通过自定义SparkSQL外部数据源实现SparkSQL读取HBase
标签: SparkSQL HBase Saprk External DataSource
package name: sparksql.hbase
Scala Class: HBaseRelation.scala
package sparksql.hbase
import java.io.Serializable
import org.apache.spark.sql._
import org.apache.spark.sql.sources.TableScan
import org.apache.hadoop.hbase.client.{Result}
import org.apache.spark.sql._
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import scala.collection.JavaConversions._
import scala.collection.JavaConverters._
import scala.collection.mutable.ArrayBuffer
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.DataType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.LongType
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.sources.BaseRelation
import sparksql.hbase.hbase._
object Resolver extends Serializable {
def resolve (hbaseField: HBaseSchemaField, result: Result ): Any = {
val cfColArray = hbaseField.fieldName.split(":",-1)
val cfName = cfColArray(0)
val colName = cfColArray(1)
var fieldRs: Any = null
//resolve row key otherwise resolve column
if(cfName=="" && colName=="key") {
fieldRs = resolveRowKey(result, hbaseField.fieldType)
} else {
fieldRs = resolveColumn(result, cfName, colName,hbaseField.fieldType)
}
fieldRs
}
def resolveRowKey (result: Result, resultType: String): Any = {
val rowkey = resultType match {
case "string" =>
result.getRow.map(_.toChar).mkString
case "int" =>
result .getRow.map(_.toChar).mkString.toInt
case "long" =>
result.getRow.map(_.toChar).mkString.toLong
}
rowkey
}
def resolveColumn (result: Result, columnFamily: String, columnName: String, resultType: String): Any = {
val column = result.containsColumn(columnFamily.getBytes, columnName.getBytes) match{
case true =>{
resultType match {
case "string" =>
Bytes.toString(result.getValue(columnFamily.getBytes,columnName.getBytes))
//result.getValue(columnFamily.getBytes,columnName.getBytes).map(_.toChar).mkString
case "int" =>
Bytes.toInt(result.getValue(columnFamily.getBytes,columnName.getBytes))
case "long" =>
Bytes.toLong(result.getValue(columnFamily.getBytes,columnName.getBytes))
case "float" =>
Bytes.toFloat(result.getValue(columnFamily.getBytes,columnName.getBytes))
case "double" =>
Bytes.toDouble(result.getValue(columnFamily.getBytes,columnName.getBytes))
}
}
case _ => {
resultType match {
case "string" =>
""
case "int" =>
0
case "long" =>
0
}
}
}
column
}
}
/**
val hbaseDDL = s"""
|CREATE TEMPORARY TABLE hbase_people
|USING com.shengli.spark.hbase
|OPTIONS (
| sparksql_table_schema '(row_key string, name string, age int, job string)',
| hbase_table_name 'people',
| hbase_table_schema '(:key , profile:name , profile:age , career:job )'
|)""".stripMargin
*/
case class HBaseRelation(@transient val hbaseProps: Map[String,String])(@transient val sqlContext: SQLContext) extends BaseRelation with Serializable with TableScan{
val hbaseTableName = hbaseProps.getOrElse("hbase_table_name", sys.error("not valid schema"))
val hbaseTableSchema = hbaseProps.getOrElse("hbase_table_schema", sys.error("not valid schema"))
val registerTableSchema = hbaseProps.getOrElse("sparksql_table_schema", sys.error("not valid schema"))
val rowRange = hbaseProps.getOrElse("row_range", "->")
//get star row and end row
val range = rowRange.split("->",-1)
val startRowKey = range(0).trim
val endRowKey = range(1).trim
val tempHBaseFields = extractHBaseSchema(hbaseTableSchema) //do not use this, a temp field
val registerTableFields = extractRegisterSchema(registerTableSchema)
val tempFieldRelation = tableSchemaFieldMapping(tempHBaseFields,registerTableFields)
val hbaseTableFields = feedTypes(tempFieldRelation)
val fieldsRelations = tableSchemaFieldMapping(hbaseTableFields,registerTableFields)
val queryColumns = getQueryTargetCloumns(hbaseTableFields)
def feedTypes( mapping: Map[HBaseSchemaField, RegisteredSchemaField]) : Array[HBaseSchemaField] = {
val hbaseFields = mapping.map{
case (k,v) =>
val field = k.copy(fieldType=v.fieldType)
field
}
hbaseFields.toArray
}
def isRowKey(field: HBaseSchemaField) : Boolean = {
val cfColArray = field.fieldName.split(":",-1)
val cfName = cfColArray(0)
val colName = cfColArray(1)
if(cfName=="" && colName=="key") true else false
}
//eg: f1:col1 f1:col2 f1:col3 f2:col1
def getQueryTargetCloumns(hbaseTableFields: Array[HBaseSchemaField]): String = {
var str = ArrayBuffer[String]()
hbaseTableFields.foreach{ field=>
if(!isRowKey(field)) {
str += field.fieldName
}
}
str.mkString(" ")
}
lazy val schema = {
val fields = hbaseTableFields.map{ field=>
val name = fieldsRelations.getOrElse(field, sys.error("table schema is not match the definition.")).fieldName
val relatedType = field.fieldType match {
case "string" =>
SchemaType(StringType,nullable = false)
case "int" =>
SchemaType(IntegerType,nullable = false)
case "long" =>
SchemaType(LongType,nullable = false)
}
StructField(name,relatedType.dataType,relatedType.nullable)
}
StructType(fields)
}
def tableSchemaFieldMapping( externalHBaseTable: Array[HBaseSchemaField], registerTable : Array[RegisteredSchemaField]): Map[HBaseSchemaField, RegisteredSchemaField] = {
if(externalHBaseTable.length != registerTable.length) sys.error("columns size not match in definition!")
val rs = externalHBaseTable.zip(registerTable)
rs.toMap
}
/**
* spark sql schema will be register
* registerTableSchema '(rowkey string, value string, column_a string)'
*/
def extractRegisterSchema(registerTableSchema: String) : Array[RegisteredSchemaField] = {
val fieldsStr = registerTableSchema.trim.drop(1).dropRight(1)
val fieldsArray = fieldsStr.split(",").map(_.trim)
fieldsArray.map{ fildString =>
val splitedField = fildString.split("\\s+", -1)
RegisteredSchemaField(splitedField(0), splitedField(1))
}
}
//externalTableSchema '(:key , f1:col1 )'
def extractHBaseSchema(externalTableSchema: String) : Array[HBaseSchemaField] = {
val fieldsStr = externalTableSchema.trim.drop(1).dropRight(1)
val fieldsArray = fieldsStr.split(",").map(_.trim)
fieldsArray.map(fildString => HBaseSchemaField(fildString,""))
}
// By making this a lazy val we keep the RDD around, amortizing the cost of locating splits.
lazy val buildScan = {
val hbaseConf = HBaseConfiguration.create()
hbaseConf.set("hbase.zookeeper.quorum", "zookeeper-name")
hbaseConf.set(TableInputFormat.INPUT_TABLE, hbaseTableName)
hbaseConf.set(TableInputFormat.SCAN_COLUMNS, queryColumns)
hbaseConf.set(TableInputFormat.SCAN_ROW_START, startRowKey)
hbaseConf.set(TableInputFormat.SCAN_ROW_STOP, endRowKey)
val hbaseRdd = sqlContext.sparkContext.newAPIHadoopRDD(
hbaseConf,
classOf[org.apache.hadoop.hbase.mapreduce.TableInputFormat],
classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
classOf[org.apache.hadoop.hbase.client.Result]
)
val rs = hbaseRdd.map(tuple => tuple._2).map(result => {
var values = new ArrayBuffer[Any]()
hbaseTableFields.foreach{field=>
values += Resolver.resolve(field,result)
}
Row.fromSeq(values.toSeq)
})
rs
}
private case class SchemaType(dataType: DataType, nullable: Boolean)
}
Scala Class: DefaultSource.scala
package sparksql.hbase
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.sources.RelationProvider
class DefaultSource extends RelationProvider {
def createRelation(sqlContext: SQLContext, parameters: Map[String, String]) = {
HBaseRelation(parameters)(sqlContext)
}
}
Package Object: package.scala
package sparksql.hbase
import org.apache.spark.sql.SQLContext
import scala.collection.immutable.HashMap
package object hbase {
abstract class SchemaField extends Serializable
case class RegisteredSchemaField(fieldName: String, fieldType: String) extends SchemaField with Serializable
case class HBaseSchemaField(fieldName: String, fieldType: String) extends SchemaField with Serializable
case class Parameter(name: String)
protected val SPARK_SQL_TABLE_SCHEMA = Parameter("sparksql_table_schema")
protected val HBASE_TABLE_NAME = Parameter("hbase_table_name")
protected val HBASE_TABLE_SCHEMA = Parameter("hbase_table_schema")
protected val ROW_RANGE = Parameter("row_range")
/**
* Adds a method, `hbaseTable`, to SQLContext that allows reading data stored in hbase table.
*/
implicit class HBaseContext(sqlContext: SQLContext) {
def hbaseTable(sparksqlTableSchema: String, hbaseTableName: String, hbaseTableSchema: String, rowRange: String = "->") = {
var params = new HashMap[String, String]
params += ( SPARK_SQL_TABLE_SCHEMA.name -> sparksqlTableSchema)
params += ( HBASE_TABLE_NAME.name -> hbaseTableName)
params += ( HBASE_TABLE_SCHEMA.name -> hbaseTableSchema)
//get star row and end row
params += ( ROW_RANGE.name -> rowRange)
sqlContext.baseRelationToDataFrame(HBaseRelation(params)(sqlContext))
}
}
}
使用实例: test.scala
package test
import org.apache.spark.SparkConf
import org.apache.spark.sql.SQLContext
import org.apache.spark.SparkContext
object SparkSqlHbaseTest {
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setMaster("yarn-client").setAppName("SparkSQL HBase Test")
val sc = new SparkContext(sparkConf)
val sqlContext = new SQLContext(sc)
var hbasetable = sqlContext.read.format("sparksql.hbase").options(Map(
"sparksql_table_schema" -> "(key string, Sequence string)",
"hbase_table_name" -> "tbTexpertR1",
"hbase_table_schema" -> "(:key , info:Sequence)"
)).load()
hbasetable.printSchema()
hbasetable.registerTempTable("tbTexpertR1")
var records = sqlContext.sql("SELECT * from tbTexpertR1 limit 10").collect
}
}
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