Create Spark DataFrame from list row keys

Question

I have a list of HBase row keys in form or Array[Row] and want to create a Spark DataFrame out of the rows that are fetched from HBase using these RowKeys.

Am thinking of something like:

def getDataFrameFromList(spark: SparkSession, rList : Array[Row]): DataFrame = {

  val conf = HBaseConfiguration.create()
  val mlRows : List[RDD[String]] = new ArrayList[RDD[String]]

  conf.set("hbase.zookeeper.quorum", "dev.server")
  conf.set("hbase.zookeeper.property.clientPort", "2181")
  conf.set("zookeeper.znode.parent","/hbase-unsecure")
  conf.set(TableInputFormat.INPUT_TABLE, "hbase_tbl1")

  rList.foreach( r => {
    var rStr = r.toString()
    conf.set(TableInputFormat.SCAN_ROW_START, rStr)
    conf.set(TableInputFormat.SCAN_ROW_STOP, rStr + "_")
    // read one row
    val recsRdd = readHBaseRdd(spark, conf)
    mlRows.append(recsRdd)
  })

  // This works, but it is only one row
  //val resourcesDf = spark.read.json(recsRdd) 

  var resourcesDf = <Code here to convert List[RDD[String]] to DataFrame>
  //resourcesDf
  spark.emptyDataFrame
}

I can do recsRdd.collect() in the for loop and convert it to string and append that json to an ArrayList[String but am not sure if its efficient, to call collect() in a for loop like this.

readHBaseRdd is using newAPIHadoopRDD to get data from HBase

def readHBaseRdd(spark: SparkSession, conf: Configuration) = {
    val hBaseRDD = spark.sparkContext.newAPIHadoopRDD(conf, classOf[TableInputFormat],
      classOf[ImmutableBytesWritable],
      classOf[Result])

    hBaseRDD.map {
      case (_: ImmutableBytesWritable, value: Result) =>
          Bytes.toString(value.getValue(Bytes.toBytes("cf"),
                                            Bytes.toBytes("jsonCol")))
        }
    }
  }

Answer 1

Use spark.union([mainRdd, recsRdd]) instead of a list or RDDs (mlRows)

And why read only one row from HBase? Try to have the largest interval as possible.

Always avoid calling collect(), do it only for debug/tests.