本文主要是介绍MapReduce生成HFile入库到HBase及源码分析，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
原文：
http://blog.pureisle.net/archives/1950.html
如果我们一次性入库hbase巨量数据，处理速度慢不说，还特别占用Region资源， 一个比较高效便捷的方法就是使用 “Bulk Loading”方法，即hbase提供的HFileOutputFormat类。
它是利用hbase的数据信息按照特定格式存储在hdfs内这一原理，直接生成这种hdfs内存储的数据格式文件，然后上传至合适位置，即完成巨量数据快速入库的办法。配合mapreduce完成，高效便捷，而且不占用region资源，增添负载。
下边给出mapreduce程序样例,数据源是hbase，结果文件输出路径自定义：
public class HFileOutput {//job 配置
public static Job configureJob(Configuration conf) throws IOException {
Job job = new Job(configuration, "countUnite1");
job.setJarByClass(HFileOutput.class);
//job.setNumReduceTasks(2);
//job.setOutputKeyClass(ImmutableBytesWritable.class);
//job.setOutputValueClass(KeyValue.class);
//job.setOutputFormatClass(HFileOutputFormat.class);
Scan scan = new Scan();
scan.setCaching(10);
scan.addFamily(INPUT_FAMILY);
TableMapReduceUtil.initTableMapperJob(inputTable, scan,
HFileOutputMapper.class, ImmutableBytesWritable.class, LongWritable.class, job);
//这里如果不定义reducer部分，会自动识别定义成KeyValueSortReducer.class 和PutSortReducer.class
job.setReducerClass(HFileOutputRedcuer.class);
//job.setOutputFormatClass(HFileOutputFormat.class);
HFileOutputFormat.configureIncrementalLoad(job, new HTable(
configuration, outputTable));
HFileOutputFormat.setOutputPath(job, new Path());
//FileOutputFormat.setOutputPath(job, new Path()); //等同上句
return job;
}
public static class HFileOutputMapper extends
TableMapper<ImmutableBytesWritable, LongWritable> {
public void map(ImmutableBytesWritable key, Result values,
Context context) throws IOException, InterruptedException {
//mapper逻辑部分
context.write(new ImmutableBytesWritable(Bytes()), LongWritable());
}
}
public static class HFileOutputRedcuer extends
Reducer<ImmutableBytesWritable, LongWritable, ImmutableBytesWritable, KeyValue> {
public void reduce(ImmutableBytesWritable key, Iterable<LongWritable> values,
Context context) throws IOException, InterruptedException {
//reducer逻辑部分
KeyValue kv = new KeyValue(row, OUTPUT_FAMILY, tmp[1].getBytes(),
Bytes.toBytes(count));
context.write(key, kv);
}
}
}
这里需要注意的是无论是map还是reduce作为最终的输出结果，输出的key和value的类型应该是：< ImmutableBytesWritable, KeyValue> 或者< ImmutableBytesWritable, Put>。否则报这样的错误：
java.lang.IllegalArgumentException: Can't read partitions file
...
Caused by: java.io.IOException: wrong key class: org.apache.hadoop.io.*** is not class org.apache.hadoop.hbase.io.ImmutableBytesWritable
上边配置部分，注释掉的其实写不写都无所谓，因为看源码（最后有贴出源码）就知道configureIncrementalLoad方法已经把固定的配置全配置完了，不固定的需要手动配置。setNumReduceTasks设置是根据region个数自动配置的。
生成的文件入库代码为：
public class TestLoadIncrementalHFileToHBase {public static void main(String[] args) throws IOException {
Configuration conf = HBaseConfiguration.create();
byte[] TABLE = Bytes.toBytes(args[0]);
HTable table = new HTable(TABLE);
LoadIncrementalHFiles loader = new LoadIncrementalHFiles(conf);
loader.doBulkLoad(new Path(args[1]), table);
}
}
另外hbase有打包好的入库jar包使用方法：
hadoop jar hbase-VERSION.jar completebulkload /myoutput mytable；
最后就是你执行你的程序时有可能遇到这样的问题：
FAILED Error: java.lang.ClassNotFoundException: com.google.common.util.concurrent.ThreadFactoryBuilder
就是你需要添加一个jar包，位置在HBASE_HOME/bin/guava-r09.jar ，添加上就OK了。
下边把HFileOutputFormat类的源码贴出来看一看：
/*** Writes HFiles. Passed KeyValues must arrive in order.
* Currently, can only write files to a single column family at a
* time. Multiple column families requires coordinating keys cross family.
* Writes current time as the sequence id for the file. Sets the major compacted
* attribute on created hfiles. Calling write(null,null) will forceably roll
* all HFiles being written.
* @see KeyValueSortReducer
*/
public class HFileOutputFormat extends FileOutputFormat<ImmutableBytesWritable, KeyValue> {
static Log LOG = LogFactory.getLog(HFileOutputFormat.class);
static final String COMPRESSION_CONF_KEY = "hbase.hfileoutputformat.families.compression";
TimeRangeTracker trt = new TimeRangeTracker();
public RecordWriter<ImmutableBytesWritable, KeyValue> getRecordWriter(final TaskAttemptContext context)
throws IOException, InterruptedException {
// Get the path of the temporary output file
final Path outputPath = FileOutputFormat.getOutputPath(context);
final Path outputdir = new FileOutputCommitter(outputPath, context).getWorkPath();
final Configuration conf = context.getConfiguration();
final FileSystem fs = outputdir.getFileSystem(conf);
// These configs. are from hbase-*.xml
final long maxsize = conf.getLong("hbase.hregion.max.filesize",
HConstants.DEFAULT_MAX_FILE_SIZE);
final int blocksize = conf.getInt("hbase.mapreduce.hfileoutputformat.blocksize",
HFile.DEFAULT_BLOCKSIZE);
// Invented config. Add to hbase-*.xml if other than default compression.
final String defaultCompression = conf.get("hfile.compression",
Compression.Algorithm.NONE.getName());
// create a map from column family to the compression algorithm
final Map<byte[], String> compressionMap = createFamilyCompressionMap(conf);
return new RecordWriter<ImmutableBytesWritable, KeyValue>() {
// Map of families to writers and how much has been output on the writer.
private final Map<byte [], WriterLength> writers =
new TreeMap<byte [], WriterLength>(Bytes.BYTES_COMPARATOR);
private byte [] previousRow = HConstants.EMPTY_BYTE_ARRAY;
private final byte [] now = Bytes.toBytes(System.currentTimeMillis());
private boolean rollRequested = false;
public void write(ImmutableBytesWritable row, KeyValue kv)
throws IOException {
// null input == user explicitly wants to flush
if (row == null && kv == null) {
rollWriters();
return;
}
byte [] rowKey = kv.getRow();
long length = kv.getLength();
byte [] family = kv.getFamily();
WriterLength wl = this.writers.get(family);
// If this is a new column family, verify that the directory exists
if (wl == null) {
fs.mkdirs(new Path(outputdir, Bytes.toString(family)));
}
// If any of the HFiles for the column families has reached
// maxsize, we need to roll all the writers
if (wl != null && wl.written + length >= maxsize) {
this.rollRequested = true;
}
// This can only happen once a row is finished though
if (rollRequested && Bytes.compareTo(this.previousRow, rowKey) != 0) {
rollWriters();
}
// create a new HLog writer, if necessary
if (wl == null || wl.writer == null) {
wl = getNewWriter(family, conf);
}
// we now have the proper HLog writer. full steam ahead
kv.updateLatestStamp(this.now);
trt.includeTimestamp(kv);
wl.writer.append(kv);
wl.written += length;
// Copy the row so we know when a row transition.
this.previousRow = rowKey;
}
private void rollWriters() throws IOException {
for (WriterLength wl : this.writers.values()) {
if (wl.writer != null) {
LOG.info("Writer=" + wl.writer.getPath() +
((wl.written == 0)? "": ", wrote=" + wl.written));
close(wl.writer);
}
wl.writer = null;
wl.written = 0;
}
this.rollRequested = false;
}
/* Create a new HFile.Writer.
* @param family
* @return A WriterLength, containing a new HFile.Writer.
* @throws IOException
*/
private WriterLength getNewWriter(byte[] family, Configuration conf)
throws IOException {
WriterLength wl = new WriterLength();
Path familydir = new Path(outputdir, Bytes.toString(family));
String compression = compressionMap.get(family);
compression = compression == null ? defaultCompression : compression;
wl.writer =
HFile.getWriterFactory(conf).createWriter(fs,
StoreFile.getUniqueFile(fs, familydir), blocksize,
compression, KeyValue.KEY_COMPARATOR);
this.writers.put(family, wl);
return wl;
}
private void close(final HFile.Writer w) throws IOException {
if (w != null) {
w.appendFileInfo(StoreFile.BULKLOAD_TIME_KEY,
Bytes.toBytes(System.currentTimeMillis()));
w.appendFileInfo(StoreFile.BULKLOAD_TASK_KEY,
Bytes.toBytes(context.getTaskAttemptID().toString()));
w.appendFileInfo(StoreFile.MAJOR_COMPACTION_KEY,
Bytes.toBytes(true));
w.appendFileInfo(StoreFile.TIMERANGE_KEY,
WritableUtils.toByteArray(trt));
w.close();
}
}
public void close(TaskAttemptContext c)
throws IOException, InterruptedException {
for (WriterLength wl: this.writers.values()) {
close(wl.writer);
}
}
};
}
/*
* Data structure to hold a Writer and amount of data written on it.
*/
static class WriterLength {
long written = 0;
HFile.Writer writer = null;
}
/**
* Return the start keys of all of the regions in this table,
* as a list of ImmutableBytesWritable.
*/
private static List<ImmutableBytesWritable> getRegionStartKeys(HTable table)
throws IOException {
byte[][] byteKeys = table.getStartKeys();
ArrayList<ImmutableBytesWritable> ret =
new ArrayList<ImmutableBytesWritable>(byteKeys.length);
for (byte[] byteKey : byteKeys) {
ret.add(new ImmutableBytesWritable(byteKey));
}
return ret;
}
/**
* Write out a SequenceFile that can be read by TotalOrderPartitioner
* that contains the split points in startKeys.
* @param partitionsPath output path for SequenceFile
* @param startKeys the region start keys
*/
private static void writePartitions(Configuration conf, Path partitionsPath,
List<ImmutableBytesWritable> startKeys) throws IOException {
if (startKeys.isEmpty()) {
throw new IllegalArgumentException("No regions passed");
}
// We're generating a list of split points, and we don't ever
// have keys < the first region (which has an empty start key)
// so we need to remove it. Otherwise we would end up with an
// empty reducer with index 0
TreeSet<ImmutableBytesWritable> sorted =
new TreeSet<ImmutableBytesWritable>(startKeys);
ImmutableBytesWritable first = sorted.first();
if (!first.equals(HConstants.EMPTY_BYTE_ARRAY)) {
throw new IllegalArgumentException(
"First region of table should have empty start key. Instead has: "
+ Bytes.toStringBinary(first.get()));
}
sorted.remove(first);
// Write the actual file
FileSystem fs = partitionsPath.getFileSystem(conf);
SequenceFile.Writer writer = SequenceFile.createWriter(fs,
conf, partitionsPath, ImmutableBytesWritable.class, NullWritable.class);
try {
for (ImmutableBytesWritable startKey : sorted) {
writer.append(startKey, NullWritable.get());
}
} finally {
writer.close();
}
}
/**
* Configure a MapReduce Job to perform an incremental load into the given
* table. This
* <ul>
* <li>Inspects the table to configure a total order partitioner</li>
* <li>Uploads the partitions file to the cluster and adds it to the DistributedCache</li>
* <li>Sets the number of reduce tasks to match the current number of regions</li>
* <li>Sets the output key/value class to match HFileOutputFormat's requirements</li>
* <li>Sets the reducer up to perform the appropriate sorting (either KeyValueSortReducer or
* PutSortReducer)</li>
* </ul>
* The user should be sure to set the map output value class to either KeyValue or Put before
* running this function.
*/
public static void configureIncrementalLoad(Job job, HTable table)
throws IOException {
Configuration conf = job.getConfiguration();
Class<? extends Partitioner> topClass;
try {
topClass = getTotalOrderPartitionerClass();
} catch (ClassNotFoundException e) {
throw new IOException("Failed getting TotalOrderPartitioner", e);
}
job.setPartitionerClass(topClass);
job.setOutputKeyClass(ImmutableBytesWritable.class);
job.setOutputValueClass(KeyValue.class);
job.setOutputFormatClass(HFileOutputFormat.class);
// Based on the configured map output class, set the correct reducer to properly
// sort the incoming values.
// TODO it would be nice to pick one or the other of these formats.
if (KeyValue.class.equals(job.getMapOutputValueClass())) {
job.setReducerClass(KeyValueSortReducer.class);
} else if (Put.class.equals(job.getMapOutputValueClass())) {
job.setReducerClass(PutSortReducer.class);
} else {
LOG.warn("Unknown map output value type:" + job.getMapOutputValueClass());
}
LOG.info("Looking up current regions for table " + table);
List<ImmutableBytesWritable> startKeys = getRegionStartKeys(table);
LOG.info("Configuring " + startKeys.size() + " reduce partitions " +
"to match current region count");
job.setNumReduceTasks(startKeys.size());
Path partitionsPath = new Path(job.getWorkingDirectory(),
"partitions_" + System.currentTimeMillis());
LOG.info("Writing partition information to " + partitionsPath);
FileSystem fs = partitionsPath.getFileSystem(conf);
writePartitions(conf, partitionsPath, startKeys);
partitionsPath.makeQualified(fs);
URI cacheUri;
try {
// Below we make explicit reference to the bundled TOP. Its cheating.
// We are assume the define in the hbase bundled TOP is as it is in
// hadoop (whether 0.20 or 0.22, etc.)
cacheUri = new URI(partitionsPath.toString() + "#" +
org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.DEFAULT_PATH);
} catch (URISyntaxException e) {
throw new IOException(e);
}
DistributedCache.addCacheFile(cacheUri, conf);
DistributedCache.createSymlink(conf);
// Set compression algorithms based on column families
configureCompression(table, conf);
TableMapReduceUtil.addDependencyJars(job);
LOG.info("Incremental table output configured.");
}
/**
* If > hadoop 0.20, then we want to use the hadoop TotalOrderPartitioner.
* If 0.20, then we want to use the TOP that we have under hadoopbackport.
* This method is about hbase being able to run on different versions of
* hadoop. In 0.20.x hadoops, we have to use the TOP that is bundled with
* hbase. Otherwise, we use the one in Hadoop.
* @return Instance of the TotalOrderPartitioner class
* @throws ClassNotFoundException If can't find a TotalOrderPartitioner.
*/
private static Class<? extends Partitioner> getTotalOrderPartitionerClass()
throws ClassNotFoundException {
Class<? extends Partitioner> clazz = null;
try {
clazz = (Class<? extends Partitioner>) Class.forName("org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner");
} catch (ClassNotFoundException e) {
clazz =
(Class<? extends Partitioner>) Class.forName("org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner");
}
return clazz;
}
/**
* Run inside the task to deserialize column family to compression algorithm
* map from the
* configuration.
*
* Package-private for unit tests only.
*
* @return a map from column family to the name of the configured compression
* algorithm
*/
static Map<byte[], String> createFamilyCompressionMap(Configuration conf) {
Map<byte[], String> compressionMap = new TreeMap<byte[], String>(Bytes.BYTES_COMPARATOR);
String compressionConf = conf.get(COMPRESSION_CONF_KEY, "");
for (String familyConf : compressionConf.split("&")) {
String[] familySplit = familyConf.split("=");
if (familySplit.length != 2) {
continue;
}
try {
compressionMap.put(URLDecoder.decode(familySplit[0], "UTF-8").getBytes(),
URLDecoder.decode(familySplit[1], "UTF-8"));
} catch (UnsupportedEncodingException e) {
// will not happen with UTF-8 encoding
throw new AssertionError(e);
}
}
return compressionMap;
}
/**
* Serialize column family to compression algorithm map to configuration.
* Invoked while configuring the MR job for incremental load.
*
* Package-private for unit tests only.
*
* @throws IOException
* on failure to read column family descriptors
*/
static void configureCompression(HTable table, Configuration conf) throws IOException {
StringBuilder compressionConfigValue = new StringBuilder();
HTableDescriptor tableDescriptor = table.getTableDescriptor();
if(tableDescriptor == null){
// could happen with mock table instance
return;
}
Collection<HColumnDescriptor> families = tableDescriptor.getFamilies();
int i = 0;
for (HColumnDescriptor familyDescriptor : families) {
if (i++ > 0) {
compressionConfigValue.append('&');
}
compressionConfigValue.append(URLEncoder.encode(familyDescriptor.getNameAsString(), "UTF-8"));
compressionConfigValue.append('=');
compressionConfigValue.append(URLEncoder.encode(familyDescriptor.getCompression().getName(), "UTF-8"));
}
// Get rid of the last ampersand
conf.set(COMPRESSION_CONF_KEY, compressionConfigValue.toString());
}
}
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