Open Source Scala Frameworks
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Scala Frameworks
View 159 business solutionsBrowse free open source Scala Frameworks and projects below. Use the toggles on the left to filter open source Scala Frameworks by OS, license, language, programming language, and project status.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Gatling
Modern Load Testing as Code
Gatling is a high-performance load testing tool built on the JVM that emphasizes realism, scalability, and developer ergonomics. Test scenarios are scripted in a concise Scala-based DSL, allowing you to model user journeys with think times, feeders (dynamic data), checks, and assertions all in code. Its asynchronous, non-blocking engine (backed by Netty) can drive very high concurrency from a single injector, reducing the need for large injector farms. Gatling supports HTTP out of the box as well as WebSocket, Server-Sent Events, and JMS, so you can exercise modern, real-time systems end to end. Rich HTML reports visualize percentiles, response time distributions, errors, and throughput, making bottlenecks and regressions easy to spot. With injection profiles (ramp, constant, spikes) and pass/fail gates, you can automate performance thresholds in CI and promote builds with confidence. -
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Metawidget is a smart User Interface widget that populates itself, at runtime, with UI components to match the properties of your business objects. Supports Swing, Java Server Faces (JSF), GWT, Spring, Struts, Android, Hibernate, Groovy, JPA and more
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Apache Spark
A unified analytics engine for large-scale data processing
Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming (microbatches) and Structured Streaming, it delivers low-latency event processing suitable for real-time analytics. The built-in MLlib library provides scalable machine learning algorithms, while GraphX enables graph computations integrated with data pipelines. Spark supports multiple languages—Scala, Java, Python, R—and connects with many storage systems like HDFS, S3, Cassandra, and streaming platforms like Kafka, making it a versatile choice for big data workloads in analytics, ETL, and data science. -
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Apache PredictionIO
Machine learning server for developers and ML engineers
Apache PredictionIO® is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. Quickly build and deploy an engine as a web service on production with customizable templates; respond to dynamic queries in real-time once deployed as a web service; evaluate and tune multiple engine variants systematically; unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics; speed up machine learning modeling with systematic processes and pre-built evaluation measures; support machine learning and data processing libraries such as Spark MLLib and OpenNLP; implement your own machine learning models and seamlessly incorporate them into your engine; simplify data infrastructure management. -
Photo and Video Editing APIs and SDKsUnlock Picsart's full editing suite by embedding our Editor SDK directly into your platform. Offer your users the power of a full design suite without leaving your site.
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Chipyard
An Agile RISC-V SoC Design Framework with in-order cores
Chipyard is a framework and generator for constructing custom RISC‑V SoC hardware. Built at UC Berkeley, it leverages Chisel/FIRRTL to generate full-stack systems—from CPU cores to peripherals—and includes simulators, FPGA deployment tools, and integration with Rocket Chip and other RISC‑V ecosystems. -
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The asyncobjects framework is intendend for implementing event-driven network applications. It provides higher level API if compared with Java NIO. Initial code is based asyncobjects prototype from sebyla.sf.net project.
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CATCore
This is distributed plugin framework for Scala
What is a plug-in architecture? Simply speaking, it is a framework that will allow a program to "look for" add-in functionality at startup, and then allow that plug-in to cooperate with itself. CATCore (Component Architecture Toolkit, Corelib) is this and more. With CATCore you can load plugins that run in there own thread(Actor) and communicate with each other via messages. But also applications can forward messages to other applications. So plugsins loaded in one app can communicate with plugins running in another app. -
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IMPORTANT: This project is no longer maintained. All the effort is targeted on project Radargun: http://radargun.sourceforge.net
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Finatra
Fast, testable, Scala services built on TwitterServer and Finagle
Finatra builds on TwitterServer and uses Finagle, therefore it is highly recommended that you familiarize yourself with those frameworks before getting started. The version of Finatra documented here is version 2.x. Version 2.x is a complete rewrite over v1.x and as such many things are different. Finatra at its core is agnostic to the type of service or application being created. It can be used to build anything based on TwitterUtil: c.t.app.App. For servers, Finatra builds on top of the features of TwitterServer (and Finagle) by allowing you to easily define a Server and controllers (a Service-like abstraction) which define and handle endpoints of the Server. You can also compose Filters either per controller, per route in a controller, or across all controllers. Powerful Feature and Integration test support. Optional JSR-330 Dependency Injection using Google Guice. -
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Gizzard
Framework for creating eventually-consistent distributed datastores
Gizzard is a Scala framework originally developed by Twitter for building scalable, fault-tolerant, distributed key-value stores that can be sharded and replicated. It provides infrastructure for routing requests through shard trees, splitting or rebalancing shards dynamically, failover, and migrations. In Gizzard, data is stored in underlying storage shards (which could be databases or other stores) and Gizzard handles the process of routing requests correctly as the cluster topology changes. Gizzard's architecture is designed for operational flexibility: you can change the shard layout over time, reassign replicas, migrate data between nodes, and have requests redirected during transitions. It also supports secondary indexing and provides hooks for custom logic in migrations and consistency. Because Gizzard handles much of the complexity of shard routing and cluster transitions, it was used to support large-scale, evolving storage backends in production. -
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Graphcool Framework
Graphcool is an open-source backend development framework
Graphcool was an open-source framework for developing and deploying GraphQL-based backends. It acts as a “backend-as-a-service / framework” that lets you define your data model via GraphQL SDL (Schema Definition Language), and in turn generates a GraphQL CRUD API, supports nested mutations, filtering, pagination, and real-time subscriptions. Graphcool separates the business logic from stateful storage components, allowing the stateful parts (database, subscription engine) to scale independently and giving flexibility in how you compose your system. Users could deploy Graphcool either locally (e.g. via Docker) or on a managed cloud offering. The framework also provided features like schema evolution, migrations, data loaders for performance, and built-in tooling to manage endpoints and deployments. -
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Guardian Frontend
The Guardian DotCom
This repository hosts a major part of The Guardian’s web application stack, historically the Play Framework–based code that serves the newspaper’s content at scale. It orchestrates rendering of articles, live blogs, and interactive pieces while integrating advertising, analytics, identity, and paywall-adjacent features. The codebase coordinates with upstream content APIs, image services, and media platforms to compose pages dynamically with caching and edge-friendly layouts. Operationally, it’s engineered for high traffic spikes—breaking news or live sports—through aggressive caching strategies, feature switches, and robust fault isolation between services. The project reflects a long-running evolution from a monolith toward a service-oriented architecture, with portions moved to separate rendering services and modern front-end stacks while this repo remains the glue for core routes and legacy paths. -
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Needle is a simple dependency injection framework for configuring POJOs from properties files using annotations. Supports all numeric types, enums, arrays, collections and maps. Just drop into your classpath and never read a properties file again!
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Play Framework
A high velocity web framework
Play Framework makes it easy to build web applications with Java & Scala. The Play Framework combines productivity and performance making it easy to build scalable web applications with Java and Scala. Play is developer friendly with a "just hit refresh" workflow and built-in testing support. With Play, applications scale predictably due to a stateless and non-blocking architecture. By being RESTful by default, including assets compilers, JSON & WebSocket support, Play is a perfect fit for modern web & mobile applications. Play is based on a lightweight, stateless, web-friendly architecture. Built on Akka, Play provides predictable and minimal resource consumption (CPU, memory, threads) for highly-scalable applications. Make your changes and simply hit refresh! All you need is a browser and a text editor. Underneath the covers Play uses a fully asynchronous model built on top of Akka. -
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RedRoo implements a framework for defining and executing hierarchical state machines. As a Red Kangaroo jumps from place to place, RedRoo will transition your application from state to state feeding on the events you pass to it. So get jumping!
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Rocket Chip
Rocket Chip Generator
Rocket Chip is a parameterized RISC-V SoC generator written in Chisel that produces synthesizable RTL for a wide range of cores and configurations. At its heart is the Rocket core, a simple, in-order, five-stage RISC-V implementation, but the generator composes much more: coherent caches, MMUs, interrupt controllers, and buses via the TileLink interconnect. A diplomacy framework (LazyModules) lets designers wire components with negotiated parameters, enabling reuse and rapid exploration of different cache sizes, port counts, and memory hierarchies. The generator supports custom accelerators through the RoCC interface, allowing domain-specific compute units to be plugged into the pipeline with shared cache and memory semantics. Tooling integrates with FIRRTL, Verilator, and commercial EDA flows, and the ecosystem around Rocket Chip (e.g., Chipyard) adds harnesses, peripherals, and verification infrastructure. -
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Scalatra
Tiny Scala high-performance, async web framework
Scalatra is a lightweight, high-performance micro web framework written in Scala, inspired by the Ruby framework Sinatra. Its goal is to provide a minimal but expressive foundation for building web applications or REST APIs in Scala without the verbosity or steep learning curve of larger frameworks. It supports asynchronous request handling, routing, filters, content negotiation, and easy integration with templating, JSON libraries, and other web middleware. Being unopinionated, it lets developers pick their persistence, dependency injection, or templating layers, rather than enforcing heavy conventions. Scalatra is well-suited for services, APIs, or small–to–medium web applications where you want fine control over latency and structure, and it benefits from Scala’s type system and ecosystem. -
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Scalding
A Scala API for Cascading
Scalding is a Scala DSL built on Cascading that simplifies writing Hadoop MapReduce jobs. It lets users describe data transformations using Scala’s functional abstractions, while abstracting away low-level Hadoop boilerplate. It enables expressive and testable pipeline definitions and integrates with various input/output formats. -
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Summingbird
Streaming MapReduce with Scalding and Storm
Summingbird is a streaming + batch hybrid computation framework developed by Twitter. Its aim is to let developers express data aggregation pipelines in a unified way, where the same logic can run either in real time (stream) or in batch mode, and the results can be merged or reconciled. In effect, Summingbird abstracts over multiple execution engines (such as Storm, Scalding, etc.) to provide one high-level program that composes transformations and aggregations, and then executes them in different runtime contexts. It is particularly useful in analytics or metrics systems where you want to update counters or aggregates continuously but also periodically recompute from historical data. Summingbird manages consistency and merging between the real-time and batch paths to avoid double-counting or data loss. -
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The Lift Web Framework
Lift Framework
Lift is the most powerful, most secure web framework available today. There are Seven Things that distinguish Lift from other web frameworks. Lift apps are resistant to common vulnerabilities including many of the OWASP Top 10. Lift apps are fast to build, concise and easy to maintain. Lift apps are high-performance and scale in the real world to handle insane traffic levels. Lift's Comet support is unparalled and Lift's ajax support is super-easy and very secure. Because Lift applications are written in Scala, an elegant JVM language, you can still use your favorite Java libraries and deploy to your favorite Servlet Container and app server. Use the code you've already written and deploy to the container you've already configured! As of Lift 3.0, you'll need to be running Java 8 to use Lift. For those using Java 6 or Java 7, you'll need to use Lift 2.6 until you can upgrade your Java installation. -
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conf4h
Yet another configuration framework for JVM languages.
This is a continuation of "Treecster" project - a tree-like configuration framework suitable for software suites with multiple environments, applications, instances, and other complications. conf4j stands for "Configuration for humans." The development of the project is suspended due to lack of significant benefits over Treecster. -
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Java OpenCL Process Virtual Machine. Spring IoC based framework for complex data analysis with OpenCL computing.
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