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Apache Storm

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Samza

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Apache Storm vs Samza: What are the differences?

Introduction

Apache Storm and Apache Samza are both distributed stream processing systems that are designed to process massive amounts of data in real-time. However, there are key differences between the two that make them suitable for different use cases.

  1. Architecture: Apache Storm follows a master-worker architecture where the Nimbus node coordinates the workers responsible for data processing. On the other hand, Apache Samza utilizes a stateful processing model with a central coordination system like Apache Kafka, where each task is assigned to a specific container with its own state. This difference in architecture impacts how tasks are managed and communication overhead between components.

  2. Latency: Apache Storm is designed for low-latency processing with the ability to achieve milliseconds-level latency, making it ideal for real-time data processing use cases. Conversely, Apache Samza is optimized for high-throughput processing with slightly higher latency compared to Storm, which makes it more suitable for scenarios where accuracy and consistency are prioritized over low latency.

  3. Fault-tolerance: Apache Storm provides fault-tolerance through message replay and acknowledgments, allowing it to recover from failures efficiently. Meanwhile, Apache Samza leverages the fault-tolerance capabilities of its underlying system (such as Kafka) for recovering state and ensuring data consistency across tasks, providing a different approach to fault-tolerance.

  4. Ease of Deployment: Apache Storm requires setting up a separate cluster for processing, which can be more complex and resource-intensive compared to Apache Samza, which can leverage existing infrastructure like Apache Kafka for deployment. This difference in deployment requirements affects the ease of adoption and scalability of each system.

  5. State Management: Apache Storm is stateless by default and requires additional setup for maintaining state across components, making it more suitable for stateless processing logic. In contrast, Apache Samza natively supports stateful processing with built-in state management capabilities, enabling it to handle complex event-driven applications with ease.

  6. Community Support: Apache Storm has a larger and more established community compared to Apache Samza, resulting in more resources, documentation, and third-party integrations available for users. This difference in community support can impact the level of assistance and innovation that users can access when utilizing each system.

In Summary, Apache Storm and Apache Samza differ in their architectures, latency characteristics, fault-tolerance mechanisms, ease of deployment, state management capabilities, and community support, making them suitable for distinct use cases in real-time stream processing applications.

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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 Samza?

    It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.

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    What are some alternatives to Apache Storm and Samza?
    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
    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.
    See all alternatives