce.OutputJobInfo;

public class GroupByGuid extends Configured implements Tool {

    @SuppressWarnings("rawtypes")
    public static class Map extends
            Mapper<WritableComparable, HCatRecord, Text, IntWritable> {
        HCatSchema schema;
        Text guid;
        IntWritable one;

        @Override
        protected void setup(org.apache.hadoop.mapreduce.Mapper.Context context)
                throws IOException, InterruptedException {
            guid = new Text();
            one = new IntWritable(1);
            schema = HCatInputFormat.getTableSchema(context);
        }

        @Override
        protected void map(WritableComparable key, HCatRecord value,
                Context context) throws IOException, InterruptedException {
            guid.set(value.getString("guid", schema));
            context.write(guid, one);
        }
    }

    @SuppressWarnings("rawtypes")
    public static class Reduce extends
            Reducer<Text, IntWritable, WritableComparable, HCatRecord> {
        HCatSchema schema;

        @Override
        protected void setup(org.apache.hadoop.mapreduce.Reducer.Context context)
                throws IOException, InterruptedException {
            schema = HCatOutputFormat.getTableSchema(context);
        }

        @Override
        protected void reduce(Text key, Iterable<IntWritable> values,
                Context context) throws IOException, InterruptedException {
            int sum = 0;
            Iterator<IntWritable> iter = values.iterator();
            while (iter.hasNext()) {
                sum++;
                iter.next();
            }
            HCatRecord record = new DefaultHCatRecord(2);
            record.setString("guid", schema, key.toString());
            record.setInteger("count", schema, sum);
            context.write(null, record);
        }
    }

    @Override
    public int run(String[] args) throws Exception {
        Configuration conf = getConf();

        String dbname = args[0];
        String inputTable = args[1];
        String filter = args[2];
        String outputTable = args[3];
        int reduceNum = Integer.parseInt(args[4]);

        Job job = new Job(conf,
                "GroupByGuid, Calculating every guid's pageview");
        HCatInputFormat.setInput(job,
                InputJobInfo.create(dbname, inputTable, filter));

        job.setJarByClass(GroupByGuid.class);
        job.setInputFormatClass(HCatInputFormat.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(WritableComparable.class);
        job.setOutputValueClass(DefaultHCatRecord.class);
        job.setNumReduceTasks(reduceNum);

        HCatOutputFormat.setOutput(job,
                OutputJobInfo.create(dbname, outputTable, null));
        HCatSchema s = HCatOutputFormat.getTableSchema(job);
        HCatOutputFormat.setSchema(job, s);

        job.setOutputFormatClass(HCatOutputFormat.class);

        return (job.waitForCompletion(true) ? 0 : 1);
    }

    public static void main(String[] args) throws Exception {
        int exitCode = ToolRunner.run(new GroupByGuid(), args);
        System.exit(exitCode);
    }
}

 

其实hcatalog还支持动态分区dynamic partition，我们可以在OutJobInfo中指定部分partition keyvalue pair，在运行时候根据传进来的值设置HCatRecord对应的其他partition keyvalue pair，这样就能在一个job中同时写多个partition了

 

本文链接http://blog.csdn.net/lalaguozhe/article/details/9083905，转载请注明