一. 前提條件

引用

成都創(chuàng)新互聯(lián)公司主營(yíng)高密網(wǎng)站建設(shè)的網(wǎng)絡(luò)公司,主營(yíng)網(wǎng)站建設(shè)方案,app開發(fā)定制,高密h5微信小程序開發(fā)搭建,高密網(wǎng)站營(yíng)銷推廣歡迎高密等地區(qū)企業(yè)咨詢

Spark Standalone集群是Master-Slaves架構(gòu)的集群模式，和大部分的Master-Slaves結(jié)構(gòu)集群一樣，存在著Master單點(diǎn)故障的問(wèn)題。如何解決這個(gè)單點(diǎn)故障的問(wèn)題，Spark提供了兩種方案：

基于文件系統(tǒng)的單點(diǎn)恢復(fù)(Single-Node Recovery with Local File System)
基于zookeeper的Standby Masters(Standby Masters with ZooKeeper)
ZooKeeper提供了一個(gè)Leader Election機(jī)制，利用這個(gè)機(jī)制可以保證雖然集群存在多個(gè)Master，但是只有一個(gè)是Active的，其他的都是Standby。當(dāng)Active的Master出現(xiàn)故障時(shí)，另外的一個(gè)Standby Master會(huì)被選舉出來(lái)。由于集群的信息，包括Worker， Driver和Application的信息都已經(jīng)持久化到文件系統(tǒng)，因此在切換的過(guò)程中只會(huì)影響新Job的提交，對(duì)于正在進(jìn)行的Job沒(méi)有任何的影響。加入ZooKeeper的集群整體架構(gòu)

Zookeeper集群正常運(yùn)行

二. 部署步驟

1. 下載Spark程序壓縮包

   wget http://mirrors.shu.edu.cn/apache/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz

2. 解壓縮并重命名

   tar -zxvf spark-2.4.0-bin-hadoop2.7.tgz -C /opt
   mv spark-2.4.0-bin-hadoop2.7 spark-2.4.0

3. 配置環(huán)境變量
   /etc/profile

   export JAVA_HOME=/usr/lib/jdk1.8.0_172
   export CLASSPATH=${JAVA_HOME}/jre/lib:${JAVA_HOME}/lib
   export HADOOP_HOME=/opt/hadoop-2.7.6
   export SPARK_HOME=/opt/spark-2.4.0
   export PATH=${JAVA_HOME}/bin:$HADOOP_HOME/bin:$SPARK_HOME/bin:$PATH

   修改機(jī)器名稱

   hostnamectl set-hostname res-spark-0001

   執(zhí)行命令使得環(huán)境變量生效

   source /etc/profile

4. 修改配置文件

cd /opt/spark-2.4.0/conf
cp log4j.properties.template log4j.properties
cp slaves.template slaves
cp spark-env.sh.template spark-env.sh
cp spark-defaults.conf.template spark-defaults.conf

4.1 slaves

res-spark-0003
res-spark-0004
res-spark-0005

4.2 spark-defaults.conf

spark.deploy.recoveryMode          ZOOKEEPER
spark.deploy.zookeeper.url         res-spark-0001:2181,res-spark-0002:2181,res-spark-0003:2181

spark.master                       spark://res-spark-0001:7077
spark.eventLog.enabled             true
spark.eventLog.dir                 hdfs://cluster1/spark/eventLog

spark.shuffle.service.enabled  true

4.3 spark-env.sh

export JAVA_HOME=/usr/lib/jdk1.8.0_172
export HADOOP_HOME=/opt/hadoop-2.7.6
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export YARN_CONF_DIR=$HADOOP_HOME/etc/hadoop

export SPARK_HOME=/opt/spark-2.4.0

export SPARK_WORKER_CORES=6
export SPARK_WORKER_MEMORY=24g

4.4 log4j.properties

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# Set everything to be logged to the console
log4j.rootCategory=INFO, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Set the default spark-shell log level to WARN. When running the spark-shell, the
# log level for this class is used to overwrite the root logger's log level, so that
# the user can have different defaults for the shell and regular Spark apps.
log4j.logger.org.apache.spark.repl.Main=WARN

# Settings to quiet third party logs that are too verbose
log4j.logger.org.spark_project.jetty=WARN
log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
log4j.logger.org.apache.parquet=ERROR
log4j.logger.parquet=ERROR

# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

5. 分發(fā)spark程序以及配置文件到其他節(jié)點(diǎn)

   scp -r /opt/spark-2.4.0 res-spark-0002:/opt
   scp -r /opt/spark-2.4.0 res-spark-0003:/opt
   scp -r /opt/spark-2.4.0 res-spark-0004:/opt
   scp -r /opt/spark-2.4.0 res-spark-0005:/opt

6. 修改 res-spark-0002節(jié)點(diǎn)的配置文件
   6.1 spark-defaults.conf

   spark.master                       spark://res-spark-0002:7077

7. 啟動(dòng)集群

cd sbin
./start-all.sh

res-spark-0002節(jié)點(diǎn)

cd sbin
./start-master.sh

8. 測(cè)試
   res-spark-0001節(jié)點(diǎn)執(zhí)行

   ./stop-master.sh

   得到如下結(jié)果
   

9. submit app

   spark-submit --master spark://res-spark-0001:7077  --driver-cores 4 --driver-memory 6g --conf spark.dynamicAllocation.enabled=true --conf spark.shuffle.service.enabled=true --class com.cloud.RuleEngine rule-engine-1.0-SNAPSHOT-jar-with-dependencies.jar

   報(bào)錯(cuò)信息

   18/12/30 08:47:41 ERROR TaskSchedulerImpl: Lost executor 3 on 172.16.0.24: Unable to create executor due to Unable to register with external shuffle server due to : Failed to connect to /172.16.0.24:7337

   官網(wǎng)：

   In standalone mode, simply start your workers with spark.shuffle.service.enabled set to true.