我想将1GB（1000万条记录）CSV文件加载到Hbase中。我为它写了Map-Reduce程序。我的代码工作正常但需要1小时才能完成。最后一个减速机需要超过半小时的时间。有人可以帮帮我吗？
我的代码如下：
Driver.Java
package com.cloudera.examples.hbase.bulkimport;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.KeyValue;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.HFileOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
/**
* HBase bulk import example
* Data preparation MapReduce job driver
*
* - args[0]: HDFS input path
*
- args[1]: HDFS output path
*
- args[2]: HBase table name
*
*/
public class Driver {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
/*
* NBA Final 2010 game 1 tip-off time (seconds from epoch)
* Thu, 03 Jun 2010 18:00:00 PDT
*/
// conf.setInt("epoch.seconds.tipoff", 1275613200);
conf.set("hbase.table.name", args[2]);
// Load hbase-site.xml
HBaseConfiguration.addHbaseResources(conf);
Job job = new Job(conf, "HBase Bulk Import Example");
job.setJarByClass(HBaseKVMapper.class);
job.setMapperClass(HBaseKVMapper.class);
job.setMapOutputKeyClass(ImmutableBytesWritable.class);
job.setMapOutputValueClass(KeyValue.class);
job.setInputFormatClass(TextInputFormat.class);
HTable hTable = new HTable(conf, args[2]);
// Auto configure partitioner and reducer
HFileOutputFormat.configureIncrementalLoad(job, hTable);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);
// Load generated HFiles into table
// LoadIncrementalHFiles loader = new LoadIncrementalHFiles(conf);
// loader.doBulkLoad(new Path(args[1]), hTable);
}
}
HColumnEnum.java
package com.cloudera.examples.hbase.bulkimport;
/**
* HBase table columns for the 'srv' column family
*/
public enum HColumnEnum {
SRV_COL_employeeid ("employeeid".getBytes()),
SRV_COL_eventdesc ("eventdesc".getBytes()),
SRV_COL_eventdate ("eventdate".getBytes()),
SRV_COL_objectname ("objectname".getBytes()),
SRV_COL_objectfolder ("objectfolder".getBytes()),
SRV_COL_ipaddress ("ipaddress".getBytes());
private final byte[] columnName;
HColumnEnum (byte[] column) {
this.columnName = column;
}
public byte[] getColumnName() {
return this.columnName;
}
}
HBaseKVMapper.java
package com.cloudera.examples.hbase.bulkimport;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.KeyValue;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import au.com.bytecode.opencsv.CSVParser;
/**
* HBase bulk import example
* <p>
* Parses Facebook and Twitter messages from CSV files and outputs
* <ImmutableBytesWritable, KeyValue>.
* <p>
* The ImmutableBytesWritable key is used by the TotalOrderPartitioner to map it
* into the correct HBase table region.
* <p>
* The KeyValue value holds the HBase mutation information (column family,
* column, and value)
*/
public class HBaseKVMapper extends
Mapper<LongWritable, Text, ImmutableBytesWritable, KeyValue> {
final static byte[] SRV_COL_FAM = "srv".getBytes();
final static int NUM_FIELDS = 6;
CSVParser csvParser = new CSVParser();
int tipOffSeconds = 0;
String tableName = "";
// DateTimeFormatter p = DateTimeFormat.forPattern("MMM dd, yyyy HH:mm:ss")
// .withLocale(Locale.US).withZone(DateTimeZone.forID("PST8PDT"));
ImmutableBytesWritable hKey = new ImmutableBytesWritable();
KeyValue kv;
/** {@inheritDoc} */
@Override
protected void setup(Context context) throws IOException,
InterruptedException {
Configuration c = context.getConfiguration();
// tipOffSeconds = c.getInt("epoch.seconds.tipoff", 0);
tableName = c.get("hbase.table.name");
}
/** {@inheritDoc} */
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
/*if (value.find("Service,Term,") > -1) {
// Skip header
return;
}*/
String[] fields = null;
try {
fields = value.toString().split(",");
//csvParser.parseLine(value.toString());
} catch (Exception ex) {
context.getCounter("HBaseKVMapper", "PARSE_ERRORS").increment(1);
return;
}
if (fields.length != NUM_FIELDS) {
context.getCounter("HBaseKVMapper", "INVALID_FIELD_LEN").increment(1);
return;
}
// Get game offset in seconds from tip-off
/* DateTime dt = null;
try {
dt = p.parseDateTime(fields[9]);
} catch (Exception ex) {
context.getCounter("HBaseKVMapper", "INVALID_DATE").increment(1);
return;
}
int gameOffset = (int) ((dt.getMillis() / 1000) - tipOffSeconds);
String offsetForKey = String.format("%04d", gameOffset);
String username = fields[2];
if (username.equals("")) {
username = fields[3];
}*/
// Key: e.g. "1200:twitter:jrkinley"
hKey.set(String.format("%s|%s|%s|%s|%s|%s", fields[0], fields[1], fields[2],fields[3],fields[4],fields[5])
.getBytes());
// Service columns
if (!fields[0].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_employeeid.getColumnName(), fields[0].getBytes());
context.write(hKey, kv);
}
if (!fields[1].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_eventdesc.getColumnName(), fields[1].getBytes());
context.write(hKey, kv);
}
if (!fields[2].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_eventdate.getColumnName(), fields[2].getBytes());
context.write(hKey, kv);
}
if (!fields[3].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_objectname.getColumnName(), fields[3].getBytes());
context.write(hKey, kv);
}
if (!fields[4].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_objectfolder.getColumnName(), fields[4].getBytes());
context.write(hKey, kv);
}
if (!fields[5].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_ipaddress.getColumnName(), fields[5].getBytes());
context.write(hKey, kv);
}
context.getCounter("HBaseKVMapper", "NUM_MSGS").increment(1);
/*
* Output number of messages per quarter and before/after game. This should
* correspond to the number of messages per region in HBase
*/
/* if (gameOffset < 0) {
context.getCounter("QStats", "BEFORE_GAME").increment(1);
} else if (gameOffset < 900) {
context.getCounter("QStats", "Q1").increment(1);
} else if (gameOffset < 1800) {
context.getCounter("QStats", "Q2").increment(1);
} else if (gameOffset < 2700) {
context.getCounter("QStats", "Q3").increment(1);
} else if (gameOffset < 3600) {
context.getCounter("QStats", "Q4").increment(1);
} else {
context.getCounter("QStats", "AFTER_GAME").increment(1);
}*/
}
}
package com.cloudera.examples.hbase.bulkimport;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.KeyValue;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import au.com.bytecode.opencsv.CSVParser;
/**
* HBase bulk import example
* <p>
* Parses Facebook and Twitter messages from CSV files and outputs
* <ImmutableBytesWritable, KeyValue>.
* <p>
* The ImmutableBytesWritable key is used by the TotalOrderPartitioner to map it
* into the correct HBase table region.
* <p>
* The KeyValue value holds the HBase mutation information (column family,
* column, and value)
*/
public class HBaseKVMapper extends
Mapper<LongWritable, Text, ImmutableBytesWritable, KeyValue> {
final static byte[] SRV_COL_FAM = "srv".getBytes();
final static int NUM_FIELDS = 6;
CSVParser csvParser = new CSVParser();
int tipOffSeconds = 0;
String tableName = "";
// DateTimeFormatter p = DateTimeFormat.forPattern("MMM dd, yyyy HH:mm:ss")
// .withLocale(Locale.US).withZone(DateTimeZone.forID("PST8PDT"));
ImmutableBytesWritable hKey = new ImmutableBytesWritable();
KeyValue kv;
/** {@inheritDoc} */
@Override
protected void setup(Context context) throws IOException,
InterruptedException {
Configuration c = context.getConfiguration();
// tipOffSeconds = c.getInt("epoch.seconds.tipoff", 0);
tableName = c.get("hbase.table.name");
}
/** {@inheritDoc} */
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
/*if (value.find("Service,Term,") > -1) {
// Skip header
return;
}*/
String[] fields = null;
try {
fields = value.toString().split(",");
//csvParser.parseLine(value.toString());
} catch (Exception ex) {
context.getCounter("HBaseKVMapper", "PARSE_ERRORS").increment(1);
return;
}
if (fields.length != NUM_FIELDS) {
context.getCounter("HBaseKVMapper", "INVALID_FIELD_LEN").increment(1);
return;
}
// Get game offset in seconds from tip-off
/* DateTime dt = null;
try {
dt = p.parseDateTime(fields[9]);
} catch (Exception ex) {
context.getCounter("HBaseKVMapper", "INVALID_DATE").increment(1);
return;
}
int gameOffset = (int) ((dt.getMillis() / 1000) - tipOffSeconds);
String offsetForKey = String.format("%04d", gameOffset);
String username = fields[2];
if (username.equals("")) {
username = fields[3];
}*/
// Key: e.g. "1200:twitter:jrkinley"
hKey.set(String.format("%s|%s|%s|%s|%s|%s", fields[0], fields[1], fields[2],fields[3],fields[4],fields[5])
.getBytes());
// Service columns
if (!fields[0].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_employeeid.getColumnName(), fields[0].getBytes());
context.write(hKey, kv);
}
if (!fields[1].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_eventdesc.getColumnName(), fields[1].getBytes());
context.write(hKey, kv);
}
if (!fields[2].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_eventdate.getColumnName(), fields[2].getBytes());
context.write(hKey, kv);
}
if (!fields[3].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_objectname.getColumnName(), fields[3].getBytes());
context.write(hKey, kv);
}
if (!fields[4].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_objectfolder.getColumnName(), fields[4].getBytes());
context.write(hKey, kv);
}
if (!fields[5].equals("")) {
kv = new KeyValue(hKey.get(), SRV_COL_FAM,
HColumnEnum.SRV_COL_ipaddress.getColumnName(), fields[5].getBytes());
context.write(hKey, kv);
}
context.getCounter("HBaseKVMapper", "NUM_MSGS").increment(1);
/*
* Output number of messages per quarter and before/after game. This should
* correspond to the number of messages per region in HBase
*/
/* if (gameOffset < 0) {
context.getCounter("QStats", "BEFORE_GAME").increment(1);
} else if (gameOffset < 900) {
context.getCounter("QStats", "Q1").increment(1);
} else if (gameOffset < 1800) {
context.getCounter("QStats", "Q2").increment(1);
} else if (gameOffset < 2700) {
context.getCounter("QStats", "Q3").increment(1);
} else if (gameOffset < 3600) {
context.getCounter("QStats", "Q4").increment(1);
} else {
context.getCounter("QStats", "AFTER_GAME").increment(1);
}*/
}
}
请帮助我提高性能，如果您有任何替代解决方案，请告知我们。
我的mapred-site.xml
hbase-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!-- Put site-specific property overrides in this file. -->
<configuration>
<property>
<name>mapred.job.tracker</name>
<value>namenode:54311</value>
</property>
<property>
<name>mapred.reduce.parallel.copies</name>
<value>20</value>
</property>
<property>
<name>tasktracker.http.threads</name>
<value>50</value>
</property>
<property>
<name>mapred.job.shuffle.input.buffer.percent</name>
<value>0.70</value>
</property>
<property>
<name>mapred.tasktracker.map.tasks.maximum</name>
<value>4</value>
</property>
<property>
<name>mapred.tasktracker.reduce.tasks.maximum</name>
<value>4</value>
</property>
<property>
<name>mapred.map.tasks</name>
<value>4</value>
</property>
<property>
<name>reduce.map.tasks</name>
<value>4</value>
</property>
<property>
<name>mapred.job.shuffle.merge.percent</name>
<value>0.65</value>
</property>
<property>
<name>mapred.task.timeout</name>
<value>1200000</value>
</property>
<property>
<name>mapred.child.java.opts</name>
<value>-Xms1024M -Xmx2048M</value>
</property>
<property>
<name>mapred.job.reuse.jvm.num.tasks</name>
<value>-1</value>
</property>
<property>
<name>mapred.compress.map.output</name>
<value>true</value>
</property>
<property>
<name>mapred.map.output.compression.codec</name>
<value>com.hadoop.compression.lzo.LzoCodec</value>
</property>
<property>
<name>io.sort.mb</name>
<value>800</value>
</property>
<property>
<name>mapred.child.ulimit</name>
<value>unlimited</value>
</property>
<property>
<name>io.sort.factor</name>
<value>100</value>
<description>More streams merged at once while sorting files.</description>
</property>
<property>
<name>mapreduce.admin.map.child.java.opts</name>
<value>-Djava.net.preferIPv4Stack=true</value>
</property>
<property>
<name>mapreduce.admin.reduce.child.java.opts</name>
<value>-Djava.net.preferIPv4Stack=true</value>
</property>
<property>
<name>mapred.min.split.size</name>
<value>0</value>
</property>
<property>
<name>mapred.job.map.memory.mb</name>
<value>-1</value>
</property>
<property>
<name>mapred.jobtracker.maxtasks.per.job</name>
<value>-1</value>
</property>
</configuration>
请帮助我，这样我可以提高我的表现。
答案 0 :(得分：5)
首先，为什么我们需要Mapreduce程序将数据加载到Hbase中以获得如此小的文件（1GB）。
根据我的经验，我使用Jackson流处理5GB Json（我不想将所有json都记入内存）并在8分钟内通过使用批处理技术在Hbase中持续存在。
我在批量列表对象100000记录中使用了hbase put。
下面是我实现此目的的代码段。解析其他格式也可以做同样的事情）
可能需要在2个地方调用此方法
1）批量为100000条记录。
2）处理提醒您的批记录小于100000
public void addRecord(final ArrayList<Put> puts, final String tableName) throws Exception {
try {
final HTable table = new HTable(HBaseConnection.getHBaseConfiguration(), getTable(tableName));
table.put(puts);
LOG.info("INSERT record[s] " + puts.size() + " to table " + tableName + " OK.");
} catch (final Throwable e) {
e.printStackTrace();
} finally {
LOG.info("Processed ---> " + puts.size());
if (puts != null) {
puts.clear();
}
}
}
答案 1 :(得分：0)
我只创建了mapper类并采用了hbase输出格式类。现在它需要10分钟。我的网络速度非常慢，这就是为什么它需要很长时间。
答案 2 :(得分：0)
可以通过指定创建Hbase表时要使用的Region拆分数来进一步微调。由于批量加载的reducer实例数量也将取决于Regions的数量。这可以使用以下命令
完成hbase org.apache.hadoop.hbase.util.RegionSplitter -c <number of regions> -f <column families> <New Hbase Table Name> <splitAlgorithm>
对于拆分算法，可以指定
UniformSplit - 将密钥视为任意字节
HexStringSplit - 将密钥视为十六进制ASCII