Edits to the source table after the CopyTable starts are not copied, so you may need to do an additional CopyTable operation to copy new data into the destination table. Run CopyTable as follows, using --help to see details about possible parameters.

$ ./bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
Usage: CopyTable [general options] [--starttime=X] [--endtime=Y] [--new.name=NEW] [--peer.adr=ADR] <tablename>

The following example creates a new table using HBase Shell in non-interactive mode, and then copies data in two ColumnFamilies in rows starting with timestamp 1265875194289 and including the last row before the CopyTable started, to the new table.

$ echo create 'NewTestTable', 'cf1', 'cf2', 'cf3' | bin/hbase shell --non-interactive
$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --starttime=1265875194289  --families=cf1,cf2,cf3 --new.name=NewTestTable TestTable

HBase uses the well-known HFile format to store its data on disk. In many situations, writing HFiles programmatically with your data, and bulk-loading that data into HBase on the RegionServer, has advantages over other data ingest mechanisms. BulkLoad operations bypass the write path completely, providing the following benefits:

• The data is available to HBase immediately but does cause additional load or latency on the cluster when it appears.
• BulkLoad operations do not use the write-ahead log (WAL) and do not cause flushes or split storms.
• BulkLoad operations do not cause excessive garbage collection.
    Note: Because they bypass the WAL, BulkLoad operations are not propagated between clusters using replication. If you need the data on all replicated clusters, you must perform the BulkLoad on each cluster.

If you use BulkLoads with HBase, your workflow is similar to the following:

1. Extract your data from its existing source. For instance, if your data is in a MySQL database, you might run the mysqldump command. The process you use depends on your data. If your data is already in TSV or CSV format, skip this step and use the included ImportTsv utility to process your data into HFiles. See the ImportTsv documentation for details.
2. Process your data into HFile format. See http://hbase.apache.org/book.html#_hfile_format_2 for details about HFile format. Usually you use a MapReduce job for the conversion, and you often need to write the Mapper yourself because your data is unique. The job must to emit the row key as the Key, and either a KeyValue, a Put, or a Delete as the Value. The Reducer is handled by HBase; configure it using HFileOutputFormat.configureIncrementalLoad() and it does the following:
   • Inspects the table to configure a total order partitioner
   • Uploads the partitions file to the cluster and adds it to the DistributedCache
   • Sets the number of reduce tasks to match the current number of regions
   • Sets the output key/value class to match HFileOutputFormat requirements
   • Sets the Reducer to perform the appropriate sorting (either KeyValueSortReducer or PutSortReducer)
3. One HFile is created per region in the output folder. Input data is almost completely re-written, so you need available disk space at least twice the size of the original data set. For example, for a 100 GB output from mysqldump, you should have at least 200 GB of available disk space in HDFS. You can delete the original input file at the end of the process.
4. Load the files into HBase. Use the LoadIncrementalHFiles command (more commonly known as the completebulkload tool), passing it a URL that locates the files in HDFS. Each file is loaded into the relevant region on the RegionServer for the region. You can limit the number of versions that are loaded by passing the --versions= N option, where N is the maximum number of versions to include, from newest to oldest (largest timestamp to smallest timestamp).

   If a region was split after the files were created, the tool automatically splits the HFile according to the new boundaries. This process is inefficient, so if your table is being written to by other processes, you should load as soon as the transform step is done.

<property>
  <name>hbase.bulkload.staging.dir</name>
  <value>/tmp/hbase-staging</value>
</property>
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint</value>
</property>

To configure your clusters for replication, see HBase Replication and Configuring Secure HBase Replication. The following is a high-level overview of the steps to enable replication.

1. Configure and start the source and destination clusters.
2. Create tables with the same names and column families on both the source and destination clusters, so that the destination cluster knows where to store data it receives. All hosts in the source and destination clusters should be reachable to each other. See Creating the Empty Table On the Destination Cluster.
3. On the source cluster, enable replication in Cloudera Manager, or by setting hbase.replication to true in hbase-site.xml.
4. Obtain Kerberos credentials as the HBase principal. Substitute your fully.qualified.domain.name and realm in the following command:

   $ kinit -k -t /etc/hbase/conf/hbase.keytab hbase/fully.qualified.domain.name@YOUR-REALM.COM

5. On the source cluster, in HBase Shell, add the destination cluster as a peer, using the add_peer command. The syntax is as follows:

   add_peer 'ID', 'CLUSTER_KEY'

   The ID must be a short integer. To compose the CLUSTER_KEY, use the following template:

   hbase.zookeeper.quorum:hbase.zookeeper.property.clientPort:zookeeper.znode.parent

   If both clusters use the same ZooKeeper cluster, you must use a different zookeeper.znode.parent, because they cannot write in the same folder.

6. On the source cluster, configure each column family to be replicated by setting its REPLICATION_SCOPE to 1, using commands such as the following in HBase Shell.

   hbase> disable 'example_table'
   hbase> alter 'example_table', {NAME => 'example_family', REPLICATION_SCOPE => '1'}
   hbase> enable 'example_table'

7. Verify that replication is occurring by examining the logs on the source cluster for messages such as the following.

   Considering 1 rs, with ratio 0.1
   Getting 1 rs from peer cluster # 0
   Choosing peer 192.0.2.49:62020

8. To verify the validity of replicated data, use the included VerifyReplication MapReduce job on the source cluster, providing it with the ID of the replication peer and table name to verify. Other options are available, such as a time range or specific families to verify.

   The command has the following form:

   hbase org.apache.hadoop.hbase.mapreduce.replication.VerifyReplication [--starttime=timestamp1] [--stoptime=timestamp] [--families=comma separated list of families] <peerId> <tablename>

   The VerifyReplication command prints GOODROWS and BADROWS counters to indicate rows that did and did not replicate correctly.

Apache Pig is a platform for analyzing large data sets using a high-level language. Apache HCatalog is a sub-project of Apache Hive, which enables reading and writing of data from one Hadoop utility to another. You can use a combination of Pig and HCatalog to import data into HBase. The initial format of your data and other details about your infrastructure determine the steps you follow to accomplish this task. The following simple example assumes that you can get your data into a TSV (text-separated value) format, such as a tab-delimited or comma-delimited text file.

1. Format the data as a TSV file. You can work with other file formats; see the Pig and HCatalog project documentation for more details.

   The following example shows a subset of data from Google's NGram Dataset, which shows the frequency of specific phrases or letter-groupings found in publications indexed by Google. Here, the first column has been added to this dataset as the row ID. The first column is formulated by combining the n-gram itself (in this case, Zones) with the line number of the file in which it occurs (z_LINE_NUM). This creates a format such as "Zones_z_6230867." The second column is the n-gram itself, the third column is the year of occurrence, the fourth column is the frequency of occurrence of that Ngram in that year, and the fifth column is the number of distinct publications. This extract is from the z file of the 1-gram dataset from version 20120701. The data is truncated at the ... mark, for the sake of readability of this document. In most real-world scenarios, you will not work with tables that have five columns. Most HBase tables have one or two columns.

   Zones_z_6230867 Zones 1507 1 1
   Zones_z_6230868 Zones 1638 1 1
   Zones_z_6230869 Zones 1656 2 1
   Zones_z_6230870 Zones 1681 8 2
   ...
   Zones_z_6231150 Zones 1996 17868 4356
   Zones_z_6231151 Zones 1997 21296 4675
   Zones_z_6231152 Zones 1998 20365 4972
   Zones_z_6231153 Zones 1999 20288 5021
   Zones_z_6231154 Zones 2000 22996 5714
   Zones_z_6231155 Zones 2001 20469 5470
   Zones_z_6231156 Zones 2002 21338 5946
   Zones_z_6231157 Zones 2003 29724 6446
   Zones_z_6231158 Zones 2004 23334 6524
   Zones_z_6231159 Zones 2005 24300 6580
   Zones_z_6231160 Zones 2006 22362 6707
   Zones_z_6231161 Zones 2007 22101 6798
   Zones_z_6231162 Zones 2008 21037 6328

2. Using the hadoop fs command, put the data into HDFS. This example places the file into an /imported_data/ directory.

   $ hadoop fs -put zones_frequency.tsv /imported_data/

3. Create and register a new HBase table in HCatalog, using the hcat command, passing it a DDL file to represent your table. You could also register an existing HBase table, using the same command. The DDL file format is specified as part of the Hive REST API. The following example illustrates the basic mechanism.

   CREATE TABLE
   zones_frequency_table (id STRING, ngram STRING, year STRING, freq STRING, sources STRING)
   STORED BY 'org.apache.hcatalog.hbase.HBaseHCatStorageHandler'
   TBLPROPERTIES (
     'hbase.table.name' = 'zones_frequency_table',
     'hbase.columns.mapping' = 'd:ngram,d:year,d:freq,d:sources',
     'hcat.hbase.output.bulkMode' = 'true'
   );

   $ hcat -f zones_frequency_table.ddl

4. Create a Pig file to process the TSV file created in step 1, using the DDL file created in step 3. Modify the file names and other parameters in this command to match your values if you use data different from this working example. USING PigStorage('\t') indicates that the input file is tab-delimited. For more details about Pig syntax, see the Pig Latin reference documentation.

   A = LOAD 'hdfs:///imported_data/zones_frequency.tsv' USING PigStorage('\t') AS (id:chararray, ngram:chararray, year:chararray, freq:chararray, sources:chararray);
   -- DUMP A;
   STORE A INTO 'zones_frequency_table' USING org.apache.hcatalog.pig.HCatStorer();

   Save the file as zones.bulkload.pig.

5. Use the pig command to bulk-load the data into HBase.

   $ pig -useHCatalog zones.bulkload.pig

   The data is now in HBase and is available to use.

The Java API is the most common mechanism for getting data into HBase, through Put operations. The Thrift and REST APIs, as well as the HBase Shell, use the Java API. The following simple example ouses the Java API to put data into an HBase table. The Java API traverses the entire write path and can cause compactions and region splits, which can adversely affect performance.

...
  HTable table = null;
  try {
    table = myCode.createTable(tableName, fam);
    int i = 1;
    List<Put> puts = new ArrayList<Put>();
    for (String labelExp : labelExps) {
      Put put = new Put(Bytes.toBytes("row" + i));
      put.add(fam, qual, HConstants.LATEST_TIMESTAMP, value);
      puts.add(put);
      i++;
    }
    table.put(puts);
  } finally {
    if (table != null) {
      table.flushCommits();
    }
  }
...

After the Thrift server is configured and running, generate Thrift bindings for the language of your choice, using an IDL file. A HBase IDL file named HBase.thrift is included as part of HBase. After generating the bindings, copy the Thrift libraries for your language into the same directory as the generated bindings. In the following Python example, these libraries provide the thrift.transport and thrift.protocol libraries. These commands show how you might generate the Thrift bindings for Python and copy the libraries on a Linux system.

$ mkdir HBaseThrift
$ cd HBaseThrift/
$ thrift -gen py /path/to/Hbase.thrift
$ mv gen-py/* .
$ rm -rf gen-py/
$ mkdir thrift
$ cp -rp ~/Downloads/thrift-0.9.0/lib/py/src/* ./thrift/

The following example shows a simple Python application using the Thrift Proxy API.

from thrift.transport import TSocket
from thrift.protocol import TBinaryProtocol
from thrift.transport import TTransport
from hbase import Hbase

# Connect to HBase Thrift server
transport = TTransport.TBufferedTransport(TSocket.TSocket(host, port))
protocol = TBinaryProtocol.TBinaryProtocolAccelerated(transport)

# Create and open the client connection
client = Hbase.Client(protocol)
transport.open()

# Modify a single row
mutations = [Hbase.Mutation(
  column='columnfamily:columndescriptor', value='columnvalue')]
client.mutateRow('tablename', 'rowkey', mutations)

# Modify a batch of rows
# Create a list of mutations per work of Shakespeare
mutationsbatch = []

for line in myDataFile:
    rowkey = username + "-" + filename + "-" + str(linenumber).zfill(6)

    mutations = [
            Hbase.Mutation(column=messagecolumncf, value=line.strip()),
            Hbase.Mutation(column=linenumbercolumncf, value=encode(linenumber)),
            Hbase.Mutation(column=usernamecolumncf, value=username)
        ]

       mutationsbatch.append(Hbase.BatchMutation(row=rowkey,mutations=mutations))

# Run the mutations for all the lines in myDataFile
client.mutateRows(tablename, mutationsbatch)

transport.close()

• Using the HBase Thrift Interface, Part 1
• Using the HBase Thrift Interface, Part 2

$ curl -H "Content-Type: text/xml" http://localhost:8000/test/testrow/test:testcolumn

HBase2Sink:

#Use the HBase2Sink
host1.sinks.sink1.type = org.apache.flume.sink.hbase2.HBase2Sink
host1.sinks.sink1.channel = ch1
host1.sinks.sink1.table = transactions
host1.sinks.sink1.columnFamily = clients
host1.sinks.sink1.column = charges
host1.sinks.sink1.batchSize = 5000
#Use the SimpleHBase2EventSerializer that comes with Flume
host1.sinks.sink1.serializer = org.apache.flume.sink.hbase2.SimpleHBase2EventSerializer
host1.sinks.sink1.serializer.incrementColumn = icol
host1.channels.ch1.type=memory

The following serializer, based on an Apache Flume blog post by Dan Sandler, splits the event body based on a delimiter and inserts each split into a different column. The row is defined in the event header. When each event is received, a counter is incremented to track the number of events received.

/**
 * A serializer for the HBaseSink, which splits the event body into
 * multiple columns and inserts them into a row whose key is available in
 * the headers
 */
public class SplittingSerializer implements HBase2EventSerializer {
    private byte[] colFam;
    private Event currentEvent;
    private byte[][] columnNames;
    private final List<Row> puts = new ArrayList<>();
    private final List<Increment> incs = new ArrayList<>();
    private byte[] currentRowKey;
    private final byte[] eventCountCol = "eventCount".getBytes();

    @Override
    public void initialize(Event event, byte[] cf) {
        this.currentEvent = event;
        String rowKeyStr = currentEvent.getHeaders().get("rowKey");
        if (rowKeyStr == null) {
            throw new FlumeException("No row key found in headers!");
        }
        currentRowKey = rowKeyStr.getBytes();

        this.colFam = cf;
    }

    @Override
    public List<Row> getActions() {
        // Split the event body and get the values for the columns
        String eventStr = new String(currentEvent.getBody());
        String[] cols = eventStr.split(",");
        puts.clear();
        for (int i = 0; i < cols.length; i++) {
            //Generate a Put for each column.
            Put put = new Put(currentRowKey);
            put.addColumn(colFam, columnNames[i], cols[i].getBytes());
            puts.add(put);
        }
        return puts;
    }

    @Override
    public List<Increment> getIncrements() {
        incs.clear();
        //Increment the number of events received
        Increment inc = new Increment("totalEvents".getBytes());
        inc.addColumn(colFam, eventCountCol, 1);
        incs.add(inc);
        return incs;
    }

    @Override
    public void close() {
        colFam = null;
        currentEvent = null;
        columnNames = null;
        currentRowKey = null;
    }

    @Override
    public void configure(Context context) {
        //Get the column names from the configuration
        String cols = context.getString("columns");
        String[] names = cols.split(",");
        byte[][] columnNames = new byte[names.length][];
        int i = 0;
        for (String name : names) {
            columnNames[i++] = name.getBytes();
        }
        this.columnNames = columnNames;
    }

    @Override
    public void configure(ComponentConfiguration conf) {
    }
}

You can write data to HBase from Apache Spark by using def saveAsHadoopDataset(conf: JobConf): Unit. This example is adapted from a post on the spark-users mailing list.

// Note: mapred package is used, instead of the
// mapreduce package which contains new hadoop APIs.

import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.client
// ... some other settings

val conf = HBaseConfiguration.create()

// general hbase settings
conf.set("hbase.rootdir",
         "hdfs://" + nameNodeURL + ":" + hdfsPort + "/hbase")
conf.setBoolean("hbase.cluster.distributed", true)
conf.set("hbase.zookeeper.quorum", hostname)
conf.setInt("hbase.client.scanner.caching", 10000)
// ... some other settings

val jobConfig: JobConf = new JobConf(conf, this.getClass)

// Note:  TableOutputFormat is used as deprecated code
// because JobConf is an old hadoop API
jobConfig.setOutputFormat(classOf[TableOutputFormat])
jobConfig.set(TableOutputFormat.OUTPUT_TABLE, outputTable)

Next, provide the mapping between how the data looks in Spark and how it should look in HBase. The following example assumes that your HBase table has two column families, col_1 and col_2, and that your data is formatted in sets of three in Spark, like (row_key, col_1, col_2).

def convert(triple: (Int, Int, Int)) = {
      val p = new Put(Bytes.toBytes(triple._1))
      p.add(Bytes.toBytes("cf"),
            Bytes.toBytes("col_1"),
            Bytes.toBytes(triple._2))
      p.add(Bytes.toBytes("cf"),
            Bytes.toBytes("col_2"),
            Bytes.toBytes(triple._3))
      (new ImmutableBytesWritable, p)
}

To write the data from Spark to HBase, you might use:

new PairRDDFunctions(localData.map(convert)).saveAsHadoopDataset(jobConfig)

This example, written in Scala, uses Apache Spark in conjunction with the Apache Kafka message bus to stream data from Spark to HBase. The example was provided in SPARK-944. It produces some random words and then stores them in an HBase table, creating the table if necessary.

package org.apache.spark.streaming.examples

import java.util.Properties

import kafka.producer._

import org.apache.hadoop.hbase.{ HBaseConfiguration, HColumnDescriptor, HTableDescriptor }
import org.apache.hadoop.hbase.client.{ HBaseAdmin, Put }
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.{ PairRDDFunctions, RDD }
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.kafka._

object MetricAggregatorHBase {
  def main(args : Array[String]) {
    if (args.length < 6) {
      System.err.println("Usage: MetricAggregatorTest <master> <zkQuorum> <group> <topics> <destHBaseTableName> <numThreads>")
      System.exit(1)
    }

    val Array(master, zkQuorum, group, topics, hbaseTableName, numThreads) = args

    val conf = HBaseConfiguration.create()
    conf.set("hbase.zookeeper.quorum", zkQuorum)

    // Initialize hBase table if necessary
    val admin = new HBaseAdmin(conf)
    if (!admin.isTableAvailable(hbaseTableName)) {
      val tableDesc = new HTableDescriptor(hbaseTableName)
      tableDesc.addFamily(new HColumnDescriptor("metric"))
      admin.createTable(tableDesc)
    }

    // setup streaming context
    val ssc = new StreamingContext(master, "MetricAggregatorTest", Seconds(2),
      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass))
    ssc.checkpoint("checkpoint")

    val topicpMap = topics.split(",").map((_, numThreads.toInt)).toMap
    val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicpMap)
      .map { case (key, value) => ((key, Math.floor(System.currentTimeMillis() / 60000).toLong * 60), value.toInt) }

    val aggr = lines.reduceByKeyAndWindow(add _, Minutes(1), Minutes(1), 2)

    aggr.foreach(line => saveToHBase(line, zkQuorum, hbaseTableName))

    ssc.start

    ssc.awaitTermination
  }

  def add(a : Int, b : Int) = { (a + b) }

  def saveToHBase(rdd : RDD[((String, Long), Int)], zkQuorum : String, tableName : String) = {
    val conf = HBaseConfiguration.create()
    conf.set("hbase.zookeeper.quorum", zkQuorum)

    val jobConfig = new JobConf(conf)
    jobConfig.set(TableOutputFormat.OUTPUT_TABLE, tableName)
    jobConfig.setOutputFormat(classOf[TableOutputFormat])

    new PairRDDFunctions(rdd.map { case ((metricId, timestamp), value) => createHBaseRow(metricId, timestamp, value) }).saveAsHadoopDataset(jobConfig)
  }

  def createHBaseRow(metricId : String, timestamp : Long, value : Int) = {
    val record = new Put(Bytes.toBytes(metricId + "~" + timestamp))

    record.add(Bytes.toBytes("metric"), Bytes.toBytes("col"), Bytes.toBytes(value.toString))

    (new ImmutableBytesWritable, record)
  }

}

// Produces some random words between 1 and 100.
object MetricDataProducer {

  def main(args : Array[String]) {
    if (args.length < 2) {
      System.err.println("Usage: MetricDataProducer <metadataBrokerList> <topic> <messagesPerSec>")
      System.exit(1)
    }

    val Array(brokers, topic, messagesPerSec) = args

    // ZooKeeper connection properties
    val props = new Properties()
    props.put("metadata.broker.list", brokers)
    props.put("serializer.class", "kafka.serializer.StringEncoder")

    val config = new ProducerConfig(props)
    val producer = new Producer[String, String](config)

    // Send some messages
    while (true) {
      val messages = (1 to messagesPerSec.toInt).map { messageNum =>
        {
          val metricId = scala.util.Random.nextInt(10)
          val value = scala.util.Random.nextInt(1000)
          new KeyedMessage[String, String](topic, metricId.toString, value.toString)
        }
      }.toArray

      producer.send(messages : _*)
      Thread.sleep(100)
    }
  }
}