Apache Druid0.15.0安装方式

Druid0.15.0概述

Druid是一个用于大数据实时查询和分析的高容错、高性能开源分布式系统，旨在快速处理大规模的数据，并能够实现快速查询和分析。尤其是当发生代码部署、机器故障以及其他产品系统遇到宕机等情况时，Druid仍能够保持100%正常运行。创建Druid的最初意图主要是为了解决查询延迟问题，Druid提供了以交互方式访问数据的能力，并权衡了查询的灵活性和性能而采取了特殊的存储格式。值得一提的是，Druid0.15开始支持SQL查询，而在之前的版本是不支持SQL查询的，只有json才能查询。

特性

  • 为局部嵌套数据结构提供列式存储格式；
  • 为快速过滤做索引；
  • 实时摄取和查询；
  • 高容错的分布式体系架构等。

业务场景

  1. 需要交互式聚合和快速探究大量数据时；
  2. 需要实时查询分析时；
  3. 对数据尤其是大数据进行实时分析时，在溢米大数据应用场景中，以上三个特性和天眼五期需求非常契合，而Druid恰好可与悟空结合实现实时入库。目前Spark+CarbonData的方式随着数据量的增加，查询速度变得缓慢，Druid是一个不错的替代方案；
  4. 需要一个高可用、高容错、高性能数据库时。

1 集群规划

  • Master包含Coordinator和Overlord,4核16G*2；
  • data包含Historical和MiddleManager,16核64G*3；
  • query包含Broker和Router，4核16G*1。
1.1 Hadoop配置文件设置

本次安装使用HDFS作为存储，进入3个data节点，/data1/druid/druid-0.15.0/conf/druid/cluster/_common目录，软链到对应hadoop的配置文件目录，此步骤为了识别Hadoop HA模式，否则深度存储使用HDFS无法识别路径。

ln -s /usr/hdp/2.6.5.0-292/hadoop/conf hadoop-xml
1.2 jdk1.8安装，此处省略。
1.3 data节点作为HDFS的datanode，此处省略
1.4 common配置

这个配置可以打印druid系统的运行日志，方便后续定位问题，文件路径和文件名可修改

  1. log4j2.xml配置
<Configuration status="WARN">
    <Properties>
        <Property name="log.path">/data1/druid/log</Property>
    </Properties>
    <Appenders>
        <Console name="Console" target="SYSTEM_OUT">
            <PatternLayout pattern="%d{ISO8601} %p [%t] %c - %m%n"/>
        </Console>
        <File name="log" fileName="${log.path}/one.log" append="false">
            <PatternLayout pattern="[%d{yyyy-MM-dd HH:mm:ss:SSS}] [%p] - %l - %m%n"/>
        </File>
        <RollingFile name="RollingFileInfo" fileName="${log.path}/druid-data.log"
                     filePattern="${log.path}/druid-data-%d{yyyy-MM-dd}-%i.out">
            <ThresholdFilter level="info" onMatch="ACCEPT" onMismatch="DENY"/>
            <PatternLayout pattern="[%d{yyyy-MM-dd HH:mm:ss:SSS}] [%p] - %l - %m%n"/>
            <Policies>
                <TimeBasedTriggeringPolicy modulate="true" interval="1"/>
                <SizeBasedTriggeringPolicy size="100 MB"/>
            </Policies>

        </RollingFile>
    </Appenders>
    <Loggers>
        <Root level="info">
            <AppenderRef ref="Console"/>
            <appender-ref ref="RollingFileInfo"/>
            <appender-ref ref="log"/>
        </Root>
    </Loggers>
</Configuration>
  1. common.runtime.properties配置， druid.host改成druid所在机器的hostname，这个配置文件是全局的配置文件，对应的参数有相应的解释。
druid.extensions.loadList=["druid-kafka-eight", "druid-histogram", "druid-datasketches", "mysql-metadata-storage","druid-hdfs-storage","druid-kafka-extraction-namespace","druid-kafka-indexing-service"]
druid.extensions.directory=/data1/druid/druid-0.15.0/extensions
# If you have a different version of Hadoop, place your Hadoop client jar files in your hadoop-dependencies directory
# and uncomment the line below to point to your directory.
druid.extensions.hadoopDependenciesDir=/data1/druid/druid-0.15.0/hadoop-dependencies


#
# Hostname
#
druid.host=bd-prod-slave06
#
# Logging
# Log all runtime properties on startup. Disable to avoid logging properties on startup:
druid.startup.logging.logProperties=true

#
# Zookeeper
#

druid.zk.service.host=bd-prod-master01:2181,bd-prod-master02:2181,bd-prod-slave01:2181
druid.zk.paths.base=/druid

#
# Metadata storage
#

# For Derby server on your Druid Coordinator (only viable in a cluster with a single Coordinator, no fail-over):
# druid.metadata.storage.type=derby
# druid.metadata.storage.connector.connectURI=jdbc:derby://localhost:1527/var/druid/metadata.db;create=true
# druid.metadata.storage.connector.host=localhost
# druid.metadata.storage.connector.port=1527

# For MySQL (make sure to include the MySQL JDBC driver on the classpath):
druid.metadata.storage.type=mysql
druid.metadata.storage.connector.connectURI=jdbc:mysql://bd-prod-master01:3306/druid?useSSL=false&amp;useUnicode=true&amp;characterEncoding=UTF-8
druid.metadata.storage.connector.user=user
druid.metadata.storage.connector.password=password

# For PostgreSQL:
#druid.metadata.storage.type=postgresql
#druid.metadata.storage.connector.connectURI=jdbc:postgresql://db.example.com:5432/druid
#druid.metadata.storage.connector.user=...
#druid.metadata.storage.connector.password=...

#
# Deep storage
#

# For local disk (only viable in a cluster if this is a network mount):
# druid.storage.type=local
# druid.storage.storageDirectory=var/druid/segments

# For HDFS:
druid.storage.type=hdfs
druid.storage.storageDirectory=hdfs://bd-prod/druid/segments

# For S3:
#druid.storage.type=s3
#druid.storage.bucket=your-bucket
#druid.storage.baseKey=druid/segments
#druid.s3.accessKey=...
#druid.s3.secretKey=...

#
# Indexing service logs
#

# For local disk (only viable in a cluster if this is a network mount):
# druid.indexer.logs.type=file
# druid.indexer.logs.directory=var/druid/indexing-logs

# For HDFS:
druid.indexer.logs.type=hdfs
druid.indexer.logs.directory=hdfs://bd-prod/druid/indexing-logs

# For S3:
#druid.indexer.logs.type=s3
#druid.indexer.logs.s3Bucket=your-bucket
#druid.indexer.logs.s3Prefix=druid/indexing-logs

#
# Service discovery
#

druid.selectors.indexing.serviceName=druid/overlord
druid.selectors.coordinator.serviceName=druid/coordinator

#
# Monitoring
#

druid.monitoring.monitors=["org.apache.druid.java.util.metrics.JvmMonitor"]
druid.emitter=noop
druid.emitter.logging.logLevel=info

# Storage type of double columns
# ommiting this will lead to index double as float at the storage layer

druid.indexing.doubleStorage=double

#
# Security
#
druid.server.hiddenProperties=["druid.s3.accessKey","druid.s3.secretKey","druid.metadata.storage.connector.password"]


#
# SQL
#
druid.sql.enable=true

#
# Lookups
#
druid.lookup.enableLookupSyncOnStartup=false
2.data节点

进入data节点，修改相应的druid.host；

2.1 historical

historical主要负责加载已经生成好的数据文件以提供数据查询。

  1. /data1/druid/druid-0.15.0/conf/druid/cluster/data/historical/jvm.config
-server
-Xms8g
-Xmx8g
-XX:MaxDirectMemorySize=12g
-XX:+ExitOnOutOfMemoryError
-Duser.timezone=UTC+0800
-Dfile.encoding=UTF-8
-Djava.io.tmpdir=/tmp
-Djava.util.logging.manager=org.apache.logging.log4j.jul.LogManager
  1. /data1/druid/druid-0.15.0/conf/druid/cluster/data/historical/runtime.properties
druid.service=druid/historical
druid.plaintextPort=9088
druid.segmentCache.numLoadingThreads=16
# HTTP server threads
druid.server.http.numThreads=60

# Processing threads and buffers
druid.processing.buffer.sizeBytes=500000000
druid.processing.numMergeBuffers=4
druid.processing.numThreads=16
druid.processing.tmpDir=/data1/druid/processing

# Segment storage
druid.segmentCache.locations=[{"path":"/data1/druid/segment-cache","maxSize":300000000000}]
druid.server.maxSize=300000000000

# Query cache
druid.historical.cache.useCache=true
druid.historical.cache.populateCache=true
druid.cache.type=caffeine
druid.cache.sizeInBytes=256000000
2.2 middleManager

middleManager主要负责索引服务的工作节点，负责接收Coordinator分配的任务，然后启动容器完成具体任务。

  1. /data1/druid/druid-0.15.0/conf/druid/cluster/data/middleManager/jvm.config
-server
-Xms128m
-Xmx128m
-XX:+ExitOnOutOfMemoryError
-Duser.timezone=UTC+0800
-Dfile.encoding=UTF-8
-Djava.io.tmpdir=/tmp
-Djava.util.logging.manager=org.apache.logging.log4j.jul.LogManager
  1. /data1/druid/druid-0.15.0/conf/druid/cluster/data/middleManager/runtime.properties
druid.service=druid/middleManager
druid.plaintextPort=8091

# Number of tasks per middleManager
druid.worker.capacity=4

# Task launch parameters
druid.indexer.runner.javaOpts=-server -Xms1g -Xmx1g -XX:MaxDirectMemorySize=1g -Duser.timezone=UTC+0800 -Dfile.encoding=UTF-8 -XX:+ExitOnOutOfMemoryError -Djava.util.logging.manager=org.apache.logging.log4j.jul.LogManager
druid.indexer.task.baseTaskDir=/data1/druid/task

# HTTP server threads
druid.server.http.numThreads=60

# Processing threads and buffers on Peons
druid.indexer.fork.property.druid.processing.numMergeBuffers=2
druid.indexer.fork.property.druid.processing.buffer.sizeBytes=100000000
druid.indexer.fork.property.druid.processing.numThreads=4

# Hadoop indexing
druid.indexer.task.hadoopWorkingPath=/data1/druid/hadoop-tmp
2.3 启动命令
 nohup ./bin/start-cluster-data-server >/dev/null 2>&1 &

3 master节点

进入master节点，修改common的druid.host选项；

3.1 coordinator-overlord

负责Historical节点的数据负载均衡，以及通过规则管理数据生命周期，也是索引服务的主节点，对外负责接收任务请求，对内负责将任务分解并下发到从节点即MiddleManager上。

  1. /data1/druid/druid-0.15.0/conf/druid/cluster/master/coordinator-overlord/jvm.config
-server
-Xms12g
-Xmx12g
-XX:+ExitOnOutOfMemoryError
-XX:+UseG1GC
-Duser.timezone=UTC+0800
-Dfile.encoding=UTF-8
-Djava.io.tmpdir=/tmp
-Djava.util.logging.manager=org.apache.logging.log4j.jul.LogManager
-Dderby.stream.error.file=/data1/druid/derby.log
  1. /data1/druid/druid-0.15.0/conf/druid/cluster/master/coordinator-overlord/runtime.properties
druid.service=druid/coordinator
druid.plaintextPort=9181

druid.coordinator.startDelay=PT10S
druid.coordinator.period=PT5S

# Run the overlord service in the coordinator process
druid.coordinator.asOverlord.enabled=true
druid.coordinator.asOverlord.overlordService=druid/overlord

druid.indexer.queue.startDelay=PT5S

druid.indexer.runner.type=remote
druid.indexer.storage.type=metadata
3.2 启动命令
 nohup ./bin/start-cluster-master-no-zk-server >/dev/null 2>&1 &

4 query节点

进入query节点，修改common的druid.host选项；

4.1 broker

broker主要对外提供数据查询服务，查询数据时，读取zookeeper上的元数据和Router，并合并查询结果数据。

  1. /data1/druid/druid-0.15.0/conf/druid/cluster/query/broker/jvm.config
-server
-Xms12g
-Xmx12g
-XX:MaxDirectMemorySize=6g
-XX:+ExitOnOutOfMemoryError
-Duser.timezone=UTC+0800
-Dfile.encoding=UTF-8
-Djava.io.tmpdir=/tmp
-Djava.util.logging.manager=org.apache.logging.log4j.jul.LogManager
  1. /data1/druid/druid-0.15.0/conf/druid/cluster/query/broker/runtime.properties
druid.service=druid/broker
druid.plaintextPort=8182

# HTTP server settings
druid.server.http.numThreads=60

# HTTP client settings
druid.broker.http.numConnections=50
druid.broker.http.maxQueuedBytes=10000000

# Processing threads and buffers
druid.processing.buffer.sizeBytes=500000000
druid.processing.numMergeBuffers=6
druid.processing.numThreads=1
druid.processing.tmpDir=/data1/druid/processing

# Query cache disabled -- push down caching and merging instead
druid.broker.cache.useCache=true
druid.broker.cache.populateCache=true
4.2 router

router顾名思义，主要是按照规则将查询路由到各个Broker上。

  1. /data1/druid/druid-0.15.0/conf/druid/cluster/query/router/jvm.config
-server
-Xms1g
-Xmx1g
-XX:+UseG1GC
-XX:MaxDirectMemorySize=256m
-XX:+ExitOnOutOfMemoryError
-Duser.timezone=UTC+0800
-Dfile.encoding=UTF-8
-Djava.io.tmpdir=/tmp
-Djava.util.logging.manager=org.apache.logging.log4j.jul.LogManager
  1. /data1/druid/druid-0.15.0/conf/druid/cluster/query/router/runtime.properties
druid.service=druid/router
druid.plaintextPort=8888

# HTTP proxy
druid.router.http.numConnections=50
druid.router.http.readTimeout=PT5M
druid.router.http.numMaxThreads=100
druid.server.http.numThreads=100

# Service discovery
druid.router.defaultBrokerServiceName=druid/broker
druid.router.coordinatorServiceName=druid/coordinator

# Management proxy to coordinator / overlord: required for unified web console.
druid.router.managementProxy.enabled=true
4.3 启动命令
nohup ./bin/start-cluster-query-server >/dev/null 2>&1 &

5 总结

Druid作为OLAP的新秀，在实时入库和预聚合上表现非常优秀，而且可以和Flink结合，作为flink的下游数据存储点，是一个非常不错的选择，而且新版的特性开始支持SQL，相信在未来一定能得到大力推广，下一期写一下有关Druid的实时入库操作。



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原文地址：https://www.cnblogs.com/ChouYarn/p/11282909.html