关键字：SparkSQL读取HBase、SparkSQL自定义外部数据源
前面文章介绍了SparSQL通过Hive操作HBase表。
SparkSQL从1.2开始支持自定义外部数据源(External DataSource)，这样就可以通过API接口来实现自己的外部数据源。这里基于Spark1.4.0，简单介绍SparkSQL自定义外部数据源，访问HBase表。
在HBase中表如下：
create 'lxw1234',{NAME => 'f1',VERSIONS => 1},{NAME => 'f2',VERSIONS => 1},{NAME => 'f3',VERSIONS => 1}
put 'lxw1234','lxw1234.com','f1:c1','name1'
put 'lxw1234','lxw1234.com','f1:c2','name2'
put 'lxw1234','lxw1234.com','f2:c1','age1'
put 'lxw1234','lxw1234.com','f2:c2','age2'
put 'lxw1234','lxw1234.com','f3:c1','job1'
put 'lxw1234','lxw1234.com','f3:c2','job2'
put 'lxw1234','lxw1234.com','f3:c3','job3'
hbase(main):025:0* scan 'lxw1234'
ROW COLUMN+CELL
lxw1234.com column=f1:c1, timestamp=1435624625198, value=name1
lxw1234.com column=f1:c2, timestamp=1435624591717, value=name2
lxw1234.com column=f2:c1, timestamp=1435624608759, value=age1
lxw1234.com column=f2:c2, timestamp=1435624635261, value=age2
lxw1234.com column=f3:c1, timestamp=1435624662282, value=job1
lxw1234.com column=f3:c2, timestamp=1435624697028, value=job2
lxw1234.com column=f3:c3, timestamp=1435624697065, value=job3
进入spark-shell
sh /usr/local/spark-1.4.0-bin-hadoop2.3/bin/spark-shell --jars /tmp/sparksql-hbase.jar --total-executor-cores 30 --executor-memory 4G --master spark://lxw1234.com:7077
运行以下代码：
import sqlContext._
var hbasetable = sqlContext.read.format("com.lxw1234.sparksql.hbase").options(Map(
"sparksql_table_schema" -> "(row_key string, c1 string, c2 string, c3 string)",
"hbase_table_name" -> "lxw1234",
"hbase_table_schema" -> "(:key , f1:c2 , f2:c2 , f3:c3 )"
)).load()
//sparksql_table_schema参数为sparksql中表的定义
//hbase_table_name参数为HBase中表名
//hbase_table_schema参数为HBase表中需要映射到SparkSQL表中的列族和列，这里映射过//去的字段要和sparksql_table_schema中定义的一致，包括顺序。
scala> hbasetable.printSchema()
root
|-- row_key: string (nullable = false)
|-- c1: string (nullable = false)
|-- c2: string (nullable = false)
|-- c3: string (nullable = false)
hbasetable.registerTempTable("lxw1234")
sqlContext.sql("SELECT * from lxw1234").collect
res3: Array[org.apache.spark.sql.Row] = Array([lxw1234.com,name2,age2,job3])
sqlContext.sql("SELECT row_key,concat(c1,'|',c2,'|',c3) from lxw1234").collect
res3: Array[org.apache.spark.sql.Row] = Array([lxw1234.com,name2|age2|job3])
源码
HBaseRelation.scala
package com.lxw1234.sparksql.hbase
import java.io.Serializable
import org.apache.hadoop.fs.Path
import org.apache.spark.sql._
import org.apache.spark.sql.sources.TableScan
import scala.collection.immutable.{HashMap, Map}
import org.apache.hadoop.hbase.client.{Result, Scan, HTable, HBaseAdmin}
import org.apache.spark.sql._
import org.apache.spark.rdd.NewHadoopRDD
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.types.MapType
import org.apache.spark.sql.sources.BaseRelation
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 = resultType match {
case "string" =>
result.getValue(columnFamily.getBytes,columnName.getBytes).map(_.toChar).mkString
case "int" =>
result.getValue(columnFamily.getBytes,columnName.getBytes).map(_.toChar).mkString.toInt
case "long" =>
result.getValue(columnFamily.getBytes,columnName.getBytes).map(_.toChar).mkString.toLong
}
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(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)
//
// private def toSqlType(hbaseSchema: Schema): SchemaType = {
// SchemaType(StringType,true)
// }
}
DefaultSource.scala
package com.lxw1234.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.scala
package com.lxw1234.sparksql
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));
//sqlContext.baseRelationToSchemaRDD(HBaseRelation(params)(sqlContext))
}
}
// implicit class HBaseSchemaRDD(schemaRDD: SchemaRDD) {
// def saveIntoTable(tableName: String): Unit = ???
// }
}
相关配置和说明
- 本来在SparkSQL中通过外部数据源建表的语法是：
CREATE TEMPORARY TABLE hbasetable
USING com.lxw1234.sparksql.hbase
OPTIONS (
sparksql_table_schema ‘(row_key string, c1 string, c2 string, c3 string)’,
hbase_table_name ‘lxw1234′,
hbase_table_schema ‘(:key , f1:c2 , f2:c2 , f3:c3)’
)
在我的Spark1.4中报错，会使用Hive的语法解析器解析这个DDL语句，因为Hive0.13中没有这种语法，因此报错。
是否是因为Spark1.4包的编译了Hive的原因？
- 上面源码的编译依赖HBase的相关jar包：
hbase-client-0.96.1.1-cdh5.0.0.jar
hbase-common-0.96.1.1-cdh5.0.0.jar
hbase-protocol-0.96.1.1-cdh5.0.0.jar
hbase-server-0.96.1.1-cdh5.0.0.jar
还有HBase的集群信息：
hbase.zookeeper.quorum
hbase.client.scanner.caching
我之前在配置时候已经将这几个jar包和参数加到Spark集群的CLASSPATH中了，可参考 http://lxw1234.com/archives/2015/07/330.htm
- 此程序是OopsOutOfMemory基于Spark1.2开发的，我只做了很小的修改。
https://github.com/OopsOutOfMemory/spark-sql-hbase
- 此程序只做学习和测试使用，并未测试性能
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