下面的代码将从hbase中读取，然后将其转换为json结构并转换为schemaRDD，但是问题是我using List要存储json字符串，然后传递给javaRDD，对于大约100 GB的数据，主数据库将被加载内存中的数据。什么是从hbase加载数据然后执行操作，然后转换为JavaRDD的正确方法。
package hbase_reader;
import java.io.IOException;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
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.rdd.RDD;
import org.apache.spark.sql.api.java.JavaSQLContext;
import org.apache.spark.sql.api.java.JavaSchemaRDD;
import org.apache.commons.cli.ParseException;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.KeyValue;
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.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableInputFormat;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.Text;
import org.apache.spark.SparkConf;
import scala.Function1;
import scala.Tuple2;
import scala.runtime.AbstractFunction1;
import com.google.common.collect.Lists;
public class hbase_reader {
public static void main(String[] args) throws IOException, ParseException {
List<String> jars = Lists.newArrayList("");
SparkConf spconf = new SparkConf();
spconf.setMaster("local[2]");
spconf.setAppName("HBase");
//spconf.setSparkHome("/opt/human/opt/spark-0.9.0-hdp1");
spconf.setJars(jars.toArray(new String[jars.size()]));
JavaSparkContext sc = new JavaSparkContext(spconf);
//spconf.set("spark.executor.memory", "1g");
JavaSQLContext jsql = new JavaSQLContext(sc);
HBaseConfiguration conf = new HBaseConfiguration();
String tableName = "HBase.CounData1_Raw_Min1";
HTable table = new HTable(conf,tableName);
try {
ResultScanner scanner = table.getScanner(new Scan());
List<String> jsonList = new ArrayList<String>();
String json = null;
for(Result rowResult:scanner) {
json = "";
String rowKey = Bytes.toString(rowResult.getRow());
for(byte[] s1:rowResult.getMap().keySet()) {
String s1_str = Bytes.toString(s1);
String jsonSame = "";
for(byte[] s2:rowResult.getMap().get(s1).keySet()) {
String s2_str = Bytes.toString(s2);
for(long s3:rowResult.getMap().get(s1).get(s2).keySet()) {
String s3_str = new String(rowResult.getMap().get(s1).get(s2).get(s3));
jsonSame += "\""+s2_str+"\":"+s3_str+",";
}
}
jsonSame = jsonSame.substring(0,jsonSame.length()-1);
json += "\""+s1_str+"\""+":{"+jsonSame+"}"+",";
}
json = json.substring(0,json.length()-1);
json = "{\"RowKey\":\""+rowKey+"\","+json+"}";
jsonList.add(json);
}
JavaRDD<String> jsonRDD = sc.parallelize(jsonList);
JavaSchemaRDD schemaRDD = jsql.jsonRDD(jsonRDD);
System.out.println(schemaRDD.take(2));
} finally {
table.close();
}
}
}
一个使用Spark（Scala）读取HBase数据的基本示例，您也可以在Java中编写以下代码：
import org.apache.hadoop.hbase.client.{HBaseAdmin, Result}
import org.apache.hadoop.hbase.{ HBaseConfiguration, HTableDescriptor }
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.spark._
object HBaseRead {
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName("HBaseRead").setMaster("local[2]")
val sc = new SparkContext(sparkConf)
val conf = HBaseConfiguration.create()
val tableName = "table1"
System.setProperty("user.name", "hdfs")
System.setProperty("HADOOP_USER_NAME", "hdfs")
conf.set("hbase.master", "localhost:60000")
conf.setInt("timeout", 120000)
conf.set("hbase.zookeeper.quorum", "localhost")
conf.set("zookeeper.znode.parent", "/hbase-unsecure")
conf.set(TableInputFormat.INPUT_TABLE, tableName)
val admin = new HBaseAdmin(conf)
if (!admin.isTableAvailable(tableName)) {
val tableDesc = new HTableDescriptor(tableName)
admin.createTable(tableDesc)
}
val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], classOf[ImmutableBytesWritable], classOf[Result])
println("Number of Records found : " + hBaseRDD.count())
sc.stop()
}
}
从Spark 1.0.x +开始，现在您还可以使用Spark-HBase连接器：
Maven依赖关系包括：
<dependency>
<groupId>it.nerdammer.bigdata</groupId>
<artifactId>spark-hbase-connector_2.10</artifactId>
<version>1.0.3</version> // Version can be changed as per your Spark version, I am using Spark 1.6.x
</dependency>
并找到以下相同的示例代码：
import org.apache.spark._
import it.nerdammer.spark.hbase._
object HBaseRead extends App {
val sparkConf = new SparkConf().setAppName("Spark-HBase").setMaster("local[4]")
sparkConf.set("spark.hbase.host", "<YourHostnameOnly>") //e.g. 192.168.1.1 or localhost or your hostanme
val sc = new SparkContext(sparkConf)
// For Example If you have an HBase Table as 'Document' with ColumnFamily 'SMPL' and qualifier as 'DocID, Title' then:
val docRdd = sc.hbaseTable[(Option[String], Option[String])]("Document")
.select("DocID", "Title").inColumnFamily("SMPL")
println("Number of Records found : " + docRdd .count())
}
从Spark 1.6.x +开始，现在您还可以使用SHC Connector（Hortonworks或HDP用户）：
Maven依赖关系包括：
<dependency>
<groupId>com.hortonworks</groupId>
<artifactId>shc</artifactId>
<version>1.0.0-2.0-s_2.11</version> // Version depends on the Spark version and is supported upto Spark 2.x
</dependency>
使用此连接器的主要优点是，它在Schema定义中具有灵活性，并且不需要像nerdammer / spark-hbase-connector中那样的硬编码参数。还要记住，它支持Spark 2.x，因此该连接器非常灵活，并在Issues和PR中提供了端到端支持。
在以下存储库路径中找到最新的自述文件和示例：
您还可以将此RDD转换为DataFrames并在其上运行SQL，也可以将这些Dataset或DataFrames映射到用户定义的Java Pojo或Case类。它运作出色。
如果您还有其他需要，请在下面发表评论。
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