Storm之——Storm+Kafka+Flume+Zookeeper+MySQL實現資料實時分析(環境搭建篇)

2018年03月04日 23:05:29 冰 河 閱讀數：1602更多

所屬專欄： Hadoop生態

版權宣告：本文為博主原創文章，未經博主允許不得轉載。 https://blog.csdn.net/l1028386804/article/details/79440511

轉載請註明出處：http://blog.csdn.net/l1028386804/article/details/79440511

一、伺服器配置

Storm:apache-storm-1.1.1.tar.gz
下載地址為：https://archive.apache.org/dist/storm/apache-storm-1.1.1/apache-storm-1.1.1.tar.gz

Zookeeper：3.4.9

具體安裝參考博文《Hadoop之——Hadoop2.5.2 HA高可靠性叢集搭建(Hadoop+Zookeeper)》或 《Storm之——搭建Storm叢集

MySQL：5.6

MySQL的安裝具體參見博文：《MySQL之——CentOS6.5 編譯安裝MySQL5.6.16》

二、環境搭建

1、安裝Zookeeper

具體安裝參考博文《Hadoop之——Hadoop2.5.2 HA高可靠性叢集搭建(Hadoop+Zookeeper)》或 《Storm之——搭建Storm叢集》

2、安裝MySQL

MySQL的安裝具體參見博文：《MySQL之——CentOS6.5 編譯安裝MySQL5.6.16》

3、Flume安裝

依次輸入以下命令安裝Flume

wget http://mirrors.shu.edu.cn/apache/flume/1.8.0/apache-flume-1.8.0-bin.tar.gz
tar -zxvf apache-flume-1.8.0-bin.tar.gz
cd apache-flume-1.8.0-bin/conf
vim flume-conf.properties
 

輸入的內容如下：

a1.sources = r1
a1.sinks = k1
a1.channels = c1

# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /home/flume/log.log

# Describe the sink
#a1.sinks.k1.type = logger
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.topic = wordCount
a1.sinks.k1.brokerList = 192.168.209.121:9092
a1.sinks.k1.requiredAcks = 1
a1.sinks.k1.batchSize = 20

# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

輸入wq退出。

注意：我們的Flume配置中，監聽了/home/flume/log.log作為日誌的資料來源，同時我們將監聽到的日誌傳送到Kafka的wordCount主題上。

輸入以下命令啟動Flume

bin/flume-ng agent --conf conf --conf-file conf/flume-conf.properties --name a1 -Dflume.root.logger=INFO,console

4、安裝Kafka

依次輸入以下命令安裝Kafka

wget http://mirrors.tuna.tsinghua.edu.cn/apache/kafka/1.0.0/kafka_2.12-1.0.0.tgz
tar -zxvf kafka_2.12-1.0.0.tgz
cd kafka_2.12-1.0.0/config
vim server.properties

內容如下：

# 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.

# see kafka.server.KafkaConfig for additional details and defaults

############################# Server Basics #############################

# The id of the broker. This must be set to a unique integer for each broker.
broker.id=0

############################# Socket Server Settings #############################

# The address the socket server listens on. It will get the value returned from 
# java.net.InetAddress.getCanonicalHostName() if not configured.
#   FORMAT:
#     listeners = listener_name://host_name:port
#   EXAMPLE:
#     listeners = PLAINTEXT://your.host.name:9092
listeners=PLAINTEXT://:9092
port=9092

# Hostname and port the broker will advertise to producers and consumers. If not set, 
# it uses the value for "listeners" if configured.  Otherwise, it will use the value
# returned from java.net.InetAddress.getCanonicalHostName().
# advertised.listeners=PLAINTEXT://192.168.209.121:9092

# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
#listener.security.protocol.map=PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL

# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3

# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8

# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400

# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400

# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600


############################# Log Basics #############################

# A comma seperated list of directories under which to store log files
log.dirs=/usr/local/kafka_2.12-1.0.0/kafka-logs

# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=1

# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1

############################# Internal Topic Settings  #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended for to ensure availability such as 3.
offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1

############################# Log Flush Policy #############################

# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
#    1. Durability: Unflushed data may be lost if you are not using replication.
#    2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
#    3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to exceessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.

# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000

# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000

############################# Log Retention Policy #############################

# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.

# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=168

# A size-based retention policy for logs. Segments are pruned from the log unless the remaining
# segments drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824

# The maximum size of a log segment file. When this size is reached a new log segment will be created.
log.segment.bytes=1073741824

# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000

############################# Zookeeper #############################

# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
zookeeper.connect=192.168.209.121:2181
advertised.host.name=192.168.209.121

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000


############################# Group Coordinator Settings #############################

# The following configuration specifies the time, in milliseconds, that the GroupCoordinator will delay the initial consumer rebalance.
# The rebalance will be further delayed by the value of group.initial.rebalance.delay.ms as new members join the group, up to a maximum of max.poll.interval.ms.
# The default value for this is 3 seconds.
# We override this to 0 here as it makes for a better out-of-the-box experience for development and testing.
# However, in production environments the default value of 3 seconds is more suitable as this will help to avoid unnecessary, and potentially expensive, rebalances during application startup.
group.initial.rebalance.delay.ms=0

之後我們輸入如下命令啟動Kafka並建立主題wordCount

啟動Kafka
./bin/kafka-server-start.sh /usr/local/kafka_2.12-1.0.0/config/server.properties  
後臺啟動Kafka
./bin/kafka-server-start.sh -daemon /usr/local/kafka_2.12-1.0.0/config/server.properties  
然後我們在Kafka上建立wordCount主題
./bin/kafka-topics.sh --create --zookeeper 192.168.209.121:2181 --replication-factor 1 -partitions 3 --topic wordCount

5、安裝Storm

依次輸入以下命令安裝配置Storm

https://archive.apache.org/dist/storm/apache-storm-1.1.1/apache-storm-1.1.1.tar.gz
tar -zxvf apache-storm-1.1.1.tar.gz
cd apache-storm-1.1.1/conf
vim storm.yaml

內容如下：

#
## List of custom kryo decorators
# topology.kryo.decorators:
#     - org.mycompany.MyDecorator
#
## Locations of the drpc servers
drpc.servers:
     - "liuyazhuang121"
#     - "server2"

## Metrics Consumers
## max.retain.metric.tuples
## - task queue will be unbounded when max.retain.metric.tuples is equal or less than 0.
## whitelist / blacklist
## - when none of configuration for metric filter are specified, it'll be treated as 'pass all'.
## - you need to specify either whitelist or blacklist, or none of them. You can't specify both of them.
## - you can specify multiple whitelist / blacklist with regular expression
## expandMapType: expand metric with map type as value to multiple metrics
## - set to true when you would like to apply filter to expanded metrics
## - default value is false which is backward compatible value
## metricNameSeparator: separator between origin metric name and key of entry from map
## - only effective when expandMapType is set to true
# topology.metrics.consumer.register:
#   - class: "org.apache.storm.metric.LoggingMetricsConsumer"
#     max.retain.metric.tuples: 100
#     parallelism.hint: 1
#   - class: "org.mycompany.MyMetricsConsumer"
#     max.retain.metric.tuples: 100
#     whitelist:
#       - "execute.*"
#       - "^__complete-latency$"
#     parallelism.hint: 1
#     argument:
#       - endpoint: "metrics-collector.mycompany.org"
#     expandMapType: true
#     metricNameSeparator: "."

## Cluster Metrics Consumers
# storm.cluster.metrics.consumer.register:
#   - class: "org.apache.storm.metric.LoggingClusterMetricsConsumer"
#   - class: "org.mycompany.MyMetricsConsumer"
#     argument:
#       - endpoint: "metrics-collector.mycompany.org"
#
# storm.cluster.metrics.consumer.publish.interval.secs: 60

storm.local.dir: /home/storm
supervisor.slots.ports:
   - 6700
   - 6701
   - 6702
   - 6703


storm.messaging.transport: "backtype.storm.messaging.netty.Context"
"storm.yaml" 92L, 3366C written
[[email protected] conf]# 
[[email protected] conf]# 
[[email protected] conf]# pwd
/usr/local/apache-storm-1.1.1/conf
[[email protected] conf]# vim storm.yaml 
#
## List of custom kryo decorators
# topology.kryo.decorators:
#     - org.mycompany.MyDecorator
#
## Locations of the drpc servers
drpc.servers:
     - "liuyazhuang121"
#     - "server2"

## Metrics Consumers
## max.retain.metric.tuples
## - task queue will be unbounded when max.retain.metric.tuples is equal or less than 0.
## whitelist / blacklist
## - when none of configuration for metric filter are specified, it'll be treated as 'pass all'.
## - you need to specify either whitelist or blacklist, or none of them. You can't specify both of them.
## - you can specify multiple whitelist / blacklist with regular expression
## expandMapType: expand metric with map type as value to multiple metrics
## - set to true when you would like to apply filter to expanded metrics
## - default value is false which is backward compatible value
## metricNameSeparator: separator between origin metric name and key of entry from map
## - only effective when expandMapType is set to true
# topology.metrics.consumer.register:
#   - class: "org.apache.storm.metric.LoggingMetricsConsumer"
#     max.retain.metric.tuples: 100
#     parallelism.hint: 1
#   - class: "org.mycompany.MyMetricsConsumer"
#     max.retain.metric.tuples: 100
#     whitelist:
#       - "execute.*"
#       - "^__complete-latency$"
#     parallelism.hint: 1
#     argument:
#       - endpoint: "metrics-collector.mycompany.org"
#     expandMapType: true
#     metricNameSeparator: "."

## Cluster Metrics Consumers
# storm.cluster.metrics.consumer.register:
#   - class: "org.apache.storm.metric.LoggingClusterMetricsConsumer"
#   - class: "org.mycompany.MyMetricsConsumer"
#     argument:
#       - endpoint: "metrics-collector.mycompany.org"
#
# storm.cluster.metrics.consumer.publish.interval.secs: 60

storm.local.dir: /home/storm
supervisor.slots.ports:
   - 6700
   - 6701
   - 6702
   - 6703


storm.messaging.transport: "backtype.storm.messaging.netty.Context"

輸入以下命令啟動storm

nohup ./bin/storm nimbus >> /dev/null &
nohup ./bin/storm ui >> /dev/null &
nohup ./bin/storm supervisor >> /dev/null &
nohup ./bin/storm drpc >> /dev/null &

至此，環境搭建完畢