主流大数据技术全体系参数与搭建与后台代码工程框架的编写(百分之70)

之前查阅源码啊,性能测试啊调优啊。。基本告一段落,项目也接近尾声,那么整理下spark所有配置参数与优化策略,方便以后开发与配置:

Spark安装配置与代码框架

spark-default.conf 配置

spark.executor.instance 参数,向Yarn申请创建的资源池实例数

spark.executor.cores 参数,每个container中所包含的core数量

spark.executor.memory 参数,每个资源池所具有的内存数

spark.dirver.memory 参数,driver端所占用的资源数

spark.storage.memoryFraction 参数,用于cache数据与计算数据集的内存使用比例

spark.kryoserializer.buffer.max 参数,序列化最大值,默认64M

spark.shuffle.consolidateFiles 参数,shuffle是否合并文件

spark.rdd.compress 参数,rdd是否进行压缩

spark.sql.shuffle.partitions 参数,shuffle过程中所创建的partition个数

spark.reducer.maxSizeInFlight 参数,设置shuffle read task的buffer缓冲大小,它将决定每次数据从远程的executors中拉取大小。这个拉取过程是由5个并行的request,从不同的executor中拉取过来,从而提升了fetch的效率。

spark.shuffle.io.retryWait 参数,每次拉取数据的等待间隔

spark.shuffle.manage 参数,使用hash,同时与参数spark.shuffle.consolidateFiles true并用。因为不需要对中间结果进行排序,同时合并中间文件的个数,从而减少打开文件的性能消耗,在spark2.0.2中不可直接配置hash,会报错,其他优化参数包括:Sort Shuffle、Tungsten Sort,这里我们要根据数据量进行选择,优缺点请参考本博客《Spark Shuffle详细过程》

spark.executor.heartbeatInterval 参数,与driver的通讯间隔,使driver知道executor有木有挂

spark.driver.maxResultSize 参数,所有分区的序列化结果的总大小限制

spark.yarn.am.cores 参数,在yarn-client模式下,申请Yarn App Master所用的CPU核数

spark.master 参数,选用的模式

spark.task.maxFailures 参数,task失败多少次后丢弃job(防止因为网络IO等问题失败,重新拉取)

spark.shuffle.file.buffer 参数,会增大Map任务的写磁盘前的cache缓存

spark-env.sh 配置

export HADOOP_CONF_DIR 参数,配置hadoop所在配置文件路径

export HADOOP_HOME 参数,配置hadoop Client的所在路径

export JAVA_HOME 参数,配置JAVA的环境变量地址

export SPARK_YARN_APP_NAME 参数,配置application的名称

export SPARK_LOG_DIR 参数,配置Spark log的输出路径

export SPARK_PID_DIR 参数,配置spark的Pid输出路径

 将hive-site.xml文件放入spark的conf下 修改spark thrift port,使其与hive的thrift的port分离开来,同时配置mysql的数据源,因为hive的meta信息存在mysql中,以及配置meta指定的hdfs路径:

<property>   <name>hive.server2.thrift.port</name>   <value>10000</value>   <description>Port number of HiveServer2 Thrift interface.   Can be overridden by setting $HIVE_SERVER2_THRIFT_PORT</description> </property>

<property>   <name>hive.metastore.warehouse.dir</name>   <value>/user/hive/warehouse</value> </property>

<property>   <name>javax.jdo.option.ConnectionDriverName</name>   <value>com.mysql.jdbc.Driver</value>   <description>Driver class name for a JDBC metastore</description> </property>

<property>   <name>javax.jdo.option.ConnectionUserName</name>   <value>root</value>   <description>username to use against metastore database</description> </property>

<property>   <name>javax.jdo.option.ConnectionPassword</name>   <value>root</value>   <description>password to use against metastore database</description> </property>

Spark动态资源分配测试:

spark.dynamicAllocation.cachedExecutorIdleTimeout 360000000 如果executor中有数据则不移除

spark.dynamicAllocation.executorIdleTimeout 60s executor空闲时间达到规定值,则将该executor移除

spark.dynamicAllocation.initialExecutors 3 如果所有的executor都移除了,重新请求时启动的初始executor数

spark.dynamicAllocation.maxExecutors 30 能够启动的最大executor数目

spark.dynamicAllocation.minExecutors 1 能够启动的最小executor数目

spark.dynamicAllocation.schedulerBacklogTimeout 1s task等待运行时间超过该值后开始启动executor

spark.dynamicAllocation.enabled True 开启动态参数配置

spark.dynamicAllocation.sustainedSchedulerBacklogTimeout 1s 启动executor的时间间隔

启动脚本:

/usr/local/spark-1.6.1/sbin/start-thriftserver.sh \

--conf spark.shuffle.service.enabled=true \

--conf spark.dynamicAllocation.enabled=true \

--conf spark.dynamicAllocation.minExecutors=2 \

--conf spark.dynamicAllocation.maxExecutors=30 \

--conf spark.dynamicAllocation.sustainedSchedulerBacklogTimeout = 5s \

--conf spark.dynamicAllocation.schedulerBacklogTimeout=1s \

--conf spark.dynamicAllocation.initialExecutors=2 \

--conf spark.dynamicAllocation.executorIdleTimeout=60s \

--conf spark.dynamicAllocation.cachedExecutorIdleTimeout=360000000s \

--conf spark.driver.memory=50g

代码框架:

首先,我们引入需要依赖的包括hadoop、spark、hbase等jar包,pom.xml配置如下:

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>sparkApp</groupId>
  <artifactId>sparkApp</artifactId>
  <version>1.0-SNAPSHOT</version>
  <inceptionYear>2008</inceptionYear>
  <properties>
      <scala.version>2.10.0</scala.version>
      <spring.version>4.0.2.RELEASE</spring.version>
      <hadoop.version>2.6.0</hadoop.version>
      <jedis.version>2.8.1</jedis.version>
  </properties>

  <repositories>
    <repository>
      <id>scala-tools.org</id>
      <name>Scala-Tools Maven2 Repository</name>
      <url>http://scala-tools.org/repo-releases</url>
    </repository>
  </repositories>

  <pluginRepositories>
    <pluginRepository>
      <id>scala-tools.org</id>
      <name>Scala-Tools Maven2 Repository</name>
      <url>http://scala-tools.org/repo-releases</url>
    </pluginRepository>
  </pluginRepositories>

  <dependencies>
    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-library</artifactId>
      <version>${scala.version}</version>
    </dependency>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.4</version>
      <scope>test</scope>
    </dependency>

      <!-- SPARK START -->
      <dependency>
          <groupId>org.apache.spark</groupId>
          <artifactId>spark-core_2.10</artifactId>
          <version>1.6.1</version>
      </dependency>

      <!--<dependency>
          <groupId>org.apache.spark</groupId>
          <artifactId>spark-streaming-kafka_2.10</artifactId>
          <version>1.6.1</version>
      </dependency>-->

      <dependency>
          <groupId>org.apache.spark</groupId>
          <artifactId>spark-sql_2.10</artifactId>
          <version>1.6.1</version>
      </dependency>

      <dependency>
          <groupId>org.apache.spark</groupId>
          <artifactId>spark-mllib_2.10</artifactId>
          <version>1.6.1</version>
      </dependency>

      <dependency>
          <groupId>org.apache.spark</groupId>
          <artifactId>spark-hive_2.10</artifactId>
          <version>1.6.1</version>
      </dependency>

      <!-- SPARK END -->

      <!-- HADOOP START -->
      <dependency>
          <groupId>org.apache.hadoop</groupId>
          <artifactId>hadoop-hdfs</artifactId>
          <version>${hadoop.version}</version>
          <exclusions>
              <exclusion>
                  <groupId>commons-logging</groupId>
                  <artifactId>commons-logging</artifactId>
              </exclusion>
              <exclusion>
                  <groupId>log4j</groupId>
                  <artifactId>log4j</artifactId>
              </exclusion>
              <exclusion>
                  <groupId>org.slf4j</groupId>
                  <artifactId>slf4j-log4j12</artifactId>
              </exclusion>
              <exclusion>
                  <groupId>org.slf4j</groupId>
                  <artifactId>slf4j-api</artifactId>
              </exclusion>
              <exclusion>
                  <groupId>javax.servlet.jsp</groupId>
                  <artifactId>jsp-api</artifactId>
              </exclusion>
          </exclusions>
      </dependency>

      <dependency>
          <groupId>org.apache.hadoop</groupId>
          <artifactId>hadoop-common</artifactId>
          <version>${hadoop.version}</version>
      </dependency>

      <!-- HADOOP END -->


      <!-- hbase START  -->
      <dependency>
          <groupId>org.apache.hbase</groupId>
          <artifactId>hbase-server</artifactId>
          <version>1.0.2</version>
      </dependency>

      <dependency>
          <groupId>org.apache.htrace</groupId>
          <artifactId>htrace-core</artifactId>
          <version>3.1.0-incubating</version>
      </dependency>

      <dependency>
          <groupId>org.apache.hbase</groupId>
          <artifactId>hbase-client</artifactId>
          <version>1.0.2</version>
      </dependency>

      <dependency>
          <groupId>org.apache.hbase</groupId>
          <artifactId>hbase-common</artifactId>
          <version>1.0.2</version>
      </dependency>

      <dependency>
          <groupId>org.apache.hbase</groupId>
          <artifactId>hbase-protocol</artifactId>
          <version>1.0.2</version>
      </dependency>

      <!-- hbase END  -->

      <dependency>
          <groupId>com.google.guava</groupId>
          <artifactId>guava</artifactId>
          <version>11.0.2</version>
      </dependency>

      <dependency>
          <groupId>org.slf4j</groupId>
          <artifactId>slf4j-api</artifactId>
          <version>1.7.12</version>
      </dependency>


      <!-- REDIS START -->
      <dependency>
          <groupId>redis.clients</groupId>
          <artifactId>jedis</artifactId>
          <version>${jedis.version}</version>
      </dependency>
      <!-- REDIS END -->

      <dependency>
          <groupId>org.apache.commons</groupId>
          <artifactId>commons-lang3</artifactId>
          <version>3.1</version>
      </dependency>

      <dependency>
          <groupId>org.apache.commons</groupId>
          <artifactId>commons-pool2</artifactId>
          <version>2.3</version>
      </dependency>

      <dependency>
          <groupId>mysql</groupId>
          <artifactId>mysql-connector-java</artifactId>
          <version>5.1.26</version>
      </dependency>

      <!--<dependency>
          <groupId>org.datanucleus</groupId>
          <artifactId>datanucleus-api-jdo</artifactId>
          <version>3.2.6</version>
      </dependency>

      <dependency>
          <groupId>org.datanucleus</groupId>
          <artifactId>datanucleus-core</artifactId>
          <version>3.2.1</version>
      </dependency>

      <dependency>
          <groupId>org.datanucleus</groupId>
          <artifactId>datanucleus-rdbms</artifactId>
          <version>3.2.9</version>
      </dependency>-->

      <dependency>
          <groupId>io.netty</groupId>
          <artifactId>netty-all</artifactId>
          <version>5.0.0.Alpha1</version>
      </dependency>

     <!-- <dependency>
          <groupId>org.apache.hive</groupId>
          <artifactId>hive-service</artifactId>
          <version>1.2.1</version>
      </dependency>-->

      <dependency>
          <groupId>org.apache.hive</groupId>
          <artifactId>hive-jdbc</artifactId>
          <version>2.1.0</version>
      </dependency>

      <dependency>
          <groupId>org.apache.hive</groupId>
          <artifactId>hive-service</artifactId>
          <version>1.2.1</version>
      </dependency>

  </dependencies>

  <build>
<!--    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>-->
    <plugins>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
          </execution>
        </executions>
        <configuration>
          <scalaVersion>${scala.version}</scalaVersion>
          <args>
            <arg>-target:jvm-1.5</arg>
          </args>
        </configuration>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-eclipse-plugin</artifactId>
        <configuration>
          <downloadSources>true</downloadSources>
          <buildcommands>
            <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>
          </buildcommands>
          <additionalProjectnatures>
            <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>
          </additionalProjectnatures>
          <classpathContainers>
            <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>
            <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>
          </classpathContainers>
        </configuration>
      </plugin>
    </plugins>
  </build>
  <reporting>
    <plugins>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <configuration>
          <scalaVersion>${scala.version}</scalaVersion>
        </configuration>
      </plugin>
    </plugins>
  </reporting>
</project>

然后将集群中hive-site.xml、hdfs-site.xml、hbase-site.xml引入项目中。

编写HBase公共方法(部分代码):

  1 package hbase
  2 
  3 import java.util.{Calendar, Date}
  4 
  5 import org.apache.hadoop.hbase.HBaseConfiguration
  6 import org.apache.hadoop.hbase.client.{Result, Scan}
  7 import org.apache.hadoop.hbase.filter._
  8 import org.apache.hadoop.hbase.io.ImmutableBytesWritable
  9 import org.apache.hadoop.hbase.mapreduce.TableInputFormat
 10 import org.apache.hadoop.hbase.protobuf.ProtobufUtil
 11 import org.apache.hadoop.hbase.util.{Base64, Bytes}
 12 import org.apache.spark.{Logging, SparkContext}
 13 import org.apache.spark.rdd.RDD
 14 import org.slf4j.{Logger, LoggerFactory}
 15 
 16 /**
 17   * Created by ysy on 2016/11/6.
 18   */
 19 object HBaseTableHelper extends Serializable {
 20 
 21   val logger: Logger = LoggerFactory.getLogger(HBaseTableHelper.getClass)
 22 
 23   //根据timestramp过滤加载Hbase数据
 24   def tableInitByTime(sc : SparkContext,tablename:String,columns :String,fromdate: Date,todate:Date):RDD[(ImmutableBytesWritable,Result)] = {
 25     val configuration = HBaseConfiguration.create()
 26     configuration.set(TableInputFormat.INPUT_TABLE, tablename)
 27 
 28     val scan = new Scan
 29     scan.setTimeRange(fromdate.getTime,todate.getTime)
 30     val column = columns.split(",")
 31     for(columnName <- column){
 32       scan.addColumn("f1".getBytes, columnName.getBytes)
 33     }
 34     configuration.set(TableInputFormat.SCAN, convertScanToString(scan))
 35     val hbaseRDD = sc.newAPIHadoopRDD(configuration, classOf[TableInputFormat], classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], classOf[org.apache.hadoop.hbase.client.Result])
 36     logger.info("-------count-------" + hbaseRDD.count() + "------------------")
 37     hbaseRDD
 38   }
 39 
 40   def convertScanToString(scan : Scan) = {
 41     val proto = ProtobufUtil.toScan(scan)
 42     Base64.encodeBytes(proto.toByteArray)
 43   }
 44 
 45   //根据时间条件filter数据
 46   def tableInitByFilter(sc : SparkContext,tablename : String,columns : String,time : String) : RDD[(ImmutableBytesWritable,Result)] = {
 47     val configuration = HBaseConfiguration.create()
 48     configuration.set(TableInputFormat.INPUT_TABLE,tablename)
 49     val filter: Filter = new RowFilter(CompareFilter.CompareOp.GREATER_OR_EQUAL, new SubstringComparator(time))
 50     val scan = new Scan
 51     scan.setFilter(filter)
 52     val column = columns.split(",")
 53     for(columnName <- column){
 54       scan.addColumn("f1".getBytes, columnName.getBytes)
 55     }
 56     val hbaseRDD = sc.newAPIHadoopRDD(configuration, classOf[TableInputFormat], classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], classOf[org.apache.hadoop.hbase.client.Result])
 57     logger.info("-------count-------" + hbaseRDD.count() + "------------------")
 58     hbaseRDD
 59   }
 60 
 61   def HBaseTableInit(): Unit ={
 62 
 63   }
 64 
 65   def hbaseToHiveTable(): Unit ={
 66 
 67   }
 68 
 69   //前N天的时间戳获取
 70   def getPassDays(beforeDay : Int): Date ={
 71     val calendar = Calendar.getInstance()
 72     var year = calendar.get(Calendar.YEAR)
 73     var dayOfYear  = calendar.get(Calendar.DAY_OF_YEAR)
 74     var j = 0
 75     for(i <- 0 to beforeDay){
 76       calendar.set(Calendar.DAY_OF_YEAR, dayOfYear - j);
 77       if (calendar.get(Calendar.YEAR) < year) {
 78         //跨年了
 79         j = 1;
 80         //更新 标记年
 81         year = year + 1;
 82         //重置日历
 83         calendar.set(year,Calendar.DECEMBER,31);
 84         //重新获取dayOfYear
 85         dayOfYear = calendar.get(Calendar.DAY_OF_YEAR);
 86       }else{
 87         j = j + 1
 88       }
 89     }
 90     calendar.getTime()
 91   }
 92 
 93   //根据startRow与endRow进行过滤
 94   def scanHbaseByStartAndEndRow(sc : SparkContext,startRow : String,stopRow : String,tableName : String) : RDD[(ImmutableBytesWritable,Result)] ={
 95     val configuration = HBaseConfiguration.create()
 96     val scan = new Scan()
 97     scan.setCacheBlocks(false)
 98     scan.setStartRow(Bytes.toBytes(startRow))
 99     scan.setStopRow(Bytes.toBytes(stopRow))
100     val filterList = new FilterList()
101     filterList.addFilter(new KeyOnlyFilter())
102     filterList.addFilter(new InclusiveStopFilter(Bytes.toBytes(stopRow)))
103     scan.setFilter(filterList)
104     configuration.set(TableInputFormat.INPUT_TABLE,tableName)
105     configuration.set(TableInputFormat.SCAN, convertScanToString(scan))
106     val hbaseRDD = sc.newAPIHadoopRDD(configuration, classOf[TableInputFormat], classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], classOf[org.apache.hadoop.hbase.client.Result])
107     logger.info("-------ScanHbaseCount-------" + hbaseRDD.count() + "------------------")
108     hbaseRDD
109   }
110 
111 }

 编写hive公共方法(部分代码):

 1 package hive
 2 
 3 import org.apache.spark.{Logging, SparkContext}
 4 import org.apache.spark.rdd.RDD
 5 import org.apache.spark.sql.Row
 6 import org.apache.spark.sql.hive.HiveContext
 7 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 8 
 9 /**
10   * Created by uatcaiwy on 2016/11/6.
11   */
12 object HiveTableHelper extends Logging {
13 
14     def hiveTableInit(sc:SparkContext): HiveContext ={
15       val sqlContext = new HiveContext(sc)
16       sqlContext
17     }
18 
19     def writePartitionTable(HCtx:HiveContext,inputRdd:RDD[Row],tabName:String,colNames:String):Unit ={
20       val schema = StructType(
21         colNames.split(" ").map(fieldName => StructField(fieldName,StringType,true))
22       )
23       val table = colNames.replace(" dt","").split(" ").map(name => name + " String").toList.toString().replace("List(","").replace(")","")
24       val df = HCtx.createDataFrame(inputRdd,schema)
25       df.show(20)
26       logInfo("----------------------------------begin write table-----------------------------------")
27       val temptb = "temp" + tabName
28       HCtx.sql("drop table if exists " + tabName)
29       df.registerTempTable(temptb)
30       HCtx.sql("CREATE EXTERNAL TABLE if not exists " + tabName +" ("+ table+ ") PARTITIONED BY (`dt` string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' STORED AS INPUTFORMAT  'org.apache.hadoop.mapred.SequenceFileIn      putFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat'")
31       HCtx.sql("set hive.exec.dynamic.partition.mode = nonstrict")
32       HCtx.sql("insert overwrite table " + tabName + " partition(`dt`)" + " select * from " + temptb)
33     }
34 }

读取hdfs文件,有时我们需要根据文件的编码来读取,否则会乱码,并改变编码公共方法:

 1 package importSplitFiletoHive
 2 
 3 import org.apache.hadoop.io.{LongWritable, Text}
 4 import org.apache.hadoop.mapred.TextInputFormat
 5 import org.apache.spark.SparkContext
 6 import org.apache.spark.rdd.RDD
 7 
 8 /**
 9   * Created by ysy on 2016/12/7.
10   */
11 object changeEncode {
12 
13   def changeFileEncoding(sc:SparkContext,path:String,encode : String):RDD[String]={
14     sc.hadoopFile(path,classOf[TextInputFormat],classOf[LongWritable],classOf[Text],1)
15       .map(p => new String(p._2.getBytes,0,p._2.getLength,encode))
16   }

 spark进行xml解析(部分代码):

 1 import hive.HiveTableHelper
 2 import org.apache.spark.Logging
 3 import org.apache.spark.rdd.RDD
 4 import org.apache.spark.sql.Row
 5 import org.apache.spark.sql.hive.HiveContext
 6 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 7 import org.slf4j.LoggerFactory
 8 
 9 import scala.xml._
10 
11 object xmlParse extends Logging{
12   val schemaString = "column1,column2...."
13   def getPBOC_V1_F1(HCtx:HiveContext,rdd:RDD[String],outputTablename:String):Unit = {
14     val tbrdd = rdd.filter(_.split("\t").length == 9)
15       .filter(_.split("\t")(8) != "RESULTS")
16       .map(data => {
17         val sp = data.split("\t")
18         val dt = sp(5).substring(0, 10).replaceAll("-", "")
19         ((sp(0), sp(1), sp(2), sp(3), sp(4), sp(5), sp(6), "RN"), sp(8), dt)
20       }).filter(_._2 != "")
21       .filter(_._2.split("<").length > 2)
22       .filter(data => !(data._2.indexOf("SingleQueryResultMessage0009") == -1 || data._2.indexOf("ReportMessage") == -1))
23       .map(data => {
24         val xml = if (XML.loadString(data._2) != null) XML.loadString(data._2) else null
25         logDebug("%%%%%%%%%%%%%%%%%%finding xml-1:" + xml + "%%%%%%%%%%%%%%%%%%")
26         val column1 = if ((xml \ "PBOC" \ "TYPE") != null) (xml \ "PBOC" \ "TYPE").text else "null"
27         val column2 = if ((xml \ "HEAD" \ "VER") != null) (xml \ "HEAD" \ "VER").text else "null"
28         val column3 = if ((xml \ "HEAD" \ "SRC") != null) (xml \ "HEAD" \ "SRC").text else "null"
29         val column4 = if ((xml \ "HEAD" \ "DES") != null) (xml \ "HEAD" \ "DES").text else "null"
30         ....
31         (data._1,column1,column2,column3...)
32       })
33  ROW(....)
34     HiveTableHelper.writePartitionTable(HCtx, tbrdd, outputTablename, schemaString)

Redis编码公共方法(部分代码):

 1 package redis
 2 
 3 import org.slf4j.{Logger, LoggerFactory}
 4 import redis.clients.jedis.{Jedis, JedisPool, JedisPoolConfig}
 5 
 6 import scala.collection.mutable.ArrayBuffer
 7 import scala.util.Random
 8 
 9 /**
10   * Created by ysy on 2016/11/21.
11   */
12 object RedisClient extends Serializable{
13   val logger: Logger = LoggerFactory.getLogger(RedisClient.getClass)
14   @transient private var jedisPool : JedisPool = null
15     private var jedisPoolList  = new ArrayBuffer[JedisPool]
16     private val poolSize = 0
17     makePool
18 
19   def makePool() ={
20       val pc = new PropConfig("redis.properties");
21     if(jedisPool == null){
22       val poolConfig : JedisPoolConfig = new JedisPoolConfig
23       poolConfig.setMaxIdle(pc.getProperty("redis.pool.maxActive").toInt)
24       poolConfig.setMaxTotal(pc.getProperty("redis.pool.maxActive").toInt)
25       poolConfig.setMaxWaitMillis(pc.getProperty("redis.pool.maxWait").toInt)
26       poolConfig.setTestOnBorrow(pc.getProperty("redis.pool.testOnBorrow").toBoolean)
27       poolConfig.setTestOnReturn(pc.getProperty("redis.pool.testOnReturn").toBoolean)
28       val hosts = pc.getProperty("redis.pool.servers").split(",")
29         .map(data => data.split(":"))
30       for(host <- hosts){
31         jedisPool = new JedisPool(poolConfig,host(0),host(1).toInt,pc.getProperty("redis.server.timeout").toInt)
32         jedisPoolList += jedisPool
33       }
34     }
35 
36   }
37 
38 
39   def getForString(key : String) : String = {
40       var value = ""
41     if(key != null && !key.isEmpty()){
42       val jedis = getJedis
43       value = jedis.get(key)
44     }
45     value
46   }
47 
48   def setForString(key : String,value : String) ={
49     if(key != null && !key.isEmpty){
50       var jedis = getJedis
51        jedis.set(key,value)
52     }else{
53 
54     }
55   }
56 
57   def zexsist(key : String) : Boolean ={
58       var flag = false
59       if(key != null && !key.isEmpty){
60         val jedis = getJedis
61 
62         val resultNum = jedis.zcard(key)
63         if("0".equals(resultNum.toLong)){
64             flag = true
65         }
66       }
67     flag
68   }
69 
70   def getJedis() : Jedis ={
71       var ramNub = 0
72       if(poolSize == 1){
73         ramNub = 0
74       }else{
75         val random = new Random
76         ramNub = Math.abs(random.nextInt % 1)
77       }
78     jedisPool = jedisPoolList(ramNub)
79     jedisPool.getResource
80   }
81 
82   def returnJedisResource(redis : Jedis): Unit ={
83     if(redis != null){
84       redis.close()
85     }
86   }
87 
88   def close: Unit = {
89       for(jedisPool <- jedisPoolList){
90           jedisPool.close
91       }
92     if(jedisPool!=null && !jedisPool.isClosed()){
93       jedisPool.close
94     }else{
95       jedisPool=null
96     }
97   }
98 
99 }

 详细就不写了,那么完整的工程框架搭建完毕:

随后通过main方法创建sparkContext对象,开始数据分析与处理,在spark路径的bin目录下或者写成脚本文件执行:

./spark-submit --conf spark.ui.port=5566 --name "sparkApp" --master yarn-client --num-executors 3 --executor-cores 2 --executor-memory 10g --class impl.spark /usr/local/spark1.6.1/sparkApp/sparkApp.jar

(注意:这里的配置参数会覆盖spark-default.conf中配置的变量,重新声明spark.ui.port的原因也是因为在同时启动spark的thrfit的时候,提交submit会造成UI占用的问题,至此spark完结)

Hadoop安装配置与MapReduce代码框架

安装:

yum install gcc 

yum install gcc-c++

yum install make

yum install autoconfautomake libtool cmake

yum install ncurses-devel

yum install openssl-devel

安装protoc(需用root用户)

 tar -xvf protobuf-2.5.0.tar.bz2 

cd protobuf-2.5.0 

 ./configure --prefix=/opt/protoc/ 

 make && make install

编译hadoop

mvn clean package -Pdist,native -DskipTests -Dtar 

编译完的hadoop在 /home/hadoop/ocdc/hadoop-2.6.0-src/hadoop-dist/target 路径下

配置hosts文件

10.1.245.244 master

10.1.245.243 slave1

命令行输入 hostname master

免密码登录:

执行命令生成密钥: ssh-keygen -t rsa -P ""

进入文件夹cd  .ssh (进入文件夹后可以执行ls  -a 查看文件)

将生成的公钥id_rsa.pub 内容追加到authorized_keys(执行命令:cat id_rsa.pub >> authorized_keys)

core-site.xml

<configuration>

<!--指定hdfs的nameservice为ns1-->

<property>

  <name>fs.defaultFS</name>

  <value>hdfs://master</value>

 </property>

 <property>

  <name>io.file.buffer.size</name>

  <value>131072</value>

 </property>

<!--指定hadoop数据存放目录-->

 <property>

  <name>hadoop.tmp.dir</name>

  <value>/home/hadoop/ocdc/hadoop-2.6.0/tmp</value>

  <description>Abasefor other temporary directories.</description>

 </property>

 <property>

  <name>hadoop.proxyuser.spark.hosts</name>

  <value>*</value>

 </property>

<property>

  <name>hadoop.proxyuser.spark.groups</name>

  <value>*</value>

 </property>

</configuration>

<!--指定zookeeper地址-->

 <property>

   <name>ha.zookeeper.quorum</name>

   <value>h4:2181,h5:2181,h6:2181</value>

 </property>

</configuration>

hdfs-site.xml

<configuration>

 <property>

  <name>dfs.namenode.secondary.http-address</name>

  <value>master:9001</value>

 </property>

  <property>

   <name>dfs.namenode.name.dir</name>

   <value>/home/hadoop/ocdc/hadoop-2.6.0/name</value>

 </property>

 <property>

  <name>dfs.datanode.data.dir</name>

  <value>/home/hadoop/ocdc/hadoop-2.6.0/data</value>

  </property>

 <property>

  <name>dfs.replication</name>

  <value>3</value>

 </property>

 <property>

  <name>dfs.webhdfs.enabled</name>

  <value>true</value>

 </property>

<property>

   <name>dfs.nameservices</name>

   <value>ns1</value>

</property>

<!-- ns1下面有两个NameNode,分别是nn1,nn2 -->

<property>

   <name>dfs.ha.namenodes.ns1</name>

   <value>nn1,nn2</value>

</property>

</configuration>

yarn-site.xml

<configuration>

<!-- Site specific YARN configuration properties -->

<!-- 指定nodemanager启动时加载server的方式为shuffle server -->

<property>

   <name>yarn.nodemanager.aux-services</name>

   <value>mapreduce_shuffle</value>

  </property>

  <property>

   <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>

   <value>org.apache.hadoop.mapred.ShuffleHandler</value>

  </property>

  <property>

   <name>yarn.resourcemanager.address</name>

   <value>master:8032</value>

  </property>

  <property>

   <name>yarn.resourcemanager.scheduler.address</name>

   <value>master:8030</value>

  </property>

  <property>

   <name>yarn.resourcemanager.resource-tracker.address</name>

   <value>master:8035</value>

  </property>

  <property>

   <name>yarn.resourcemanager.admin.address</name>

   <value>master:8033</value>

  </property>

  <property>

   <name>yarn.resourcemanager.webapp.address</name>

   <value>master:8088</value>

  </property>

<property>

<name>yarn.nodemanager.resource.memory-mb</name>

<value>16384</value>

</property>

<!-- 指定resourcemanager地址 -->

   <property>

       <name>yarn.resourcemanager.hostname</name>

            <value>h3</value>

   </property>

</configuration>

mapred-site.xml

<configuration>

<property>

<name>mapreduce.framework.name</name>

   <value>yarn</value>

 </property>

 <property>

  <name>mapreduce.jobhistory.address</name>

  <value>master:10020</value>

 </property>

 <property>

  <name>mapreduce.jobhistory.webapp.address</name>

  <value>master:19888</value>

 </property>

<property>

    <name>yarn.scheduler.maximum-allocation-mb</name>

    <value>16384</value>

  </property>

</configuration>

关于MapReduce,1年多前大家都觉得很神秘,其实 就相当于在Map阶段或者Reduce阶段中,进行数据的处理,也可以在Map中读取写入hbase、redis都可以~其实就相当于在MapReduce中写业务处理逻辑,代码如下:

 1 public static class Map extends MapReduceBase implments Mapper<LongWritable,Text,Text,IntWritable>{
 2     //设置常量1,用来形成<word,1>形式的输出
 3     private fianll static IntWritable one = new IntWritable(1)
 4     private Text word = new Text();
 5 
 6 public void map(LongWritable key,Text value,OutputCollector<Text,output,Reporter reporter) throws IOException{
 7    //hadoop执行map函数时为是一行一行的读取数据处理,有多少行,就会执行多少次map函数
 8     String line = value.toString();
 9     //进行单词的分割,可以多传入进行分割的参数
10     StringTokenizer tokenizer = new StringTokenizer(line);
11     //遍历单词
12     while(tokenizer.hasMoreTokens()){
13        //往Text中写入<word,1>
14         word.set(tokenizer.nextToken());
15         output.collect(word,one);
16     }
17     }
18 }
19 //需要注意的是,reduce将相同key值(这里是word)的value值收集起来,形成<word,list of 1>的形式,再将这些1累加
20 public static class Reduce extends MapReduceBase implements Reducer<Text IntWritable,Text,IntWritable>{
21         public void reduce(Text key,Iterator<IntWritable> values,OutputCollector<Text,IntWritable> output,Reporter reporter) throws IOException{
22     //初始word个数设置
23     int sum = 0;
24     while(values,hasNext()){
25      //单词个数相加   
26         sum += value.next().get();
27     }
28     output.collect(key,new IntWritbale(sum));
29     }
30 }

HBASE安装配置与hbase代码框架

由于我使用的是外置的zookeeper,所以这里HBASE_MANAGES_ZK设置为,设置参数:

export JAVA_HOME=/usr/local/yangsy/jdk1.7.0_55

export HBASE_CLASSPATH=/usr/local/hbase-1.0.2/conf

export HBASE_MANAGES_ZK=false

hbase-site.xml

<configuration>

//设置将数据写入hdfs的目录 <property>   <name>hbase.rootdir</name>   <value>hdfs://master:9000/usr/local/hadoop-2.6.0/hbaseData</value> </property>

//设置hbase的模式为集群模式 <property>   <name>hbase.cluster.distributed</name>   <value>true</value> </property>

//设置hbase的master端口地址

<property>   <name>hbase.master</name>   <value>hdfs://master:60000</value> </property>

//HBase Master Web借鉴绑定的默认端口,默认为0.0.0.0

<property>   <name>hbase.master.info.port</name>   <value>60010</value> </property>

//设置zookeeper的连接地址(必须为基数个)

<property>   <name>hbase.zookeeper.property.clientPort</name>   <value>2183</value> </property>

//zookeeper的节点

<property>   <name>hbase.zookeeper.quorum</name>   <value>master,slave1,slave2</value> </property>

//zookeeper数据地址

<property>   <name>hbase.zookeeper.property.dataDir</name>   <value>/usr/local/zookeeper-3.4.6/data</value> </property>

//zookeeper连接超时时间

<property>   <name>zookeeper.session.timeout</name>

  <value>60000</value>   </property>

</configuration>

这里要注意的是,如果选择外置的zookeeper集群,则需要将zookeeper的zoo.cfg拷贝至HBase的conf下。在启动HBase时,将会自动加载该配置文件。

regionServers中配置regionserver节点的地址

代码结构:

  1 package HbaseTest;
  2 
  3 import akka.io.Tcp;
  4 import org.apache.hadoop.conf.Configuration;
  5 import org.apache.hadoop.hbase.*;
  6 import org.apache.hadoop.hbase.client.*;
  7 
  8 import java.util.ArrayList;
  9 import java.util.List;
 10 
 11 /**
 12  * Created by root on 5/30/16.
 13  */
 14 public class HbaseTest {
 15     private Configuration conf;
 16     public void init(){
 17         conf = HBaseConfiguration.create();
 18     }
 19 
 20    public void createTable(){
 21        Connection conn = null;
 22        try{
 23            conn = ConnectionFactory.createConnection(conf);
 24            HBaseAdmin hadmin = (HBaseAdmin)conn.getAdmin();
 25            HTableDescriptor desc = new HTableDescriptor("TableName".valueOf("yangsy"));
 26 
 27            desc.addFamily(new HColumnDescriptor("f1"));
 28            if(hadmin.tableExists("yangsy")){
 29                System.out.println("table is exists!");
 30                System.exit(0);
 31            }else{
 32                hadmin.createTable(desc);
 33                System.out.println("create table success");
 34            }
 35        }catch (Exception e){
 36            e.printStackTrace();
 37        }finally {
 38            {
 39                if(null != conn){
 40                    try{
 41                        conn.close();
 42                    }catch(Exception e){
 43                        e.printStackTrace();
 44                    }
 45                }
 46            }
 47        }
 48    }
 49 
 50     public void query(){
 51         Connection conn = null;
 52         HTable table = null;
 53         ResultScanner scan = null;
 54         try{
 55             conn = ConnectionFactory.createConnection(conf);
 56             table = (HTable)conn.getTable(TableName.valueOf("yangsy"));
 57 
 58             scan = table.getScanner(new Scan());
 59 
 60             for(Result rs : scan){
 61                 System.out.println("rowkey:" + new String(rs.getRow()));
 62 
 63                 for(Cell cell : rs.rawCells()){
 64                     System.out.println("column:" + new String(CellUtil.cloneFamily(cell)));
 65 
 66                     System.out.println("columnQualifier:"+new String(CellUtil.cloneQualifier(cell)));
 67 
 68                     System.out.println("columnValue:" + new String(CellUtil.cloneValue(cell)));
 69 
 70                     System.out.println("----------------------------");
 71                 }
 72             }
 73         }catch(Exception e){
 74             e.printStackTrace();
 75         }finally{
 76             try {
 77                 table.close();
 78                 if(null != conn) {
 79                     conn.close();
 80                 }
 81             }catch (Exception e){
 82                 e.printStackTrace();
 83             }
 84         }
 85     }
 86 
 87     public void queryByRowKey(){
 88         Connection conn = null;
 89         ResultScanner scann = null;
 90         HTable table = null;
 91         try {
 92             conn = ConnectionFactory.createConnection(conf);
 93             table = (HTable)conn.getTable(TableName.valueOf("yangsy"));
 94 
 95             Result rs = table.get(new Get("1445320222118".getBytes()));
 96             System.out.println("yangsy the value of rokey:1445320222118");
 97             for(Cell cell : rs.rawCells()){
 98                 System.out.println("family" + new String(CellUtil.cloneFamily(cell)));
 99                 System.out.println("value:"+new String(CellUtil.cloneValue(cell)));
100             }
101         }catch (Exception e){
102             e.printStackTrace();
103         }finally{
104             if(null != table){
105                 try{
106                     table.close();
107                 }catch (Exception e){
108                     e.printStackTrace();
109                 }
110             }
111         }
112     }
113 
114     public void insertData(){
115         Connection conn = null;
116         HTable hTable = null;
117         try{
118             conn = ConnectionFactory.createConnection(conf);
119             hTable = (HTable)conn.getTable(TableName.valueOf("yangsy"));
120 
121             Put put1 = new Put(String.valueOf("1445320222118").getBytes());
122 
123             put1.addColumn("f1".getBytes(),"Column_1".getBytes(),"123".getBytes());
124             put1.addColumn("f1".getBytes(),"Column_2".getBytes(),"456".getBytes());
125             put1.addColumn("f1".getBytes(),"Column_3".getBytes(),"789".getBytes());
126 
127             Put put2 = new Put(String.valueOf("1445320222119").getBytes());
128 
129             put2.addColumn("f1".getBytes(),"Column_1".getBytes(),"321".getBytes());
130             put2.addColumn("f1".getBytes(),"Column_2".getBytes(),"654".getBytes());
131             put2.addColumn("f1".getBytes(),"Column_3".getBytes(),"987".getBytes());
132 
133             List<Put> puts = new ArrayList<Put>();
134             puts.add(put1);
135             puts.add(put2);
136             hTable.put(puts);
137         }catch(Exception e){
138             e.printStackTrace();
139         }finally{
140             try {
141                 if (null != hTable) {
142                     hTable.close();
143                 }
144             }catch(Exception e){
145                 e.printStackTrace();
146             }
147         }
148     }
149 
150 public static void main(String args[]){
151     HbaseTest test = new HbaseTest();
152     test.init();
153     test.createTable();
154     test.insertData();
155     test.query();
156 }
157 
158 
159 }

Storm安装配置与代码框架

拓扑构造: 编写topology实体类,在构造方法中加入配置参数,序列化等。通过CommandlLine获取启动时的workers与calc数量,最终调用StormSubmitter的submitTopologyWithProgressBar,传入topo的名称,配置项,以及TopologyBuilder实例。

数据接收Spout:

Storm从kafka中获取数据,创建于BasicTopology,其中配置参数:

kafka.brokerZkStr Kafka使用的zookeeper服务器地址

kafka.brokerZkPath 保存offset的zookeeper服务器地址

kafka.offset.zkPort 保存offset的zookeeper端口 默认2181

kafka.offset.zkRoot 保存offset的zookeeper路径 /kafka-offset

stateUpdateIntervalMs 把offset信息写入zookeeper的间隔时间 30000

spout、bolt初始化时,构建对象stormBeanFactory,其后使用getBean方法从BeanFactory中获取对象

Bolt数据处理:

自定义bolt继承自extendsBaseRichBolt,实现它的prepare、declareOutputFileds方法。在prepare方法中,获取StormBeanFactory的配置,加载业务实体类。

Storm配置参数:

dev.zookeeper.path : '/tmp/dev-storm-zookeeper'     以dev.zookeeper.path配置的值作为本地目录,以storm.zookeeper.port配置的值作为端口,启动一个新的zookeeper服务 drpc.childopts: '-Xms768m'   drpc.invocations.port 3773 drpc.port : 3772 drpc.queue.size : 128 drpc.request.timeout.secs : 600 drpc.worker.threads : 64 java.library.path : '' logviewer.appender.name : 'A1' logviewer.childopts : '-Xms128m' logviewr.port : 8000 metrics.reporter.register : 'org.apache.hadoop.metrics2.sink.storm.StormTimelineMetricsReporter' nimbus.childopts : '-Xmx1024m -javaagent:/usr/hdp/current/storm-nimbus/contrib/storm-jmxetric/lib/jmxetric-1.0.4.jar=host=localhost,port=8649,wireformat31x-true...' nimbus.cleanup.inbox.freq.secs : 600 nimbus.file.copy.exiration.secs : 600

storm.yaml

nimbus.host  nimbus的配置地址

nimbus.inbox.jar.expiration.secs   主要负责清理nimbus的inbox文件夹最后一次修改时间,默认3600秒

nimbus.monitor.freq.secs : 120 nimbus.reassign : true  当发现task失败时nimbus是否重新分配执行。默认为真,不建议修改 nimbus.supervisor.timeout.secs : 60 nimbus.task.launch.secs : 120 nimbus.task.timeout.secs : 30  心跳超时时间,超时后nimbus会认为task死掉并重分配给另一个地址 nimbus.thrift.max_buffer_size : 1048576 nimbus.thrift.port : 6627 nimbus.topology.validator : 'backtype.storm.nimbus.DefaultTopologyValidator' storm.cluster.metrics.consumer.register : [{"class" : "org.apache.hadoop.metrics2.sink.storm.Strorm.StormTimeLineMetricsReporter"}] storm.cluster.mode : 'distributed' storm.local.dir : '/storage/disk1/hadoop/storm' storm.local.mode.zmq : false storm.log.dir : '/var/log/storm' storm.messaging.netty.buffer_size : 5242880  为每次批量发送的Tuple 序列化之后的Task Message 消息的大小 storm.messaging.netty.client_worker_threads : 10  指定netty服务器工作线程数量  storm.messaging.netty.max_retries : 60  指定最大重试次数 storm.messaging.netty.max_wait_ms : 2000  指定最大等待时间(毫秒) storm.messaging.netty.min_wait_ms : 100 指定最小等待时间(毫秒) storm.messaging.netty.server_worker_threads : 10 指定netty服务器工作线程数量 storm.messaging.transport : 'backtype.storm.messaging.netty.Context' 指定传输协议 storm.thrift.transport : 'backtype.storm.security.auth.SimpleTransportPlugin' storm.zookeeper.connection.timeout : 15000 storm.zookeeper.port : 2181 storm.zookeeper.retry.interval : 1000 storm.zookeeper.retry.intervalceiling.millis : 30000 storm.zookeeper.retry.times : 5 storm.zookeeper.root : '/storm'  ZooKeeper中Storm的根目录位置 storm.zookeeper.servers : ['',''.....]   zookeeper服务器列表 storm.zookeeper.session.timeout : 20000  客户端连接ZooKeeper超时时间 supervisor.childopts :    在storm-deploy项目中使用,用来配置supervisor守护进程的jvm选项 supervisor.heartbeat.frequency.secs : 5  supervisor心跳发送频率(多久发送一次) supervisor.monitor.frequency.secs : 3  supervisor检查worker心跳的频率 supervisor.slots.ports : [6700,6701,....]   supervisor上能够运行workers的端口列表.每个worker占用一个端口,且每个端口只运行一个worker.通过这项配置可以调整每台机器上运行的worker数.(调整slot数/每机) supervisor.worker.start.timeout.secs : 120 supervisor.worker.timeout.secs : 30  supervisor中的worker心跳超时时间,一旦超时supervisor会尝试重启worker进程. task.heartbeat.frequency.secs : 3  task汇报状态心跳时间间隔 task.refresh.poll.secs : 10  ask与其他tasks之间链接同步的频率.(如果task被重分配,其他tasks向它发送消息需要刷新连接).一般来讲,重分配发生时其他tasks会理解得到通知。该配置仅仅为了防止未通知的情况。 topology.acker.executors : null topology.builtin.metrics.bucket.size.secs : 60 topology.debug : false topology.disruptor.wait.strategy : 'com.lmax.disruptor.BlockingWaitStrategy' topology.enable.message.timeouts : true topology.error.throttle.interval.secs : 10 topology.executor.receive.buffer.size : 1024 topology.executor.send.buffer.size : 1024 topology.fall.back.on.java.serialization : true topology.kryo.factory : 'backtype.storm.serialization.DefaultKryoFactory' topology.max.error.report.per.interval : 5 topology.max.spout.pending : null  一个spout task中处于pending状态的最大的tuples数量.该配置应用于单个task,而不是整个spouts或topology. topology.max.task.parallelism : null  在一个topology中能够允许的最大组件并行度.该项配置主要用在本地模式中测试线程数限制. topology.message.timeout.secs : 30  topology中spout发送消息的最大处理超时时间.如果一条消息在该时间窗口内未被成功ack,Storm会告知spout这条消息失败。而部分spout实现了失败消息重播功能。 topology.metrics.aggregate.metric.evict.secs : 5 topology.metrics.aggregate.per.worker : true topology.metrics.consumer.register : topology.metrics.expand.map.type : true topology.metrics.metric.name.separator : ',' topology.optimize : true topology.skip.missing.kryo.registrations : false  Storm是否应该跳过它不能识别的kryo序列化方案.如果设置为否task可能会装载失败或者在运行时抛出错误. topology.sleep.spout.wait.strategy.time.ms : 1 topology.spoout.wait.strategy : 'backtype.storm.spout.SleeSpoutWaitStrategy' topology.state.synchronization.timeout.secs : 60 topology.stats.sample.rate : 0.05 topology.tick.tuple.freq.secs : null topology.transfer.buffer.size : 1024 topology.trident.batch.emit.interval.millis : 500 topology.tuple.serializer : 'backtype.storm.serialization.types.ListDelegateSerializer' topology.worker.childopts : null topology.worker.shared.thread.pool.size : 4 topology.workers : 40  执行该topology集群中应当启动的进程数量.每个进程内部将以线程方式执行一定数目的tasks.topology的组件结合该参数和并行度提示来优化性能 transactional.zookeeper.port : null transactional.zookeeper.root : '/transactional' transactional.zookeeper.servers : null ui.childopts : '-Xmx2048m' ui.filter : null ui.port : 8744  Storm UI的服务端口 worker.childopts : worker.heartbeet.frequency.secs : 1 zmq.hwm : 0 zmq.linger.millis : 5000  当连接关闭时,链接尝试重新发送消息到目标主机的持续时长.这是一个不常用的高级选项,基本上可以忽略. zmq.threads : 1  每个worker进程内zeromq通讯用到的线程数

storm代码框架总结

基类topology,BasicTopology.java

  1 import java.io.UnsupportedEncodingException;
  2 import java.math.BigDecimal;
  3 import java.util.Date;
  4 import java.util.HashMap;
  5 import java.util.List;
  6 import java.util.Map;
  7 
  8 import org.apache.commons.cli.CommandLine;
  9 import org.apache.commons.cli.CommandLineParser;
 10 import org.apache.commons.cli.DefaultParser;
 11 import org.apache.commons.cli.HelpFormatter;
 12 import org.apache.commons.cli.Options;
 13 import org.apache.commons.lang3.StringUtils;
 14 import org.apache.storm.guava.base.Preconditions;
 15 import org.edm.storm.topo.util.DelayKafkaSpout;
 16 import org.edm.storm.topo.util.StormBeanFactory;
 17 import org.joda.time.DateTime;
 18 
 19 import storm.kafka.BrokerHosts;
 20 import storm.kafka.KafkaSpout;
 21 import storm.kafka.SpoutConfig;
 22 import storm.kafka.ZkHosts;
 23 import backtype.storm.Config;
 24 import backtype.storm.LocalCluster;
 25 import backtype.storm.StormSubmitter;
 26 import backtype.storm.topology.TopologyBuilder;
 27 import backtype.storm.tuple.Fields;
 28 
 29 public abstract class BasicTopology {
 30     
 31     public static final String HASH_TAG = "hashTag";
 32     
 33     public static final Fields HASH_FIELDS = new Fields(HASH_TAG);
 34     
 35     protected Options options = new Options();
 36     
 37     protected StormBeanFactory stormBeanFactory;
 38     
 39     protected Config config = new Config();
 40     
 41     protected String configFile;
 42     
 43     public BasicTopology(){
 44         config.setFallBackOnJavaSerialization(false);
 45         
 46         config.setSkipMissingKryoRegistrations(false);
 47         config.registerSerialization(Date.class);
 48         config.registerSerialization(BigDecimal.class);
 49         config.registerSerialization(HashMap.class);
 50         config.registerSerialization(Map.class);
 51 
 52         options.addOption("name", true, "拓扑运行时名称");
 53         options.addOption("conf", false, "配置文件路径");
 54         options.addOption("workers", true, "虚拟机数量");
 55     }
 56     
 57     protected void setupConfig(CommandLine cmd) throws UnsupportedEncodingException{
 58         //配置文件名称
 59         String confLocation = cmd.getOptionValue("conf",getConfigName());
 60         //创建stormBeanFactory
 61         stormBeanFactory = new StormBeanFactory(confLocation);
 62         Map<String,Object> stormConfig = stormBeanFactory.getBean("stormConfig",Map.class);
 63         Preconditions.checkNotNull(stormConfig);
 64         config.putAll(stormConfig);
 65         config.put(StormBeanFactory.SPRING_BEAN_FACTORY_XML, stormBeanFactory.getXml());
 66         //先默认加载,然后再加载命令行
 67         String numWorkers = cmd.getOptionValue("workers");
 68         if(numWorkers != null){
 69             config.setNumWorkers(Integer.parseInt(numWorkers));
 70         }else{
 71             config.setNumWorkers(getNumWorkers());
 72         }
 73         config.put(Config.TOPOLOGY_MESSAGE_TIMEOUT_SECS, 180);
 74         config.put(Config.TOPOLOGY_MAX_SPOUT_PENDING, 2000);
 75         config.put(Config.TOPOLOGY_EXECUTOR_RECEIVE_BUFFER_SIZE, 16384);
 76         config.put(Config.TOPOLOGY_EXECUTOR_SEND_BUFFER_SIZE, 16384);
 77         config.put(Config.TOPOLOGY_ACKER_EXECUTORS, config.get(Config.TOPOLOGY_WORKERS));
 78     }
 79     
 80     @SuppressWarnings("unchecked")
 81     protected SpoutConfig getSpoutConfig(String topic){
 82         String brokerZkStr = (String)config.get("kafka.brokerZkStr");
 83         String brokerZkPath = (String)config.get("kafka.brokerZkPath");
 84         
 85         List<String> zkServers = (List<String>)config.get("kafka.offset.zkServers");
 86         Integer zkPort = Integer.parseInt(String.valueOf(config.get("kafka.offset.zkPort")));
 87         String zkRoot = (String)config.get("kafka.offset.zkRoot");
 88         String id = StringUtils.join(getTopoName(),"-",topic);
 89         BrokerHosts kafkaBrokerZk = new ZkHosts(brokerZkStr, brokerZkPath);
 90         SpoutConfig spoutConfig = new SpoutConfig(kafkaBrokerZk, topic, zkRoot, id);
 91         spoutConfig.zkServers = zkServers;
 92         spoutConfig.zkPort = zkPort;
 93         spoutConfig.zkRoot = zkRoot;
 94         spoutConfig.stateUpdateIntervalMs = 30000;
 95         return spoutConfig;
 96     }
 97     
 98     //创建kafkaspout
 99     public KafkaSpout getKafkaSpout(String topic){
100         SpoutConfig spoutConfig = getSpoutConfig(topic);
101         return new DelayKafkaSpout(spoutConfig);
102     }
103     
104     
105     /**
106      * 拓扑可部署多次,但从kafka获取数据,做唯一次过滤等用的
107      * 
108      * @return
109      */
110     public abstract String getTopoName();
111     
112     public abstract String getConfigName();
113     
114     public abstract int getNumWorkers();
115     
116     public void registerKryo(Config config){
117         
118     }
119     
120     public abstract void addOptions(Options options);
121         
122     public abstract void setupOptionValue(CommandLine cmd);
123     
124     public abstract void createTopology(TopologyBuilder builder);
125     
126     public void runLocat(String[] args) throws Exception{
127         CommandLineParser parser = new DefaultParser();
128         CommandLine cmd = parser.parse(options, args);
129         HelpFormatter formatter = new HelpFormatter();
130         formatter.printHelp("topology", options);
131         setupConfig(cmd);
132         
133         config.setDebug(true);
134         config.setNumWorkers(1);
135         TopologyBuilder builder = new TopologyBuilder();
136         createTopology(builder);
137         LocalCluster cluster = new LocalCluster();
138         String topoName = cmd.getOptionValue("name",
139                 StringUtils.join(getTopoName(), "-", new DateTime().toString("yyyyMMdd-HHmmss")));
140         cluster.submitTopology(topoName,config,builder.createTopology());
141         
142     }
143     
144     public void run(String args[]) throws Exception{
145         CommandLineParser parser = new DefaultParser();
146         CommandLine cmd = parser.parse(options, args);
147         HelpFormatter formatter = new HelpFormatter();
148         formatter.printHelp("topology", options);
149         setupConfig(cmd);
150         setupOptionValue(cmd);
151         
152         TopologyBuilder builder = new TopologyBuilder();
153         createTopology(builder);
154         String topoName = cmd.getOptionValue("name",StringUtils.join(getTopoName(),new DateTime().toString("yyyyMMdd-HHmmss")));
155         StormSubmitter.submitTopologyWithProgressBar(topoName, config, builder.createTopology());
156     }
157 }

 用于实现数据流spout,bolt的业务实现Topology:

 1 import org.apache.commons.cli.CommandLine;
 2 import org.apache.commons.cli.Options;
 3 
 4 import storm.kafka.KafkaSpout;
 5 
 6 import backtype.storm.topology.TopologyBuilder;
 7 
 8 import com.practice.impl.BasicTopology;
 9 
10 public class LbsCalcTopology extends BasicTopology{
11     
12     private int spoutParallelism = 1;
13     
14     private int onceParallelism = 1;
15     
16     private int calculateParallelism = 1;
17     
18 
19     @Override
20     public void addOptions(Options options) {
21         options.addOption("spout",true,"spoutParallelism");
22         options.addOption("once",true,"onceParallelism");
23         options.addOption("calc",true,"calculateParallelism");
24     }
25 
26     @Override
27     public void setupOptionValue(CommandLine cmd) {
28         spoutParallelism = Integer.parseInt(cmd.getOptionValue("spout","1"));
29         onceParallelism = Integer.parseInt(cmd.getOptionValue("once","1"));
30         calculateParallelism = Integer.parseInt(cmd.getOptionValue("calc","1"));
31     }
32 
33     @Override
34     public void createTopology(TopologyBuilder builder) {
35         KafkaSpout kafkaSpout = getKafkaSpout("lbs");
36         LbsOnceBolt exactlyOnceBolt = new LbsOnceBolt(getTopoName(),"lbs","lbs");
37         LbsCalcBolt calculateBolt = new LbsCalcBolt();
38         
39         builder.setSpout("kafkaSpout", kafkaSpout,spoutParallelism);
40         builder.setBolt("onceParallelism", exactlyOnceBolt,onceParallelism).shuffleGrouping("kafkaSpout");
41         builder.setBolt("calculateBolt", calculateBolt, calculateParallelism).fieldsGrouping("onceParallelism", BasicTopology.HASH_FIELDS);
42     }
43     
44     @Override
45     public String getTopoName() {
46         return "lbs";
47     }
48 
49     @Override
50     public String getConfigName() {
51         return "lbs-topology.xml";
52     }
53 
54     @Override
55     public int getNumWorkers() {
56         return 3;
57     }
58     
59     public static void main(String args[]) throws Exception{
60         LbsCalcTopology topo = new LbsCalcTopology();
61         topo.run(args);
62     }
63 }

具体业务时间bolt:

 1 import java.util.Map;
 2 
 3 import backtype.storm.task.OutputCollector;
 4 import backtype.storm.task.TopologyContext;
 5 import backtype.storm.topology.OutputFieldsDeclarer;
 6 import backtype.storm.topology.base.BaseRichBolt;
 7 import backtype.storm.tuple.Tuple;
 8 
 9 public class LbsCalcBolt extends BaseRichBolt{
10 
11     @Override
12     public void execute(Tuple input) {
13         try{
14             String msg = input.getString(0);
15             if(msg != null){
16                 System.out.println(msg);
17             }
18         }catch(Exception e){
19             e.printStackTrace();
20         }
21     }
22 
23     @Override
24     public void prepare(Map arg0, TopologyContext arg1, OutputCollector arg2) {
25         // TODO Auto-generated method stub
26         
27     }
28 
29     @Override
30     public void declareOutputFields(OutputFieldsDeclarer arg0) {
31         // TODO Auto-generated method stub
32         
33     }
34 
35 }

Kafka安装配置与代码框架

1、主要配置参数说明 -- 日志数据存储目录 log.dirs = /storage/disk17/kafka-logs -- Kafka server端口 port = 6667 -- 强制切分日志文件的间隔时间(切分的文件大小由log.segment.bytes决定,默认1073741824 bytes) log.roll.hours = 168 -- 日志文件保留时间 log.retention.hours = 168 -- 每次flush到磁盘的最大消息数量,数量太大会让写磁盘时间过长(IO阻塞),导致客户端发生延迟 log.flush.interval.messages=10000 -- 每次flush消息的间隔时间 log.flush.scheduler.interval.ms=3000 -- zookeeper 连接超时时间 zookeeper.connection.timeout.ms = 20000 -- zookeeper 集群的节点地址,多个以,号分隔 zookeeper.connect=127.0.0.1:2181 -- zooKeeper集群中leader和follower之间的同步时间 zookeeper.sync.time.ms=2000 -- 是否允许自动创建主题 auto.create.topics.enable=true -- 默认复制因子数量 default.replication.factor=1 -- 默认分区数量 num.partitions=1 2、消息以及日志数据的存储策略 kafka和JMS(Java Message Service)实现(activeMQ)不同的是:即使消息被消费,消息仍然不会被立即删除,日志文件将会根据broker中的配置要求,保留一定的时间之后删除,比如log.retention.hours=168,那么七天后,文件会被清除,无论其中的消息是否被消费。kafka通过这种简单的手段,来释放磁盘空间,以及减少消息消费之后对文件内容改动的磁盘IO开销。 对于consumer而言,它需要保存消费消息的offset(如果不指定则存储在默认的zookeeper目录kafak-broker),对于offset的保存和使用,由consumer来控制。当consumer正常消费消息时,offset将会"线性"的向前驱动,即消息将依次顺序被消费。事实上consumer可以使用任意顺序消费消息,它只需要将offset重置为任意值。 kafka集群几乎不需要维护任何consumer和producer状态信息,这些信息由zookeeper保存。因此producer和consumer的客户端实现非常轻量级,它们可以随意离开,而不会对集群造成额外的影响。 3、常用操作命令 3.1、创建消息主题(kafka topic) ./kafka-topics.sh --zookeeper 127.0.0.1:2181 --topic topicName --partitions 2 --replication-factor 3 --create --zookeeper zookeeper 服务端地址 --topic 消息主题名称 --partitions 分区数量(如果不指定数量则由Kafka的配置决定) --replication-factor 复制因子数量(副本数量,如果不指定数量则由Kafka的配置决定) --create 创建主题 3.2、删除消息主题 * 慎用,这条命令只是删除zookeeper中主题的元数据,日志文件并不会删除,不建议直接删除日志文件。如需删除,安全的措施是停止kafka集群服务,然后执行这条命令再删除日志文件,最后恢复服务,恢复服务后执行--describe命令(3.3)检查主题是否已经移除! ./kafka-run-class.sh kafka.admin.DeleteTopicCommand --zookeeper 127.0.0.1:2181 --topic topicName 3.3、查看消息主题 ./kafka-topics.sh --zookeeper 127.0.0.1:2181 --topic topicName --describe --zookeeper zookeeper 服务端地址 --topic 消息主题名称 ----describe 主题信息 命令将输出如下信息,如topicName主题有2个分区,复制因子为3,以及主题所在的leader。 Topic:topicName PartitionCount:2 ReplicationFactor:3 Configs: Topic: topicName Partition: 0 Leader: 2 Replicas: 2,1,0 Isr: 2,1,0 Topic: topicName Partition: 1 Leader: 0 Replicas: 0,2,1 Isr: 0,2,1 3.4、其他命令 -- 列出所有的主题 ./kafka-topics.sh --zookeeper 127.0.0.1:2181 --list -- 修改主题分区数量 ./kafka-topics.sh --zookeeper 127.0.0.1:2181 --topic topicName --partitions 4 --alter

Redis安装配置与代码框架

Jettry配置与代码框架

 Jetty服务一共启动8个线程进行数据的接入。存储于bLOCKINGqUEUE中,进行消费,启启动多个线程的作用在于并发消费接入的报文数据。同时,同一事件启动多个服务,通过nginx进行负载均衡。

一、Jetty服务的启动

1、创建ApplicationContext,通过AbstractApplicationContext创建。 AbstractApplicationContext applicationContext = new FileSystemXmlApplicationContext(configXmlFile);

2、创建PlatformMBeanServer MBeanServer mbs = java.lang.management.ManagementFactory.getPlatformMBeanServer();

3、注册ApplicationServer

ObjectName mbeanName = getApplicationObjectName();
ApplicationServer applicationServer = new ApplicationServer();
....
mbs.registerMBean(applicationServer,mbeanNamer);

二、Jettry服务的停止 ......

HIVE安装配置与代码框架

Rstudio与Spark安装配置与代码框架

Nginx配置

user www-data; #运行用户 worker_processes 1; #启动进程,通常设置成和cpu的数量相等 error_log /var/log/nginx/error.log; #全局错误日志及PID文件 pid /var/run/nginx.pid;#PID文件

#工作模式及连接数上限 events { use epoll; #epoll是多路复用IO(I/O Multiplexing)中的一种方式,但是仅用于linux2.6以上内核,可以大大提高nginx的性能 worker_connections 1024;#单个后台worker process进程的最大并发链接数 # multi_accept on; }

#设定http服务器,利用它的反向代理功能提供负载均衡支持 http { #设定mime类型,类型由mime.type文件定义 include /etc/nginx/mime.types; default_type application/octet-stream; #设定日志格式 access_log /var/log/nginx/access.log;

#sendfile 指令指定 nginx 是否调用 sendfile 函数(zero copy 方式)来输出文件,对于普通应用, #必须设为 on,如果用来进行下载等应用磁盘IO重负载应用,可设置为 off,以平衡磁盘与网络I/O处理速度,降低系统的uptime. sendfile on; #tcp_nopush on;

#连接超时时间 #keepalive_timeout 0; keepalive_timeout 65; tcp_nodelay on; #开启gzip压缩 gzip on; gzip_disable "MSIE [1-6]\.(?!.*SV1)";

#设定请求缓冲 client_header_buffer_size 1k; large_client_header_buffers 4 4k;

include /etc/nginx/conf.d/*.conf; include /etc/nginx/sites-enabled/*;

#设定负载均衡的服务器列表 upstream mysvr { #weigth参数表示权值,权值越高被分配到的几率越大 #本机上的Squid开启3128端口 server 192.168.8.1:3128 weight=5; server 192.168.8.2:80 weight=1; server 192.168.8.3:80 weight=6; }

server { #侦听80端口 listen 80; #定义使用www.xx.com访问 server_name www.xx.com;(虚拟主机ip)

#设定本虚拟主机的访问日志 access_log logs/www.xx.com.access.log main;

#默认请求 location / { root /root; #定义服务器的默认网站根目录位置 index index.php index.html index.htm; #定义首页索引文件的名称

fastcgi_pass www.xx.com; fastcgi_param SCRIPT_FILENAME $document_root/$fastcgi_script_name; include /etc/nginx/fastcgi_params; }

# 定义错误提示页面 error_page 500 502 503 504 /50x.html; location = /50x.html { root /root; }

#静态文件,nginx自己处理 location ~ ^/(images|javascript|js|css|flash|media|static)/ { root /var/www/virtual/htdocs; #过期30天,静态文件不怎么更新,过期可以设大一点,如果频繁更新,则可以设置得小一点。 expires 30d; } #PHP 脚本请求全部转发到 FastCGI处理. 使用FastCGI默认配置. location ~ \.php$ { root /root; fastcgi_pass 127.0.0.1:9000; fastcgi_index index.php; fastcgi_param SCRIPT_FILENAME /home/www/www$fastcgi_script_name; include fastcgi_params; } #设定查看Nginx状态的地址 location /NginxStatus { stub_status on; access_log on; auth_basic "NginxStatus"; auth_basic_user_file conf/htpasswd; } #禁止访问 .htxxx 文件 location ~ /\.ht { deny all; } } }

Sentry配置

Hue配置(3.11版本)

hue安装 rpm -ivh hue-* --force --nodeps rpm -ivh sentry-* --force --nodeps

hue 启动 /usr/lib/hue/build/env/bin/hue runserver ip:port > /var/log/hue/hue-start.out 2> /var/log/hue/hue-error.out &

spark livy 集成 hue sqlited is locked desktopdefault_hdfs_superuserhadoopHDFS管理用户desktophttp_host10.10.4.125Hue Web Server所在主机/IPdesktophttp_port8000Hue Web Server服务端口desktopserver_userhadoop运行Hue Web Server的进程用户desktopserver_grouphadoop运行Hue Web Server的进程用户组desktopdefault_useryanjunHue管理员

1. 新建hue用户 #usradd hue 将包放到/home/hue/底下(可以自己选择位置) #rpm -ivh hue-* --force --nodeps 安装完成之后,hue的目录在/usr/lib/hue,配置文件目录在/etc/hue/conf/hue.ini 2. 配置hue(/etc/hue/conf/hue.ini) [beeswax] # Host where HiveServer2 is running. # If Kerberos security is enabled, use fully-qualified domain name (FQDN). hive_server_host=172.19.189.50(hiveserver2的地址) # Port where HiveServer2 Thrift server runs on. hive_server_port=10010(hiveserver2端口) # Hive configuration directory, where hive-site.xml is located hive_conf_dir=/etc/conf(hive配置文件路径) # Timeout in seconds for thrift calls to Hive service server_conn_timeout=120(连接hive超时时间) # Choose whether to use the old GetLog() thrift call from before Hive 0.14 to retrieve the logs. # If false, use the FetchResults() thrift call from Hive 1.0 or more instead. ## use_get_log_api=false # Limit the number of partitions that can be listed. list_partitions_limit=10000(不限制时查询界面上list的partition数据) # The maximum number of partitions that will be included in the SELECT * LIMIT sample query for partitioned tables. query_partitions_limit=10(查询语句中最多查询10个partition) # A limit to the number of cells (rows * columns) that can be downloaded from a query # (e.g. - 10K rows * 1K columns = 10M cells.) # A value of -1 means there will be no limit. download_cell_limit=10000000(download最大量) # Hue will try to close the Hive query when the user leaves the editor page. # This will free all the query resources in HiveServer2, but also make its results inaccessible. close_queries=true(关闭界面时直接断开该用户与hiveserver2的连接) # Thrift version to use when communicating with HiveServer2. # New column format is from version 7. thrift_version=7(默认值,不建议改会报错) [[yarn_clusters]] [[[default]]] # Enter the host on which you are running the ResourceManager resourcemanager_host= (RM地址) # The port where the ResourceManager IPC listens on resourcemanager_port=8032(RM端口) # Whether to submit jobs to this cluster submit_to=True # Resource Manager logical name (required for HA) logical_name=ActiveResourceManager(HA,给activeRM命名) # Change this if your YARN cluster is Kerberos-secured ## security_enabled=false # URL of the ResourceManager API resourcemanager_api_url=http://172.19.xxx.xx:8088(RM URL) # URL of the ProxyServer API ## proxy_api_url=http://localhost:8088 # URL of the HistoryServer API history_server_api_url=http://172.19.xx.xx:19888 # URL of the Spark History Server #spark_history_server_url=http://172.30.xx.xx:18088 # In secure mode (HTTPS), if SSL certificates from YARN Rest APIs # have to be verified against certificate authority ## ssl_cert_ca_verify=True # HA support by specifying multiple clusters. # Redefine different properties there. # e.g. [[[ha]]] # Resource Manager logical name (required for HA) logical_name=StandbyResourceManager(RM的HA从节点name) # Un-comment to enable submit_to=True # URL of the ResourceManager API resourcemanager_api_url=http://172.19.189.50:8088(RM从节点地址) [hadoop] # Configuration for HDFS NameNode # ------------------------------------------------------------------------ [[hdfs_clusters]] # HA support by using HttpFs [[[default]]] # Enter the filesystem uri fs_defaultfs=hdfs://hdp(HDFS的filesystem名字) # NameNode logical name. logical_name=MasterNamenode(NN name) # Use WebHdfs/HttpFs as the communication mechanism. # Domain should be the NameNode or HttpFs host. # Default port is 14000 for HttpFs. webhdfs_url=http://172.19.189.50:50070/webhdfs/v1(hue的获取hdfs的nn HA是通过WebHDFS来切换的,这个地址是启动Webhdfs的地址) # Change this if your HDFS cluster is Kerberos-secured ## security_enabled=false # In secure mode (HTTPS), if SSL certificates from YARN Rest APIs # have to be verified against certificate authority ## ssl_cert_ca_verify=True # Directory of the Hadoop configuration hadoop_conf_dir=/etc/hadoop/conf(hadoop配置文件路径) [desktop] # Set this to a random string, the longer the better. # This is used for secure hashing in the session store. ## secret_key= # Execute this script to produce the Django secret key. This will be used when # 'secret_key' is not set. ## secret_key_script= # Webserver listens on this address and port http_host=0.0.0.0 http_port=8888 # Time zone name time_zone=Asia/Shanghai # Enable or disable Django debug mode. #django_debug_mode=false # Enable or disable database debug mode. #database_logging=false # Whether to send debug messages from JavaScript to the server logs. #send_dbug_messages=true # Enable or disable backtrace for server error http_500_debug_mode=true # Enable or disable memory profiling. ## memory_profiler=false # Server email for internal error messages ## django_server_email='hue@localhost.localdomain' # Email backend ## django_email_backend=django.core.mail.backends.smtp.EmailBackend # Webserver runs as this user server_user=hue server_group=hadoop(hue界面登录用户) # This should be the Hue admin and proxy user default_user=hue(hue管理员用户) # This should be the hadoop cluster admin default_hdfs_superuser=hue(hue在hdfs上的管理员用户) [[database]] #engine=sqlite3 #name=/var/lib/hue/desktop.db # Database engine is typically one of: # postgresql_psycopg2, mysql, sqlite3 or oracle. # Note that for sqlite3, 'name', below is a path to the filename. For other backends, it is the database name # Note for Oracle, options={"threaded":true} must be set in order to avoid crashes. # Note for Oracle, you can use the Oracle Service Name by setting "host=" and "port=" and then "name=<host>:<port>/<service_name>". # Note for MariaDB use the 'mysql' engine. engine=mysql host=172.30.115.60 port=3306 user=hue password=hue name=hue (hue的元数据存储位置) # Execute this script to produce the database password. This will be used when 'password' is not set. ## password_script=/path/script ## name=desktop/desktop.db ## options={} [librdbms] # The RDBMS app can have any number of databases configured in the databases # section. A database is known by its section name # (IE sqlite, mysql, psql, and oracle in the list below). [[databases]] # sqlite configuration. ## [[[sqlite]]] # Name to show in the UI. ## nice_name=SQLite # For SQLite, name defines the path to the database. ## name=/tmp/sqlite.db # Database backend to use. ## engine=sqlite # Database options to send to the server when connecting. # https://docs.djangoproject.com/en/1.4/ref/databases/ ## options={} # mysql, oracle, or postgresql configuration. [[[mysql]]](开启mysql) # Name to show in the UI. nice_name="HUE DB"(界面显示mysql的名字) # For MySQL and PostgreSQL, name is the name of the database. # For Oracle, Name is instance of the Oracle server. For express edition # this is 'xe' by default. name=hue(连接的mysql数据库) # Database backend to use. This can be: # 1. mysql # 2. postgresql # 3. oracle engine=mysql(连接引擎) # IP or hostname of the database to connect to. host=172.30.115.60(mysql地址) # Port the database server is listening to. Defaults are: # 1. MySQL: 3306 # 2. PostgreSQL: 5432 # 3. Oracle Express Edition: 1521 port=3306(端口) # Username to authenticate with when connecting to the database. user=hue(连接数据库的用户) # Password matching the username to authenticate with when # connecting to the database. password=hue(连接数据库的密码) # Database options to send to the server when connecting. # https://docs.djangoproject.com/en/1.4/ref/databases/ ## options={} 修改hue源码 1. /usr/lib/hue/desktop/libs/notebook/src/notebook/connectors/hiveserver2.py 添加调用query_server方法: def _get_db(self, snippet):

LOG.info('log by ck get_db') if snippet['type'] == 'hive':

name = 'beeswax'

elif snippet['type'] == 'impala':

name = 'impala'

else:

name = 'spark-sql'

return dbms.get_by_type(self.user, 'data', query_server=get_query_server_config_for_meta(name=name)) 修改计算(exector)的执行方法 @query_error_handler def execute(self, notebook, snippet): db = self._get_db(snippet)

statement = self._get_current_statement(db, snippet) session = self._get_session(notebook, snippet['type']) query = self._prepare_hql_query(snippet, statement['statement'], session)

try: if statement.get('statement_id') == 0: db.use(query.database) handle = db.client.query(query) except QueryServerException, ex: raise QueryError(ex.message, handle=statement)

# All good server_id, server_guid = handle.get() response = { 'secret': server_id, 'guid': server_guid, 'operation_type': handle.operation_type, 'has_result_set': handle.has_result_set, 'modified_row_count': handle.modified_row_count, 'log_context': handle.log_context, } response.update(statement)

return response 2. /usr/lib/hue/apps/beeswax/src/beeswax/server/dbms.py 添加query执行的路径:(修改ip为spark的ip和端口) def get_query_server_config_for_meta(name='beeswax', server=None):

if name == 'impala':

from impala.dbms import get_query_server_config as impala_query_server_config

query_server = impala_query_server_config()

else:

kerberos_principal = hive_site.get_hiveserver2_kerberos_principal(HIVE_SERVER_HOST.get())

query_server = {

'server_name': 'beeswax', # Aka HiveServer2 now

'server_host': '172.30.115.58',#HIVE_SERVER_HOST.get(),

'server_port': '10010',#HIVE_SERVER_PORT.get(),

'principal': kerberos_principal,

'http_url': '%(protocol)s://%(host)s:%(port)s/%(end_point)s' % {

'protocol': 'https' if hiveserver2_use_ssl() else 'http',

'host': '172.30.115.58',#HIVE_SERVER_HOST.get(),

'port': hive_site.hiveserver2_thrift_http_port(),

'end_point': hive_site.hiveserver2_thrift_http_path()

},

'transport_mode': 'http' if hive_site.hiveserver2_transport_mode() == 'HTTP' else 'socket',

'auth_username': AUTH_USERNAME.get(),

'auth_password': AUTH_PASSWORD.get()

}

if name == 'sparksql': # Spark SQL is almost the same as Hive

from spark.conf import SQL_SERVER_HOST as SPARK_SERVER_HOST, SQL_SERVER_PORT as SPARK_SERVER_PORT

query_server.update({

'server_name': 'sparksql',

'server_host': SPARK_SERVER_HOST.get(),

'server_port': SPARK_SERVER_PORT.get()

})

debug_query_server = query_server.copy()

debug_query_server['auth_password_used'] = bool(debug_query_server.pop('auth_password'))

LOG.info("Query Server: %s" % debug_query_server)

return query_server 添加query的cache locking DBMS_CACHE = {}

DBMS_CACHE_LOCK = threading.Lock()

DBMS_META_CACHE = {}

DBMS_META_CACHE_LOCK = threading.Lock()

DBMS_DATA_CACHE = {}

DBMS_DATA_CACHE_LOCK = threading.Lock()

def get(user, query_server=None):

global DBMS_CACHE

global DBMS_CACHE_LOCK

if query_server is None:

query_server = get_query_server_config()

DBMS_CACHE_LOCK.acquire()

try:

DBMS_CACHE.setdefault(user.username, {})

if query_server['server_name'] not in DBMS_CACHE[user.username]:

# Avoid circular dependency

from beeswax.server.hive_server2_lib import HiveServerClientCompatible

if query_server['server_name'] == 'impala':

from impala.dbms import ImpalaDbms

from impala.server import ImpalaServerClient

DBMS_CACHE[user.username][query_server['server_name']] = ImpalaDbms(HiveServerClientCompatible(ImpalaServerClient(query_server, user)), QueryHistory.SERVER_TYPE[1][0])

else:

from beeswax.server.hive_server2_lib import HiveServerClient

DBMS_CACHE[user.username][query_server['server_name']] = HiveServer2Dbms(HiveServerClientCompatible(HiveServerClient(query_server, user)), QueryHistory.SERVER_TYPE[1][0])

return DBMS_CACHE[user.username][query_server['server_name']]

finally:

DBMS_CACHE_LOCK.release()

def get_by_type(user, type, query_server=None):

global DBMS_META_CACHE

global DBMS_META_CACHE_LOCK

global DBMS_DATA_CACHE

global DBMS_DATA_CACHE_LOCK

if query_server is None:

query_server = get_query_server_config()

if type == 'meta':

DBMS_META_CACHE_LOCK.acquire()

try:

DBMS_META_CACHE.setdefault(user.username, {})

if query_server['server_name'] not in DBMS_META_CACHE[user.username]:

# Avoid circular dependency

from beeswax.server.hive_server2_lib import HiveServerClientCompatible

if query_server['server_name'] == 'impala':

from impala.dbms import ImpalaDbms

from impala.server import ImpalaServerClient

DBMS_META_CACHE[user.username][query_server['server_name']] = ImpalaDbms(HiveServerClientCompatible(ImpalaServerClient(query_server, user)), QueryHistory.SERVER_TYPE[1][0])

else:

from beeswax.server.hive_server2_lib import HiveServerClient

DBMS_META_CACHE[user.username][query_server['server_name']] = HiveServer2Dbms(HiveServerClientCompatible(HiveServerClient(query_server, user)), QueryHistory.SERVER_TYPE[1][0])

return DBMS_META_CACHE[user.username][query_server['server_name']]

finally:

DBMS_META_CACHE_LOCK.release()

else:

DBMS_DATA_CACHE_LOCK.acquire()

try:

DBMS_DATA_CACHE.setdefault(user.username, {})

if query_server['server_name'] not in DBMS_DATA_CACHE[user.username]:

# Avoid circular dependency

from beeswax.server.hive_server2_lib import HiveServerClientCompatible

if query_server['server_name'] == 'impala':

from impala.dbms import ImpalaDbms

from impala.server import ImpalaServerClient

DBMS_DATA_CACHE[user.username][query_server['server_name']] = ImpalaDbms(HiveServerClientCompatible(ImpalaServerClient(query_server, user)), QueryHistory.SERVER_TYPE[1][0])

else:

from beeswax.server.hive_server2_lib import HiveServerClient

DBMS_DATA_CACHE[user.username][query_server['server_name']] = HiveServer2Dbms(HiveServerClientCompatible(HiveServerClient(query_server, user)), QueryHistory.SERVER_TYPE[1][0])

return DBMS_DATA_CACHE[user.username][query_server['server_name']]

finally:

DBMS_DATA_CACHE_LOCK.release() 3. /usr/lib/hue/apps/filebrowser/src/filebrowser/templates/listdir.mako 删除filebrower中的delete和drop < !-- ko if: $root.isS3 --> < button

class ="btn fileToolbarBtn delete-link" title="${_('Delete forever')}" data-bind="enable: selectedFiles().length > 0, click: deleteSelected" > < i class ="fa fa-bolt" > < / i > ${_('Delete forever')} < / button >

< !-- / ko -->

< ul

class ="dropdown-menu" >

< li > < a

href = "#"

class ="delete-link" title="${_('Delete forever')}" data-bind="enable: selectedFiles().length > 0, click: deleteSelected" > < i class ="fa fa-bolt" > < / i > ${_('Delete forever')} < / a > < / li >

< / ul > 4. /usr/lib/hue/apps/filebrowser/src/filebrowser/templates/listdir_components.mako < li > < a href = "#" class ="delete-link" title="${_('Delete forever')}" data-bind="enable: $root.selectedFiles().length > 0, click: $root.deleteSelected" > < i class ="fa fa-fw fa-bolt" > < / i > ${_('Delete forever')} < / a > < / li > 5. /usr/lib/hue/apps/filebrowser/src/filebrowser/views.py request.fs.do_as_user(request.user, request.fs.rmtree, arg['path'], True) # the old code was ('skip_trash' in request.GET) 修改hdfs配置 hdfs-site.xml文件 修改:fs.permissions.umask-mode 000 (hue建表用户为web登录用户,在用spark做计算引擎时会在hdfs的表目录中创建文件,后台spark的用户为hive无法写入) 添加:添加hue用户为hdfs代理用户(superuser) hadoop.proxyuser.hue.hosts * hadoop.proxyuser.hue.groups *

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