如何从映射器中访问Cassandra列族？具体来说，如何将map（）方法的参数转换回我期望的java类型？
Key {logType} - ＆gt; {列名：timeUUID，列值：csv log line，ttl：1year}
package com.xxx.hadoop;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.util.Iterator;
import java.util.SortedMap;
import org.apache.cassandra.db.IColumn;
import org.apache.cassandra.hadoop.ColumnFamilyInputFormat;
import org.apache.cassandra.hadoop.ConfigHelper;
import org.apache.cassandra.thrift.SlicePredicate;
import org.apache.cassandra.thrift.SliceRange;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.thrift.TBaseHelper;
import com.xxx.parser.LogParser;
import com.netflix.astyanax.serializers.StringSerializer;
public class LogTypeCounterByDate extends Configured implements Tool {
private static final String KEYSPACE = "LogKS";
private static final String COLUMN_FAMILY = "LogBlock";
private static final String JOB_NAME = "LOG_LINE_COUNT";
private static final String INPUT_PARTITIONER = "org.apache.cassandra.dht.RandomPartitioner";
private static final String INPUT_RPC_PORT = "9160";
private static final String INPUT_INITIAL_ADDRESS = "192.168.1.21";
private static final String OUTPUT_PATH = "/logOutput/results";
@Override
public int run(String[] args) throws Exception {
//Configuration conf = new Configuration();
Job job = new Job(getConf(), JOB_NAME);
job.setJarByClass(LogTypeCounterByDate.class);
job.setMapperClass(LogTypeCounterByDateMapper.class);
job.setReducerClass(LogTypeCounterByDateReducer.class);
job.setInputFormatClass(ColumnFamilyInputFormat.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongWritable.class);
job.setNumReduceTasks(1);
ConfigHelper.setRangeBatchSize(getConf(), 1000);
/*SlicePredicate predicate = new SlicePredicate().setSlice_range(new SliceRange(ByteBuffer.wrap(new byte[0]),
ByteBuffer.wrap(new byte[0]), true, 1));*/
SliceRange sliceRange = new SliceRange(ByteBuffer.wrap(new byte[0]),
ByteBuffer.wrap(new byte[0]), true, 1000);
SlicePredicate slicePredicate = new SlicePredicate();
slicePredicate.setSlice_range(sliceRange);
ConfigHelper.setInputColumnFamily(job.getConfiguration(), KEYSPACE, COLUMN_FAMILY);
ConfigHelper.setInputRpcPort(job.getConfiguration(), INPUT_RPC_PORT);
ConfigHelper.setInputInitialAddress(job.getConfiguration(), INPUT_INITIAL_ADDRESS);
ConfigHelper.setInputPartitioner(job.getConfiguration(), INPUT_PARTITIONER);
ConfigHelper.setInputSlicePredicate(job.getConfiguration(), slicePredicate);
FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH));
job.waitForCompletion(true);
return job.isSuccessful() ? 0 : 1;
}
public static void main(String[] args) throws Exception{
ToolRunner.run(new Configuration(), new LogTypeCounterByDate(), args);
System.exit(0);
}
public static class LogTypeCounterByDateMapper extends Mapper<ByteBuffer, SortedMap<ByteBuffer, IColumn>, Text, LongWritable>
{
@SuppressWarnings("rawtypes")
@Override
protected void setup(Mapper.Context context){
}
@SuppressWarnings({ })
public void map(ByteBuffer key, SortedMap<ByteBuffer, IColumn> columns, Context context) throws IOException, InterruptedException{
//String[] lines = columns.;
String rowkey = StringSerializer.get().fromByteBuffer(TBaseHelper.rightSize(key));
Iterator<ByteBuffer> iter = columns.keySet().iterator();
IColumn column;
String line;
LogParser lp = null;
while(iter.hasNext()){
column = columns.get(iter.next());
line = StringSerializer.get().fromByteBuffer(TBaseHelper.rightSize(column.value()));
lp = new LogParser(line);
context.write(new Text(rowkey + "\t" + "LineCount"), new LongWritable(1L));
context.write(new Text(rowkey + "\t" + "Minutes"), new LongWritable(lp.getTotalDuration()));
}
}
}
public static class LogTypeCounterByDateReducer extends Reducer<Text, LongWritable, Text, LongWritable>
{
public void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException{
long total = 0;
for(LongWritable val : values){
total += val.get();
}
context.write(key, new LongWritable(total));
}
}
}
ConfigHelper.setRangeBatchSize(getConf(), 1000);
/*SlicePredicate predicate = new SlicePredicate().setSlice_range(new SliceRange(ByteBuffer.wrap(new byte[0]),
ByteBuffer.wrap(new byte[0]), true, 1));*/
SliceRange sliceRange = new SliceRange(ByteBuffer.wrap(new byte[0]),
ByteBuffer.wrap(new byte[0]), true, 1000);
SlicePredicate slicePredicate = new SlicePredicate();
slicePredicate.setSlice_range(sliceRange);
上面的代码只为每行的mapper提供了1000列，因为我希望每次以1000列的批量为每行提供所有列。
请有人帮助我。
答案 0 :(得分：4)
给出参数：
ByteBuffer key;
SortedMap<ByteBuffer, IColumn> columns;
您将使用：
String rowkey = StringSerializer.get().fromByteBuffer(TBaseHelper.rightSize(key))
获取反序列化的键值。请注意，此处的假设是行键是String。如果是其他类型，则必须使用适当的序列化程序类。
要获取列值，请执行以下操作：
Iterator<ByteBuffer> = columns.keySet().iterator();
while (iter.hasNext()) {
IColumn col = columns.get(iter.next());
xxx colVal = xxxSerializer.get().fromByteBuffer(TBaseHelper.rightSize(col.value()));
}
其中xxx是列值的Java类型，xxxSerializer是相应的序列化器。
顺便说一下，TBaseHelper类用于将内部字节数组中的值的偏移量校正为零，从而强制执行串行器实现的假设。
还有一件事......如果您正在检索时间序列，那么每列都是它自己的时间序列值，您需要包含适当的映射器逻辑（如某种数学运算和对上下文的写入）在列上的迭代循环内部。相反，如果您有一个更静态的列族（更像传统的sql表），那么您可能只需要对整行进行一次上下文写入。