Alternatives to Akka logo

Alternatives to Akka

Spring, Scala, Erlang, Kafka, and Spring Boot are the most popular alternatives and competitors to Akka.
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What is Akka and what are its top alternatives?

Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM.
Akka is a tool in the Concurrency Frameworks category of a tech stack.
Akka is an open source tool with 11.2K GitHub stars and 3.3K GitHub forks. Here’s a link to Akka's open source repository on GitHub

Top Alternatives to Akka

  • Spring

    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

    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. ...

  • Erlang

    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. ...

  • Kafka

    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

    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

    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. ...

  • Orleans

    Orleans

    Orleans is a framework that provides a straightforward approach to building distributed high-scale computing applications, without the need to learn and apply complex concurrency or other scaling patterns. It was created by Microsoft Research and designed for use in the cloud. ...

  • Netty

    Netty

    Netty is a NIO client server framework which enables quick and easy development of network applications such as protocol servers and clients. It greatly simplifies and streamlines network programming such as TCP and UDP socket server. ...

Akka alternatives & related posts

related Spring posts

I am consulting for a company that wants to move its current CubeCart e-commerce site to another PHP based platform like PrestaShop or Magento. I was interested in alternatives that utilize Node.js as the primary platform. I currently don't know PHP, but I have done full stack dev with Java, Spring, Thymeleaf, etc.. I am just unsure that learning a set of technologies not commonly used makes sense. For example, in PrestaShop, I would need to work with JavaScript better and learn PHP, Twig, and Bootstrap. It seems more cumbersome than a Node JS system, where the language syntax stays the same for the full stack. I am looking for thoughts and advice on the relevance of PHP skillset into the future AND whether the Node based e-commerce open source options can compete with Magento or Prestashop.

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Hi

I’ve been using Django for the last year on and off to do my backend API. I’m getting a bit frustrated with the Django REST framework with the setup of the serializers and Django for the lack of web sockets. I’m considering either Spring or .NET Core. I’m familiar with Kotlin and C# but I’ve not built any substantial projects with them. I like OOP, building a desktop app, web API, and also the potential to get a job in the future or building a tool at work to manage my documents, dashboard and processes point cloud data.

I’m familiar with c/cpp, TypeScript.

I would love your insights on where I should go.

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related Scala posts

Shared insights
on
Java
Scala
Apache Spark

I am new to Apache Spark and Scala both. I am basically a Java developer and have around 10 years of experience in Java.

I wish to work on some Machine learning or AI tech stacks. Please assist me in the tech stack and help make a clear Road Map. Any feedback is welcome.

Technologies apart from Scala and Spark are also welcome. Please note that the tools should be relevant to Machine Learning or Artificial Intelligence.

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Michael Binshtock
Principal Software Architect at Microsoft · | 5 upvotes · 63K views

I use Visual Studio Code because its the best IDE for my open source projects using Python, Node.js, TypeScript, Ruby and Scala. Extension exist for everything, great integration with GitHub. It makes development easy and fun.

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Erlang logo

Erlang

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A programming language used to build massively scalable soft real-time systems with requirements on high availability
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related Erlang posts

Sebastian Gębski

Another major decision was to adopt Elixir and Phoenix Framework - the DX (Developer eXperience) is pretty similar to what we know from RoR, but this tech is running on the top of rock-solid Erlang platform which is powering planet-scale telecom solutions for 20+ years. So we're getting pretty much the best from both worlds: minimum friction & smart conventions that eliminate the excessive boilerplate AND highly concurrent EVM (Erlang's Virtual Machine) that makes all the scalability problems vanish. The transition was very smooth - none of Ruby developers we had decided to leave because of Elixir. What is more, we kept recruiting Ruby developers w/o any requirement regarding Elixir proficiency & we still were able to educate them internally in almost no time. Obviously Elixir comes with some more tools in the stack: Credo , Hex , AppSignal (required to properly monitor BEAM apps).

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Shared insights
on
Consul
Elixir
Erlang
at

Postmates built a tool called Bazaar that helps onboard new partners and handles several routine tasks, like nightly emails to merchants alerting them about items that are out of stock.

Since they ran Bazaar across multiple instances, the team needed to avoid sending multiple emails to their partners by obtaining lock across multiple hosts. To solve their challenge, they created and open sourced ConsulMutEx, and an Elixir module for acquiring and releasing locks with Consul and other backends.

It works with Consul’s KV store, as well as other backends, including ets, Erlang’s in-memory database.

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related Kafka posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 20 upvotes · 1.6M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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John Kodumal

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

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related Spring Boot posts

Praveen Mooli
Engineering Manager at Taylor and Francis · | 13 upvotes · 1.5M views

We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

To build #Webapps we decided to use Angular 2 with RxJS

#Devops - GitHub , Travis CI , Terraform , Docker , Serverless

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Eva Maciejko
Fullstack Web developer · | 9 upvotes · 143K views

Hello, I am a fullstack web developer. I have been working for a company with Java/ Spring Boot and client-side JavaScript(mainly jQuery, some AngularJS) for the past 4 years. As I wish to now work as a freelancer, I am faced with a dilemma: which stack to choose given my current knowledge and the state of the market?

I've heard PHP is very popular in the freelance world. I don't know PHP. However, I'm sure it wouldn't be difficult to learn since it has many similarities with Java (OOP). It seems to me that Laravel has similarities with Spring Boot (it's MVC and OOP). Also, people say Laravel works well with Vue.js, which is my favorite JS framework.

On the other hand, I already know the Javascript language, and I like Vue.js, so I figure I could go the fullstack Javascript route with ExpressJS. However, I am not sure if these techs are ripe for freelancing (with regards to RAD, stability, reliability, security, costs, etc.) Is it true that Express is almost always used with MongoDB? Because my experience is mostly with SQL databases.

The projects I would like to work on are custom web applications/websites for small businesses. I have developed custom ERPs before and found that Java was a good fit, except for it taking a long time to develop. I cannot make a choice, and I am constantly switching between trying PHP and Node.js/Express. Any real-world advice would be welcome! I would love to find a stack that I enjoy while doing meaningful freelance coding.

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Apache Spark logo

Apache Spark

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Fast and general engine for large-scale data processing
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related Apache Spark posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 20 upvotes · 1.6M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 811.9K views

Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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Orleans logo

Orleans

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An approach to building distributed applications in .NET
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Netty logo

Netty

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Asynchronous event-driven network application framework
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CONS OF NETTY
    No cons available

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