來源於 https://www.cnblogs.com/swordfall/p/10527423.html
1. HBase連接的方式概況
主要分為：
- 純Java API讀寫HBase的方式；
- Spark讀寫HBase的方式；
- Flink讀寫HBase的方式；
- HBase通過Phoenix讀寫的方式；
第一種方式是HBase自身提供的比較原始的高效操作方式，而第二、第三則分別是Spark、Flink集成HBase的方式，最后一種是第三方插件Phoenix集成的JDBC方式，Phoenix集成的JDBC操作方式也能在Spark、Flink中調用。
注意：
這里我們使用HBase2.1.2版本，flink1.7.2版本，scala-2.12版本。
2. Flink Streaming和Flink DataSet讀寫HBase
Flink上讀取HBase數據有兩種方式：
- 繼承RichSourceFunction重寫父類方法（flink streaming）
- 實現自定義TableInputFormat接口（flink streaming和flink dataSet）
Flink上將數據寫入HBase也有兩種方式：
- 繼承RichSinkFunction重寫父類方法（flink streaming）
- 實現OutputFormat接口（flink streaming和flink dataSet）
注意：
① Flink Streaming流式處理有上述兩種方式；但是Flink DataSet批處理，讀只有“實現TableInputFormat接口”一種方式，寫只有”實現OutputFormat接口“一種方式。
②TableInputFormat接口是在flink-hbase-2.12-1.7.2里面的，而該jar包對應的hbase版本是1.4.3，而項目中我們使用HBase2.1.2版本，故需要對TableInputFormat重寫。
2.1 Flink讀取HBase的兩種方式
注意：讀取HBase之前可以先執行節點2.2.2實現OutputFormat接口：Flink dataSet 批處理寫入HBase的方法，確保HBase test表里面有數據，數據如下：
2.1.1 繼承RichSourceFunction重寫父類方法：
package cn.swordfall.hbaseOnFlink
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.functions.source.RichSourceFunction
import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext
import org.apache.hadoop.hbase.{Cell, HBaseConfiguration, HConstants, TableName}
import org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Scan, Table}
import org.apache.hadoop.hbase.util.Bytes
import scala.collection.JavaConverters._
/**
* @Author: Yang JianQiu
* @Date: 2019/2/28 18:05
*
* 以HBase為數據源
* 從HBase中獲取數據，然后以流的形式發射
*
* 從HBase讀取數據
* 第一種：繼承RichSourceFunction重寫父類方法
*/
class HBaseReader extends RichSourceFunction[(String, String)]{
private var conn: Connection = null
private var table: Table = null
private var scan: Scan = null
/**
* 在open方法使用HBase的客戶端連接
* @param parameters
*/
override def open(parameters: Configuration): Unit = {
val config: org.apache.hadoop.conf.Configuration = HBaseConfiguration.create()
config.set(HConstants.ZOOKEEPER_QUORUM, "192.168.187.201")
config.set(HConstants.ZOOKEEPER_CLIENT_PORT, "2181")
config.setInt(HConstants.HBASE_CLIENT_OPERATION_TIMEOUT, 30000)
config.setInt(HConstants.HBASE_CLIENT_SCANNER_TIMEOUT_PERIOD, 30000)
val tableName: TableName = TableName.valueOf("test")
val cf1: String = "cf1"
conn = ConnectionFactory.createConnection(config)
table = conn.getTable(tableName)
scan = new Scan()
scan.withStartRow(Bytes.toBytes("100"))
scan.withStopRow(Bytes.toBytes("107"))
scan.addFamily(Bytes.toBytes(cf1))
}
/**
* run方法來自java的接口文件SourceFunction，使用IDEA工具Ctrl + o 無法便捷獲取到該方法，直接override會提示
* @param sourceContext
*/
override def run(sourceContext: SourceContext[(String, String)]): Unit = {
val rs = table.getScanner(scan)
val iterator = rs.iterator()
while (iterator.hasNext){
val result = iterator.next()
val rowKey = Bytes.toString(result.getRow)
val sb: StringBuffer = new StringBuffer()
for (cell:Cell <- result.listCells().asScala){
val value = Bytes.toString(cell.getValueArray, cell.getValueOffset, cell.getValueLength)
sb.append(value).append("_")
}
val valueString = sb.replace(sb.length() - 1, sb.length(), "").toString
sourceContext.collect((rowKey, valueString))
}
}
/**
* 必須添加
*/
override def cancel(): Unit = {
}
/**
* 關閉hbase的連接，關閉table表
*/
override def close(): Unit = {
try {
if (table != null) {
table.close()
}
if (conn != null) {
conn.close()
}
} catch {
case e:Exception => println(e.getMessage)
}
}
}
調用繼承RichSourceFunction的HBaseReader類，Flink Streaming流式處理的方式：
/**
* 從HBase讀取數據
* 第一種：繼承RichSourceFunction重寫父類方法
*/
def readFromHBaseWithRichSourceFunction(): Unit ={
val env = StreamExecutionEnvironment.getExecutionEnvironment
env.enableCheckpointing(5000)
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
val dataStream: DataStream[(String, String)] = env.addSource(new HBaseReader)
dataStream.map(x => println(x._1 + " " + x._2))
env.execute()
}
2.1.2 實現自定義的TableInputFormat接口：
由於版本不匹配，這里我們需要對flink-hbase-2.12-1.7.2里面的三個文件進行重寫，分別是TableInputSplit、AbstractTableInputFormat、TableInputFormat
TableInputSplit重寫為CustomTableInputSplit：
package cn.swordfall.hbaseOnFlink.flink172_hbase212;
import org.apache.flink.core.io.LocatableInputSplit;
/**
* @Author: Yang JianQiu
* @Date: 2019/3/19 11:50
*/
public class CustomTableInputSplit extends LocatableInputSplit {
private static final long serialVersionUID = 1L;
/** The name of the table to retrieve data from. */
private final byte[] tableName;
/** The start row of the split. */
private final byte[] startRow;
/** The end row of the split. */
private final byte[] endRow;
/**
* Creates a new table input split.
*
* @param splitNumber
* the number of the input split
* @param hostnames
* the names of the hosts storing the data the input split refers to
* @param tableName
* the name of the table to retrieve data from
* @param startRow
* the start row of the split
* @param endRow
* the end row of the split
*/
CustomTableInputSplit(final int splitNumber, final String[] hostnames, final byte[] tableName, final byte[] startRow,
final byte[] endRow) {
super(splitNumber, hostnames);
this.tableName = tableName;
this.startRow = startRow;
this.endRow = endRow;
}
/**
* Returns the table name.
*
* @return The table name.
*/
public byte[] getTableName() {
return this.tableName;
}
/**
* Returns the start row.
*
* @return The start row.
*/
public byte[] getStartRow() {
return this.startRow;
}
/**
* Returns the end row.
*
* @return The end row.
*/
public byte[] getEndRow() {
return this.endRow;
}
}
AbstractTableInputFormat重寫為CustomeAbstractTableInputFormat：
package cn.swordfall.hbaseOnFlink.flink172_hbase212;
import org.apache.flink.addons.hbase.AbstractTableInputFormat;
import org.apache.flink.api.common.io.LocatableInputSplitAssigner;
import org.apache.flink.api.common.io.RichInputFormat;
import org.apache.flink.api.common.io.statistics.BaseStatistics;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.core.io.InputSplitAssigner;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.ResultScanner;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.hbase.util.Pair;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
/**
* @Author: Yang JianQiu
* @Date: 2019/3/19 11:16
*
* 由於flink-hbase_2.12_1.7.2 jar包所引用的是hbase1.4.3版本，而現在用到的是hbase2.1.2，版本不匹配
* 故需要重寫flink-hbase_2.12_1.7.2里面的AbstractTableInputFormat，主要原因是AbstractTableInputFormat里面調用的是hbase1.4.3版本的api，
* 而新版本hbase2.1.2已經去掉某些api
*/
public abstract class CustomAbstractTableInputFormat<T> extends RichInputFormat<T, CustomTableInputSplit> {
protected static final Logger LOG = LoggerFactory.getLogger(AbstractTableInputFormat.class);
// helper variable to decide whether the input is exhausted or not
protected boolean endReached = false;
protected transient HTable table = null;
protected transient Scan scan = null;
/** HBase iterator wrapper. */
protected ResultScanner resultScanner = null;
protected byte[] currentRow;
protected long scannedRows;
/**
* Returns an instance of Scan that retrieves the required subset of records from the HBase table.
*
* @return The appropriate instance of Scan for this use case.
*/
protected abstract Scan getScanner();
/**
* What table is to be read.
*
* <p>Per instance of a TableInputFormat derivative only a single table name is possible.
*
* @return The name of the table
*/
protected abstract String getTableName();
/**
* HBase returns an instance of {@link Result}.
*
* <p>This method maps the returned {@link Result} instance into the output type {@link T}.
*
* @param r The Result instance from HBase that needs to be converted
* @return The appropriate instance of {@link T} that contains the data of Result.
*/
protected abstract T mapResultToOutType(Result r);
/**
* Creates a {@link Scan} object and opens the {@link HTable} connection.
*
* <p>These are opened here because they are needed in the createInputSplits
* which is called before the openInputFormat method.
*
* <p>The connection is opened in this method and closed in {@link #closeInputFormat()}.
*
* @param parameters The configuration that is to be used
* @see Configuration
*/
@Override
public abstract void configure(Configuration parameters);
@Override
public void open(CustomTableInputSplit split) throws IOException {
if (table == null) {
throw new IOException("The HBase table has not been opened! " +
"This needs to be done in configure().");
}
if (scan == null) {
throw new IOException("Scan has not been initialized! " +
"This needs to be done in configure().");
}
if (split == null) {
throw new IOException("Input split is null!");
}
logSplitInfo("opening", split);
// set scan range
currentRow = split.getStartRow();
/* scan.setStartRow(currentRow);
scan.setStopRow(split.getEndRow());*/
scan.withStartRow(currentRow);
scan.withStopRow(split.getEndRow());
resultScanner = table.getScanner(scan);
endReached = false;
scannedRows = 0;
}
@Override
public T nextRecord(T reuse) throws IOException {
if (resultScanner == null) {
throw new IOException("No table result scanner provided!");
}
try {
Result res = resultScanner.next();
if (res != null) {
scannedRows++;
currentRow = res.getRow();
return mapResultToOutType(res);
}
} catch (Exception e) {
resultScanner.close();
//workaround for timeout on scan
LOG.warn("Error after scan of " + scannedRows + " rows. Retry with a new scanner...", e);
/*scan.setStartRow(currentRow);*/
scan.withStartRow(currentRow);
resultScanner = table.getScanner(scan);
Result res = resultScanner.next();
if (res != null) {
scannedRows++;
currentRow = res.getRow();
return mapResultToOutType(res);
}
}
endReached = true;
return null;
}
private void logSplitInfo(String action, CustomTableInputSplit split) {
int splitId = split.getSplitNumber();
String splitStart = Bytes.toString(split.getStartRow());
String splitEnd = Bytes.toString(split.getEndRow());
String splitStartKey = splitStart.isEmpty() ? "-" : splitStart;
String splitStopKey = splitEnd.isEmpty() ? "-" : splitEnd;
String[] hostnames = split.getHostnames();
LOG.info("{} split (this={})[{}|{}|{}|{}]", action, this, splitId, hostnames, splitStartKey, splitStopKey);
}
@Override
public boolean reachedEnd() throws IOException {
return endReached;
}
@Override
public void close() throws IOException {
LOG.info("Closing split (scanned {} rows)", scannedRows);
currentRow = null;
try {
if (resultScanner != null) {
resultScanner.close();
}
} finally {
resultScanner = null;
}
}
@Override
public void closeInputFormat() throws IOException {
try {
if (table != null) {
table.close();
}
} finally {
table = null;
}
}
@Override
public CustomTableInputSplit[] createInputSplits(final int minNumSplits) throws IOException {
if (table == null) {
throw new IOException("The HBase table has not been opened! " +
"This needs to be done in configure().");
}
if (scan == null) {
throw new IOException("Scan has not been initialized! " +
"This needs to be done in configure().");
}
// Get the starting and ending row keys for every region in the currently open table
final Pair<byte[][], byte[][]> keys = table.getRegionLocator().getStartEndKeys();
if (keys == null || keys.getFirst() == null || keys.getFirst().length == 0) {
throw new IOException("Expecting at least one region.");
}
final byte[] startRow = scan.getStartRow();
final byte[] stopRow = scan.getStopRow();
final boolean scanWithNoLowerBound = startRow.length == 0;
final boolean scanWithNoUpperBound = stopRow.length == 0;
final List<CustomTableInputSplit> splits = new ArrayList<CustomTableInputSplit>(minNumSplits);
for (int i = 0; i < keys.getFirst().length; i++) {
final byte[] startKey = keys.getFirst()[i];
final byte[] endKey = keys.getSecond()[i];
final String regionLocation = table.getRegionLocator().getRegionLocation(startKey, false).getHostnamePort();
// Test if the given region is to be included in the InputSplit while splitting the regions of a table
if (!includeRegionInScan(startKey, endKey)) {
continue;
}
// Find the region on which the given row is being served
final String[] hosts = new String[]{regionLocation};
// Determine if regions contains keys used by the scan
boolean isLastRegion = endKey.length == 0;
if ((scanWithNoLowerBound || isLastRegion || Bytes.compareTo(startRow, endKey) < 0) &&
(scanWithNoUpperBound || Bytes.compareTo(stopRow, startKey) > 0)) {
final byte[] splitStart = scanWithNoLowerBound || Bytes.compareTo(startKey, startRow) >= 0 ? startKey : startRow;
final byte[] splitStop = (scanWithNoUpperBound || Bytes.compareTo(endKey, stopRow) <= 0)
&& !isLastRegion ? endKey : stopRow;
int id = splits.size();
final CustomTableInputSplit split = new CustomTableInputSplit(id, hosts, table.getName().getName(), splitStart, splitStop);
splits.add(split);
}
}
LOG.info("Created " + splits.size() + " splits");
for (CustomTableInputSplit split : splits) {
logSplitInfo("created", split);
}
return splits.toArray(new CustomTableInputSplit[splits.size()]);
}
/**
* Test if the given region is to be included in the scan while splitting the regions of a table.
*
* @param startKey Start key of the region
* @param endKey End key of the region
* @return true, if this region needs to be included as part of the input (default).
*/
protected boolean includeRegionInScan(final byte[] startKey, final byte[] endKey) {
return true;
}
@Override
public InputSplitAssigner getInputSplitAssigner(CustomTableInputSplit[] inputSplits) {
return new LocatableInputSplitAssigner(inputSplits);
}
@Override
public BaseStatistics getStatistics(BaseStatistics cachedStatistics) {
return null;
}
}
TableInputFormat重寫為CustomTableInputFormat：
package cn.swordfall.hbaseOnFlink.flink172_hbase212;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.configuration.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
/**
* @Author: Yang JianQiu
* @Date: 2019/3/19 11:15
* 由於flink-hbase_2.12_1.7.2 jar包所引用的是hbase1.4.3版本，而現在用到的是hbase2.1.2，版本不匹配
* 故需要重寫flink-hbase_2.12_1.7.2里面的TableInputFormat
*/
public abstract class CustomTableInputFormat<T extends Tuple> extends CustomAbstractTableInputFormat<T> {
private static final long serialVersionUID = 1L;
/**
* Returns an instance of Scan that retrieves the required subset of records from the HBase table.
* @return The appropriate instance of Scan for this usecase.
*/
@Override
protected abstract Scan getScanner();
/**
* What table is to be read.
* Per instance of a TableInputFormat derivative only a single tablename is possible.
* @return The name of the table
*/
@Override
protected abstract String getTableName();
/**
* The output from HBase is always an instance of {@link Result}.
* This method is to copy the data in the Result instance into the required {@link Tuple}
* @param r The Result instance from HBase that needs to be converted
* @return The appropriate instance of {@link Tuple} that contains the needed information.
*/
protected abstract T mapResultToTuple(Result r);
/**
* Creates a {@link Scan} object and opens the {@link HTable} connection.
* These are opened here because they are needed in the createInputSplits
* which is called before the openInputFormat method.
* So the connection is opened in {@link #configure(Configuration)} and closed in {@link #closeInputFormat()}.
*
* @param parameters The configuration that is to be used
* @see Configuration
*/
@Override
public void configure(Configuration parameters) {
table = createTable();
if (table != null) {
scan = getScanner();
}
}
/**
* Create an {@link HTable} instance and set it into this format.
*/
private HTable createTable() {
LOG.info("Initializing HBaseConfiguration");
//use files found in the classpath
org.apache.hadoop.conf.Configuration hConf = HBaseConfiguration.create();
try {
return null;
} catch (Exception e) {
LOG.error("Error instantiating a new HTable instance", e);
}
return null;
}
@Override
protected T mapResultToOutType(Result r) {
return mapResultToTuple(r);
}
}
繼承自定義的CustomTableInputFormat，進行hbase連接、讀取操作：
package cn.swordfall.hbaseOnFlink
import java.io.IOException
import cn.swordfall.hbaseOnFlink.flink172_hbase212.CustomTableInputFormat
import org.apache.flink.api.java.tuple.Tuple2
import org.apache.flink.addons.hbase.TableInputFormat
import org.apache.flink.configuration.Configuration
import org.apache.hadoop.hbase.{Cell, HBaseConfiguration, HConstants, TableName}
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.util.Bytes
import scala.collection.JavaConverters._
/**
* @Author: Yang JianQiu
* @Date: 2019/3/1 1:14
*
* 從HBase讀取數據
* 第二種：實現TableInputFormat接口
*/
class HBaseInputFormat extends CustomTableInputFormat[Tuple2[String, String]]{
// 結果Tuple
val tuple2 = new Tuple2[String, String]
/**
* 建立HBase連接
* @param parameters
*/
override def configure(parameters: Configuration): Unit = {
val tableName: TableName = TableName.valueOf("test")
val cf1 = "cf1"
var conn: Connection = null
val config: org.apache.hadoop.conf.Configuration = HBaseConfiguration.create
config.set(HConstants.ZOOKEEPER_QUORUM, "192.168.187.201")
config.set(HConstants.ZOOKEEPER_CLIENT_PORT, "2181")
config.setInt(HConstants.HBASE_CLIENT_OPERATION_TIMEOUT, 30000)
config.setInt(HConstants.HBASE_CLIENT_SCANNER_TIMEOUT_PERIOD, 30000)
try {
conn = ConnectionFactory.createConnection(config)
table = conn.getTable(tableName).asInstanceOf[HTable]
scan = new Scan()
scan.withStartRow(Bytes.toBytes("001"))
scan.withStopRow(Bytes.toBytes("201"))
scan.addFamily(Bytes.toBytes(cf1))
} catch {
case e: IOException =>
e.printStackTrace()
}
}
/**
* 對獲取的數據進行加工處理
* @param result
* @return
*/
override def mapResultToTuple(result: Result): Tuple2[String, String] = {
val rowKey = Bytes.toString(result.getRow)
val sb = new StringBuffer()
for (cell: Cell <- result.listCells().asScala){
val value = Bytes.toString(cell.getValueArray, cell.getValueOffset, cell.getValueLength)
sb.append(value).append("_")
}
val value = sb.replace(sb.length() - 1, sb.length(), "").toString
tuple2.setField(rowKey, 0)
tuple2.setField(value, 1)
tuple2
}
/**
* tableName
* @return
*/
override def getTableName: String = "test"
/**
* 獲取Scan
* @return
*/
override def getScanner: Scan = {
scan
}
}
調用實現CustomTableInputFormat接口的類HBaseInputFormat，Flink Streaming流式處理的方式：
/**
* 從HBase讀取數據
* 第二種：實現TableInputFormat接口
*/
def readFromHBaseWithTableInputFormat(): Unit ={
val env = StreamExecutionEnvironment.getExecutionEnvironment
env.enableCheckpointing(5000)
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
val dataStream = env.createInput(new HBaseInputFormat)
dataStream.filter(_.f0.startsWith("10")).print()
env.execute()
}
而Flink DataSet批處理的方式為：
/**
* 讀取HBase數據方式：實現TableInputFormat接口
*/
def readFromHBaseWithTableInputFormat(): Unit ={
val env = ExecutionEnvironment.getExecutionEnvironment
val dataStream = env.createInput(new HBaseInputFormat)
dataStream.filter(_.f1.startsWith("20")).print()
}
2.2 Flink寫入HBase的兩種方式
這里Flink Streaming寫入HBase，需要從Kafka接收數據，可以開啟kafka單機版，利用kafka-console-producer.sh往topic "test"寫入如下數據：
100,hello,20 101,nice,24 102,beautiful,26
2.2.1 繼承RichSinkFunction重寫父類方法：
package cn.swordfall.hbaseOnFlink
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.functions.sink.{RichSinkFunction, SinkFunction}
import org.apache.hadoop.hbase.{HBaseConfiguration, HConstants, TableName}
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.util.Bytes
/**
* @Author: Yang JianQiu
* @Date: 2019/3/1 1:34
*
* 寫入HBase
* 第一種：繼承RichSinkFunction重寫父類方法
*
* 注意：由於flink是一條一條的處理數據，所以我們在插入hbase的時候不能來一條flush下，
* 不然會給hbase造成很大的壓力，而且會產生很多線程導致集群崩潰，所以線上任務必須控制flush的頻率。
*
* 解決方案：我們可以在open方法中定義一個變量，然后在寫入hbase時比如500條flush一次，或者加入一個list，判斷list的大小滿足某個閥值flush一下
*/
class HBaseWriter extends RichSinkFunction[String]{
var conn: Connection = null
val scan: Scan = null
var mutator: BufferedMutator = null
var count = 0
/**
* 建立HBase連接
* @param parameters
*/
override def open(parameters: Configuration): Unit = {
val config:org.apache.hadoop.conf.Configuration = HBaseConfiguration.create
config.set(HConstants.ZOOKEEPER_QUORUM, "192.168.187.201")
config.set(HConstants.ZOOKEEPER_CLIENT_PORT, "2181")
config.setInt(HConstants.HBASE_CLIENT_OPERATION_TIMEOUT, 30000)
config.setInt(HConstants.HBASE_CLIENT_SCANNER_TIMEOUT_PERIOD, 30000)
conn = ConnectionFactory.createConnection(config)
val tableName: TableName = TableName.valueOf("test")
val params: BufferedMutatorParams = new BufferedMutatorParams(tableName)
//設置緩存1m，當達到1m時數據會自動刷到hbase
params.writeBufferSize(1024 * 1024) //設置緩存的大小
mutator = conn.getBufferedMutator(params)
count = 0
}
/**
* 處理獲取的hbase數據
* @param value
* @param context
*/
override def invoke(value: String, context: SinkFunction.Context[_]): Unit = {
val cf1 = "cf1"
val array: Array[String] = value.split(",")
val put: Put = new Put(Bytes.toBytes(array(0)))
put.addColumn(Bytes.toBytes(cf1), Bytes.toBytes("name"), Bytes.toBytes(array(1)))
put.addColumn(Bytes.toBytes(cf1), Bytes.toBytes("age"), Bytes.toBytes(array(2)))
mutator.mutate(put)
//每滿2000條刷新一下數據
if (count >= 2000){
mutator.flush()
count = 0
}
count = count + 1
}
/**
* 關閉
*/
override def close(): Unit = {
if (conn != null) conn.close()
}
}
調用繼承RichSinkFunction的HBaseWriter類，Flink Streaming流式處理的方式：
/**
* 寫入HBase
* 第一種：繼承RichSinkFunction重寫父類方法
*/
def write2HBaseWithRichSinkFunction(): Unit = {
val topic = "test"
val props = new Properties
props.put("bootstrap.servers", "192.168.187.201:9092")
props.put("group.id", "kv_flink")
props.put("enable.auto.commit", "true")
props.put("auto.commit.interval.ms", "1000")
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
env.enableCheckpointing(5000)
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
val myConsumer = new FlinkKafkaConsumer[String](topic, new SimpleStringSchema, props)
val dataStream: DataStream[String] = env.addSource(myConsumer)
//寫入HBase
dataStream.addSink(new HBaseWriter)
env.execute()
}
2.2.2 實現OutputFormat接口：
package cn.swordfall.hbaseOnFlink
import org.apache.flink.api.common.io.OutputFormat
import org.apache.flink.configuration.Configuration
import org.apache.hadoop.hbase.{HBaseConfiguration, HConstants, TableName}
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.util.Bytes
/**
* @Author: Yang JianQiu
* @Date: 2019/3/1 1:40
*
* 寫入HBase提供兩種方式
* 第二種：實現OutputFormat接口
*/
class HBaseOutputFormat extends OutputFormat[String]{
val zkServer = "192.168.187.201"
val port = "2181"
var conn: Connection = null
var mutator: BufferedMutator = null
var count = 0
/**
* 配置輸出格式。此方法總是在實例化輸出格式上首先調用的
*
* @param configuration
*/
override def configure(configuration: Configuration): Unit = {
}
/**
* 用於打開輸出格式的並行實例，所以在open方法中我們會進行hbase的連接，配置，建表等操作。
*
* @param i
* @param i1
*/
override def open(i: Int, i1: Int): Unit = {
val config: org.apache.hadoop.conf.Configuration = HBaseConfiguration.create
config.set(HConstants.ZOOKEEPER_QUORUM, zkServer)
config.set(HConstants.ZOOKEEPER_CLIENT_PORT, port)
config.setInt(HConstants.HBASE_CLIENT_OPERATION_TIMEOUT, 30000)
config.setInt(HConstants.HBASE_CLIENT_SCANNER_TIMEOUT_PERIOD, 30000)
conn = ConnectionFactory.createConnection(config)
val tableName: TableName = TableName.valueOf("test")
val params: BufferedMutatorParams = new BufferedMutatorParams(tableName)
//設置緩存1m，當達到1m時數據會自動刷到hbase
params.writeBufferSize(1024 * 1024) //設置緩存的大小
mutator = conn.getBufferedMutator(params)
count = 0
}
/**
* 用於將數據寫入數據源，所以我們會在這個方法中調用寫入hbase的API
*
* @param it
*/
override def writeRecord(it: String): Unit = {
val cf1 = "cf1"
val array: Array[String] = it.split(",")
val put: Put = new Put(Bytes.toBytes(array(0)))
put.addColumn(Bytes.toBytes(cf1), Bytes.toBytes("name"), Bytes.toBytes(array(1)))
put.addColumn(Bytes.toBytes(cf1), Bytes.toBytes("age"), Bytes.toBytes(array(2)))
mutator.mutate(put)
//每4條刷新一下數據，如果是批處理調用outputFormat，這里填寫的4必須不能大於批處理的記錄總數量，否則數據不會更新到hbase里面
if (count >= 4){
mutator.flush()
count = 0
}
count = count + 1
}
/**
* 關閉
*/
override def close(): Unit = {
try {
if (conn != null) conn.close()
} catch {
case e: Exception => println(e.getMessage)
}
}
}
調用實現OutputFormat的HBaseOutputFormat類，Flink Streaming流式處理的方式：
/**
* 寫入HBase
* 第二種：實現OutputFormat接口
*/
def write2HBaseWithOutputFormat(): Unit = {
val topic = "test"
val props = new Properties
props.put("bootstrap.servers", "192.168.187.201:9092")
props.put("group.id", "kv_flink")
props.put("enable.auto.commit", "true")
props.put("auto.commit.interval.ms", "1000")
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
env.enableCheckpointing(5000)
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
val myConsumer = new FlinkKafkaConsumer[String](topic, new SimpleStringSchema, props)
val dataStream: DataStream[String] = env.addSource(myConsumer)
dataStream.writeUsingOutputFormat(new HBaseOutputFormat)
env.execute()
}
而Flink DataSet批處理的方式為：
/**
* 寫入HBase方式：實現OutputFormat接口
*/
def write2HBaseWithOutputFormat(): Unit = {
val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
//2.定義數據
val dataSet: DataSet[String] = env.fromElements("103,zhangsan,20", "104,lisi,21", "105,wangwu,22", "106,zhaolilu,23")
dataSet.output(new HBaseOutputFormat)
//運行下面這句話，程序才會真正執行，這句代碼針對的是data sinks寫入數據的
env.execute()
}
注意：
如果是批處理調用的，應該要注意HBaseOutputFormat類的writeRecord方法每次批量刷新的數據量不能大於批處理的總記錄數據量，否則數據更新不到hbase里面。
3. 總結
【其他相關文章】
HBase連接的幾種方式（一）java篇 查看純Java API讀寫HBase
HBase連接的幾種方式（二）spark篇 查看Spark上讀寫HBase
github地址：
https://github.com/SwordfallYeung/HBaseDemo（flink讀寫hbase包括java和scala兩個版本的代碼）
【參考資料】
https://blog.csdn.net/liguohuabigdata/article/details/78588861
https://blog.csdn.net/aA518189/article/details/86544844