Zookeeper & Kafka Install
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Apache Kafka
A Kafka cluster has a much higher throughput compared to other message brokers such as ActiveMQ/RabbitMQ.
Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Its storage layer is essentially a "massively scalable pub/sub message queue architected as a distributed transaction log," making it highly valuable for enterprise infrastructures to process streaming data. Additionally, Kafka connects to external systems (for data import/export) via Kafka Connect and provides Kafka Streams, a Java stream processing library. - Apache Kafka
Kafka was created at LinkedIn to handle large volumes of event data. Like many other message brokers, it deals with publisher-consumer and queue semantics by grouping data into topics. As an application, you write to a topic and consume from a topic. An important distinction, or a shift in design with Kafka is that the complexity moves from producer to consumers, and it heavily uses the file system cache. These design decisions, coupled with it being distributed from scratch, makes it a winner in many high volume streaming use cases. - Just Enough Kafka for the Elastic Stack, Part 1
Java install
Apache Kafka needs a Java runtime environment:
$ sudo apt-get install default-jre
Create a User : "kafka"
This is an optional section.
Let's create a user called "kafka" including home directory and add it to the sudo group so that it can install Kafka's dependencies:
$ sudo useradd -m kafka $ sudo adduser kafka sudo
Install ZooKeeper
ZooKeeper coordinates and synchronizes configuration information of distributed nodes.
Kafka cluster depends on ZooKeeper to perform operations such as electing leaders and detecting failed nodes. In other words, Kafka brokers need it to form a cluster, and the topic configuration is stored in ZK nodes, etc. Note that newer versions of Kafka have decoupled the clients - consumers and producers - from having to communicate with ZooKeeper. In Kafka 0.9 and 0.10, offsets are stored in topics by default instead of in ZK. Either way, we still need ZooKeeper to run Kafka brokers.
Actually, once we install Kafka, we can use the ZooKeeper that comes with Kafka. But in this chapter, we'll use ZooKeeper package that's available in Ubuntu's default repositories.
$ sudo apt-get install zookeeperd
Now, ZooKeeper is installed, and it will be started as a daemon automatically.
ZK itself does not need much hand-holding once set up. We just have to make sure the instances are up and are monitored.
We can see it's listening on port 2181:
$ sudo netstat -nlpt | grep ':2181' tcp6 0 0 :::2181 :::* LISTEN 10462/java
Or we can check the 2181 port using telnet:
$ telnet 52.8.9.227 2181 Trying 52.8.9.227... Connected to 52.8.9.227. Escape character is '^]'. ruok imok
We can see ZooKeeper is working and it responded back with "imok" to "ruok"!
The ZooKeeper directory structure looks like this:
We may start zookeeper in the following way:
$ sudo bin/zkServer.sh start ZooKeeper JMX enabled by default Using config: /etc/zookeeper/conf/zoo.cfg Starting zookeeper ... STARTED
The usage of bin/zkServer.sh is like this:
$ bin/zkServer.sh {start|start-foreground|stop|restart|status|upgrade|print-cmd}
Kafka
Apache Kafka is a scalable and fault-tolerant distributed message broker, and it can handle large volumes of real-time data efficiently. Compared to other message brokers such as RabbitMQ or ActiveMQ, it has a much higher throughput.
Kafka's pub/sub messaging system is probably the most widely used feature, however, we can also use it for log aggregation since it provides persistent storage for published messages.
Kafka has 5 components:
- Topic: A topic is a category or feed name to which messages are published by the message producers. Topics are partitioned and each partition is represented by the ordered immutable sequence of messages. Each message in the partition is assigned a unique sequential ID (offset).
- Broker: A Kafka cluster consists of servers where each one may have server processes (brokers). Topics are created within the context of broker processes.
- Zookeeper: Zookeeper serves as the coordinator between the Kafka broker and consumers.
- Producers: Producers publish data to the topics by choosing the appropriate partition within the topic.
- Consumers: Consumers are the applications or processes that subscribe to topics and process the feed of published messages.
Install Kafka
Kafka is available from http://kafka.apache.org/downloads.html.
$ mkdir -p ~/kafka $ cd kafka $ wget http://www-us.apache.org/dist/kafka/0.10.2.0/kafka_2.10-0.10.2.0.tgz
Change directory to "kafka", and then extract the file with "--strip 1" option to extract files directly into the directory:
$ tar xvzf kafka_2.10-0.10.2.0.tgz --strip 1
The files in kafka directory look like this:
Kafka server configuration
To be able to delete topics, take off '#' from ~/kafka/config/server.properties:
# Switch to enable topic deletion or not, default value is false delete.topic.enable=true
Start Kafka server (broker)
To start server, we ned to run kafka-server-start.sh
$ ~/kafka/bin/kafka-server-start.sh ~/kafka/config/server.properties
Now, we can check listening ports:
- ZooKeeper : 2181
- Kafka : 9092
$ netstat -nlpt ... tcp6 0 0 :::9092 :::* LISTEN 1746/java tcp6 0 0 :::2181 :::* LISTEN -
After starting Kafka Broker, type the command jps on a terminal and we would see the following response:
$ jps 23057 QuorumPeerMain 24385 Jps 23350 Kafka
As we can see from the output, now two daemons running on the terminal where QuorumPeerMain is ZooKeeper daemon and another one is Kafka daemon.
Stopping Kafka server (broker)
After performing all the operations, we can stop the server using the following command:
$ bin/kafka-server-stop.sh config/server.properties
Next : Zookeeper & Kafka - single node single broker cluster
Big Data & Hadoop Tutorials
Hadoop 2.6 - Installing on Ubuntu 14.04 (Single-Node Cluster)
Hadoop 2.6.5 - Installing on Ubuntu 16.04 (Single-Node Cluster)
Hadoop - Running MapReduce Job
Hadoop - Ecosystem
CDH5.3 Install on four EC2 instances (1 Name node and 3 Datanodes) using Cloudera Manager 5
CDH5 APIs
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QuickStart VMs for CDH 5.3 II - Testing with wordcount
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Scheduled start and stop CDH services
CDH 5.8 Install with QuickStarts Docker
Zookeeper & Kafka Install
Zookeeper & Kafka - single node single broker
Zookeeper & Kafka - Single node and multiple brokers
OLTP vs OLAP
Apache Hadoop Tutorial I with CDH - Overview
Apache Hadoop Tutorial II with CDH - MapReduce Word Count
Apache Hadoop Tutorial III with CDH - MapReduce Word Count 2
Apache Hadoop (CDH 5) Hive Introduction
CDH5 - Hive Upgrade to 1.3 to from 1.2
Apache Hive 2.1.0 install on Ubuntu 16.04
Apache Hadoop : HBase in Pseudo-Distributed mode
Apache Hadoop : Creating HBase table with HBase shell and HUE
Apache Hadoop : Hue 3.11 install on Ubuntu 16.04
Apache Hadoop : Creating HBase table with Java API
Apache HBase : Map, Persistent, Sparse, Sorted, Distributed and Multidimensional
Apache Hadoop - Flume with CDH5: a single-node Flume deployment (telnet example)
Apache Hadoop (CDH 5) Flume with VirtualBox : syslog example via NettyAvroRpcClient
List of Apache Hadoop hdfs commands
Apache Hadoop : Creating Wordcount Java Project with Eclipse Part 1
Apache Hadoop : Creating Wordcount Java Project with Eclipse Part 2
Apache Hadoop : Creating Card Java Project with Eclipse using Cloudera VM UnoExample for CDH5 - local run
Apache Hadoop : Creating Wordcount Maven Project with Eclipse
Wordcount MapReduce with Oozie workflow with Hue browser - CDH 5.3 Hadoop cluster using VirtualBox and QuickStart VM
Spark 1.2 using VirtualBox and QuickStart VM - wordcount
Spark Programming Model : Resilient Distributed Dataset (RDD) with CDH
Apache Spark 1.2 with PySpark (Spark Python API) Wordcount using CDH5
Apache Spark 1.2 Streaming
Apache Spark 2.0.2 with PySpark (Spark Python API) Shell
Apache Spark 2.0.2 tutorial with PySpark : RDD
Apache Spark 2.0.0 tutorial with PySpark : Analyzing Neuroimaging Data with Thunder
Apache Spark Streaming with Kafka and Cassandra
Apache Drill with ZooKeeper - Install on Ubuntu 16.04
Apache Drill - Query File System, JSON, and Parquet
Apache Drill - HBase query
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Apache Drill - MongoDB query
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