Spark SQL 初步
已经Spark Submit 2013哪里有介绍Spark SQL。就在很多人都介绍Catalyst查询优化框架。经过一年的发展后，。今年Spark Submit 2014在。Databricks放弃Shark 发育。和开关Spark SQL。是Shark继承了Hive太多，优化出现了瓶颈。如图：
今天把Spark最新的代码签了下来。測试了一下：
1、编译SparkSQL
-bash-3.2$ git config --global http.sslVerify false
-bash-3.2$ git clone https://github.com/apache/spark.git
正克隆到 'spark'...
remote: Reusing existing pack: 107821, done.
remote: Counting objects: 103, done.
remote: Compressing objects: 100% (72/72), done.
remote: Total 107924 (delta 20), reused 64 (delta 16)
Receiving objects: 100% (107924/107924), 69.06 MiB | 3.39 MiB/s, done.
Resolving deltas: 100% (50174/50174), done.
这里还是须要先build一下的，sbt/sbt assembly（怎样build匹配版本号，请參考Spark编译及集群搭建）
执行 sbt/sbt hive/console也会进行编译。
最新的spark sql提供了一个console，在这里能够直接的执行交互式查下。也提供了几个样例。
2、运行Spark SQL
官方提供给我们了一个測试用例。
通过查看log，find . -name TestHive* 找到了位于：
/app/hadoop/shengli/spark/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveTestHive.scala 有兴趣能够自己打开编译调试下看看。
首先进入控制台：
sbt/sbt hive/console
[info] Starting scala interpreter...
[info]
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.dsl._
import org.apache.spark.sql.catalyst.errors._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules._
import org.apache.spark.sql.catalyst.types._
import org.apache.spark.sql.catalyst.util._
import org.apache.spark.sql.execution
import org.apache.spark.sql.hive._
import org.apache.spark.sql.hive.test.TestHive._
import org.apache.spark.sql.parquet.ParquetTestData
Welcome to Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.6.0_20).
Type in expressions to have them evaluated.
Type :help for more information.
scala>
查看一下当前RunTime下都提供了哪些方法：
scala>
<init> DslAttribute DslExpression DslString DslSymbol
ParquetTestData SqlCmd analyzer autoConvertJoinSize binaryToLiteral
booleanToLiteral byteToLiteral cacheTable cacheTables catalog
classOf clear clone configure contains
createParquetFile createSchemaRDD createTable decimalToLiteral describedTable
doubleToLiteral emptyResult eq equals executePlan
executeSql execution finalize floatToLiteral get
getAll getClass getHiveFile getOption hashCode
hiveDevHome hiveFilesTemp hiveHome hivePlanner hiveQTestUtilTables
hiveconf hiveql hql inRepoTests inferSchema
intToLiteral isCached joinBroadcastTables jsonFile jsonRDD
loadTestTable logger logicalPlanToSparkQuery longToLiteral metastorePath
ne notify notifyAll numShufflePartitions optimizer
originalUdfs outputBuffer parquetFile parseSql parser
planner prepareForExecution registerRDDAsTable registerTestTable reset
runHive runSqlHive sessionState set shortToLiteral
sparkContext sql stringToLiteral symbolToUnresolvedAttribute synchronized
table testTables timestampToLiteral toDebugString toString
uncacheTable wait warehousePath
我们发现，这个測试用例里面有一个testTables。因为这些成员都是lazy的。所以一開始没有被载入：
查看測试用例要载入哪些表：
scala> testTables
14/07/02 18:45:59 INFO spark.SecurityManager: Changing view acls to: hadoop
14/07/02 18:45:59 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop)
14/07/02 18:46:00 INFO slf4j.Slf4jLogger: Slf4jLogger started
14/07/02 18:46:00 INFO Remoting: Starting remoting
14/07/02 18:46:00 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://spark@web02.dw:42984]
14/07/02 18:46:00 INFO Remoting: Remoting now listens on addresses: [akka.tcp://spark@web02.dw:42984]
14/07/02 18:46:00 INFO spark.SparkEnv: Registering MapOutputTracker
14/07/02 18:46:00 INFO spark.SparkEnv: Registering BlockManagerMaster
14/07/02 18:46:00 INFO storage.DiskBlockManager: Created local directory at /tmp/spark-local-20140702184600-9e16
14/07/02 18:46:00 INFO network.ConnectionManager: Bound socket to port 48348 with id = ConnectionManagerId(web02.dw,48348)
14/07/02 18:46:00 INFO storage.MemoryStore: MemoryStore started with capacity 1097.0 MB
14/07/02 18:46:00 INFO storage.BlockManagerMaster: Trying to register BlockManager
14/07/02 18:46:00 INFO storage.BlockManagerInfo: Registering block manager web02.dw:48348 with 1097.0 MB RAM
14/07/02 18:46:00 INFO storage.BlockManagerMaster: Registered BlockManager
14/07/02 18:46:00 INFO spark.HttpServer: Starting HTTP Server
14/07/02 18:46:01 INFO server.Server: jetty-8.1.14.v20131031
14/07/02 18:46:01 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:36260
14/07/02 18:46:01 INFO broadcast.HttpBroadcast: Broadcast server started at http://10.1.8.207:36260
14/07/02 18:46:01 INFO spark.HttpFileServer: HTTP File server directory is /tmp/spark-ca40f66c-edc3-484f-b317-d3f512aab244
14/07/02 18:46:01 INFO spark.HttpServer: Starting HTTP Server
14/07/02 18:46:01 INFO server.Server: jetty-8.1.14.v20131031
14/07/02 18:46:01 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:57821
14/07/02 18:46:01 INFO server.Server: jetty-8.1.14.v20131031
14/07/02 18:46:02 INFO server.AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040
14/07/02 18:46:02 INFO ui.SparkUI: Started SparkUI at http://web02.dw:4040
metastore path is /tmp/sparkHiveMetastore8060064816530828092
warehousePath path is /tmp/sparkHiveWarehouse5366068035857129261
hiveHome path is Some(/home/hadoop/Java/lib/hive-0.6.0)
hiveDevHome path is None
res0: scala.collection.mutable.HashMap[String,org.apache.spark.sql.hive.test.TestHive.TestTable] = Map(sales -> TestTable(sales,WrappedArray(<function0>, <function0>)), src -> TestTable(src,WrappedArray(<function0>, <function0>)), src1 -> TestTable(src1,WrappedArray(<function0>, <function0>)), serdeins -> TestTable(serdeins,WrappedArray(<function0>, <function0>)), src_thrift -> TestTable(src_thrift,WrappedArray(<function0>)), srcpart -> TestTable(srcpart,WrappedArray(<function0>)), episodes -> TestTable(episodes,WrappedArray(<function0>, <function0>)), srcpart1 -> TestTable(srcpart1,WrappedArray(<function0>)))
測试select语句
1.首先声明一个sql
2.这是測试用例会用hive的metastore。创建一个derby的数据库
3.创建上述的所以表。并把数据载入进去。
4.Parse这条select * from sales 语句。
5. 生成SchemaRDD并产生查询计划。
6. 当对querySales这个RDD运行Action的时候。会计算这条sql的运行。
下面是运行的具体结果：（能够看到log打出的大概运行步骤）
scala> val querySales = sql("select * from sales")
14/07/02 18:51:19 INFO test.TestHive$: Loading test table sales
14/07/02 18:51:19 INFO parse.ParseDriver: Parsing command: CREATE TABLE IF NOT EXISTS sales (key STRING, value INT)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES ("input.regex" = "([^ ]*) ([^ ]*)")
14/07/02 18:51:19 INFO parse.ParseDriver: Parse Completed
14/07/02 18:51:19 INFO analysis.Analyzer: Max iterations (2) reached for batch MultiInstanceRelations
14/07/02 18:51:19 INFO analysis.Analyzer: Max iterations (2) reached for batch CaseInsensitiveAttributeReferences
14/07/02 18:51:19 INFO sql.SQLContext$$anon$1: Max iterations (2) reached for batch Add exchange
14/07/02 18:51:19 INFO sql.SQLContext$$anon$1: Max iterations (2) reached for batch Prepare Expressions
14/07/02 18:51:19 INFO ql.Driver: <PERFLOG method=Driver.run>
14/07/02 18:51:19 INFO ql.Driver: <PERFLOG method=TimeToSubmit>
14/07/02 18:51:19 INFO ql.Driver: <PERFLOG method=compile>
14/07/02 18:51:19 INFO ql.Driver: <PERFLOG method=parse>
14/07/02 18:51:19 INFO parse.ParseDriver: Parsing command: CREATE TABLE IF NOT EXISTS sales (key STRING, value INT)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES ("input.regex" = "([^ ]*) ([^ ]*)")
14/07/02 18:51:19 INFO parse.ParseDriver: Parse Completed
14/07/02 18:51:19 INFO ql.Driver: </PERFLOG method=parse start=1404298279883 end=1404298279885 duration=2>
14/07/02 18:51:19 INFO ql.Driver: <PERFLOG method=semanticAnalyze>
14/07/02 18:51:19 INFO parse.SemanticAnalyzer: Starting Semantic Analysis
14/07/02 18:51:19 INFO parse.SemanticAnalyzer: Creating table sales position=27
14/07/02 18:51:20 INFO metastore.HiveMetaStore: 0: Opening raw store with implemenation class:org.apache.hadoop.hive.metastore.ObjectStore
14/07/02 18:51:20 INFO metastore.ObjectStore: ObjectStore, initialize called
14/07/02 18:51:20 INFO DataNucleus.Persistence: Property datanucleus.cache.level2 unknown - will be ignored
14/07/02 18:51:21 WARN bonecp.BoneCPConfig: Max Connections < 1. Setting to 20
14/07/02 18:51:25 INFO metastore.ObjectStore: Setting MetaStore object pin classes with hive.metastore.cache.pinobjtypes="Table,StorageDescriptor,SerDeInfo,Partition,Database,Type,FieldSchema,Order"
14/07/02 18:51:25 INFO metastore.ObjectStore: Initialized ObjectStore
14/07/02 18:51:26 WARN bonecp.BoneCPConfig: Max Connections < 1. Setting to 20
14/07/02 18:51:26 WARN metastore.ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 0.12.0
14/07/02 18:51:27 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:27 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:27 INFO DataNucleus.Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema" is tagged as "embedded-only" so does not have its own datastore table.
14/07/02 18:51:27 INFO DataNucleus.Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only" so does not have its own datastore table.
14/07/02 18:51:28 INFO ql.Driver: Semantic Analysis Completed
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=semanticAnalyze start=1404298279885 end=1404298288331 duration=8446>
14/07/02 18:51:28 INFO ql.Driver: Returning Hive schema: Schema(fieldSchemas:null, properties:null)
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=compile start=1404298279840 end=1404298288340 duration=8500>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=Driver.execute>
14/07/02 18:51:28 INFO ql.Driver: Starting command: CREATE TABLE IF NOT EXISTS sales (key STRING, value INT)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES ("input.regex" = "([^ ]*) ([^ ]*)")
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=TimeToSubmit start=1404298279840 end=1404298288351 duration=8511>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=runTasks>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=task.DDL.Stage-0>
14/07/02 18:51:28 INFO metastore.HiveMetaStore: 0: create_table: Table(tableName:sales, dbName:default, owner:hadoop, createTime:1404298288, lastAccessTime:0, retention:0, sd:StorageDescriptor(cols:[FieldSchema(name:key, type:string, comment:null), FieldSchema(name:value, type:int, comment:null)], location:null, inputFormat:org.apache.hadoop.mapred.TextInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.RegexSerDe, parameters:{serialization.format=1, input.regex=([^ ]*) ([^ ]*)}), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{}, viewOriginalText:null, viewExpandedText:null, tableType:MANAGED_TABLE, privileges:PrincipalPrivilegeSet(userPrivileges:null, groupPrivileges:null, rolePrivileges:null))
14/07/02 18:51:28 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=create_table: Table(tableName:sales, dbName:default, owner:hadoop, createTime:1404298288, lastAccessTime:0, retention:0, sd:StorageDescriptor(cols:[FieldSchema(name:key, type:string, comment:null), FieldSchema(name:value, type:int, comment:null)], location:null, inputFormat:org.apache.hadoop.mapred.TextInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.RegexSerDe, parameters:{serialization.format=1, input.regex=([^ ]*) ([^ ]*)}), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{}, viewOriginalText:null, viewExpandedText:null, tableType:MANAGED_TABLE, privileges:PrincipalPrivilegeSet(userPrivileges:null, groupPrivileges:null, rolePrivileges:null))
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=task.DDL.Stage-0 start=1404298288351 end=1404298288589 duration=238>
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=runTasks start=1404298288351 end=1404298288589 duration=238>
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=Driver.execute start=1404298288340 end=1404298288589 duration=249>
14/07/02 18:51:28 INFO ql.Driver: OK
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=releaseLocks>
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=releaseLocks start=1404298288590 end=1404298288590 duration=0>
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=Driver.run start=1404298279839 end=1404298288590 duration=8751>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=releaseLocks>
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=releaseLocks start=1404298288590 end=1404298288590 duration=0>
14/07/02 18:51:28 INFO parse.ParseDriver: Parsing command: LOAD DATA LOCAL INPATH 'sql/hive/src/test/resources/data/files/sales.txt' INTO TABLE sales
14/07/02 18:51:28 INFO parse.ParseDriver: Parse Completed
14/07/02 18:51:28 INFO analysis.Analyzer: Max iterations (2) reached for batch MultiInstanceRelations
14/07/02 18:51:28 INFO analysis.Analyzer: Max iterations (2) reached for batch CaseInsensitiveAttributeReferences
14/07/02 18:51:28 INFO sql.SQLContext$$anon$1: Max iterations (2) reached for batch Add exchange
14/07/02 18:51:28 INFO sql.SQLContext$$anon$1: Max iterations (2) reached for batch Prepare Expressions
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=Driver.run>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=TimeToSubmit>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=compile>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=parse>
14/07/02 18:51:28 INFO parse.ParseDriver: Parsing command: LOAD DATA LOCAL INPATH 'sql/hive/src/test/resources/data/files/sales.txt' INTO TABLE sales
14/07/02 18:51:28 INFO parse.ParseDriver: Parse Completed
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=parse start=1404298288629 end=1404298288629 duration=0>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=semanticAnalyze>
14/07/02 18:51:28 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:28 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:28 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/07/02 18:51:28 INFO ql.Driver: Semantic Analysis Completed
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=semanticAnalyze start=1404298288630 end=1404298288942 duration=312>
14/07/02 18:51:28 INFO ql.Driver: Returning Hive schema: Schema(fieldSchemas:null, properties:null)
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=compile start=1404298288628 end=1404298288943 duration=315>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=Driver.execute>
14/07/02 18:51:28 INFO ql.Driver: Starting command: LOAD DATA LOCAL INPATH 'sql/hive/src/test/resources/data/files/sales.txt' INTO TABLE sales
14/07/02 18:51:28 INFO ql.Driver: </PERFLOG method=TimeToSubmit start=1404298288628 end=1404298288943 duration=315>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=runTasks>
14/07/02 18:51:28 INFO ql.Driver: <PERFLOG method=task.COPY.Stage-0>
14/07/02 18:51:28 INFO exec.Task: Copying data from file:/app/hadoop/spark/sql/hive/src/test/resources/data/files/sales.txt to file:/tmp/hive-hadoop/hive_2014-07-02_18-51-28_629_2309366591646930035-1/-ext-10000
14/07/02 18:51:28 INFO exec.Task: Copying file: file:/app/hadoop/spark/sql/hive/src/test/resources/data/files/sales.txt
14/07/02 18:51:29 INFO ql.Driver: </PERFLOG method=task.COPY.Stage-0 start=1404298288943 end=1404298289037 duration=94>
14/07/02 18:51:29 INFO ql.Driver: <PERFLOG method=task.MOVE.Stage-1>
14/07/02 18:51:29 INFO exec.Task: Loading data to table default.sales from file:/tmp/hive-hadoop/hive_2014-07-02_18-51-28_629_2309366591646930035-1/-ext-10000
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: alter_table: db=default tbl=sales newtbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=alter_table: db=default tbl=sales newtbl=sales
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:29 INFO ql.Driver: </PERFLOG method=task.MOVE.Stage-1 start=1404298289037 end=1404298289196 duration=159>
14/07/02 18:51:29 INFO ql.Driver: <PERFLOG method=task.STATS.Stage-2>
14/07/02 18:51:29 INFO exec.StatsTask: Executing stats task
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: alter_table: db=default tbl=sales newtbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=alter_table: db=default tbl=sales newtbl=sales
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:29 INFO exec.Task: Table default.sales stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 13, raw_data_size: 0]
14/07/02 18:51:29 INFO ql.Driver: </PERFLOG method=task.STATS.Stage-2 start=1404298289196 end=1404298289282 duration=86>
14/07/02 18:51:29 INFO ql.Driver: </PERFLOG method=runTasks start=1404298288943 end=1404298289282 duration=339>
14/07/02 18:51:29 INFO ql.Driver: </PERFLOG method=Driver.execute start=1404298288943 end=1404298289282 duration=339>
14/07/02 18:51:29 INFO ql.Driver: OK
14/07/02 18:51:29 INFO ql.Driver: <PERFLOG method=releaseLocks>
14/07/02 18:51:29 INFO ql.Driver: </PERFLOG method=releaseLocks start=1404298289282 end=1404298289282 duration=0>
14/07/02 18:51:29 INFO ql.Driver: </PERFLOG method=Driver.run start=1404298288628 end=1404298289282 duration=654>
14/07/02 18:51:29 INFO ql.Driver: <PERFLOG method=releaseLocks>
14/07/02 18:51:29 INFO ql.Driver: </PERFLOG method=releaseLocks start=1404298289282 end=1404298289282 duration=0>
14/07/02 18:51:29 INFO analysis.Analyzer: Max iterations (2) reached for batch MultiInstanceRelations
14/07/02 18:51:29 INFO analysis.Analyzer: Max iterations (2) reached for batch CaseInsensitiveAttributeReferences
14/07/02 18:51:29 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=sales
14/07/02 18:51:29 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=sales
14/07/02 18:51:29 INFO storage.MemoryStore: ensureFreeSpace(355913) called with curMem=0, maxMem=1150314086
14/07/02 18:51:29 INFO storage.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 347.6 KB, free 1096.7 MB)
14/07/02 18:51:29 INFO sql.SQLContext$$anon$1: Max iterations (2) reached for batch Add exchange
14/07/02 18:51:29 INFO sql.SQLContext$$anon$1: Max iterations (2) reached for batch Prepare Expressions
querySales: org.apache.spark.sql.SchemaRDD =
SchemaRDD[0] at RDD at SchemaRDD.scala:100
== Query Plan ==
HiveTableScan [key#2,value#3], (MetastoreRelation default, sales, None), None
运行spark sql
scala> querySales.collect()
14/07/02 18:57:32 WARN snappy.LoadSnappy: Snappy native library is available
14/07/02 18:57:32 WARN snappy.LoadSnappy: Snappy native library not loaded
14/07/02 18:57:32 INFO mapred.FileInputFormat: Total input paths to process : 1
14/07/02 18:57:32 INFO spark.SparkContext: Starting job: collect at SparkPlan.scala:52
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Got job 0 (collect at SparkPlan.scala:52) with 3 output partitions (allowLocal=false)
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Final stage: Stage 0(collect at SparkPlan.scala:52)
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Parents of final stage: List()
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Missing parents: List()
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Submitting Stage 0 (MappedRDD[5] at map at SparkPlan.scala:52), which has no missing parents
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Submitting 3 missing tasks from Stage 0 (MappedRDD[5] at map at SparkPlan.scala:52)
14/07/02 18:57:32 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 3 tasks
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Re-computing pending task lists.
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Starting task 0.0:0 as TID 0 on executor localhost: localhost (PROCESS_LOCAL)
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Serialized task 0.0:0 as 3606 bytes in 20 ms
14/07/02 18:57:32 INFO executor.Executor: Running task ID 0
14/07/02 18:57:32 INFO storage.BlockManager: Found block broadcast_0 locally
14/07/02 18:57:32 INFO rdd.HadoopRDD: Input split: file:/tmp/sparkHiveWarehouse5366068035857129261/sales/sales.txt:0+6
14/07/02 18:57:32 INFO executor.Executor: Serialized size of result for 0 is 1947
14/07/02 18:57:32 INFO executor.Executor: Sending result for 0 directly to driver
14/07/02 18:57:32 INFO executor.Executor: Finished task ID 0
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Starting task 0.0:1 as TID 1 on executor localhost: localhost (PROCESS_LOCAL)
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Serialized task 0.0:1 as 3606 bytes in 0 ms
14/07/02 18:57:32 INFO executor.Executor: Running task ID 1
14/07/02 18:57:32 INFO storage.BlockManager: Found block broadcast_0 locally
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Completed ResultTask(0, 0)
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Finished TID 0 in 243 ms on localhost (progress: 1/3)
14/07/02 18:57:32 INFO rdd.HadoopRDD: Input split: file:/tmp/sparkHiveWarehouse5366068035857129261/sales/sales.txt:6+6
14/07/02 18:57:32 INFO executor.Executor: Serialized size of result for 1 is 1948
14/07/02 18:57:32 INFO executor.Executor: Sending result for 1 directly to driver
14/07/02 18:57:32 INFO executor.Executor: Finished task ID 1
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Starting task 0.0:2 as TID 2 on executor localhost: localhost (PROCESS_LOCAL)
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Serialized task 0.0:2 as 3606 bytes in 1 ms
14/07/02 18:57:32 INFO executor.Executor: Running task ID 2
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Finished TID 1 in 36 ms on localhost (progress: 2/3)
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Completed ResultTask(0, 1)
14/07/02 18:57:32 INFO storage.BlockManager: Found block broadcast_0 locally
14/07/02 18:57:32 INFO rdd.HadoopRDD: Input split: file:/tmp/sparkHiveWarehouse5366068035857129261/sales/sales.txt:12+1
14/07/02 18:57:32 INFO executor.Executor: Serialized size of result for 2 is 1721
14/07/02 18:57:32 INFO executor.Executor: Sending result for 2 directly to driver
14/07/02 18:57:32 INFO executor.Executor: Finished task ID 2
14/07/02 18:57:32 INFO scheduler.TaskSetManager: Finished TID 2 in 96 ms on localhost (progress: 3/3)
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Completed ResultTask(0, 2)
14/07/02 18:57:32 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
14/07/02 18:57:32 INFO scheduler.DAGScheduler: Stage 0 (collect at SparkPlan.scala:52) finished in 0.366 s
14/07/02 18:57:32 INFO spark.SparkContext: Job finished: collect at SparkPlan.scala:52, took 0.454512333 s
res1: Array[org.apache.spark.sql.Row] = Array([Joe,2], [Hank,2])
运行结果：
Array([Joe,2], [Hank,2])
查询计划优化：
<pre name="code" class="python">scala> val query = sql("SELECT * FROM (SELECT * FROM src) a")
query: org.apache.spark.sql.SchemaRDD =
SchemaRDD[6] at RDD at SchemaRDD.scala:100
== Query Plan ==
<span style="font-family: Arial, Helvetica, sans-serif;">HiveTableScan [key#6,value#7], (MetastoreRelation default, src, None), None</span>
3、Spark SQL LINQ
在Spark相关的框架里。一切的核心体都是RDD，SchemaRDD提供类似LINQ的语法api：
such as take, where... etc
scala> query.
++ aggregate as asInstanceOf baseLogicalPlan baseSchemaRDD
cache cartesian checkpoint coalesce collect compute
context count countApprox countApproxDistinct countByValue countByValueApprox
dependencies distinct filter filterWith first flatMap
flatMapWith fold foreach foreachPartition foreachWith generate
getCheckpointFile getPartitions getStorageLevel glom groupBy id
insertInto intersection isCheckpointed isInstanceOf iterator join
keyBy limit map mapPartitions mapPartitionsWithContext mapPartitionsWithIndex
mapPartitionsWithSplit mapWith max min name name_=
orderBy partitioner partitions persist pipe preferredLocations
printSchema queryExecution randomSplit reduce registerAsTable repartition
sample saveAsObjectFile saveAsParquetFile saveAsTable saveAsTextFile schemaString
select setName sortBy sparkContext sqlContext subtract
take takeOrdered takeSample toArray toDebugString toJavaRDD
toJavaSchemaRDD toLocalIterator toSchemaRDD toString top union
unionAll unpersist where zip zipPartitions zipWithIndex
zipWithUniqueId
注意key前面带了一撇。这个是Catalyst的查下语法，以后我会写一篇具体介绍：
scala> query.where('key === 100).collect()
14/07/02 19:07:55 INFO analysis.Analyzer: Max iterations (2) reached for batch MultiInstanceRelations
14/07/02 19:07:55 INFO analysis.Analyzer: Max iterations (2) reached for batch CaseInsensitiveAttributeReferences
14/07/02 19:07:55 INFO metastore.HiveMetaStore: 0: get_table : db=default tbl=src
14/07/02 19:07:55 INFO HiveMetaStore.audit: ugi=hadoop ip=unknown-ip-addr cmd=get_table : db=default tbl=src
14/07/02 19:07:55 INFO storage.MemoryStore: ensureFreeSpace(358003) called with curMem=713876, maxMem=1150314086
14/07/02 19:07:55 INFO storage.MemoryStore: Block broadcast_3 stored as values in memory (estimated size 349.6 KB, free 1096.0 MB)
14/07/02 19:07:55 INFO sql.SQLContext$$anon$1: Max iterations (2) reached for batch Add exchange
14/07/02 19:07:55 INFO sql.SQLContext$$anon$1: Max iterations (2) reached for batch Prepare Expressions
14/07/02 19:07:55 INFO mapred.FileInputFormat: Total input paths to process : 1
14/07/02 19:07:55 INFO spark.SparkContext: Starting job: collect at SparkPlan.scala:52
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Got job 2 (collect at SparkPlan.scala:52) with 2 output partitions (allowLocal=false)
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Final stage: Stage 2(collect at SparkPlan.scala:52)
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Parents of final stage: List()
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Missing parents: List()
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Submitting Stage 2 (MappedRDD[20] at map at SparkPlan.scala:52), which has no missing parents
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Submitting 2 missing tasks from Stage 2 (MappedRDD[20] at map at SparkPlan.scala:52)
14/07/02 19:07:55 INFO scheduler.TaskSchedulerImpl: Adding task set 2.0 with 2 tasks
14/07/02 19:07:55 INFO scheduler.TaskSetManager: Starting task 2.0:0 as TID 5 on executor localhost: localhost (PROCESS_LOCAL)
14/07/02 19:07:55 INFO scheduler.TaskSetManager: Serialized task 2.0:0 as 3854 bytes in 0 ms
14/07/02 19:07:55 INFO executor.Executor: Running task ID 5
14/07/02 19:07:55 INFO storage.BlockManager: Found block broadcast_3 locally
14/07/02 19:07:55 INFO rdd.HadoopRDD: Input split: file:/tmp/sparkHiveWarehouse5366068035857129261/src/kv1.txt:0+2906
14/07/02 19:07:55 INFO executor.Executor: Serialized size of result for 5 is 1951
14/07/02 19:07:55 INFO executor.Executor: Sending result for 5 directly to driver
14/07/02 19:07:55 INFO scheduler.TaskSetManager: Starting task 2.0:1 as TID 6 on executor localhost: localhost (PROCESS_LOCAL)
14/07/02 19:07:55 INFO scheduler.TaskSetManager: Serialized task 2.0:1 as 3854 bytes in 0 ms
14/07/02 19:07:55 INFO executor.Executor: Finished task ID 5
14/07/02 19:07:55 INFO executor.Executor: Running task ID 6
14/07/02 19:07:55 INFO scheduler.TaskSetManager: Finished TID 5 in 44 ms on localhost (progress: 1/2)
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Completed ResultTask(2, 0)
14/07/02 19:07:55 INFO storage.BlockManager: Found block broadcast_3 locally
14/07/02 19:07:55 INFO rdd.HadoopRDD: Input split: file:/tmp/sparkHiveWarehouse5366068035857129261/src/kv1.txt:2906+2906
14/07/02 19:07:55 INFO executor.Executor: Serialized size of result for 6 is 1951
14/07/02 19:07:55 INFO executor.Executor: Sending result for 6 directly to driver
14/07/02 19:07:55 INFO executor.Executor: Finished task ID 6
14/07/02 19:07:55 INFO scheduler.TaskSetManager: Finished TID 6 in 19 ms on localhost (progress: 2/2)
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Completed ResultTask(2, 1)
14/07/02 19:07:55 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 2.0, whose tasks have all completed, from pool
14/07/02 19:07:55 INFO scheduler.DAGScheduler: Stage 2 (collect at SparkPlan.scala:52) finished in 0.062 s
14/07/02 19:07:55 INFO spark.SparkContext: Job finished: collect at SparkPlan.scala:52, took 0.06947625 s
res6: Array[org.apache.spark.sql.Row] = Array([100,val_100], [100,val_100])
查询出2个key为100的结果。
4、总结：
Spark SQL 提供了一种Catalyst查询优化框架，在把SQL解析成逻辑运行计划。对运行计划优化，最后变成RDD操作。多种框架一种API，简单，规范。
本文暂且为止，兴许还会继续相关的深入研究。
——EOF——
原创文章。转载请注明来自http://blog.csdn.net/oopsoom/article/details/36440821
Spark SQL 初步的更多相关文章
- spark SQL学习（认识spark SQL）
spark SQL初步认识 spark SQL是spark的一个模块,主要用于进行结构化数据的处理.它提供的最核心的编程抽象就是DataFrame. DataFrame:它可以根据很多源进行构建,包括 ...
- Spark SQL数据源
[TOC] 背景 Spark SQL是Spark的一个模块,用于结构化数据的处理. ++++++++++++++ +++++++++++++++++++++ | SQL | | Dataset API ...
- 我的Spark SQL单元测试实践
最近加入一个Spark项目,作为临时的开发人员协助进行开发工作.该项目中不存在测试的概念,开发人员按需求进行编码工作后,直接向生产系统部署,再由需求的提出者在生产系统检验程序运行结果的正确性.在这种原 ...
- spark SQL读取ORC文件从Driver启动到开始执行Task(或stage)间隔时间太长（计算Partition时间太长）且产出orc单个文件中stripe个数太多问题解决方案
1.背景: 控制上游文件个数每天7000个,每个文件大小小于256M,50亿条+,orc格式.查看每个文件的stripe个数,500个左右,查询命令:hdfs fsck viewfs://hadoop ...
- Spark SQL入门用法与原理分析
Spark SQL是为了让开发人员摆脱自己编写RDD等原生Spark代码而产生的,开发人员只需要写一句SQL语句或者调用API,就能生成(翻译成)对应的SparkJob代码并去执行,开发变得更简洁 注 ...
- Spark SQL join的三种实现方式
引言 join是SQL中的常用操作,良好的表结构能够将数据分散到不同的表中,使其符合某种规范(mysql三大范式),可以最大程度的减少数据冗余,更新容错等,而建立表和表之间关系的最佳方式就是join操 ...
- 整理对Spark SQL的理解
Catalyst Catalyst是与Spark解耦的一个独立库,是一个impl-free的运行计划的生成和优化框架. 眼下与Spark Core还是耦合的.对此user邮件组里有人对此提出疑问,见m ...
- Spark SQL 之 Data Sources
#Spark SQL 之 Data Sources 转载请注明出处:http://www.cnblogs.com/BYRans/ 数据源(Data Source) Spark SQL的DataFram ...
- Spark SQL 之 DataFrame
Spark SQL 之 DataFrame 转载请注明出处:http://www.cnblogs.com/BYRans/ 概述(Overview) Spark SQL是Spark的一个组件,用于结构化 ...
随机推荐
- android用shape画虚线，怎么也不显示
一直以为android的shape能画直线.虚线.矩形,圆形等.画直线也就算了.用一个view设一下高度和颜色,就能够出来一条直线了.所以说这个对我来说常常不用,圆形是能够,看看我应用里的消息提 ...
- 通过OpenSSL解码X509证书文件
在Windows平台下.假设要解析一个X509证书文件,最直接的办法是使用微软的CryptoAPI. 可是在非Windows平台下,就仅仅能使用强大的开源跨平台库OpenSSL了.一个X509证书通过 ...
- spring mvc 接受多对象的处置
spring mvc 接受多对象的处理 spring mvc感觉非常好用,尤其是对接收对象參数的自己主动绑定非常简便,但对于同一时候传多个对象时有些困扰. 同一时候项目并没有直接使用spring的fo ...
- C嵌入汇编
概述:linux内核源码中,有很多C语言中嵌入了汇编语句,如何理解这些汇编语句,对理解内核有很重要的作用. 具有输入和输出参数的嵌入式汇编语句的基本格式为: asm("汇编语句" ...
- 【ASP.NET Web API教程】4.2 路由与动作选择
原文:[ASP.NET Web API教程]4.2 路由与动作选择 注:本文是[ASP.NET Web API系列教程]的一部分,如果您是第一次看本系列教程,请先看前面的内容. 4.2 Routing ...
- 基于Predictive Parsing的ABNF语法分析器（十）——AbnfParser文法解析器之数值类型（num-val）
ANBF语法中的数值类型有3种:二进制.十进制和十六进制,可以是一个以点号分隔的数列,也可以是一个数值的范围.例如,%d11.22.33.44.55表示五个有次序的十进制数字“11.22.33.44. ...
- 为HttpStatusCodeResult加入customErrors
asp.net mvc的action返回值为HttpStatusCodeResult时的customErrors总是不起作用 (404和exception时的500,因为他们并不是HttpStatus ...
- uva 10602 Editor Nottoobad(排序)
题目连接:10602 Editor Nottoobad 题目大意:要输入n个单词,现在有三种操作, 1.输入一个字符,需要按下一次按键. 2.通过声控删除一个字符.3.通过声控复制一遍上面的单词.现 ...
- RBAC用户角色权限设计方案
RBAC(Role-Based Access Control,基于角色的访问控制),就是用户通过角色与权限进行关联.简单地说,一个用户拥有若干角色,每一个角色拥有若干权限.这样,就构造成“用 户-角色 ...
- [Android阅读代码]圆形旋转菜单CircleMenu
项目名称:圆形旋转菜单CircleMenu 原版项目代码下载 感谢原作者开源