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The Apache Cassandra Project develops a highly scalable second-generation distributed database, bringing together Dynamo's fully distributed design and Bigtable's ColumnFamily-based data model.
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
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* See the License for the specific language governing permissions and
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package org.apache.cassandra.hadoop.cql3;
import java.io.IOException;
import java.util.*;
import java.util.concurrent.*;
import com.datastax.driver.core.Cluster;
import com.datastax.driver.core.Host;
import com.datastax.driver.core.Metadata;
import com.datastax.driver.core.ResultSet;
import com.datastax.driver.core.Row;
import com.datastax.driver.core.Session;
import com.datastax.driver.core.TokenRange;
import org.apache.cassandra.config.SchemaConstants;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapred.InputSplit;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.TaskAttemptID;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.apache.cassandra.db.SystemKeyspace;
import org.apache.cassandra.dht.*;
import org.apache.cassandra.thrift.KeyRange;
import org.apache.cassandra.hadoop.*;
import static java.util.stream.Collectors.toMap;
/**
* Hadoop InputFormat allowing map/reduce against Cassandra rows within one ColumnFamily.
*
* At minimum, you need to set the KS and CF in your Hadoop job Configuration.
* The ConfigHelper class is provided to make this
* simple:
* ConfigHelper.setInputColumnFamily
*
* You can also configure the number of rows per InputSplit with
* 1: ConfigHelper.setInputSplitSize. The default split size is 64k rows.
* or
* 2: ConfigHelper.setInputSplitSizeInMb. InputSplit size in MB with new, more precise method
* If no value is provided for InputSplitSizeInMb, we default to using InputSplitSize.
*
* CQLConfigHelper.setInputCQLPageRowSize. The default page row size is 1000. You
* should set it to "as big as possible, but no bigger." It set the LIMIT for the CQL
* query, so you need set it big enough to minimize the network overhead, and also
* not too big to avoid out of memory issue.
*
* other native protocol connection parameters in CqlConfigHelper
*/
public class CqlInputFormat extends org.apache.hadoop.mapreduce.InputFormat implements org.apache.hadoop.mapred.InputFormat
{
public static final String MAPRED_TASK_ID = "mapred.task.id";
private static final Logger logger = LoggerFactory.getLogger(CqlInputFormat.class);
private String keyspace;
private String cfName;
private IPartitioner partitioner;
public RecordReader getRecordReader(InputSplit split, JobConf jobConf, final Reporter reporter)
throws IOException
{
TaskAttemptContext tac = HadoopCompat.newMapContext(
jobConf,
TaskAttemptID.forName(jobConf.get(MAPRED_TASK_ID)),
null,
null,
null,
new ReporterWrapper(reporter),
null);
CqlRecordReader recordReader = new CqlRecordReader();
recordReader.initialize((org.apache.hadoop.mapreduce.InputSplit)split, tac);
return recordReader;
}
@Override
public org.apache.hadoop.mapreduce.RecordReader createRecordReader(
org.apache.hadoop.mapreduce.InputSplit arg0, TaskAttemptContext arg1) throws IOException,
InterruptedException
{
return new CqlRecordReader();
}
protected void validateConfiguration(Configuration conf)
{
if (ConfigHelper.getInputKeyspace(conf) == null || ConfigHelper.getInputColumnFamily(conf) == null)
{
throw new UnsupportedOperationException("you must set the keyspace and table with setInputColumnFamily()");
}
if (ConfigHelper.getInputInitialAddress(conf) == null)
throw new UnsupportedOperationException("You must set the initial output address to a Cassandra node with setInputInitialAddress");
if (ConfigHelper.getInputPartitioner(conf) == null)
throw new UnsupportedOperationException("You must set the Cassandra partitioner class with setInputPartitioner");
}
public List getSplits(JobContext context) throws IOException
{
Configuration conf = HadoopCompat.getConfiguration(context);
validateConfiguration(conf);
keyspace = ConfigHelper.getInputKeyspace(conf);
cfName = ConfigHelper.getInputColumnFamily(conf);
partitioner = ConfigHelper.getInputPartitioner(conf);
logger.trace("partitioner is {}", partitioner);
// canonical ranges, split into pieces, fetching the splits in parallel
ExecutorService executor = new ThreadPoolExecutor(0, 128, 60L, TimeUnit.SECONDS, new LinkedBlockingQueue());
List splits = new ArrayList<>();
try (Cluster cluster = CqlConfigHelper.getInputCluster(ConfigHelper.getInputInitialAddress(conf).split(","), conf);
Session session = cluster.connect())
{
List>> splitfutures = new ArrayList<>();
KeyRange jobKeyRange = ConfigHelper.getInputKeyRange(conf);
Range jobRange = null;
if (jobKeyRange != null)
{
if (jobKeyRange.start_key != null)
{
if (!partitioner.preservesOrder())
throw new UnsupportedOperationException("KeyRange based on keys can only be used with a order preserving partitioner");
if (jobKeyRange.start_token != null)
throw new IllegalArgumentException("only start_key supported");
if (jobKeyRange.end_token != null)
throw new IllegalArgumentException("only start_key supported");
jobRange = new Range<>(partitioner.getToken(jobKeyRange.start_key),
partitioner.getToken(jobKeyRange.end_key));
}
else if (jobKeyRange.start_token != null)
{
jobRange = new Range<>(partitioner.getTokenFactory().fromString(jobKeyRange.start_token),
partitioner.getTokenFactory().fromString(jobKeyRange.end_token));
}
else
{
logger.warn("ignoring jobKeyRange specified without start_key or start_token");
}
}
Metadata metadata = cluster.getMetadata();
// canonical ranges and nodes holding replicas
Map> masterRangeNodes = getRangeMap(keyspace, metadata);
for (TokenRange range : masterRangeNodes.keySet())
{
if (jobRange == null)
{
// for each tokenRange, pick a live owner and ask it to compute bite-sized splits
splitfutures.add(executor.submit(new SplitCallable(range, masterRangeNodes.get(range), conf, session)));
}
else
{
TokenRange jobTokenRange = rangeToTokenRange(metadata, jobRange);
if (range.intersects(jobTokenRange))
{
for (TokenRange intersection: range.intersectWith(jobTokenRange))
{
// for each tokenRange, pick a live owner and ask it to compute bite-sized splits
splitfutures.add(executor.submit(new SplitCallable(intersection, masterRangeNodes.get(range), conf, session)));
}
}
}
}
// wait until we have all the results back
for (Future> futureInputSplits : splitfutures)
{
try
{
splits.addAll(futureInputSplits.get());
}
catch (Exception e)
{
throw new IOException("Could not get input splits", e);
}
}
}
finally
{
executor.shutdownNow();
}
assert splits.size() > 0;
Collections.shuffle(splits, new Random(System.nanoTime()));
return splits;
}
private TokenRange rangeToTokenRange(Metadata metadata, Range range)
{
return metadata.newTokenRange(metadata.newToken(partitioner.getTokenFactory().toString(range.left)),
metadata.newToken(partitioner.getTokenFactory().toString(range.right)));
}
private Map getSubSplits(String keyspace, String cfName, TokenRange range, Configuration conf, Session session)
{
int splitSize = ConfigHelper.getInputSplitSize(conf);
int splitSizeMb = ConfigHelper.getInputSplitSizeInMb(conf);
try
{
return describeSplits(keyspace, cfName, range, splitSize, splitSizeMb, session);
}
catch (Exception e)
{
throw new RuntimeException(e);
}
}
private Map> getRangeMap(String keyspace, Metadata metadata)
{
return metadata.getTokenRanges()
.stream()
.collect(toMap(p -> p, p -> metadata.getReplicas('"' + keyspace + '"', p)));
}
private Map describeSplits(String keyspace, String table, TokenRange tokenRange, int splitSize, int splitSizeMb, Session session)
{
String query = String.format("SELECT mean_partition_size, partitions_count " +
"FROM %s.%s " +
"WHERE keyspace_name = ? AND table_name = ? AND range_start = ? AND range_end = ?",
SchemaConstants.SYSTEM_KEYSPACE_NAME,
SystemKeyspace.SIZE_ESTIMATES);
ResultSet resultSet = session.execute(query, keyspace, table, tokenRange.getStart().toString(), tokenRange.getEnd().toString());
Row row = resultSet.one();
long meanPartitionSize = 0;
long partitionCount = 0;
int splitCount = 0;
if (row != null)
{
meanPartitionSize = row.getLong("mean_partition_size");
partitionCount = row.getLong("partitions_count");
splitCount = splitSizeMb > 0
? (int)(meanPartitionSize * partitionCount / splitSizeMb / 1024 / 1024)
: (int)(partitionCount / splitSize);
}
// If we have no data on this split or the size estimate is 0,
// return the full split i.e., do not sub-split
// Assume smallest granularity of partition count available from CASSANDRA-7688
if (splitCount == 0)
{
Map wrappedTokenRange = new HashMap<>();
wrappedTokenRange.put(tokenRange, (long) 128);
return wrappedTokenRange;
}
List splitRanges = tokenRange.splitEvenly(splitCount);
Map rangesWithLength = new HashMap<>();
for (TokenRange range : splitRanges)
rangesWithLength.put(range, partitionCount/splitCount);
return rangesWithLength;
}
// Old Hadoop API
public InputSplit[] getSplits(JobConf jobConf, int numSplits) throws IOException
{
TaskAttemptContext tac = HadoopCompat.newTaskAttemptContext(jobConf, new TaskAttemptID());
List newInputSplits = this.getSplits(tac);
InputSplit[] oldInputSplits = new InputSplit[newInputSplits.size()];
for (int i = 0; i < newInputSplits.size(); i++)
oldInputSplits[i] = (ColumnFamilySplit)newInputSplits.get(i);
return oldInputSplits;
}
/**
* Gets a token tokenRange and splits it up according to the suggested
* size into input splits that Hadoop can use.
*/
class SplitCallable implements Callable>
{
private final TokenRange tokenRange;
private final Set hosts;
private final Configuration conf;
private final Session session;
public SplitCallable(TokenRange tr, Set hosts, Configuration conf, Session session)
{
this.tokenRange = tr;
this.hosts = hosts;
this.conf = conf;
this.session = session;
}
public List call() throws Exception
{
ArrayList splits = new ArrayList<>();
Map subSplits;
subSplits = getSubSplits(keyspace, cfName, tokenRange, conf, session);
// turn the sub-ranges into InputSplits
String[] endpoints = new String[hosts.size()];
// hadoop needs hostname, not ip
int endpointIndex = 0;
for (Host endpoint : hosts)
endpoints[endpointIndex++] = endpoint.getAddress().getHostName();
boolean partitionerIsOpp = partitioner instanceof OrderPreservingPartitioner || partitioner instanceof ByteOrderedPartitioner;
for (Map.Entry subSplitEntry : subSplits.entrySet())
{
List ranges = subSplitEntry.getKey().unwrap();
for (TokenRange subrange : ranges)
{
ColumnFamilySplit split =
new ColumnFamilySplit(
partitionerIsOpp ?
subrange.getStart().toString().substring(2) : subrange.getStart().toString(),
partitionerIsOpp ?
subrange.getEnd().toString().substring(2) : subrange.getEnd().toString(),
subSplitEntry.getValue(),
endpoints);
logger.trace("adding {}", split);
splits.add(split);
}
}
return splits;
}
}
}
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