Open Source Scala Software Development Software - Page 4
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Scala Software Development Software
View 5728 business solutionsBrowse free open source Scala Software Development Software and projects below. Use the toggles on the left to filter open source Scala Software Development Software by OS, license, language, programming language, and project status.
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Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
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Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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Regular Expression Builder for Java (reb4j) is a simple set of classes that encapsulate the regular expressions used by java.util.regex.Pattern and provide facilities for composing larger expressions from sub-expressions.
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A web application that helps students learn Java. Students can submit their solutions to programming exercises and get immediate feedback through automated tests. The application is built with Scala and Lift.
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SageMaker Spark
A Spark library for Amazon SageMaker
SageMaker Spark is an open-source Spark library for Amazon SageMaker. With SageMaker Spark you construct Spark ML Pipelines using Amazon SageMaker stages. These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting. With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrames using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrames against SageMaker endpoints hosting your trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrames with your own algorithms -- all at Spark scale. SageMaker Spark depends on hadoop-aws-2.8.1. To run Spark applications that depend on SageMaker Spark, you need to build Spark with Hadoop 2.8. However, if you are running Spark applications on EMR, you can use Spark built with Hadoop 2.7. -
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Sangria
Scala GraphQL implementation
Sangria is a Scala GraphQL implementation. It is an example of GraphQL server written with Play framework and Sangria. It also serves as a playground, where you can interactively execute GraphQL queries and play with some examples. If you want to use sangria with a react-relay framework, then you also may be interested in sangria-relay. Sangria is a spec-compliant GraphQL implementation, so it works out of the box with Apollo, Relay, GraphiQL and other GraphQL tools and libraries. Since GraphQL has a type system, the server defines a schema that the client can query using the introspection API. This provides the client with a set of possibilities. After the client got this information and decided which parts of the data it needs, it is able to describe its data requirements in form of a GraphQL query. An important aspect of GraphQL is that it’s completely backend agnostic. -
No-Nonsense Code-to-Cloud Security for Devs | AikidoAikido provides a unified security platform for developers, combining 12 powerful scans like SAST, DAST, and CSPM. AI-driven AutoFix and AutoTriage streamline vulnerability management, while runtime protection blocks attacks.
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Math expression parser and evaluator written in Scala. Usable from Java (Sun JRE 1.6) Provides float, integral, boolean and vector data types, some string processing support. Variables may be defined internally or im- and exported through a binding.
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Scala Native
Your favorite language gets closer to bare metal
Scala Native is an optimizing ahead-of-time compiler and lightweight managed runtime designed specifically for Scala. Pointers, structs, you name it. Low-level primitives let you hand-tune your application to make it work exactly as you want it to. You’re in control. Calling C code has never been easier. With the help of extern objects you can seamlessly call native code without any runtime overhead. Scala Native is compiled ahead of time via LLVM. This means that there is no sluggish warm-up phase that’s common for just-in-time compilers. Your code is immediately fast and ready for action. Scala Native requires Clang, which is part of the LLVM toolchain. The recommended LLVM version is the most recent available for your system provided that it works with Scala Native. Scala Native uses the Immix garbage collector by default. You can use the Boehm garbage collector instead. If you chose to use that alternate garbage collector both the native library and header files must be provided. -
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Scala Parser Combinators
simple combinator-based parsing for Scala
scala-parser-combinators is a library that offers simple combinator-based parsing for Scala. Originally part of the Scala standard library, it allows developers to construct parsers using combinators directly in Scala code. -
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Library allowing scala classes to easily implement state charts. The library handles asynchronous consumption of events and traversal through state charts.
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Scala.js
Scala.js, the Scala to JavaScript compiler
Strong typing guarantees your code is free of silly mistakes; no more mixing up strings or numbers, forgetting what keys an object has, or worrying about typos in your method names. Scala.js takes care of all this tedious book-keeping for you, letting you focus on the actual, more interesting problem your application is trying to solve. Scala.js optimizes your Scala code into highly efficient JavaScript. Incremental compilation guarantees speedy (1-2s) turn-around times when your code changes. The generated JavaScript is both fast and small, starting from 45kB gzipped for a full application. Scala.js loves JavaScript libraries, including React and AngularJS. You can use any JavaScript library right from your Scala.js code, either in a statically or dynamically typed way. You won't even notice you're crossing a language border! -
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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ScalaCheck
Property-based testing for Scala
ScalaCheck is a library for property-based testing in Scala (and Java), inspired by Haskell’s QuickCheck. It automatically generates test inputs based on specifications, validating that properties hold across randomized scenarios, thereby enabling robust, declarative testing of edge cases and invariants. -
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ScalaFish is a library to facilitate programming the fischertechnik ROBO TX Controller using the Scala programming language.
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Scalaffinity is a library providing a core of functionalities for any social networking web site extracting and leveraging collective intelligence. Scalaffinity is built using Scala, Spring, JPA+Hibernate(Search)+EHCache, JUnit+DBUnit, Maven...
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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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Scalaz
Principled Functional Programming in Scala
Scalaz is a foundational functional-programming library for Scala that provides type classes, data types, and syntax to write pure, composable code. It implements classic abstractions such as Functor, Applicative, Monad, Monoid, Foldable, and Traverse, along with powerful transformers (ReaderT, StateT, WriterT, OptionT, and more) to structure effects. The library offers rich data structures—\/ (disjunction), Validation, NonEmptyList, IList, and Free—that help model errors, invariants, and interpretable programs. Its type class–oriented design lets you write generic algorithms over capabilities rather than concrete types, improving reuse and testability. Scalaz also contributes optics, equality/ordering abstractions, and lawful instances with property-based tests to ensure algebraic laws hold. While the Scala ecosystem now includes sibling projects, Scalaz remains a deep toolbox for principled FP in Scala and a reference point for category-theory-inspired design on the JVM. -
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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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ScalikeJDBC
A tidy SQL-based DB access library for Scala developers
ScalikeJDBC is a tidy SQL-based DB access library for Scala developers. This library naturally wraps JDBC APIs and provides you easy-to-use and very flexible APIs. What’s more, QueryDSL makes your code type-safe and reusable. ScalikeJDBC is a practical and production-ready one. Use this library for your real projects. Whether you like it or not, JDBC is a stable standard interface. Since most of RDBMS supports JDBC interface, we can access RDBMS in the same way. We believe that ScalikeJDBC basically works with any other RDBMS (Oracle, SQL Server and so on). If you can access some datastore via JDBC interface, that means you can access them via ScalikeJDBC too. Recently, Amazon Redshift and Facebook Presto support JDBC interface. You can access them via ScalikeJDBC! Of course, you can use c3p0 (or others) instead of commons-dbcp though ConnectionPool implementation for that isn’t provided by default. -
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Scaloid
Scaloid makes your Android code easy to understand and maintain
Scaloid is a Scala library designed to make Android app development in Scala more concise, idiomatic, and less verbose compared to using the standard Android APIs. It provides wrappers, syntactic sugar, and implicit conversions so that interacting with Android UI elements, layouts, intents, and other Android primitives feels more natural to Scala developers. For example, it offers DSL-style helpers for defining views and layouts with less boilerplate, implicit conversions to help with event listeners, and utility methods to simplify common tasks. Scaloid aims to capture the expressiveness of Scala—higher-order methods, implicit conversions, extension methods—to let Android development be more functional and succinct. It lowers friction for Scala developers targeting Android, making UI code more compact and composable while still maintaining access to full Android platform capabilities. -
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ScenarLang
A DSL that ease (shortens) the set up mockito based unit tests
This domain specific language lets you declare complex java objects with a Json-like notation. In addition to that, it lets you specify in a natural way what the mockito mocks should return when called with a given value. -
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Scrooge
A Thrift parser/generator
Scrooge is a thrift code generator written in Scala, which currently generates code for Scala, Java, Cocoa, Android, and Lua. It’s meant to be a replacement for the Apache thrift code generator and generates conforming, binary-compatible codecs by building on top of libthrift. Since Scala is API-compatible with Java, you can use the apache thrift code generator to generate Java files and use them from within Scala, but the generated code uses Java collections and mutable “bean” classes, causing some annoying boilerplate conversions to be hand-written. This is an attempt to bypass the problem by generating Scala code directly. It also uses Scala syntax so the generated code is much more compact. There is a fairly comprehensive set of unit tests, which actually generate code, compile it, and execute it to verify expectations. -
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Shapeless
Generic programming for Scala
Shapeless is a powerful generic programming library for Scala, enabling compile-time abstraction and manipulation of types. It provides features such as HLists (heterogenous lists), generic derivation of type class instances, dependent types, and polymorphic functions—allowing developers to write boilerplate-free, type-safe code. -
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Skunk
A data access library for Scala + Postgres
Skunk is a Postgres library for Scala. Skunk is powered by cats, cats-effect, scodec, and fs2. Skunk is purely functional, non-blocking, and provides a tagless-final API. Skunk gives very good error messages. Skunk embraces the Scala Code of Conduct. Skunk is pre-release software! Code and documentation are under active development! Skunk is published for Scala 2.12/2.13/3.1 and can be included in your project.Query and Command types are usually inferrable, but specifying a type ensures that the chosen encoders and decoders are consistent with the expected input and output Scala types. Postgres provides a protocol for execution of simple queries, returning all rows at once (Skunk returns them as a list). -
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Slick database
Slick (Scala Language Integrated Connection Kit) is a modern database
Slick is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred. You can write your database queries in Scala instead of SQL, thus profiting from the static checking, compile-time safety and compositionality of Scala. Slick features an extensible query compiler which can generate code for different backends. It allows you to work with relational databases almost as if you were using Scala collections, while at the same time giving you full control over when a database access happens and what data is transferred. By writing your queries in Scala you can benefit from the static type checking, compile-time safety, and compositionality of Scala, while retaining the ability to drop down to raw SQL where needed for custom or advanced database features. -
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Spark JobServer
REST job server for Apache Spark
Spark Job Server offers a RESTful interface for submitting, managing, and running jobs or contexts on Apache Spark. Rather than requiring every application to embed Spark or manage Spark contexts manually, this server abstracts a long-lived service where clients can upload JARs, start and stop contexts, submit jobs synchronously or asynchronously, and manage named objects (RDDs / DataFrames) across job executions. It supports multiple modes (transient jobs, persistent contexts for reuse, streaming, SQL/Hive, etc.), and can be integrated with authentication/authorization systems (e.g. via Apache Shiro). The architecture isolates Spark contexts (optionally in separate JVMs), isolates job dependencies, and persists job / jar metadata via pluggable DAOs. It supports deployment across cluster managers (YARN, Mesos, etc.) and aims to simplify Spark-as-a-service scenarios. -
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SpinalHDL
Scala based HDL
SpinalHDL is a hardware description (HDL) framework embedded in Scala, enabling hardware designers to build digital circuits with modern programming abstractions. Instead of writing in Verilog or VHDL directly, users describe hardware components and their interconnects using Scala code and Spinal’s domain-specific library, which then emits synthesizable hardware (e.g. as Verilog). Because SpinalHDL is embedded in Scala, it allows reuse of functional abstractions, parameterization, modular composition, and higher-level constructs to manage complexity. It supports building systems at various levels—single modules, pipelines, memories, controllers, etc.—while letting the designer control timing, pipelining, and resource sharing explicitly. The generated hardware can be synthesized for FPGAs or ASIC flows, making it practical for real designs.
