Java Spark读取Hbase

本文记录Spark读取Hbase基本操作，及读取多版本Hbase数据示例。

Hbase数据示例如下：

示例代码如下

package org.HbaseLearn.Util;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableInputFormat;
import org.apache.hadoop.hbase.protobuf.ProtobufUtil;
import org.apache.hadoop.hbase.protobuf.generated.ClientProtos;
import org.apache.hadoop.hbase.util.Base64;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import scala.Tuple2;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import org.apache.spark.sql.SparkSession;
import org.apache.log4j.Level;
import org.apache.log4j.Logger;

public class ReadSpark {

    private static JavaSparkContext jsc;

    public static void main(String[] args) throws InterruptedException {
        Logger.getLogger("org").setLevel(Level.ERROR);
        SparkSession spark = SparkSession.builder().master("yarn").master("local").appName("hello-wrold")
                // .config("spark.some.config.option", "some-value")
                .getOrCreate();
        jsc = new JavaSparkContext(spark.sparkContext());
        Configuration conf = HBaseConfiguration.create();

        Scan scan = new Scan();
        // 指定列，可以不写相当于select * from XX
        byte[] family_bytes = "info".getBytes();
        byte[] birthday_bytes = "birthday".getBytes();
        byte[] gender_bytes = "gender".getBytes();
        byte[] user_type_bytes = "user_type".getBytes();
        scan.addFamily(family_bytes);
        scan.addColumn(family_bytes, birthday_bytes);
        scan.addColumn(family_bytes, gender_bytes);
        scan.addColumn(family_bytes, user_type_bytes);
        // 设置读取的最大的版本数
        scan.setMaxVersions(3);
        try {
            // 将scan编码
            ClientProtos.Scan proto = ProtobufUtil.toScan(scan);
            String scanToString = Base64.encodeBytes(proto.toByteArray());
            // 表名
            String tableName = "test_info";
            conf.set(TableInputFormat.INPUT_TABLE, tableName);
            conf.set(TableInputFormat.SCAN, scanToString);
            // ZooKeeper集群
            conf.set("hbase.zookeeper.quorum", "node35,node34,node26");
            conf.set("hbase.zookeeper.property.clientPort", "2181");
            conf.set("hbase.master", "master");

            // 将HBase数据转成RDD
            JavaPairRDD<ImmutableBytesWritable, Result> HBaseRdd = jsc.newAPIHadoopRDD(conf, TableInputFormat.class,
                    ImmutableBytesWritable.class, Result.class);

            // 再将以上结果转成Row类型RDD
            JavaRDD<Row> HBaseRow = HBaseRdd.map(new Function<Tuple2<ImmutableBytesWritable, Result>, Row>() {
                private static final long serialVersionUID = 1L;

                @Override
                public Row call(Tuple2<ImmutableBytesWritable, Result> tuple2) throws IOException {
                    Result result = tuple2._2;
                    String rowKey = Bytes.toString(result.getRow());
                    String birthday = Bytes.toString(result.getValue(family_bytes, birthday_bytes));
                    String gender = Bytes.toString(result.getValue(family_bytes, gender_bytes));
                    String user_type = Bytes.toString(result.getValue(family_bytes, user_type_bytes));
                    return RowFactory.create(rowKey, birthday, gender, user_type);
                }
            });
            // 顺序必须与构建RowRDD的顺序一致
            List<StructField> structFields = Arrays.asList(
                    DataTypes.createStructField("user_id", DataTypes.StringType, true),
                    DataTypes.createStructField("birthday", DataTypes.StringType, true),
                    DataTypes.createStructField("gender", DataTypes.StringType, true),
                    DataTypes.createStructField("user_type", DataTypes.StringType, true));
            // 构建schema
            StructType schema = DataTypes.createStructType(structFields);
            // 生成DataFrame
            Dataset<Row> HBaseDF = spark.createDataFrame(HBaseRow, schema);
            HBaseDF.show();
            // 获取birthday多版本数据
            JavaRDD<Row> multiVersionHBaseRow = HBaseRdd
                    .mapPartitions(new FlatMapFunction<Iterator<Tuple2<ImmutableBytesWritable, Result>>, Row>() {

                        private static final long serialVersionUID = 1L;

                        @Override
                        public Iterator<Row> call(Iterator<Tuple2<ImmutableBytesWritable, Result>> t) throws Exception {
                            // TODO Auto-generated method stub
                            List<Row> rows = new ArrayList<Row>();
                            while (t.hasNext()) {
                                Result result = t.next()._2();
                                String rowKey = Bytes.toString(result.getRow());
                                // 获取当前rowKey的family_bytes列族对应的所有Cell
                                List<Cell> cells = result.getColumnCells(family_bytes, birthday_bytes);
                                for (Cell cell : cells) {
                                    String birthday = Bytes.toString(CellUtil.cloneValue(cell));
                                    rows.add(RowFactory.create(rowKey, birthday, cell.getTimestamp()));
                                }
                            }
                            return rows.iterator();
                        }
                    });
            List<StructField> multiVersionStructFields = Arrays.asList(
                    DataTypes.createStructField("rowKey", DataTypes.StringType, true),
                    DataTypes.createStructField("birthday", DataTypes.StringType, true),
                    DataTypes.createStructField("timestamp", DataTypes.LongType, true));
            // 构建schema
            StructType multiVersionSchema = DataTypes.createStructType(multiVersionStructFields);
            // 生成DataFrame
            Dataset<Row> multiVersionHBaseDF = spark.createDataFrame(multiVersionHBaseRow, multiVersionSchema);
            multiVersionHBaseDF.show();

        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            if (spark != null) {
                spark.close();
            }
        }

        spark.close();

    }

}

 

运行结果

+-------+----------+------+---------+
|user_id| birthday|gender|user_type|
+-------+----------+------+---------+
| 0001|2018-05-01|female| 1|
| 0002|2019-01-01| male| 2|
| 0003|2018-01-01| male| 2|
+-------+----------+------+---------+

+------+----------+-------------+
|rowKey| birthday| timestamp|
+------+----------+-------------+
| 0001|2018-05-01|1589014338166|
| 0001|2020-01-01|1589008384004|
| 0002|2019-01-01|1589008489848|
| 0003|2018-01-01|1589008489949|
+------+----------+-------------+

 

 

pom依赖如下

　　　　　<dependency> <!-- Spark dependency -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.2.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase</artifactId>
            <version>1.0.0</version>
            <type>pom</type>
        </dependency>


        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-common</artifactId>
            <version>1.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.6.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.1.2</version>
        </dependency>