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Akka vs Erlang: What are the differences?

Introduction

Akka and Erlang are both popular programming frameworks used for building concurrent and distributed systems. While they have similarities in terms of their capabilities and goals, there are several key differences between Akka and Erlang.

  1. Scala vs Erlang: Akka is written in Scala and runs on the Java Virtual Machine (JVM), while Erlang is its own programming language. This difference in programming languages affects the syntax and style of the code, with Akka relying on Scala's object-oriented and functional programming features, while Erlang has its own unique syntax.

  2. Actor Model: Both Akka and Erlang are based on the concept of the actor model, which allows for concurrent and parallel processing. However, the implementation and features of the actor model differ in the two frameworks. Akka provides a more flexible and extensible actor model, allowing actors to be created and managed in a hierarchical fashion, while Erlang has a simpler actor model with built-in fault tolerance mechanisms.

  3. Concurrency and Parallelism: Akka and Erlang approach concurrency and parallelism differently. Akka utilizes a thread-based approach, where each actor is associated with its own thread, allowing for fine-grained control over concurrent processing. On the other hand, Erlang uses lightweight processes (also known as Erlang processes) that are managed by the virtual machine, allowing for millions of concurrent processes to be created and executed efficiently.

  4. Fault Tolerance: Both Akka and Erlang prioritize fault tolerance and provide mechanisms to handle failures in distributed systems. However, Erlang has a stronger emphasis on fault tolerance, with built-in features such as supervisors and the "let-it-crash" philosophy. Akka, while also providing fault tolerance mechanisms, allows for more flexibility in defining fault-handling strategies by leveraging Scala's object-oriented and functional programming features.

  5. Ecosystem and Community: Akka and Erlang have different ecosystems and communities surrounding them. Akka benefits from its integration with the Java ecosystem and has a larger community of developers. This results in a wider range of libraries, tools, and resources available for Akka developers. Erlang, on the other hand, has a smaller but dedicated community that has contributed to a rich set of libraries and tools specific to the Erlang ecosystem.

  6. Performance and Scalability: Akka and Erlang have different performance characteristics and scalability capabilities. Akka, being built on the JVM, can take advantage of the underlying infrastructure and optimizations provided by the JVM, resulting in high-performance applications. Erlang, on the other hand, is known for its lightweight processes and message-passing concurrency model, which makes it highly scalable and efficient in handling concurrent workloads.

In summary, Akka and Erlang differ in terms of the programming language they are built upon, their actor model implementations, concurrency and parallelism approaches, fault tolerance mechanisms, ecosystems and communities, and performance and scalability characteristics. Despite these differences, both frameworks excel at building distributed and concurrent systems.

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Pros of Akka
Pros of Erlang
  • 32
    Great concurrency model
  • 17
    Fast
  • 12
    Actor Library
  • 10
    Open source
  • 7
    Resilient
  • 5
    Message driven
  • 5
    Scalable
  • 60
    Concurrency Support
  • 60
    Real time, distributed applications
  • 56
    Fault tolerance
  • 35
    Soft real-time
  • 31
    Open source
  • 21
    Functional programming
  • 20
    Message passing
  • 15
    Immutable data
  • 13
    Works as expected
  • 5
    Facebook chat uses it at backend
  • 4
    Practical
  • 4
    Knowledgeable community
  • 3
    Bullets included

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Cons of Akka
Cons of Erlang
  • 3
    Mixing futures with Akka tell is difficult
  • 2
    Closing of futures
  • 2
    No type safety
  • 1
    Very difficult to refactor
  • 1
    Typed actors still not stable
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    What is Akka?

    Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM.

    What is Erlang?

    Some of Erlang's uses are in telecoms, banking, e-commerce, computer telephony and instant messaging. Erlang's runtime system has built-in support for concurrency, distribution and fault tolerance. OTP is set of Erlang libraries and design principles providing middle-ware to develop these systems.

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    What companies use Akka?
    What companies use Erlang?
    See which teams inside your own company are using Akka or Erlang.
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    What tools integrate with Akka?
    What tools integrate with Erlang?

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    What are some alternatives to Akka and Erlang?
    Spring
    A key element of Spring is infrastructural support at the application level: Spring focuses on the "plumbing" of enterprise applications so that teams can focus on application-level business logic, without unnecessary ties to specific deployment environments.
    Scala
    Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Spring Boot
    Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can "just run". We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss. Most Spring Boot applications need very little Spring configuration.
    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.
    See all alternatives