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Accessing Hbase from Apache Spark
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| import org.apache.spark.rdd.NewHadoopRDD | |
| import org.apache.hadoop.hbase.mapreduce.TableInputFormat | |
| import org.apache.hadoop.hbase.HBaseConfiguration | |
| import org.apache.hadoop.hbase.client.Result | |
| import org.apache.hadoop.hbase.io.ImmutableBytesWritable | |
| import scala.collection.JavaConversions._ | |
| import scala.collection.JavaConverters._ | |
| val sc = new SparkContext("local", "Simple App") | |
| val hbaseConfiguration = (tableName: String) => { | |
| val hbaseConfiguration = HBaseConfiguration.create() | |
| hbaseConfiguration.set(TableInputFormat.INPUT_TABLE, tableName) | |
| hbaseConfiguration | |
| } | |
| val tableRDD = (table: String) => { | |
| val rdd = new NewHadoopRDD( | |
| sc, | |
| classOf[TableInputFormat], | |
| classOf[ImmutableBytesWritable], | |
| classOf[Result], | |
| hbaseConfiguration(table) | |
| ) | |
| rdd | |
| } | |
| val rdd = tableRDD("table-with-data") | |
| /** Convert columns to strings **/ | |
| val columns = rdd.map(tuple => tuple._2).map(result => result.getColumn("Column Family".getBytes(), | |
| "ColumnQualifier".getBytes())).map(keyValues => { | |
| new String(keyValues.asScala.reduceLeft{ | |
| (a,b) => if (a.getTimestamp > b.getTimestamp) a else b | |
| }.getValue.map(_.toChar))}) | |
| /** another way to get multiple columns */ | |
| val cols = rdd.map(tuple => tuple._2).map(result => result.getColumn("CF".getBytes, "CQ1".getBytes) :: result.getColumn("CF".getBytes, "CQ2".getBytes) :: result.getColumn("CF".getBytes, "CQ2".getBytes):: Nil) | |
| // remove invalid items from rdd | |
| val filtered = cols.filter( row.map(_.length > 0).reduce((acc, tip) => acc & tip) ) | |
| /** convert all values to strings **/ | |
| val row_vals = filtered.map(row => row.map(ele => new String(ele.head.getValue.map(_.toChar)))) |
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