大数据进阶之路——Spark SQL基本配置
文章目录
-
-
- Spark安装
- 编译失败
- 环境搭建
- Standalone
- 本地IDE
- HiveContextAPP
- SparkSessinon
- Spark Shell
- Spark Sql
- thriftserver/beeline的使用
- jdbc
-
MapReduce的局限性：
1）代码繁琐；
2）只能够支持map和reduce方法；
3）执行效率低下；
4）不适合迭代多次、交互式、流式的处理；
框架多样化：
1）批处理（离线）：MapReduce、Hive、Pig
2）流式处理（实时）： Storm、JStorm
3）交互式计算：Impala
学习、运维成本无形中都提高了很多
===> Spark
Spark安装
前置要求：
1）Building Spark using Maven requires Maven 3.3.9 or newer and Java 7+
2）export MAVEN_OPTS="-Xmx2g -XX:ReservedCodeCacheSize=512m"
mvn编译命令：
./build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
[hadoop@hadoop001 spark-2.1.0]$ cat pom.xml
[hadoop@hadoop001 spark-2.1.0]$ pwd
/home/hadoop/source/spark-2.1.0
<properties>
<hadoop.version>2.2.0hadoop.version>
<protobuf.version>2.5.0protobuf.version>
<yarn.version>${hadoop.version}yarn.version>
......
properties>
...............
<profile>
<id>hadoop-2.6id>
<properties>
<hadoop.version>2.6.4hadoop.version>
<jets3t.version>0.9.3jets3t.version>
<zookeeper.version>3.4.6zookeeper.version>
<curator.version>2.6.0curator.version>
properties>
profile>
路径下执行
[hadoop@hadoop001 spark-2.1.0]$ pwd
/home/hadoop/source/spark-2.1.0
==> ./build/mvn -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver -Dhadoop.version=2.6.0-cdh5.7.0 -DskipTests clean package
编译可以运行的包
./dev/make-distribution.sh --name 2.6.0-cdh5.7.0 --tgz -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver -Dhadoop.version=2.6.0-cdh5.7.0
make-distribution.sh
spark-$VERSION-bin-$NAME.tgz
—>spark-2.1.0-bin-2.6.0-cdh5.7.0.tgz
编译失败
Failed to execute goal on project ...: Could not resolve dependencies for project ...
在pom.xml中添加
<repository>
<id>clouderaid>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/url>
repository>
如果scala是2.10
需要添加./dev/change-scala-version.sh 2.10
环境搭建
local
-
tar -zxvf park-2.1.0-bin-2.6.0-cdh5.7.0.tgz -C ~/app/ -
配置环境
SPARK_HOME -
source ~./bash_profile
运行
spark-shell --master local[2]
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
a)
Caused by: org.datanucleus.exceptions.NucleusException: Attempt to invoke the "BONECP" plugin to create a ConnectionPool gave an error : The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH. Please check your CLASSPATH specification, and the name of the driver.
at org.datanucleus.store.rdbms.ConnectionFactoryImpl.generateDataSources(ConnectionFactoryImpl.java:259)
java:104)
.............................................
at org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)
571)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)
at org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)
at org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:325)
at org.datanucleus.store.AbstractStoreManager.registerConnectionFactory(AbstractStoreManager.java:282)
at org.datanucleus.store.AbstractStoreManager.<init>(AbstractStoreManager.java:240)
Caused by: org.datanucleus.store.rdbms.connectionpool.DatastoreDriverNotFoundException: The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH. Please check your CLASSPATH specification, and the name of the driver.
at org.datanucleus.store.rdbms.connectionpool.AbstractConnectionPoolFactory.loadDriver(AbstractConnectionPoolFactory.java:58)
at org.datanucleus.store.rdbms.connectionpool.BoneCPConnectionPoolFactory.createConnectionPool(BoneCPConnectionPoolFactory.java:54)
at org.datanucleus.store.rdbms.ConnectionFactoryImpl.generateDataSources(ConnectionFactoryImpl.java:238)
... 145 more
原因没有引入mysql驱动
spark-shell --master local[2] --jar /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar
[hadoop@hadoop001 software]$ spark-shell --master local[2] --jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
20/10/16 20:42:32 WARN SparkContext: Support for Java 7 is deprecated as of Spark 2.0.0
20/10/16 20:42:33 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
20/10/16 20:42:35 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
20/10/16 20:42:50 ERROR ObjectStore: Version information found in metastore differs 1.1.0 from expected schema version 1.2.0. Schema verififcation is disabled hive.metastore.schema.verification so setting version.
20/10/16 20:42:53 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Spark context Web UI available at http://192.168.43.214:4041
Spark context available as 'sc' (master = local[2], app id = local-1602906155852).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.1.0
/_/
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51)
Type in expressions to have them evaluated.
Type :help for more information.
Standalone
Spark Standalone模式的架构和Hadoop HDFS/YARN很类似的
1 master + n worker
spark-env.sh
SPARK_MASTER_HOST=hadoop001
SPARK_WORKER_CORES=2
SPARK_WORKER_MEMORY=2g
SPARK_WORKER_INSTANCES=1
master:
hadoop1
slaves:
hadoop2
hadoop3
hadoop4
....
hadoop10
==> start-all.sh 会在 hadoop1机器上启动master进程，在slaves文件配置的所有hostname的机器上启动worker进程
Spark WordCount统计
val file = spark.sparkContext.textFile(“file:///home/hadoop/data/wc.txt”)
val wordCounts = file.flatMap(line => line.split(",")).map((word => (word, 1))).reduceByKey(_ + _)
wordCounts.collect
本地IDE
A master URL must be set in your configuration
点击edit configuration，在左侧点击该项目。在右侧VM options中输入“-Dspark.master=local”，指示本程序本地单线程运行，再次运行即可。
package org.example
import org.apache.spark.sql.SQLContext
import org.apache.spark.{
SparkConf, SparkContext}
object SQLContextAPP {
def main(args: Array[String]): Unit = {
//1创建相应的Spark
val sparkConf = new SparkConf()
sparkConf.setAppName("SQLContextAPP")
val sc = new SparkContext(sparkConf)
val sqlContext = new SQLContext(sc)
//2数据处理
val people = sqlContext.read.format("json").load("people.json")
people.printSchema()
people.show()
//3关闭资源
sc.stop()
}
}
root
|-- age: long (nullable = true)
|-- name: string (nullable = true)
.........................
| age| name|
+----+-------+
|null|Michael|
| 30| Andy|
| 19| Justin|
+----+-------+
配置maven环境变量cmd控制台提示：mvn不是内部或外部命令，也不是可运行的程序或批处理文件
首先maven环境变量：
变量名：MAVEN_HOME
变量值：E:\apache-maven-3.2.3
变量名：Path
变量值：;%MAVEN_HOME%\bin
然后到项目的目录下直接执行
C:\Users\jacksun\IdeaProjects\SqarkSQL\ mvn clean package -DskipTests
在集群上测试
spark-submit \
--name SQLContextApp \
--class org.example.SQLContextApp \
--master local[2] \
/home/hadoop/lib/sql-1.0.jar \
/home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/examples/src/main/resources/people.json
HiveContextAPP
注意：
1）To use a HiveContext, you do not need to have an existing Hive setup
2）hive-site.xml
package org.example
import org.apache.spark.{
SparkConf, SparkContext}
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.hive.HiveContext
object HiveContextAPP {
def main(args: Array[String]): Unit = {
//1创建相应的Spark
val path =args(0)
val sparkConf = new SparkConf()
//测试和生产中AppName和Master是通过脚本执行的
//sparkConf.setAppName("HiveContextAPP").setMaster("local[2]")
val sc = new SparkContext(sparkConf)
val hiveContext = new HiveContext(sc)
//2数据处理
hiveContext.table("emp").show
//3关闭资源
sc.stop()
}
}
spark-submit \
--name HiveContextApp \
--class org.example.HiveContextApp \
--master local[2] \
/home/hadoop/lib/sql-1.0.jar \
--jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar
SparkSessinon
package org.example
import org.apache.spark.sql.SparkSession
object SparkSessionApp {
def main(args: Array[String]) {
val spark = SparkSession.builder().appName("SparkSessionApp")
.master("local[2]").getOrCreate()
val people = spark.read.json("people.json")
people.show()
spark.stop()
}
}
Spark Shell
- 启动hive
[hadoop@hadoop001 bin]$ pwd
/home/hadoop/app/hive-1.1.0-cdh5.7.0/bin
[hadoop@hadoop001 bin]$ hive
ls: cannot access /home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/lib/spark-assembly-*.jar: No such file or directory
which: no hbase in (/home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/bin:/home/hadoop/app/scala-2.11.8/bin:/home/hadoop/app/hive-1.1.0-cdh5.7.0/bin:/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/bin:/home/hadoop/app/apache-maven-3.3.9/bin:/home/hadoop/app/jdk1.7.0_51/bin:/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/sbin)
Logging initialized using configuration in jar:file:/home/hadoop/app/hive-1.1.0-cdh5.7.0/lib/hive-common-1.1.0-cdh5.7.0.jar!/hive-log4j.properties
WARNING: Hive CLI is deprecated and migration to Beeline is recommended.
hive>
-
拷贝
[hadoop@hadoop001 conf]$ cp hive-site.xml ~/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/conf/ -
启动Spark
spark-shell --master local[2] --jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar
scala> spark.sql("show tables").show
+--------+------------+-----------+
|database| tableName|isTemporary|
+--------+------------+-----------+
| default| dept| false|
| default| emp| false|
| default|hive_table_1| false|
| default|hive_table_2| false|
| default| t| false|
+--------+------------+-----------+
hive> show tables;
OK
dept
emp
hive_wordcount
scala> spark.sql("select * from emp e join dept d on e.deptno=d.deptno").show
+-----+------+---------+----+----------+------+------+------+------+----------+--------+
|empno| ename| job| mgr| hiredate| sal| comm|deptno|deptno| dname| loc|
+-----+------+---------+----+----------+------+------+------+------+----------+--------+
| 7369| SMITH| CLERK|7902|1980-12-17| 800.0| null| 20| 20| RESEARCH| DALLAS|
| 7499| ALLEN| SALESMAN|7698| 1981-2-20|1600.0| 300.0| 30| 30| SALES| CHICAGO|
| 7521| WARD| SALESMAN|7698| 1981-2-22|1250.0| 500.0| 30| 30| SALES| CHICAGO|
| 7566| JONES| MANAGER|7839| 1981-4-2|2975.0| null| 20| 20| RESEARCH| DALLAS|
| 7654|MARTIN| SALESMAN|7698| 1981-9-28|1250.0|1400.0| 30| 30| SALES| CHICAGO|
| 7698| BLAKE| MANAGER|7839| 1981-5-1|2850.0| null| 30| 30| SALES| CHICAGO|
| 7782| CLARK| MANAGER|7839| 1981-6-9|2450.0| null| 10| 10|ACCOUNTING|NEW YORK|
| 7788| SCOTT| ANALYST|7566| 1987-4-19|3000.0| null| 20| 20| RESEARCH| DALLAS|
| 7839| KING|PRESIDENT|null|1981-11-17|5000.0| null| 10| 10|ACCOUNTING|NEW YORK|
| 7844|TURNER| SALESMAN|7698| 1981-9-8|1500.0| 0.0| 30| 30| SALES| CHICAGO|
| 7876| ADAMS| CLERK|7788| 1987-5-23|1100.0| null| 20| 20| RESEARCH| DALLAS|
| 7900| JAMES| CLERK|7698| 1981-12-3| 950.0| null| 30| 30| SALES| CHICAGO|
| 7902| FORD| ANALYST|7566| 1981-12-3|3000.0| null| 20| 20| RESEARCH| DALLAS|
| 7934|MILLER| CLERK|7782| 1982-1-23|1300.0| null| 10| 10|ACCOUNTING|NEW YORK|
+-----+------+---------+----+----------+------+------+------+------+----------+--------+
hive> select * from emp e join dept d on e.deptno=d.deptno
> ;
Query ID = hadoop_20201020054545_f7fbda3e-439e-409e-b2ce-3c553d969ed4
Total jobs = 1
20/10/20 05:48:46 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Execution log at: /tmp/hadoop/hadoop_20201020054545_f7fbda3e-439e-409e-b2ce-3c553d969ed4.log
2020-10-20 05:48:49 Starting to launch local task to process map join; maximum memory = 477102080
2020-10-20 05:48:51 Dump the side-table for tag: 1 with group count: 4 into file: file:/tmp/hadoop/5c8577b3-c00d-4ece-9899-c0e3de66f2f2/hive_2020-10-20_05-48-27_437_556791932773953494-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile01--.hashtable
2020-10-20 05:48:51 Uploaded 1 File to: file:/tmp/hadoop/5c8577b3-c00d-4ece-9899-c0e3de66f2f2/hive_2020-10-20_05-48-27_437_556791932773953494-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile01--.hashtable (404 bytes)
2020-10-20 05:48:51 End of local task; Time Taken: 2.691 sec.
Execution completed successfully
MapredLocal task succeeded
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1602849227137_0002, Tracking URL = http://hadoop001:8088/proxy/application_1602849227137_0002/
Kill Command = /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/bin/hadoop job -kill job_1602849227137_0002
Hadoop job information for Stage-3: number of mappers: 1; number of reducers: 0
2020-10-20 05:49:13,663 Stage-3 map = 0%, reduce = 0%
2020-10-20 05:49:36,950 Stage-3 map = 100%, reduce = 0%, Cumulative CPU 13.08 sec
MapReduce Total cumulative CPU time: 13 seconds 80 msec
Ended Job = job_1602849227137_0002
MapReduce Jobs Launched:
Stage-Stage-3: Map: 1 Cumulative CPU: 13.08 sec HDFS Read: 7639 HDFS Write: 927 SUCCESS
Total MapReduce CPU Time Spent: 13 seconds 80 msec
OK
7369 SMITH CLERK 7902 1980-12-17 800.0 NULL 20 20 RESEARCH DALLAS
7499 ALLEN SALESMAN 7698 1981-2-20 1600.0 300.0 30 30 SALES CHICAGO
7521 WARD SALESMAN 7698 1981-2-22 1250.0 500.0 30 30 SALES CHICAGO
7566 JONES MANAGER 7839 1981-4-2 2975.0 NULL 20 20 RESEARCH DALLAS
7654 MARTIN SALESMAN 7698 1981-9-28 1250.0 1400.0 30 30 SALES CHICAGO
7698 BLAKE MANAGER 7839 1981-5-1 2850.0 NULL 30 30 SALES CHICAGO
7782 CLARK MANAGER 7839 1981-6-9 2450.0 NULL 10 10 ACCOUNTING NEW YORK
7788 SCOTT ANALYST 7566 1987-4-19 3000.0 NULL 20 20 RESEARCH DALLAS
7839 KING PRESIDENT NULL 1981-11-17 5000.0 NULL 10 10 ACCOUNTINGNEW YORK
7844 TURNER SALESMAN 7698 1981-9-8 1500.0 0.0 30 30 SALES CHICAGO
7876 ADAMS CLERK 7788 1987-5-23 1100.0 NULL 20 20 RESEARCH DALLAS
7900 JAMES CLERK 7698 1981-12-3 950.0 NULL 30 30 SALES CHICAGO
7902 FORD ANALYST 7566 1981-12-3 3000.0 NULL 20 20 RESEARCH DALLAS
7934 MILLER CLERK 7782 1982-1-23 1300.0 NULL 10 10 ACCOUNTING NEW YORK
Time taken: 71.998 seconds, Fetched: 14 row(s)
hive>
SPARK SQL 基本秒出结果,hive比较耗时
- hive-site.xml
删除警告
<property>
<name>hive.metastore.schema.verificationname>
<value>falsevalue>
property>
Spark Sql
20/10/20 06:20:09 INFO DAGScheduler: Job 1 finished: processCmd at CliDriver.java:376, took 0.261151 s
7369 SMITH CLERK 7902 1980-12-17 800.0 NULL 20 20 RESEARCH DALLAS
7499 ALLEN SALESMAN 7698 1981-2-20 1600.0 300.0 30 30 SALES CHICAGO
7521 WARD SALESMAN 7698 1981-2-22 1250.0 500.0 30 30 SALES CHICAGO
7566 JONES MANAGER 7839 1981-4-2 2975.0 NULL 20 20 RESEARCH DALLAS
7654 MARTIN SALESMAN 7698 1981-9-28 1250.0 1400.0 30 30 SALES CHICAGO
7698 BLAKE MANAGER 7839 1981-5-1 2850.0 NULL 30 30 SALES CHICAGO
7782 CLARK MANAGER 7839 1981-6-9 2450.0 NULL 10 10 ACCOUNTING NEW YORK
7788 SCOTT ANALYST 7566 1987-4-19 3000.0 NULL 20 20 RESEARCH DALLAS
7839 KING PRESIDENT NULL 1981-11-17 5000.0 NULL 10 10 ACCOUNTINGNEW YORK
7844 TURNER SALESMAN 7698 1981-9-8 1500.0 0.0 30 30 SALES CHICAGO
7876 ADAMS CLERK 7788 1987-5-23 1100.0 NULL 20 20 RESEARCH DALLAS
7900 JAMES CLERK 7698 1981-12-3 950.0 NULL 30 30 SALES CHICAGO
7902 FORD ANALYST 7566 1981-12-3 3000.0 NULL 20 20 RESEARCH DALLAS
7934 MILLER CLERK 7782 1982-1-23 1300.0 NULL 10 10 ACCOUNTING NEW YORK
Time taken: 13.625 seconds, Fetched 14 row(s)
20/10/20 06:20:09 INFO CliDriver: Time taken: 13.625 seconds, Fetched 14 row(s)
explain extended select a.key*(2+3), b.value from t a join t b on a.key = b.key and a.key > 3;
== Parsed Logical Plan ==
'Project [unresolvedalias(('a.key * (2 + 3)), None), 'b.value]
+- 'Join Inner, (('a.key = 'b.key) && ('a.key > 3))
:- 'UnresolvedRelation `t`, a
+- 'UnresolvedRelation `t`, b
== Analyzed Logical Plan ==
(CAST(key AS DOUBLE) * CAST((2 + 3) AS DOUBLE)): double, value: string
Project [(cast(key#321 as double) * cast((2 + 3) as double)) AS (CAST(key AS DOUBLE) * CAST((2 + 3) AS DOUBLE))#325, value#324]
+- Join Inner, ((key#321 = key#323) && (cast(key#321 as double) > cast(3 as double)))
:- SubqueryAlias a
: +- MetastoreRelation default, t
+- SubqueryAlias b
+- MetastoreRelation default, t
== Optimized Logical Plan ==
Project [(cast(key#321 as double) * 5.0) AS (CAST(key AS DOUBLE) * CAST((2 + 3) AS DOUBLE))#325, value#324]
+- Join Inner, (key#321 = key#323)
:- Project [key#321]
: +- Filter (isnotnull(key#321) && (cast(key#321 as double) > 3.0))
: +- MetastoreRelation default, t
+- Filter (isnotnull(key#323) && (cast(key#323 as double) > 3.0))
+- MetastoreRelation default, t
== Physical Plan ==
*Project [(cast(key#321 as double) * 5.0) AS (CAST(key AS DOUBLE) * CAST((2 + 3) AS DOUBLE))#325, value#324]
+- *SortMergeJoin [key#321], [key#323], Inner
:- *Sort [key#321 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(key#321, 200)
: +- *Filter (isnotnull(key#321) && (cast(key#321 as double) > 3.0))
: +- HiveTableScan [key#321], MetastoreRelation default, t
+- *Sort [key#323 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(key#323, 200)
+- *Filter (isnotnull(key#323) && (cast(key#323 as double) > 3.0))
+- HiveTableScan [key#323, value#324], MetastoreRelation default, t
thriftserver/beeline的使用
spark下的sbin
- 启动thriftserver:
./start-thriftserver.sh --master local[2] --jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar
默认端口是10000 ，可以修改
./start-thriftserver.sh \
--master local[2] \
--jars ~/software/mysql-connector-java-5.1.27-bin.jar \
--hiveconf hive.server2.thrift.port=14000
2）启动beeline
beeline -u jdbc:hive2://localhost:10000 -n hadoop
beeline -u jdbc:hive2://localhost:14000 -n hadoop
thriftserver和普通的spark-shell/spark-sql有什么区别？
1）spark-shell、spark-sql都是一个spark application；
2）thriftserver
- 不管你启动多少个客户端(beeline/code)，永远都是一个spark application
- 解决了一个数据共享的问题，多个客户端可以共享数据；
jdbc
注意事项：在使用jdbc开发时，一定要先启动thriftserver
Exception in thread "main" java.sql.SQLException:
Could not open client transport with JDBC Uri: jdbc:hive2://hadoop001:14000:
java.net.ConnectException: Connection refused
org.spark-project.hive
hive-jdbc
1.2.1.spark2
package org.example
import java.sql.DriverManager
object JDBCApp {
def main(args: Array[String]) {
Class.forName("org.apache.hive.jdbc.HiveDriver")
val conn = DriverManager.getConnection("jdbc:hive2://192.168.43.214:10000","hadoop","")
val pstmt = conn.prepareStatement("select empno, ename, sal from emp")
val rs = pstmt.executeQuery()
while (rs.next()) {
println("empno:" + rs.getInt("empno") +
" , ename:" + rs.getString("ename") +
" , sal:" + rs.getDouble("sal"))
}
rs.close()
pstmt.close()
conn.close()
}
}
