How does Spark read local HBase files?

To read local HBase files in Spark, you can utilize the HBase Java API and the Spark HBase Connector. The following are the general steps for using Spark to read local HBase files.

1. Add dependencies for HBase and Spark in the pom.xml file (if it’s a Maven project) or build.sbt file (if it’s an SBT project). For example, for a Maven project, you can add the following dependencies:

<dependencies>
    <!-- HBase -->
    <dependency>
        <groupId>org.apache.hbase</groupId>
        <artifactId>hbase-client</artifactId>
        <version>2.4.6</version>
    </dependency>
    
    <!-- Spark -->
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.12</artifactId>
        <version>3.2.0</version>
    </dependency>
    
    <!-- HBase Connector for Spark -->
    <dependency>
        <groupId>org.apache.hbase</groupId>
        <artifactId>hbase-spark</artifactId>
        <version>3.0.0</version>
    </dependency>
</dependencies>

1. Import the necessary classes in the Spark application.

import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.spark.sql.SparkSession
import org.apache.hadoop.hbase.spark.HBaseContext

1. Instantiate a SparkSession object:

val spark = SparkSession.builder()
  .appName("Read HBase File")
  .master("local")
  .getOrCreate()

1. Create an HBase configuration object and set the necessary parameters.

val hbaseConf = HBaseConfiguration.create()
hbaseConf.set("hbase.zookeeper.quorum", "localhost")
hbaseConf.set("hbase.zookeeper.property.clientPort", "2181")

1. Create an HBaseContext object.

val hbaseContext = new HBaseContext(spark.sparkContext, hbaseConf)

1. fetch large quantities

val tableName = "my_table"
val cf = "my_column_family"
val columns = Seq("column1", "column2")

val rdd = hbaseContext.bulkGet[Array[Byte], Array[(Array[Byte], Array[Byte], Array[Byte])]](
  tableName,
  2, // 并行度
  spark.sparkContext.parallelize(Seq("rowkey1", "rowkey2")), // 要读取的行键
  record => {
    // 创建Get对象并设置要获取的列族和列
    val get = new Get(record)
    columns.foreach(column => {
      get.addColumn(Bytes.toBytes(cf), Bytes.toBytes(column))
    })
    get
  },
  (result: Result) => {
    // 将结果转换为Array[(Array[Byte], Array[Byte], Array[Byte])]
    result.rawCells().map(cell => (cell.getRowArray, cell.getFamilyArray, cell.getValueArray))
  }
)

1. You can further process the data in RDD, for example, by converting it into a DataFrame for analysis.

import spark.implicits._

val df = rdd.map(row => (Bytes.toString(row._1), Bytes.toString(row._2), Bytes.toString(row._3)))
  .toDF("rowkey", "column_family", "value")

df.show()

This allows you to read local HBase files and perform further processing and analysis in Spark. Please note that the above example assumes that the HBase configuration and ZooKeeper connection parameters have been correctly set up.