我一直在尝试使用cassandra附带的wordcount示例来使用hadoop。 源代码：
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import java.io.IOException;
import java.nio.ByteBuffer;
import java.util.*;
import java.util.Map.Entry;
import org.apache.cassandra.hadoop.cql3.CqlConfigHelper;
import org.apache.cassandra.hadoop.cql3.CqlOutputFormat;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.apache.cassandra.hadoop.cql3.CqlPagingInputFormat;
import org.apache.cassandra.hadoop.ConfigHelper;
import org.apache.cassandra.utils.ByteBufferUtil;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 java.nio.charset.CharacterCodingException;
/**
* This counts the occurrences of words in ColumnFamily
* cql3_worldcount ( user_id text,
* category_id text,
* sub_category_id text,
* title text,
* body text,
* PRIMARY KEY (user_id, category_id, sub_category_id))
*
* For each word, we output the total number of occurrences across all body texts.
*
* When outputting to Cassandra, we write the word counts to column family
* output_words ( row_id1 text,
* row_id2 text,
* word text,
* count_num text,
* PRIMARY KEY ((row_id1, row_id2), word))
* as a {word, count} to columns: word, count_num with a row key of "word sum"
*/
public class WordCount extends Configured implements Tool
{
private static final Logger logger = LoggerFactory.getLogger(WordCount.class);
static final String KEYSPACE = "cql3_worldcount";
static final String COLUMN_FAMILY = "inputs";
static final String OUTPUT_REDUCER_VAR = "output_reducer";
static final String OUTPUT_COLUMN_FAMILY = "output_words";
private static final String OUTPUT_PATH_PREFIX = "/tmp/word_count";
private static final String PRIMARY_KEY = "row_key";
public static void main(String[] args) throws Exception
{
// Let ToolRunner handle generic command-line options
ToolRunner.run(new Configuration(), new WordCount(), args);
System.exit(0);
}
public static class TokenizerMapper extends Mapper<Map<String, ByteBuffer>, Map<String, ByteBuffer>, Text, IntWritable>
{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
private ByteBuffer sourceColumn;
protected void setup(org.apache.hadoop.mapreduce.Mapper.Context context)
throws IOException, InterruptedException
{
}
public void map(Map<String, ByteBuffer> keys, Map<String, ByteBuffer> columns, Context context) throws IOException, InterruptedException
{
for (Entry<String, ByteBuffer> column : columns.entrySet())
{
if (!"body".equalsIgnoreCase(column.getKey()))
continue;
String value = ByteBufferUtil.string(column.getValue());
logger.debug("read {}:{}={} from {}",
new Object[] {toString(keys), column.getKey(), value, context.getInputSplit()});
StringTokenizer itr = new StringTokenizer(value);
while (itr.hasMoreTokens())
{
word.set(itr.nextToken());
context.write(word, one);
}
}
}
private String toString(Map<String, ByteBuffer> keys)
{
String result = "";
try
{
for (ByteBuffer key : keys.values())
result = result + ByteBufferUtil.string(key) + ":";
}
catch (CharacterCodingException e)
{
logger.error("Failed to print keys", e);
}
return result;
}
}
public static class ReducerToFilesystem extends Reducer<Text, IntWritable, Text, IntWritable>
{
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException
{
int sum = 0;
for (IntWritable val : values)
sum += val.get();
context.write(key, new IntWritable(sum));
}
}
public static class ReducerToCassandra extends Reducer<Text, IntWritable, Map<String, ByteBuffer>, List<ByteBuffer>>
{
private Map<String, ByteBuffer> keys;
private ByteBuffer key;
protected void setup(org.apache.hadoop.mapreduce.Reducer.Context context)
throws IOException, InterruptedException
{
keys = new LinkedHashMap<String, ByteBuffer>();
String[] partitionKeys = context.getConfiguration().get(PRIMARY_KEY).split(",");
keys.put("row_id1", ByteBufferUtil.bytes(partitionKeys[0]));
keys.put("row_id2", ByteBufferUtil.bytes(partitionKeys[1]));
}
public void reduce(Text word, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException
{
int sum = 0;
for (IntWritable val : values)
sum += val.get();
context.write(keys, getBindVariables(word, sum));
}
private List<ByteBuffer> getBindVariables(Text word, int sum)
{
List<ByteBuffer> variables = new ArrayList<ByteBuffer>();
keys.put("word", ByteBufferUtil.bytes(word.toString()));
variables.add(ByteBufferUtil.bytes(String.valueOf(sum)));
return variables;
}
}
public int run(String[] args) throws Exception
{
String outputReducerType = "filesystem";
if (args != null && args[0].startsWith(OUTPUT_REDUCER_VAR))
{
String[] s = args[0].split("=");
if (s != null && s.length == 2)
outputReducerType = s[1];
}
logger.info("output reducer type: " + outputReducerType);
Job job = new Job(getConf(), "wordcount");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
if (outputReducerType.equalsIgnoreCase("filesystem"))
{
job.setCombinerClass(ReducerToFilesystem.class);
job.setReducerClass(ReducerToFilesystem.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH_PREFIX));
}
else
{
job.setReducerClass(ReducerToCassandra.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Map.class);
job.setOutputValueClass(List.class);
job.setOutputFormatClass(CqlOutputFormat.class);
ConfigHelper.setOutputColumnFamily(job.getConfiguration(), KEYSPACE, OUTPUT_COLUMN_FAMILY);
job.getConfiguration().set(PRIMARY_KEY, "word,sum");
String query = "UPDATE " + KEYSPACE + "." + OUTPUT_COLUMN_FAMILY +
" SET count_num = ? ";
CqlConfigHelper.setOutputCql(job.getConfiguration(), query);
ConfigHelper.setOutputInitialAddress(job.getConfiguration(), "localhost");
ConfigHelper.setOutputPartitioner(job.getConfiguration(), "Murmur3Partitioner");
}
job.setInputFormatClass(CqlPagingInputFormat.class);
ConfigHelper.setInputRpcPort(job.getConfiguration(), "9160");
ConfigHelper.setInputInitialAddress(job.getConfiguration(), "localhost");
ConfigHelper.setInputColumnFamily(job.getConfiguration(), KEYSPACE, COLUMN_FAMILY);
ConfigHelper.setInputPartitioner(job.getConfiguration(), "Murmur3Partitioner");
CqlConfigHelper.setInputCQLPageRowSize(job.getConfiguration(), "3");
//this is the user defined filter clauses, you can comment it out if you want count all titles
CqlConfigHelper.setInputWhereClauses(job.getConfiguration(), "title='A'");
job.waitForCompletion(true);
return 0;
}
}
编译并生成jar文件后，当我尝试使用hadoop运行它时，程序运行到job.waitForCompletion（true）;点和冻结，它不输出任何与mapreduce或任何错误相关的内容。 我正在使用hadoop 1.2.1和cassandra 2.0.4
有谁知道问题是什么？ 感谢