Real-time Data Processing with Apache Storm: A Comprehensive Guide!

Are you ready to take your real-time data processing skills to the next level? Do you want to learn how to handle data streams in a highly scalable and reliable way? If your answer is yes, then you are in the right place! Apache Storm is a distributed, real-time data processing system that can handle high velocity data streams with ease.

In this comprehensive guide, we will take a deep dive into the world of Apache Storm, covering every aspect from installation to data processing, to help you become an expert in real-time data processing.

Why Apache Storm?

Data streams are a rapidly growing data source that businesses are utilizing to gather meaningful insights that drive strategic decisions. These data streams can be generated from various sources, such as sensors, IoT devices, social media, and many other sources.

Real-time data processing system such as Apache Storm is considered the best solution to handle high-velocity data streams. Apache Storm processes streams of data in real-time, with low latency and high reliability. It enables businesses to make informed decisions in real-time using historical data analysis.

Some of the key benefits of Apache Storm are:

• Fast processing: Apache Storm can process millions of messages per second with low latency, making it ideal for real-time data processing use cases.

• Fault-tolerant: In case any part of the Storm cluster fails, it can quickly recover without affecting the performance of the system.

• Scalable: Apache Storm can scale horizontally by adding more machines to the cluster, making it easy to handle data streams of any size.

Now that we know why we need Apache Storm let's jump into its architecture.

Apache Storm Architecture

Apache Storm has a master-slave architecture, where the master node is also known as the Nimbus node, and the slave nodes are called Supervisor nodes. The master node is responsible for distributing the code, assign tasks to supervisor nodes, and monitor the system. The Supervisor nodes execute the code as directed by the Nimbus node.

Components of Apache Storm

1. Zookeeper: Apache Storm uses Zookeeper for coordination between the Nimbus and Supervisor nodes, to ensure the reliability of the system.

2. Nimbus: It is the master node that assigns tasks to the Supervisor nodes and monitors the system.

3. Supervisor: The slave nodes that run the tasks assigned to them by the Nimbus node. Each Supervisor node runs one or more workers.

4. Worker: It is the process that runs a specific task assigned by the Nimbus node.

5. Topology: It is the data flow graph that defines how data flows from one component to another.

Concepts of Apache Storm

Apache Storm has a few fundamental concepts that we should be aware of before proceeding further.

• Bolts: Bolts are processing units in a topology that receive input data and perform functions on it, such as filtering, joining, calculating averages, or any other functions required by the application.

• Spouts: Spouts are the source of input data in a topology. They can read data from various systems such as Kafka, Twitter, or any other source.

• Streams: A stream is an unbounded, infinite sequence of data processed in a topology.

• Tuples: Tuples represent a single piece of data that flows through a topology. Each tuple consists of a collection of fields that can be of any data type, such as integers, strings, or floating-point numbers.

With the fundamental concepts in mind, let's take a practical look at how to set up Apache Storm.

Setting up Apache Storm

Prerequisites

Before setting up Apache Storm, you need to ensure that you have the following prerequisites installed on your system:

1. Java Development Kit (JDK) 8 or later.
2. Apache Maven for building the topology code.
3. Git for downloading the source code.
4. Storm, the Apache project.

Installing Storm

To install Storm, follow these steps:

1. Download the latest version of Storm from https://storm.apache.org/downloads.html

2. Extract the downloaded package to a file path on your local machine.

3. In a terminal window, navigate to the extracted folder.

4. Start the ZooKeeper service by running the following command:

   zookeeper/bin/zkServer.sh start

5. Next, start the Storm process by running the following command:

   storm/bin/storm nimbus

6. Finally, the last step is to start the Supervisor node process by running the following command:

   storm/bin/storm supervisor

Running Basic Applications

Now, that we have set up the environment, let's take a look at some basic applications.

Word Count Example

The Word Count Example is the most commonly used demo on Apache Storm. It generates a word count from the input text.

1. Clone the following Git repository to get the code for the Word Count Example: https://github.com/apache/storm/tree/master/examples/storm-starter

2. Navigate to the storm-starter directory.

3. Build the code using the following command:

   mvn clean install

4. Run the following command to submit the jar file to the Storm cluster:

   storm jar target/storm-starter-{version}-shadow.jar org.apache.storm.starter.WordCountTopology

5. View the word count results in the output console.

Twitter Sentiment Analysis Example

In this example, we will build a Twitter sentiment analysis application. We will use Twitter data as input and perform sentiment analysis on it to determine whether the tweet is positive, negative, or neutral.

1. Clone the following Git repository to get the code for the Twitter Sentiment Analysis Example: https://github.com/gwenshap/storm-sentiment-analysis

2. Navigate to the storm-sentiment-analysis directory.

3. Update the OAuth credentials for Twitter authorization. Refer to the comments in the file SentimentScoreBolt.java for more information.

4. Build the code using the following command:

   mvn clean install

5. Run the following command to submit the jar file to the Storm cluster:

   storm jar target/storm-sentiment-analysis-{version}-jar-with-dependencies.jar storm.starter.SentimentTopology

6. Monitor the console for the sentiment analysis results.

With these examples, we have covered the basics of Apache Storm. Let's move on to advanced topics.

Advanced Topics

Trident

Trident is a high-level API on top of Apache Storm, which provides transactional, fault-tolerant processing of streams. With Trident, you can build complex stream processing applications without worrying about low-level Storm details, such as tuple ordering or at-least-once processing.

Trident provides several features that are essential for stream processing applications, such as:

• Data partitioning: Trident provides a convenient way to partition data streams into multiple substreams, enabling parallel processing for improved performance.

• Stateful aggregations: With Trident, you can perform stateful aggregations over long time windows, without losing consistency or experiencing performance issues.

• Transactional Spouts: Trident supports transactional spouts to ensure that the data is processed exactly once.

Here is a code example of how to use Trident for word count:

TridentTopology topology = new TridentTopology();
TridentState wordCounts = topology
                        .newStream("spout1", new Spout()...)
                        .each(new Fields("word"), new SplitFunction(), new Fields("word_split"))
                        .groupBy(new Fields("word_split"))
                        .persistentAggregate(new MemoryMapState.Factory(), new CountFunction(), new Fields("count"))
                        .parallelismHint(6);

topology.newDRPCStream("words", drpc)
        .each(new Fields("args"), new SplitFunction(), new Fields("word_split"))
        .groupBy(new Fields("word_split"))
        .stateQuery(wordCounts, new Fields("word_split"), new MapGet(), new Fields("count"))
        .aggregate(new Fields("count"), new Sum(), new Fields("sum"));

In the above example, we are using a Trident topology to calculate the frequency of words. We first split the words into individual components, group them by individual words, and then aggregate them to get the count.

Integration with Apache Kafka

Apache Kafka is a distributed streaming platform that is widely used with Apache Storm to handle data streams. Kafka is designed to be fast, scalable and fault-tolerant, making it an ideal pairing for Apache Storm.

Here is an example of how to integrate Kafka with Apache Storm:

BrokerHosts hosts = new ZkHosts("localhost:2181");
SpoutConfig spoutConfig = new SpoutConfig(hosts, "mytopic", "/" + "kafka" + "/mytopic", "spout");

KafkaSpout kafkaSpout = new KafkaSpout(spoutConfig);

TopologyBuilder builder = new TopologyBuilder();

builder.setSpout("kafka-spout", kafkaSpout);
builder.setBolt("user-bolt", new UserBolt()).shuffleGrouping("kafka-spout");

In the above example, we are first configuring the Kafka Spout with the Zookeeper address and the topic name. We then create a topology builder and add the Kafka spout to it. We also add a Bolt that will process the data that comes from the Kafka Spout.

Conclusion

We have covered the basics of Apache Storm, its architecture, the fundamental concepts, and how to set it up. We also explored advanced topics like Apache Trident and integration with Apache Kafka. Now you should have a good understanding of how Apache Storm works and how it can be used to process data streams in real-time.

If you are interested in learning more about real-time data processing, check out our website realtimestreaming.dev where we cover other topics like time series databases, Spark, Beam, Kafka, Flink, and many more. Happy streaming!

Sources

• https://storm.apache.org/index.html
• https://www.tutorialspoint.com/apache_storm/apache_storm_introduction.htm
• https://www.edureka.co/blog/apache-storm-architecture/
• https://cwiki.apache.org/confluence/display/STORM/Trident+Tutorial
• https://docs.confluent.io/platform/current/streams/index.html

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