Bulkload Hive表到HBase
1.描述
HBase可以随机读写海量的数据，但是如果把这海量数据导入到HBase却是一个挑战。如，将Hive表尽可能快的导入到HBase中。这里有以下三种解决方案：
- 使用API把数据一条一条地写入HBase。
- 用HBaseIntegration方法。
- 使用HBase自带的Bulkload功能。
但是，第一种方法明显是最低效的；第二种方法我之前已经提到过，同样比较慢；那么，第三种方法呢？毋庸置疑，Bulkload方法是最高效的解决方案。因为Bulkload方法可以让你绕过创建HFile（HBase Data Files），而是直接把已经生成的HFile拷贝到HDFS上。如果你的Hive和HBase部署在同一个集群上时，Bulkload的处理过程包括两个步骤：
- 用MapReduce生成HFile。
- 使用LoadIncrementalHFiles.doBulkLoad将HFile导入到HBase。
public class Driver extends Configured implements Tool{
private static Configuration conf = new Configuration();
private static Configuration hconf = null;
private static HBaseAdmin hadmin = null;
public static void connectHBase(){
final String HBASE_CONFIG_ZOOKEEPER_CLIENT = "hbase.zookeeper.property.clientPort";
final String HBASE_ZOOKEEPER_CLIENT_PORT = "2181";
final String HBASE_CONFIG_ZOOKEEPER_QUORUM = "hbase.zookeeper.quorum";
final String HBASE_ZOOKEEPER_SERVER = "hbase38,hbase43,hbase00";
conf.set(HBASE_CONFIG_ZOOKEEPER_CLIENT, HBASE_ZOOKEEPER_CLIENT_PORT);
conf.set(HBASE_CONFIG_ZOOKEEPER_QUORUM, HBASE_ZOOKEEPER_SERVER);
hconf = HBaseConfiguration.create(conf);
try{
hadmin = new HBaseAdmin(hconf);
}
catch (Exception e){
e.printStackTrace();
}
}
public static void main(String[] args)throws Exception{
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if(otherArgs.length != 4){
System.err.println("Usage: ");
System.exit(1);
}
String path = System.getProperty("user.dir") + otherArgs[2];
List fieldNames = HiveTableUtils.getFieldName(path);
StringBuilder sb = new StringBuilder(fieldNames.get(0));
int size = fieldNames.size();
for(int i = 1; i < size; i++){
sb.append(":").append(fieldNames.get(i));
}
conf.set("schema", sb.toString());
if(ToolRunner.run(conf, new Driver(), otherArgs) == 0){
// Importing the generated HFiles into a HBase table
LoadIncrementalHFiles loader = new LoadIncrementalHFiles(conf);
loader.doBulkLoad(new Path(otherArgs[1], otherArgs[3]);
System.exit(0);
}
else{
System.exit(1);
}
}
@SuppressWarnings("deprecation")
@Override
public int run(String[] strings) throws Exception {
Configuration config = getConf();
Driver.connectHBase();
Job job = new Job(config, "RCFile to HFile");
job.setJarByClass(Driver.class);
job.setMapperClass(RCFileToHFile.ParseMapper.class);
job.setMapOutputKeyClass(ImmutableBytesWritable.class);
job.setMapOutputValueClass(KeyValue.class);
//Reduce's number is 0.
job.setNumReduceTasks(0);
job.setPartitionerClass(SimpleTotalOrderPartitioner.class);
job.setInputFormatClass(RCFileMapReduceInputFormat.class);
// job.setOutputFormatClass(HFileOutputFormat.class);
HTable table = new HTable(config, strings[3]);
HFileOutputFormat.configureIncrementalLoad(job, table);
RCFileMapReduceInputFormat.addInputPath(job, new Path(strings[0]));
FileOutputFormat.setOutputPath(job, new Path(strings[1]));
return job.waitForCompletion(true) ? 0 : 1;
}
} - 用MapReduce生成HFile。
- 将已生成的HFile从Hive集群拷贝到HBase集群。
- 用HBase的命令将HFile导入到HBase中（你也可以用Java代码如上述代码，这样就相对复杂了）。
我们先看第一步的代码：
public class Driver extends Configured implements Tool{
private static Configuration conf = new Configuration();
private static Configuration hconf = null;
private static HBaseAdmin hadmin = null;
public static void connectHBase(){
final String HBASE_CONFIG_ZOOKEEPER_CLIENT = "hbase.zookeeper.property.clientPort";
final String HBASE_ZOOKEEPER_CLIENT_PORT = "2181";
final String HBASE_CONFIG_ZOOKEEPER_QUORUM = "hbase.zookeeper.quorum";
final String HBASE_ZOOKEEPER_SERVER = "hbase38,hbase43,hbase00";
conf.set(HBASE_CONFIG_ZOOKEEPER_CLIENT, HBASE_ZOOKEEPER_CLIENT_PORT);
conf.set(HBASE_CONFIG_ZOOKEEPER_QUORUM, HBASE_ZOOKEEPER_SERVER);
hconf = HBaseConfiguration.create(conf);
try{
hadmin = new HBaseAdmin(hconf);
}
catch (Exception e){
e.printStackTrace();
}
}
public static void main(String[] args)throws Exception{
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if(otherArgs.length != 4){
System.err.println("Usage: ");
System.exit(1);
}
String path = System.getProperty("user.dir") + otherArgs[2];
List fieldNames = HiveTableUtils.getFieldName(path);
StringBuilder sb = new StringBuilder(fieldNames.get(0));
int size = fieldNames.size();
for(int i = 1; i < size; i++){
sb.append(":").append(fieldNames.get(i));
}
conf.set("schema", sb.toString());
System.exit(ToolRunner.run(conf, new Driver(), otherArgs));
}
@SuppressWarnings("deprecation")
@Override
public int run(String[] strings) throws Exception {
Configuration config = getConf();
Driver.connectHBase();
Job job = new Job(config, "RCFile to HFile");
job.setJarByClass(Driver.class);
job.setMapperClass(RCFileToHFile.ParseMapper.class);
job.setMapOutputKeyClass(ImmutableBytesWritable.class);
job.setMapOutputValueClass(KeyValue.class);
//Reduce's number is 0.
job.setNumReduceTasks(0);
job.setPartitionerClass(SimpleTotalOrderPartitioner.class);
job.setInputFormatClass(RCFileMapReduceInputFormat.class);
// job.setOutputFormatClass(HFileOutputFormat.class);
HTable table = new HTable(config, strings[3]);
HFileOutputFormat.configureIncrementalLoad(job, table);
RCFileMapReduceInputFormat.addInputPath(job, new Path(strings[0]));
FileOutputFormat.setOutputPath(job, new Path(strings[1]));
return job.waitForCompletion(true) ? 0 : 1;
}
} 第二步，拷贝命令 distcp：
# Distributed copy HFile to mycluster-hbase.
hadoop distcp hdfs://mycluster-hive/hfile/hbase hdfs://mycluster-hbase/hbase/test第三步，Bulkload：
# BulkLoad HFile into hbase table on mycluster-hbase.
hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles /hbase/test hbase_table2.生成HFile
MapReduce的作用是生成HFile。由于Hive采用的是RCFile的存储结构，所以我的MapReduce的输入数据是RCFile文件，你可以查看
MapReduce读/写RCFile文件了解更多细节。值得一提的是，在生成HFile之前，你需要得到Hive表的schema，即字段名称。你可以通过以下方式得到Hive表的字段名。
- 解析存储Hive表元数据的文件。
- 获取Hive表的元数据，
- 访问MySQL
- 用HCatalog访问Hive表
但是，我个人认为解析文件的方式更为高效，因为我们的Hive的metadata一般有数千列。
public class HiveTableUtils {
//Gain hive table columns by parsing file.
public static List getFieldName(String filePath){
File file = new File(filePath);
BufferedReader reader = null;
List fieldName = new ArrayList();
try {
if (file.exists()) {
reader = new BufferedReader(new FileReader(file));
String tmp = null;
while ((tmp = reader.readLine()) != null) {
if (tmp.contains("`") && tmp.contains("COMMENT")) {
int start = tmp.indexOf("`");
int end = tmp.lastIndexOf("`");
fieldName.add(tmp.substring(start + 1, end));
}
}
} else {
System.err.println("The file doesn't exist!");
System.exit(1);
}
reader.close();
}
catch (Exception e) {
e.printStackTrace();
}
return fieldName;
}
至于Mapper的输入类型，就是
ImmutableBytesWritable, KeyValue。这些类使用了随后的Partitioner和Reducer来创建HFile。当然，你不需要实现自己的Reducer，代码中的
HFileOutputFormat.configureIncrementalLoad()会自动为你设置合理的Partitioner和Reducer。
public class RCFileToHFile {
public static class ParseMapper extends Mapper{
// private List fieldName = null;
private String[] fieldName = null;
@Override
protected void setup(Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
super.setup(context);
Configuration conf = context.getConfiguration();
String schema = conf.get("schema");
fieldName = schema.split(":");
// fieldName = new ArrayList();
// fieldName.add("id");
// fieldName.add("name");
// fieldName.add("age");
}
@Override
protected void map(LongWritable key, BytesRefArrayWritable values,
Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
Text line = new Text();
List fields = new ArrayList();
int size = values.size();
for(int i = 0; i < size; i++){
BytesRefWritable value = values.get(i);
line.set(value.getData(), value.getStart(), value.getLength());
fields.add(line.toString());
}
String rowKey = fields.get(0);
String columnFamily = "cf";
int length = fieldName.length;
ImmutableBytesWritable hKey = new ImmutableBytesWritable();
hKey.set(rowKey.getBytes());
KeyValue kv = null;
for(int i = 1; i < length; i++){
kv = new KeyValue(hKey.get(), columnFamily.getBytes(), fieldName[i].getBytes(), fields.get(i).getBytes());
context.write(hKey, kv);
}
}
}
} 注意：
- 生成HFile时，连接的HBase表必须存在。
- 连接的HBase表，可以与最后Bulkload的目标HBase表可以不同名，但是表的结构必须相同（如，Column Family的个数）。
- 版本之间的匹配。
完整的代码托管在GitHub中： https://github.com/GatsbyNewton/hive-bulkload-hbase