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About the Tutorial
Storm was originally created by Nathan Marz and team at BackType. BackType is a social analytics company. Later, Storm was acquired and open-sourced by Twitter. In a short time, Apache Storm became a standard for distributed real-time processing system that allows you to process large amount of data, similar to Hadoop. Apache Storm is written in Java and Clojure.
It is continuing to be a leader in real-time analytics.
This tutorial will explore the principles of Apache Storm, distributed messaging, installation, creating Storm topologies and deploy them to a Storm cluster, workflow of Trident, real-time applications and finally concludes with some useful examples.
Audience
This tutorial has been prepared for professionals aspiring to make a career in Big Data Analytics using Apache Storm framework. This tutorial will give you enough understanding on creating and deploying a Storm cluster in a distributed environment.
Prerequisites
Before proceeding with this tutorial, you must have a good understanding of Core Java and any of the Linux flavors.
Copyright & Disclaimer
© Copyright 2014 by Tutorials Point (I) Pvt. Ltd.
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Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher.
We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. Ltd.
provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. If you discover any errors on our website or in this tutorial, please notify us at [email protected]
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Table of Contents
About the Tutorial ... i
Audience... i
Prerequisites ... i
Copyright & Disclaimer ... i
Table of Contents ... ii
1. APACHE STORM – INTRODUCTION ... 1
What is Apache Storm? ... 1
Apache Storm vs Hadoop ... 1
Use-Cases of Apache Storm ... 2
Apache Storm – Benefits ... 3
2. APACHE STORM – CORE CONCEPTS ... 4
Topology... 5
Tasks ... 5
Workers ... 6
Stream Grouping ... 6
3. STORM – CLUSTER ARCHITECTURE ... 9
4. APACHE STORM – WORKFLOW ... 11
5. STORM – DISTRIBUTED MESSAGING SYSTEM ... 12
What is Distributed Messaging System? ... 12
Thrift Protocol ... 13
6. APACHE STORM – INSTALLATION ... 14
Step 1: Verifying Java Installation ... 14
Step 2: ZooKeeper Framework Installation ... 15
Step 3: Apache Storm Framework Installation ... 17
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7. APACHE STORM – WORKING EXAMPLE ... 19
Scenario – Mobile Call Log Analyzer ... 19
Spout Creation ... 19
Bolt Creation ... 23
Call log Creator Bolt ... 24
Call log Counter Bolt ... 26
Creating Topology ... 27
Local Cluster ... 28
Building and Running the Application ... 29
Non-JVM languages ... 30
8. APACHE STORM – TRIDENT ... 32
Trident Topology ... 32
Trident Tuples ... 32
Trident Spout ... 32
Trident Operations ... 33
State Maintenance ... 37
Distributed RPC ... 37
When to Use Trident? ... 37
Working Example of Trident ... 37
Building and Running the Application ... 41
9. APACHE STORM IN TWITTER ... 43
Twitter ... 43
Hashtag Reader Bolt ... 47
Hashtag Counter Bolt ... 49
Submitting a Topology ... 50
Building and Running the Application ... 51
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10. APACHE STORM IN YAHOO! FINANCE ... 53
Spout Creation ... 53
Bolt Creation ... 55
Submitting a Topology ... 57
Building and Running the Application ... 58
11. APACHE STORM – APPLICATIONS ... 59
Klout ... 59
The Weather Channel ... 59
Telecom Industry ... 59
Apache Storm
1
What is Apache Storm?
Apache Storm is a distributed real-time big data-processing system. Storm is designed to process vast amount of data in a fault-tolerant and horizontal scalable method. It is a streaming data framework that has the capability of highest ingestion rates. Though Storm is stateless, it manages distributed environment and cluster state via Apache ZooKeeper. It is simple and you can execute all kinds of manipulations on real-time data in parallel.
Apache Storm is continuing to be a leader in real-time data analytics. Storm is easy to setup, operate and it guarantees that every message will be processed through the topology at least once.
Apache Storm vs Hadoop
Basically Hadoop and Storm frameworks are used for analyzing big data. Both of them complement each other and differ in some aspects. Apache Storm does all the operations except persistency, while Hadoop is good at everything but lags in real-time computation. The following table compares the attributes of Storm and Hadoop.
1. Apache Storm – Introduction
2
Use-Cases of Apache Storm
Apache Storm is very famous for real-time big data stream processing. For this reason, most of the companies are using Storm as an integral part of their system. Some notable examples are as follows:
Twitter – Twitter is using Apache Storm for its range of “Publisher Analytics products”.
“Publisher Analytics Products” process each and every tweets and clicks in the Twitter Platform.
Apache Storm is deeply integrated with Twitter infrastructure.
NaviSite – NaviSite is using Storm for Event log monitoring/auditing system. Every logs generated in the system will go through the Storm. Storm will check the message against the configured set of regular expression and if there is a match, then that particular message will be saved to the database.
Wego – Wego is a travel metasearch engine located in Singapore. Travel related data comes from many sources all over the world with different timing. Storm helps Wego to search real- time data, resolves concurrency issues and find the best match for the end-user.
Storm Hadoop
Real-time stream processing Batch processing
Stateless Stateful
Master/Slave architecture with ZooKeeper based coordination. The master node is called as nimbus and slaves are
supervisors.
Master-slave architecture with/without ZooKeeper based coordination. Master node is job tracker and slave node is task tracker.
A Storm streaming process can access tens of thousands messages per second on cluster.
Hadoop Distributed File System (HDFS) uses MapReduce framework to process vast amount of data that takes minutes or hours.
Storm topology runs until shutdown by the
user or an unexpected unrecoverable failure. MapReduce jobs are executed in a sequential order and completed eventually.
Both are distributed and fault-tolerant If nimbus / supervisor dies, restarting makes
it continue from where it stopped, hence nothing gets affected.
If the JobTracker dies, all the running jobs are lost.
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Apache Storm – Benefits
Here is a list of the benefits that Apache Storm offers:
Storm is open source, robust, and user friendly. It could be utilized in small companies as well as large corporations.
Storm is fault tolerant, flexible, reliable, and supports any programming language.
Allows real-time stream processing.
Storm is unbelievably fast because it has enormous power of processing the data.
Storm can keep up the performance even under increasing load by adding resources linearly. It is highly scalable.
Storm performs data refresh and end-to-end delivery response in seconds or minutes depends upon the problem. It has very low latency.
Storm has operational intelligence.
Storm provides guaranteed data processing even if any of the connected nodes in the cluster die or messages are lost.
Apache Storm
4 Apache Storm reads raw stream of real-time data from one end and passes it through a sequence of small processing units and output the processed / useful information at the other end.
The following diagram depicts the core concept of Apache Storm.
Let us now have a closer look at the components of Apache Storm:
Components Description
Tuple Tuple is the main data structure in Storm. It is a list of ordered elements.
By default, a Tuple supports all data types. Generally, it is modelled as a set of comma separated values and passed to a Storm cluster.
Stream Stream is an unordered sequence of tuples.
Spouts
Source of stream. Generally, Storm accepts input data from raw data sources like Twitter Streaming API, Apache Kafka queue, Kestrel queue, etc. Otherwise you can write spouts to read data from datasources.
“ISpout" is the core interface for implementing spouts. Some of the specific interfaces are IRichSpout, BaseRichSpout, KafkaSpout, etc.
Bolts
Bolts are logical processing units. Spouts pass data to bolts and bolts process and produce a new output stream. Bolts can perform the operations of filtering, aggregation, joining, interacting with data sources and databases. Bolt receives data and emits to one or more bolts. “IBolt”
is the core interface for implementing bolts. Some of the common interfaces are IRichBolt, IBasicBolt, etc.
2. Apache Storm – Core Concepts
5 Let’s take a real-time example of “Twitter Analysis” and see how it can be modelled in Apache Storm. The following diagram depicts the structure.
The input for the “Twitter Analysis” comes from Twitter Streaming API. Spout will read the tweets of the users using Twitter Streaming API and output as a stream of tuples. A single tuple from the spout will have a twitter username and a single tweet as comma separated values. Then, this steam of tuples will be forwarded to the Bolt and the Bolt will split the tweet into individual word, calculate the word count, and persist the information to a configured datasource. Now, we can easily get the result by querying the datasource.
Topology
Spouts and bolts are connected together and they form a topology. Real-time application logic is specified inside Storm topology. In simple words, a topology is a directed graph where vertices are computation and edges are stream of data.
A simple topology starts with spouts. Spout emits the data to one or more bolts. Bolt represents a node in the topology having the smallest processing logic and the output of a bolt can be emitted into another bolt as input.
Storm keeps the topology always running, until you kill the topology. Apache Storm’s main job is to run the topology and will run any number of topology at a given time.
Tasks
Now you have a basic idea on spouts and bolts. They are the smallest logical unit of the topology and a topology is built using a single spout and an array of bolts. They should be executed properly in a particular order for the topology to run successfully. The execution of each and
6 every spout and bolt by Storm is called as “Tasks”. In simple words, a task is either the execution of a spout or a bolt. At a given time, each spout and bolt can have multiple instances running in multiple separate threads.
Workers
A topology runs in a distributed manner, on multiple worker nodes. Storm spreads the tasks evenly on all the worker nodes. The worker node’s role is to listen for jobs and start or stop the processes whenever a new job arrives.
Stream Grouping
Stream of data flows from spouts to bolts or from one bolt to another bolt. Stream grouping controls how the tuples are routed in the topology and helps us to understand the tuples flow in the topology. There are four in-built groupings as explained below.
Shuffle Grouping
In shuffle grouping, an equal number of tuples is distributed randomly across all of the workers executing the bolts. The following diagram depicts the structure.
7
Field Grouping
The fields with same values in tuples are grouped together and the remaining tuples kept outside. Then, the tuples with the same field values are sent forward to the same worker executing the bolts. For example, if the stream is grouped by the field “word”, then the tuples with the same string, “Hello” will move to the same worker. The following diagram shows how Field Grouping works.
Global Grouping
All the streams can be grouped and forward to one bolt. This grouping sends tuples generated by all instances of the source to a single target instance (specifically, pick the worker with lowest ID).
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All Grouping
All Grouping sends a single copy of each tuple to all instances of the receiving bolt. This kind of grouping is used to send signals to bolts. All grouping is useful for join operations.
Apache Storm
9 One of the main highlight of the Apache Storm is that it is a fault-tolerant, fast with no “Single Point of Failure” (SPOF) distributed application. We can install Apache Storm in as many systems as needed to increase the capacity of the application.
Let’s have a look at how the Apache Storm cluster is designed and its internal architecture. The following diagram depicts the cluster design.
Apache Storm has two type of nodes, Nimbus (master node) and Supervisor (worker node).
Nimbus is the central component of Apache Storm. The main job of Nimbus is to run the Storm topology. Nimbus analyzes the topology and gathers the task to be executed. Then, it will distributes the task to an available supervisor.
A supervisor will have one or more worker process. Supervisor will delegate the tasks to worker processes. Worker process will spawn as many executors as needed and run the task. Apache Storm uses an internal distributed messaging system for the communication between nimbus and supervisors.
Components Description
Nimbus
Nimbus is a master node of Storm cluster. All other nodes in the cluster are called as worker nodes. Master node is responsible for distributing data among all the worker nodes, assign tasks to worker nodes and monitoring failures.
3. Storm – Cluster Architecture
10 Supervisor
The nodes that follow instructions given by the nimbus are called as Supervisors. A supervisor has multiple worker processes and it governs worker processes to complete the tasks assigned by the nimbus.
Worker process
A worker process will execute tasks related to a specific topology. A worker process will not run a task by itself, instead it creates executors and asks them to perform a particular task. A worker process will have multiple executors.
Executor An executor is nothing but a single thread spawn by a worker process. An executor runs one or more tasks but only for a specific spout or bolt.
Task A task performs actual data processing. So, it is either a spout or a bolt.
ZooKeeper framework
Apache ZooKeeper is a service used by a cluster (group of nodes) to coordinate between themselves and maintaining shared data with robust synchronization techniques. Nimbus is stateless, so it depends on ZooKeeper to monitor the working node status.
ZooKeeper helps the supervisor to interact with the nimbus. It is responsible to maintain the state of nimbus and supervisor.
Storm is stateless in nature. Even though stateless nature has its own disadvantages, it actually helps Storm to process real-time data in the best possible and quickest way.
Storm is not entirely stateless though. It stores its state in Apache ZooKeeper. Since the state is available in Apache ZooKeeper, a failed nimbus can be restarted and made to work from where it left. Usually, service monitoring tools like monit will monitor Nimbus and restart it if there is any failure.
Apache Storm also have an advanced topology called Trident Topology with state maintenance and it also provides a high-level API like Pig. We will discuss all these features in the coming chapters.
Apache Storm
11 A working Storm cluster should have one nimbus and one or more supervisors. Another important node is Apache ZooKeeper, which will be used for the coordination between the nimbus and the supervisors.
Let us now take a close look at the workflow of Apache Storm:
Initially, the nimbus will wait for the “Storm Topology” to be submitted to it. The
Once a topology is submitted, it will process the topology and gather all the tasks that are to be carried out and the order in which the task is to be executed.
Then, the nimbus will evenly distribute the tasks to all the available supervisors.
At a particular time interval, all supervisors will send heartbeats to the nimbus to inform that they are still alive.
When a supervisor dies and doesn’t send a heartbeat to the nimbus, then the nimbus assigns the tasks to another supervisor.
When the nimbus itself dies, supervisors will work on the already assigned task without any issue.
Once all the tasks are completed, the supervisor will wait for a new task to come in.
In the meantime, the dead nimbus will be restarted automatically by service monitoring tools.
The restarted nimbus will continue from where it stopped. Similarly, the dead supervisor can also be restarted automatically. Since both the nimbus and the supervisor can be restarted automatically and both will continue as before, Storm is guaranteed to process all the task at least once.
Once all the topologies are processed, the nimbus waits for a new topology to arrive and similarly the supervisor waits for new tasks.
By default, there are two modes in a Storm cluster:
Local mode: This mode is used for development, testing, and debugging because it is the easiest way to see all the topology components working together. In this mode, we can adjust parameters that enable us to see how our topology runs in different Storm configuration environments. In Local mode, storm topologies run on the local machine in a single JVM.
Production mode: In this mode, we submit our topology to the working storm cluster, which is composed of many processes, usually running on different machines. As discussed in the workflow of storm, a working cluster will run indefinitely until it is shutdown.
4. Apache Storm – Workflow
Apache Storm
12 Apache Storm processes real-time data and the input normally comes from a message queuing system. An external distributed messaging system will provide the input necessary for the real- time computation. Spout will read the data from the messaging system and convert it into tuples and input into the Apache Storm. The interesting fact is that Apache Storm uses its own distributed messaging system internally for the communication between its nimbus and supervisor.
What is Distributed Messaging System?
Distributed messaging is based on the concept of reliable message queuing. Messages are queued asynchronously between client applications and messaging systems. A distributed messaging system provides the benefits of reliability, scalability, and persistence.
Most of the messaging patterns follow the publish-subscribe model (simply Pub-Sub) where the senders of the messages are called publishers and those who want to receive the messages are called subscribers.
Once the message has been published by the sender, the subscribers can receive the selected message with the help of a filtering option. Usually we have two types of filtering, one is topic- based filtering and another one is content-based filtering.
Note that the pub-sub model can communicate only via messages. It is a very loosely coupled architecture; even the senders don’t know who their subscribers are. Many of the message patterns enable with message broker to exchange publish messages for timely access by many subscribers. A real-life example is Dish TV, which publishes different channels like sports, movies, music, etc., and anyone can subscribe to their own set of channels and get them whenever their subscribed channels are available.
5. Storm – Distributed Messaging System
13 The following table describes some of the popular high throughput messaging systems:
Distributed messaging
system Description
Apache Kafka
Kafka was developed at LinkedIn corporation and later it became a sub-project of Apache. Apache Kafka is based on broker- enabled, persistent, distributed publish-subscribe model. Kafka is fast, scalable, and highly efficient.
RabbitMQ RabbitMQ is an open source distributed robust messaging application. It is easy to use and runs on all platforms.
JMS(Java Message Service)
JMS is an open source API that supports creating, reading, and sending messages from one application to another. It provides guaranteed message delivery and follows publish-subscribe model.
ActiveMQ ActiveMQ messaging system is an open source API of JMS.
ZeroMQ ZeroMQ is broker-less peer-peer message processing. It provides push-pull, router-dealer message patterns.
Kestrel Kestrel is a fast, reliable, and simple distributed message queue.
Thrift Protocol
Thrift was built at Facebook for cross-language services development and remote procedure call (RPC). Later, it became an open source Apache project. Apache Thrift is an Interface Definition Language and allows to define new data types and services implementation on top of the defined data types in an easy manner.
Apache Thrift is also a communication framework that supports embedded systems, mobile applications, web applications, and many other programming languages. Some of the key features associated with Apache Thrift are its modularity, flexibility, and high performance. In addition, it can perform streaming, messaging, and RPC in distributed applications.
Storm extensively uses Thrift Protocol for its internal communication and data definition. Storm topology is simply Thrift Structs. Storm Nimbus that runs the topology in Apache Storm is a Thrift service.
Apache Storm
14 Let us now see how to install Apache Storm framework on your machine. There are three major steps here:
Install Java on your system, if you don’t have it already.
Install ZooKeeper framework.
Install Apache Storm framework.
Step 1: Verifying Java Installation
Use the following command to check whether you have Java already installed on your system.
$ java -version
If Java is already there, then you would see its version number. Else, download the latest version of JDK.
Step 1.1: Download JDK
Download the latest version of JDK by using the following link:
http://www.oracle.com/technetwork/java/javase/downloads/index.html
The latest version is JDK 8u 60 and the file is “jdk-8u60-linux-x64.tar.gz”. Download the file on your machine.
Step 1.2: Extract files
Generally files are being downloaded onto the downloads folder. Extract the tar setup using the following commands.
$ cd /go/to/download/path
$ tar -zxf jdk-8u60-linux-x64.gz
Step 1.3: Move to opt directory
To make Java available to all users, move the extracted java content to “/usr/local/java” folder.
$ su
password: (type password of root user)
$ mkdir /opt/jdk
$ mv jdk-1.8.0_60 /opt/jdk/
6. Apache Storm – Installation
15
Step 1.4: Set path
To set path and JAVA_HOME variables, add the following commands to ~/.bashrc file.
export JAVA_HOME =/usr/jdk/jdk-1.8.0_60 export PATH=$PATH:$JAVA_HOME/bin
Now apply all the changes in to the current running system.
$ source ~/.bashrc
Step 1.5: Java Alternatives
Use the following command to change Java alternatives.
update-alternatives --install /usr/bin/java java /opt/jdk/jdk1.8.0_60/bin/java 100
Step 1.6
Now verify the Java installation using the verification command (java -version) explained in Step 1.
Step 2: ZooKeeper Framework Installation
Step 2.1: Download ZooKeeper
To install ZooKeeper framework on your machine, visit the following link and download the latest version of ZooKeeper.
http://zookeeper.apache.org/releases.html
As of now, the latest version of ZooKeeper is 3.4.6 (ZooKeeper-3.4.6.tar.gz).
Step 2.2: Extract tar file
Extract the tar file using the following commands:
$ cd opt/
$ tar -zxf zookeeper-3.4.6.tar.gz
$ cd zookeeper-3.4.6
$ mkdir data
Step 2.3: Create configuration file
Open configuration file named “conf/zoo.cfg” using the command "vi conf/zoo.cfg" and setting all the following parameters as starting point.
16
$ vi conf/zoo.cfg tickTime=2000
dataDir=/path/to/zookeeper/data clientPort=2181
initLimit=5 syncLimit=2
Once the configuration file has been saved successfully, you can start the ZooKeeper server.
Step 2.4: Start ZooKeeper Server
Use the following command to start the ZooKeeper server.
$ bin/zkServer.sh start
After executing this command, you will get a response as follows:
$ JMX enabled by default
$ Using config: /Users/../zookeeper-3.4.6/bin/../conf/zoo.cfg
$ Starting zookeeper ... STARTED
Step 2.5: Start CLI
Use the following command to start the CLI.
$ bin/zkCli.sh
After executing the above command, you will be connected to the ZooKeeper server and get the following response.
Connecting to localhost:2181 ...
...
...
Welcome to ZooKeeper!
...
...
WATCHER::
WatchedEvent state:SyncConnected type: None path:null [zk: localhost:2181(CONNECTED) 0]
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Step 2.6: Stop ZooKeeper Server
After connecting the server and performing all the operations, you can stop the ZooKeeper server by using the following command.
bin/zkServer.sh stop
You have successfully installed Java and ZooKeeper on your machine. Let us now see the steps to install Apache Storm framework.
Step 3: Apache Storm Framework Installation
Step 3.1 Download Storm
To install Storm framework on your machine, visit the following link and download the latest version of Storm.
http://storm.apache.org/downloads.html
As of now, the latest version of Storm is “apache-storm-0.9.5.tar.gz”.
Step 3.2: Extract tar file
Extract the tar file using the following commands:
$ cd opt/
$ tar -zxf apache-storm-0.9.5.tar.gz
$ cd apache-storm-0.9.5
$ mkdir data
Step 3.3: Open configuration file
The current release of Storm contains a file at “conf/storm.yaml” that configures Storm daemons. Add the following information to that file.
$ vi conf/storm.yaml
storm.zookeeper.servers:
- "localhost"
storm.local.dir: “/path/to/storm/data(any path)”
nimbus.host: "localhost"
supervisor.slots.ports:
- 6700 - 6701
18 - 6702
- 6703
After applying all the changes, save and return to terminal.
Step 3.4: Start the Nimbus
$ bin/storm nimbus
Step 3.5: Start the Supervisor
$ bin/storm supervisor
Step 3.6 Start the UI
$ bin/storm ui
After starting Storm user interface application, type the URL http://localhost:8080 in your favorite browser and you could see Storm cluster information and its running topology. The page should look similar to the following screenshot.
Apache Storm
19 We have gone through the core technical details of the Apache Storm and now it is time to code some simple scenarios.
Scenario – Mobile Call Log Analyzer
Mobile call and its duration will be given as input to Apache Storm and the Storm will process and group the call between the same caller and receiver and their total number of calls.
Spout Creation
Spout is a component which is used for data generation. Basically, a spout will implement an IRichSpout interface. “IRichSpout” interface has the following important methods:
open – Provides the spout with an environment to execute. The executors will run this method to initialize the spout.
nextTuple – Emits the generated data through the collector.
close – This method is called when a spout is going to shutdown.
declareOutputFields – Declares the output schema of the tuple.
ack – Acknowledges that a specific tuple is processed.
fail – Specifies that a specific tuple is not processed and not to be reprocessed.
Open
The signature of the open method is as follows:
open(Map conf, TopologyContext context, SpoutOutputCollector collector)
conf – Provides storm configuration for this spout.
context – Provides complete information about the spout place within the topology, its task id, input and output information.
collector – Enables us to emit the tuple that will be processed by the bolts.
nextTuple
The signature of the nextTuple method is as follows:
nextTuple()
nextTuple() is called periodically from the same loop as the ack() and fail() methods. It must release control of the thread when there is no work to do, so that the other methods have a
7. Apache Storm – Working Example
20 chance to be called. So the first line of nextTuple checks to see if processing has finished. If so, it should sleep for at least one millisecond to reduce load on the processor before returning.
close
The signature of the close method is as follows:
close()
declareOutputFields
The signature of the declareOutputFields method is as follows:
declareOutputFields(OutputFieldsDeclarer declarer)
declarer – It is used to declare output stream ids, output fields, etc.
This method is used to specify the output schema of the tuple.
ack
The signature of the ack method is as follows:
ack(Object msgId)
This method acknowledges that a specific tuple has been processed.
fail
The signature of the nextTuple method is as follows:
ack(Object msgId)
This method informs that a specific tuple has not been fully processed. Storm will reprocess the specific tuple.
FakeCallLogReaderSpout
In our scenario, we need to collect the call log details. The information of the call log contains caller number
receiver number duration
Since, we don’t have real-time information of call logs, we will generate fake call logs. The fake information will be created using Random class. The complete program code is given below.
21
Coding: FakeCallLogReaderSpout.java
import java.util.*;
//import storm tuple packages import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Values;
//import Spout interface packages
import backtype.storm.topology.IRichSpout;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.spout.SpoutOutputCollector;
import backtype.storm.task.TopologyContext;
//Create a class FakeLogReaderSpout which implement IRichSpout interface to access functionalities public class FakeCallLogReaderSpout implements IRichSpout {
//Create instance for SpoutOutputCollector which passes tuples to bolt.
private SpoutOutputCollector collector;
private boolean completed = false;
//Create instance for TopologyContext which contains topology data.
private TopologyContext context;
//Create instance for Random class.
private Random randomGenerator = new Random();
private Integer idx = 0;
@Override
public void open(Map conf, TopologyContext context, SpoutOutputCollector collector) {
this.context = context;
this.collector = collector;
}
@Override
public void nextTuple() { if(this.idx <= 1000) {
List<String> mobileNumbers = new ArrayList<String>();
22 mobileNumbers.add("1234123401");
mobileNumbers.add("1234123402");
mobileNumbers.add("1234123403");
mobileNumbers.add("1234123404");
Integer localIdx = 0;
while(localIdx++ < 100 && this.idx++ < 1000) {
String fromMobileNumber = mobileNumbers.get(randomGenerator.nextInt(4));
String toMobileNumber = mobileNumbers.get(randomGenerator.nextInt(4));
while(fromMobileNumber == toMobileNumber) {
toMobileNumber = mobileNumbers.get(randomGenerator.nextInt(4));
}
Integer duration = randomGenerator.nextInt(60);
this.collector.emit(new Values(fromMobileNumber, toMobileNumber, duration));
} } }
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("from", "to", "duration"));
}
//Override all the interface methods @Override
public void close() { }
public boolean isDistributed() { return false;
}
@Override
public void activate() { }
@Override
23 public void deactivate() {
}
@Override
public void ack(Object msgId) { }
@Override
public void fail(Object msgId) { }
@Override
public Map<String, Object> getComponentConfiguration() { return null;
} }
Bolt Creation
Bolt is a component that takes tuples as input, processes the tuple, and produces new tuples as output. Bolts will implement IRichBolt interface. In this program, two bolt classes CallLogCreatorBolt and CallLogCounterBolt are used to perform the operations.
IRichBolt interface has the following methods:
prepare – Provides the bolt with an environment to execute. The executors will run this method to initialize the spout.
execute – Process a single tuple of input.
cleanup – Called when a bolt is going to shutdown.
declareOutputFields – Declares the output schema of the tuple.
Prepare
The signature of the prepare method is as follows:
prepare(Map conf, TopologyContext context, OutputCollector collector) conf – Provides Storm configuration for this bolt.
context – Provides complete information about the bolt place within the topology, its task id, input and output information, etc.
collector – Enables us to emit the processed tuple.
24
execute
The signature of the execute method is as follows:
execute(Tuple tuple)
Here tuple is the input tuple to be processed.
The execute method processes a single tuple at a time. The tuple data can be accessed by getValue method of Tuple class. It is not necessary to process the input tuple immediately.
Multiple tuple can be processed and output as a single output tuple. The processed tuple can be emitted by using the OutputCollector class.
cleanup
The signature of the cleanup method is as follows:
cleanup()
declareOutputFields
The signature of the declareOutputFields method is as follows:
declareOutputFields(OutputFieldsDeclarer declarer)
Here the parameter declarer is used to declare output stream ids, output fields, etc.
This method is used to specify the output schema of the tuple.
Call log Creator Bolt
Call log creator bolt receives the call log tuple. The call log tuple has caller number, receiver number, and call duration. This bolt simply creates a new value by combining the caller number and the receiver number. The format of the new value is "Caller number – Receiver number"
and it is named as new field, "call". The complete code is given below.
Coding: CallLogCreatorBolt.java
//import util packages import java.util.HashMap;
import java.util.Map;
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Values;
import backtype.storm.task.OutputCollector;
import backtype.storm.task.TopologyContext;
//import Storm IRichBolt package
25 import backtype.storm.topology.IRichBolt;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.tuple.Tuple;
//Create a class CallLogCreatorBolt which implement IRichBolt interface public class CallLogCreatorBolt implements IRichBolt {
//Create instance for OutputCollector which collects and emits tuples to produce output private OutputCollector collector;
@Override
public void prepare(Map conf, TopologyContext context, OutputCollector collector) { this.collector = collector;
}
@Override
public void execute(Tuple tuple) { String from = tuple.getString(0);
String to = tuple.getString(1);
Integer duration = tuple.getInteger(2);
collector.emit(new Values(from + " - " + to, duration));
}
@Override
public void cleanup() { }
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("call", "duration"));
}
@Override
public Map<String, Object> getComponentConfiguration() { return null;
26 }
}
Call log Counter Bolt
Call log counter bolt receives call and its duration as a tuple. This bolt initializes a dictionary (Map) object in the prepare method. In execute method, it checks the tuple and creates a new entry in the dictionary object for every new “call” value in the tuple and sets a value 1 in the dictionary object. For the already available entry in the dictionary, it just increment its value. In simple terms, this bolt saves the call and its count in the dictionary object. Instead of saving the call and its count in the dictionary, we can also save it to a datasource. The complete program code is as follows:
Coding:CallLogCounterBolt.java
import java.util.HashMap;
import java.util.Map;
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Values;
import backtype.storm.task.OutputCollector;
import backtype.storm.task.TopologyContext;
import backtype.storm.topology.IRichBolt;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.tuple.Tuple;
public class CallLogCounterBolt implements IRichBolt { Map<String, Integer> counterMap;
private OutputCollector collector;
@Override
public void prepare(Map conf, TopologyContext context, OutputCollector collector) { this.counterMap = new HashMap<String, Integer>();
this.collector = collector;
}
@Override
public void execute(Tuple tuple) { String call = tuple.getString(0);
Integer duration = tuple.getInteger(1);
27
if(!counterMap.containsKey(call)){
counterMap.put(call, 1);
}else{
Integer c = counterMap.get(call) + 1;
counterMap.put(call, c);
}
collector.ack(tuple);
}
@Override
public void cleanup() {
for(Map.Entry<String, Integer> entry:counterMap.entrySet()){
System.out.println(entry.getKey()+" : " + entry.getValue());
} }
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("call"));
}
@Override
public Map<String, Object> getComponentConfiguration() { return null;
} }
Creating Topology
The Storm topology is basically a Thrift structure. TopologyBuilder class provides simple and easy methods to create complex topologies. The TopologyBuilder class has methods to set spout (setSpout) and to set bolt (setBolt). Finally, TopologyBuilder has createTopology to create topology. Use the following code snippet to create a topology:
TopologyBuilder builder = new TopologyBuilder();
builder.setSpout("call-log-reader-spout", new FakeCallLogReaderSpout());
builder.setBolt("call-log-creator-bolt", new CallLogCreatorBolt())
28 .shuffleGrouping("call-log-reader-spout");
builder.setBolt("call-log-counter-bolt", new CallLogCounterBolt())
.fieldsGrouping("call-log-creator-bolt", new Fields("call"));
shuffleGrouping and fieldsGrouping methods help to set stream grouping for spout and bolts.
Local Cluster
For development purpose, we can create a local cluster using "LocalCluster" object and then submit the topology using "submitTopology" method of "LocalCluster" class. One of the arguments for "submitTopology" is an instance of "Config" class. The "Config" class is used to set configuration options before submitting the topology. This configuration option will be merged with the cluster configuration at run time and sent to all task (spout and bolt) with the prepare method. Once topology is submitted to the cluster, we will wait 10 seconds for the cluster to compute the submitted topology and then shutdown the cluster using “shutdown”
method of "LocalCluster". The complete program code is as follows:
Coding: LogAnalyserStorm.java
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Values;
//import storm configuration packages import backtype.storm.Config;
import backtype.storm.LocalCluster;
import backtype.storm.topology.TopologyBuilder;
//Create main class LogAnalyserStorm submit topology.
public class LogAnalyserStorm {
public static void main(String[] args) throws Exception{
//Create Config instance for cluster configuration Config config = new Config();
config.setDebug(true);
//
TopologyBuilder builder = new TopologyBuilder();
29 builder.setSpout("call-log-reader-spout", new FakeCallLogReaderSpout());
builder.setBolt("call-log-creator-bolt", new CallLogCreatorBolt()) .shuffleGrouping("call-log-reader-spout");
builder.setBolt("call-log-counter-bolt", new CallLogCounterBolt()) .fieldsGrouping("call-log-creator-bolt", new Fields("call"));
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("LogAnalyserStorm", config, builder.createTopology());
Thread.sleep(10000);
//Stop the topology cluster.shutdown();
} }
Building and Running the Application
The complete application has four Java codes. They are:
FakeCallLogReaderSpout.java CallLogCreaterBolt.java CallLogCounterBolt.java LogAnalyerStorm.java
The application can be built using the following command:
javac -cp “/path/to/storm/apache-storm-0.9.5/lib/*” *.java The application can be run using the following command:
java -cp “/path/to/storm/apache-storm-0.9.5/lib/*”:. LogAnalyserStorm
Output
Once the application is started, it will output the complete details about the cluster startup process, spout and bolt processing, and finally, the cluster shutdown process. In
"CallLogCounterBolt", we have printed the call and its count details. This information will be displayed on the console as follows:
30 1234123402 - 1234123401 : 78
1234123402 - 1234123404 : 88 1234123402 - 1234123403 : 105 1234123401 - 1234123404 : 74 1234123401 - 1234123403 : 81 1234123401 - 1234123402 : 81 1234123403 - 1234123404 : 86 1234123404 - 1234123401 : 63 1234123404 - 1234123402 : 82 1234123403 - 1234123402 : 83 1234123404 - 1234123403 : 86 1234123403 - 1234123401 : 93
Non-JVM languages
Storm topologies are implemented by Thrift interfaces which makes it easy to submit topologies in any language. Storm supports Ruby, Python and many other languages. Let’s take a look at python binding.
Python Binding
Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Storm supports Python to implement its topology. Python supports emitting, anchoring, acking, and logging operations.
As you know, bolts can be defined in any language. Bolts written in another language are executed as sub-processes, and Storm communicates with those sub-processes with JSON messages over stdin/stdout. First take a sample bolt WordCount that supports python binding.
public static class WordCount implements IRichBolt { public WordSplit() {
super("python", "splitword.py");
}
public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("word"));
} }
Here the class WordCount implements the IRichBolt interface and running with python implementation specified super method argument "splitword.py". Now create a python implementation named "splitword.py".
31 import storm
class WordCountBolt(storm.BasicBolt):
def process(self, tup):
words = tup.values[0].split(" ") for word in words:
storm.emit([word])
WordCountBolt().run()
This is the sample implementation for Python that counts the words in a given sentence. Similarly you can bind with other supporting languages as well.
Apache Storm
32 Trident is an extension of Storm. Like Storm, Trident was also developed by Twitter. The main reason behind developing Trident is to provide a high-level abstraction on top of Storm along with stateful stream processing and low latency distributed querying.
Trident uses spout and bolt, but these low-level components are auto-generated by Trident before execution. Trident has functions, filters, joins, grouping, and aggregation.
Trident processes streams as a series of batches which are referred as transactions. Generally the size of those small batches will be on the order of thousands or millions of tuples, depending on the input stream. This way, Trident is different from Storm, which performs tuple-by-tuple processing.
Batch processing concept is very similar to database transactions. Every transaction is assigned a transaction ID. The transaction is considered successful, once all its processing complete.
However, a failure in processing one of the transaction's tuples will cause the entire transaction to be retransmitted. For each batch, Trident will call beginCommit at the beginning of the transaction, and commit at the end of it.
Trident Topology
Trident API exposes an easy option to create Trident topology using “TridentTopology” class.
Basically, Trident topology receives input stream from spout and do ordered sequence of operation (filter, aggregation, grouping, etc.,) on the stream. Storm Tuple is replaced by Trident Tuple and Bolts are replaced by operations. A simple Trident topology can be created as follow
TridentTopology topology = new TridentTopology();
Trident Tuples
Trident tuple is a named list of values. The TridentTuple interface is the data model of a Trident topology. The TridentTuple interface is the basic unit of data that can be processed by a Trident topology.
Trident Spout
Trident spout is similar to Storm spout, with additional options to use the features of Trident.
Actually, we can still use the IRichSpout, which we have used in Storm topology, but it will be non-transactional in nature and we won’t be able to use the advantages provided by Trident.
The basic spout having all the functionality to use the features of Trident is "ITridentSpout". It supports both transactional and opaque transactional semantics. The other spouts are IBatchSpout, IPartitionedTridentSpout, and IOpaquePartitionedTridentSpout.
In addition to these generic spouts, Trident has many sample implementation of trident spout.
One of them is FeederBatchSpout spout, which we can use to send named list of trident tuples easily without worrying about batch processing, parallelism, etc.
8. Apache Storm – Trident
33 FeederBatchSpout creation and data feeding can be done as shown below:
TridentTopology topology = new TridentTopology();
FeederBatchSpout testSpout = new FeederBatchSpout(
ImmutableList.of("fromMobileNumber", "toMobileNumber", “duration”));
topology.newStream("fixed-batch-spout", testSpout)
testSpout.feed(ImmutableList.of(new Values("1234123401", "1234123402", 20)));
Trident Operations
Trident relies on the “Trident Operation” to process the input stream of trident tuples. Trident API has a number of in-built operations to handle simple-to-complex stream processing. These operations range from simple validation to complex grouping and aggregation of trident tuples.
Let us go through the most important and frequently used operations.
Filter
Filter is an object used to perform the task of input validation. A Trident filter gets a subset of trident tuple fields as input and returns either true or false depending on whether certain conditions are satisfied or not. If true is returned, then the tuple is kept in the output stream;
otherwise, the tuple is removed from the stream. Filter will basically inherit from the BaseFilter class and implement the isKeep method. Here is a sample implementation of filter operation:
public class MyFilter extends BaseFilter { public boolean isKeep(TridentTuple tuple) { return tuple.getInteger(1) % 2 == 0;
} }
input
[1, 2]
[1, 3]
[1, 4]
output
[1, 2]
[1, 4]
34 Filter function can be called in the topology using “each” method. “Fields” class can be used to specify the input (subset of trident tuple). The sample code is as follows:
TridentTopology topology = new TridentTopology();
topology.newStream("spout", spout)
.each(new Fields("a", "b"), new MyFilter())
Function
Function is an object used to perform a simple operation on a single trident tuple. It takes a subset of trident tuple fields and emits zero or more new trident tuple fields.
Function basically inherits from the BaseFunction class and implements the execute method.
A sample implementation is given below:
public class MyFunction extends BaseFunction {
public void execute(TridentTuple tuple, TridentCollector collector) { int a = tuple.getInteger(0);
int b = tuple.getInteger(1);
collector.emit(new Values(a + b));
} }
input
[1, 2]
[1, 3]
[1, 4]
output
[1, 2, 3]
[1, 3, 4]
[1, 4, 5]
35 Just like Filter operation, Function operation can be called in a topology using the each method.
The sample code is as follows:
TridentTopology topology = new TridentTopology();
topology.newStream("spout", spout)
.each(new Fields(“a, b"), new MyFunction(), new Fields(“d")));
Aggregation
Aggregation is an object used to perform aggregation operations on an input batch or partition or stream. Trident has three types of aggregation. They are as follows:
aggregate: Aggregates each batch of trident tuple in isolation. During the aggregate process, the tuples are initially repartitioned using the global grouping to combine all partitions of the same batch into a single partition.
partitionAggregate: Aggregates each partition instead of the entire batch of trident tuple. The output of the partition aggregate completely replaces the input tuple. The output of the partition aggregate contains a single field tuple.
persistentaggregate: Aggregates on all trident tuple across all batch and stores the result in either memory or database
TridentTopology topology = new TridentTopology();
// aggregate operation
topology.newStream("spout", spout)
.each(new Fields(“a, b"), new MyFunction(), new Fields(“d”)) .aggregate(new Count(), new Fields(“count”))
// partitionAggregate operation topology.newStream("spout", spout)
.each(new Fields(“a, b"), new MyFunction(), new Fields(“d”)) .partitionAggregate(new Count(), new Fields(“count"))
// persistentAggregate - saving the count to memory topology.newStream("spout", spout)
.each(new Fields(“a, b"), new MyFunction(), new Fields(“d”))
.persistentAggregate(new MemoryMapState.Factory(), new Count(), new Fields("count"));
Aggregation operation can be created using either CombinerAggregator, ReducerAggregator, or generic Aggregator interface. The "count” aggregator used in the above example is one of the build-in aggregators. It is implemented using “CombinerAggregator”. The implementation is as follows:
36 public class Count implements CombinerAggregator<Long> {
@Override
public Long init(TridentTuple tuple) { return 1L;
}
@Override
public Long combine(Long val1, Long val2) { return val1 + val2;
}
@Override
public Long zero() { return 0L;
} }
Grouping
Grouping operation is an inbuilt operation and can be called by the groupBy method. The groupBy method repartitions the stream by doing a partitionBy on the specified fields, and then within each partition, it groups tuples together whose group fields are equal. Normally, we use
“groupBy” along with “persistentAggregate” to get the grouped aggregation. The sample code is as follows:
TridentTopology topology = new TridentTopology();
// persistentAggregate - saving the count to memory topology.newStream("spout", spout)
.each(new Fields(“a, b"), new MyFunction(), new Fields(“d”)) .groupBy(new Fields(“d”)
.persistentAggregate(new MemoryMapState.Factory(), new Count(), new Fields("count"));
Merging and Joining
Merging and joining can be done by using “merge” and “join” method respectively. Merging combines one or more streams. Joining is similar to merging, except the fact that joining uses trident tuple field from both sides to check and join two streams. Moreover, joining will work under batch level only. The sample code is as follows:
37 TridentTopology topology = new TridentTopology();
topology.merge(stream1, stream2, stream3);
topology.join(stream1, new Fields("key"), stream2, new Fields("x"), new Fields("key",
"a", "b", "c"));
State Maintenance
Trident provides a mechanism for state maintenance. State information can be stored in the topology itself, otherwise you can store it in a separate database as well. The reason is to maintain a state that if any tuple fails during processing, then the failed tuple is retried. This creates a problem while updating the state because you are not sure whether the state of this tuple has been updated previously or not. If the tuple has failed before updating the state, then retrying the tuple will make the state stable. However, if the tuple has failed after updating the state, then retrying the same tuple will again increase the count in the database and make the state unstable. One needs to perform the following steps to ensure a message is processed only once:
Process the tuples in small batches.
Assign a unique ID to each batch. If the batch is retried, it is given the same unique ID.
The state updates are ordered among batches. For example, the state update of the second batch will not be possible until the state update for the first batch has completed.
Distributed RPC
Distributed RPC is used to query and retrieve the result from the Trident topology. Storm has an inbuilt distributed RPC server. The distributed RPC server receives the RPC request from the client and passes it to the topology. The topology processes the request and sends the result to the distributed RPC server, which is redirected by the distributed RPC server to the client.
Trident's distributed RPC query executes like a normal RPC query, except for the fact that these queries are run in parallel.
When to Use Trident?
As in many use-cases, if the requirement is to process a query only once, we can achieve it by writing a topology in Trident. On the other hand, it will be difficult to achieve exactly once processing in the case of Storm. Hence Trident will be useful for those use-cases where you require exactly once processing. Trident is not for all use cases, especially high-performance use-cases because it adds complexity to Storm and manages the state.
Working Example of Trident
We are going to convert our call log analyzer application worked out in the previous section to Trident framework. Trident application will be relatively easy as compared to plain storm, thanks to its high-level API. Storm will be basically required to perform any one of Function, Filter, Aggregate, GroupBy, Join and Merge operations in Trident. Finally we will start the DRPC Server using the LocalDRPC class and search some keyword using the execute method of LocalDRPC class.
38
Formatting the call information
The purpose of the FormatCall class is to format the call information comprising “Caller number”
and “Receiver number”. The complete program code is as follows:
Coding: FormatCall.java
import backtype.storm.tuple.Values;
import storm.trident.operation.BaseFunction;
import storm.trident.operation.TridentCollector;
import storm.trident.tuple.TridentTuple;
public class FormatCall extends BaseFunction {
@Override
public void execute(TridentTuple tuple, TridentCollector collector) { String fromMobileNumber = tuple.getString(0);
String toMobileNumber = tuple.getString(1);
collector.emit(new Values(fromMobileNumber + " - " + toMobileNumber));
} }
CSVSplit
The purpose of the CSVSplit class is to split the input string based on “comma (,)” and emit every word in the string. This function is used to parse the input argument of distributed querying. The complete code is as follows:
Coding: CSVSplit.java
import backtype.storm.tuple.Values;
import storm.trident.operation.BaseFunction;
import storm.trident.operation.TridentCollector;
import storm.trident.tuple.TridentTuple;
public class CSVSplit extends BaseFunction {
@Override
public void execute(TridentTuple tuple, TridentCollector collector) { for(String word: tuple.getString(0).split(",")) {
if(word.length() > 0) {
39 collector.emit(new Values(word));
} } } }
Log Analyzer
This is the main application. Initially, the application will initialize the TridentTopology and feed caller information using FeederBatchSpout. Trident topology stream can be created using the newStream method of TridentTopology class. Similarly, Trident topology DRPC stream can be created using the newDRCPStream method of TridentTopology class. A simple DRCP server can be created using LocalDRPC class. LocalDRPC has execute method to search some keyword.
The complete code is given below.
Coding: LogAnalyserTrident.java
import java.util.*;
import backtype.storm.Config;
import backtype.storm.LocalCluster;
import backtype.storm.LocalDRPC;
import backtype.storm.utils.DRPCClient;
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Values;
import storm.trident.TridentState;
import storm.trident.TridentTopology;
import storm.trident.tuple.TridentTuple;
import storm.trident.operation.builtin.FilterNull;
import storm.trident.operation.builtin.Count;
import storm.trident.operation.builtin.Sum;
import storm.trident.operation.builtin.MapGet;
import storm.trident.operation.builtin.Debug;
import storm.trident.operation.BaseFilter;
import storm.trident.testing.FixedBatchSpout;
import storm.trident.testing.FeederBatchSpout;
import storm.trident.testing.Split;
import storm.trident.testing.MemoryMapState;
40 import com.google.common.collect.ImmutableList;
public class LogAnalyserTrident {
public static void main(String[] args) throws Exception { System.out.println("Log Analyser Trident");
TridentTopology topology = new TridentTopology();
FeederBatchSpout testSpout = new
FeederBatchSpout(ImmutableList.of("fromMobileNumber", "toMobileNumber", "duration"));
TridentState callCounts = topology
.newStream("fixed-batch-spout", testSpout)
.each(new Fields("fromMobileNumber", "toMobileNumber"), new FormatCall(), new Fields("call"))
.groupBy(new Fields("call"))
.persistentAggregate(new MemoryMapState.Factory(), new Count(), new Fields("count"));
LocalDRPC drpc = new LocalDRPC();
topology.newDRPCStream("call_count", drpc)
.stateQuery(callCounts, new Fields("args"), new MapGet(), new Fields("count"));
topology.newDRPCStream("multiple_call_count", drpc)
.each(new Fields("args"), new CSVSplit(), new Fields("call")) .groupBy(new Fields("call"))
.stateQuery(callCounts, new Fields("call"), new MapGet(), new Fields("count")) .each(new Fields("call", "count"), new Debug())
.each(new Fields("count"), new FilterNull())
.aggregate(new Fields("count"), new Sum(), new Fields("sum"));
Config conf = new Config();
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("trident", conf, topology.build());
41 Random randomGenerator = new Random();
int idx = 0;
while(idx < 10) { testSpout.feed(
ImmutableList.of(new Values("1234123401", "1234123402", randomGenerator.nextInt(60))));
testSpout.feed(
ImmutableList.of(new Values("1234123401", "1234123403", randomGenerator.nextInt(60))));
testSpout.feed(
ImmutableList.of(new Values("1234123401", "1234123404", randomGenerator.nextInt(60))));
testSpout.feed(
ImmutableList.of(new Values("1234123402", "1234123403", randomGenerator.nextInt(60))));
idx = idx + 1;
}
System.out.println("DRPC : Query starts");
System.out.println(drpc.execute("call_count","1234123401 - 1234123402"));
System.out.println(drpc.execute("multiple_call_count", "1234123401 - 1234123402,1234123401 - 1234123403"));
System.out.println("DRPC : Query ends");
cluster.shutdown();
drpc.shutdown();
// DRPCClient client = new DRPCClient("drpc.server.location", 3772);
} }
Building and Running the Application
The complete application has three Java codes. They are as follows:
42 FormatCall.java
CSVSplit.java
LogAnalyerTrident.java
The application can be built by using the following command:
javac -cp “/path/to/storm/apache-storm-0.9.5/lib/*” *.java The application can be run by using the following command:
java -cp “/path/to/storm/apache-storm-0.9.5/lib/*”:. LogAnalyserTrident
Output
Once the application is started, the application will output the complete details about the cluster startup process, operations processing, DRPC Server and client information, and finally, the cluster shutdown process. This output will be displayed on the console as shown below.
DRPC : Query starts
[["1234123401 - 1234123402",10]]
DEBUG: [1234123401 - 1234123402, 10]
DEBUG: [1234123401 - 1234123403, 10]
[[20]]
DRPC : Query ends
