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Apache Storm vs Heron: What are the differences?


Apache Storm and Heron are both open-source distributed stream processing systems used for real-time analytics and data processing. While they have similar functionalities, there are some key differences between the two.

  1. Scalability: Apache Storm is designed to run on a cluster of machines, which can be scaled horizontally to handle large workloads. On the other hand, Heron is built with scalability in mind from the ground up, allowing it to handle massive amounts of data more efficiently.

  2. Performance: Heron achieves better performance compared to Apache Storm due to its more efficient and optimized runtime. Heron's architecture allows for lower latency and higher throughput, making it ideal for latency-sensitive applications.

  3. Fault-tolerance and Reliability: Apache Storm uses the concept of "acknowledgments" to ensure reliability and fault-tolerance. However, this approach can sometimes lead to over-acknowledgment and additional overhead. In contrast, Heron introduces the concept of "exactly-once processing" which ensures end-to-end reliability without requiring explicit acknowledgments.

  4. Ease of Use and Development: Apache Storm provides a relatively simpler programming model as compared to Heron, making it more suitable for beginners and developers who prefer a straightforward approach. However, Heron offers a more advanced programming model and a more extensible API, allowing developers to build more complex stream processing applications.

  5. Operational Management: Apache Storm lacks built-in operational tools for managing and monitoring running topologies. However, Heron includes a wide range of operational tools like an interactive dashboard, metrics collection, and distributed tracing, making it easier to monitor, debug, and manage the execution of streaming applications.

  6. Community and Support: Apache Storm has been around for longer and has a larger community of users and contributors, resulting in a larger ecosystem of libraries, connectors, and resources. Heron, being a more recent project, has a smaller but growing community, with a focus on performance and innovation.

In Summary, Apache Storm and Heron differ in terms of scalability, performance, fault-tolerance, ease of use, operational management, and community support. While Storm offers simplicity and a mature ecosystem, Heron excels in scalability, performance, and operational tooling.

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Pros of Apache Storm
Pros of Heron
  • 10
  • 6
    Easy setup
  • 4
    Event Processing
  • 3
  • 2
    Real Time
  • 1
    Support most popular container environment
  • 1
    Highly Customizable
  • 1
    Operation friendly
  • 1
    Realtime Stream Processing

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What is Apache Storm?

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

What is Heron?

Heron is realtime analytics platform developed by Twitter. It is the direct successor of Apache Storm, built to be backwards compatible with Storm's topology API but with a wide array of architectural improvements.

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    What are some alternatives to Apache Storm and Heron?
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Amazon Kinesis
    Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
    Apache Flume
    It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
    Apache Flink
    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
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