Open Source Scala Software Development Software

Scala Software Development Software

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  • Find Hidden Risks in Windows Task Scheduler Icon
    Find Hidden Risks in Windows Task Scheduler

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  • 1
    lichess.org

    lichess.org

    The forever free, ad-free and open source chess server

    lichess.org (also known as Lila - lichess in Scala) is a free and open source chess server written in Scala 2.13 that focuses on real time gameplay and ease of use. It’s where countless chess players and chess enthusiasts can gather and watch or play from a selection of over a million games every day, analyze games, learn and improve their playing. lichess is equipped with a search engine, computer analysis, tournaments, exhibitions, a mobile app, a shared analysis board, and so much more. Thanks to its large active community, the UI is available in more than 130 different languages. lichess is one of the most popular chess websites in the world and remains totally free and ad-free. Visit https://lichess.org today to know more and see what it’s about!
    Downloads: 50 This Week
    Last Update:
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  • 2
    Play Framework

    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.
    Downloads: 13 This Week
    Last Update:
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  • 3
    Gatling

    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.
    Downloads: 11 This Week
    Last Update:
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  • 4
    Apache Spark

    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.
    Downloads: 8 This Week
    Last Update:
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  • Atera - The depth of a full-stack IT platform, with the power of AI. Icon
    Atera - The depth of a full-stack IT platform, with the power of AI.

    Atera introduces your autonomous AI agent - Ensure operational efficiency at any scale with 24/7 autonomous IT support.

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  • 5
    sbt

    sbt

    sbt, the interactive build tool

    Define your tasks in Scala. Run them in parallel from sbt's interactive shell. sbt is built for Scala and Java projects. It is the build tool of choice for 93.6% of the Scala developers (2019). One of the examples of a Scala-specific feature is the ability to cross-build your project against multiple Scala versions. build.sbt is a Scala-based DSL to express parallel processing task graph. Typos in build.sbt will be caught as a compilation error. With Zinc incremental compiler and file watch (~), edit-compile-test loop is fast and incremental. Adding support for new tasks and platforms (like Scala.js) is as easy as writing build.sbt. Join 100+ community-maintained plugins to share and reuse sbt tasks. Continuous compilation and testing with triggered execution. Supports mixed Scala/Java projects. Supports testing with ScalaCheck, specs, and ScalaTest. JUnit is supported by a plugin. Starts the Scala REPL with project classes and dependencies on the classpath.
    Downloads: 7 This Week
    Last Update:
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  • 6
    Mill

    Mill

    Your shiny new Java/Scala build tool

    Your shiny new Scala build tool! Confused by SBT? Frustrated by Maven? Perplexed by Gradle? Give Mill a try. In-process tests live in the .test sub-modules of the various Mill modules. These range from tiny unit tests, to larger integration tests that instantiate a TestUtil.BaseModule in-process and a TestEvaluator to evaluate tasks on it. Note that the in-memory tests compile the BaseModule together with the test suite, and do not exercise the Mill script-file bootstrapping, transformation, and compilation process.
    Downloads: 6 This Week
    Last Update:
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  • 7
    node2vec

    node2vec

    Learn continuous vector embeddings for nodes in a graph using biased R

    The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible.
    Downloads: 3 This Week
    Last Update:
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  • 8
    Coursier

    Coursier

    Pure Scala Artifact Fetching

    Coursier is the Scala application and artifact manager. It can install Scala applications and setup your Scala development environment. It can also download and cache artifacts from the web.
    Downloads: 2 This Week
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  • 9
    RISC-V BOOM

    RISC-V BOOM

    SonicBOOM: The Berkeley Out-of-Order Machine

    The riscv-boom project (also called BOOM or SonicBOOM) implements a high-performance, synthesizable out-of-order RISC-V core written in the Chisel hardware construction language. It targets the RV64GC (i.e. 64-bit with general + compressed + floating point) instruction set and supports features such as virtual memory, caches, atomics, and IEEE-754 floating point. The design is parameterizable, meaning users can tune pipeline widths, buffer sizes, functional units, and other microarchitectural knobs to explore tradeoffs. It is capable of booting Linux and running standard benchmarks, and its performance (measured in CoreMarks/MHz) is competitive with commercial cores. The project is intended primarily for hardware/architecture research and teaching, rather than production silicon, and typically is used in conjunction with SoC frameworks (for example via Chipyard) to integrate BOOM into larger systems.
    Downloads: 2 This Week
    Last Update:
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  • Grafana: The open and composable observability platform Icon
    Grafana: The open and composable observability platform

    Faster answers, predictable costs, and no lock-in built by the team helping to make observability accessible to anyone.

    Grafana is the open source analytics & monitoring solution for every database.
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  • 10
    Scala Steward

    Scala Steward

    A bot that helps you keep your projects up-to-date

    Scala Steward is an automated tool that helps to keep Scala libraries and plugins up to date by checking for dependency updates and sending pull requests.
    Downloads: 2 This Week
    Last Update:
    See Project
  • 11
    TextTeaser

    TextTeaser

    TextTeaser is an automatic summarization algorithm

    textteaser is an automatic text summarization algorithm implemented in Python. It extracts the most important sentences from an article to generate concise summaries that retain the core meaning of the original text. The algorithm uses features such as sentence length, keyword frequency, and position within the document to determine which sentences are most relevant. By combining these features with a simple scoring mechanism, it produces summaries that are both readable and informative. Originally inspired by research and earlier implementations, textteaser provides a lightweight solution for summarization without requiring heavy machine learning models. It is particularly useful for developers, researchers, or content platforms seeking a simple, rule-based approach to article summarization.
    Downloads: 2 This Week
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  • 12
    X's Recommendation Algorithm

    X's Recommendation Algorithm

    Source code for the X Recommendation Algorithm

    The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs. Written primarily in Scala, it shows the architecture of large-scale recommendation systems, including candidate sourcing, ranking, and heuristics. While certain components (such as safety layers, spam detection, or private data) are excluded, the release provides valuable insights into the design of real-world machine learning–driven ranking systems. The project is intended as a reference for researchers, developers, and the public to study, experiment with, and better understand the mechanisms behind social media content.
    Downloads: 2 This Week
    Last Update:
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  • 13
    scalafmt

    scalafmt

    Code formatter for Scala

    The Scala plugin compatible with recent versions of IntelliJ IDEA has built-in support for Scalafmt. Spend more time discussing important issues in code review and less time on code style. Scalafmt formats code so that it looks consistent between people on your team. Run scalafmt from your editor, build tool or terminal. Scalafmt has integrations with IntelliJ, sbt, Maven, Gradle and Mill. Choose the scalafmt formatter and IntelliJ's Reformat Code action will then use Scalafmt when formatting files. Scalafmt is primarily designed to operate on entire text files—formatting selected ranges of code may produce undesirable results. For this reason, IntelliJ uses its own formatter for ranges by default. It is not recommended to change this, and is instead recommended to format files when saving.
    Downloads: 2 This Week
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  • 14
    Mozart-Oz Programming System

    Mozart-Oz Programming System

    Runtime and development environment of Oz

    The Mozart Programming System is an open source implementation of the programming language Oz 3. Oz is a multi-paradigm language that supports declarative programming, object-oriented programming, constraint programming, concurrency and distributed programming as part of a coherent whole.
    Downloads: 19 This Week
    Last Update:
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  • 15
    Algebird

    Algebird

    Abstract Algebra for Scala

    Algebird is Twitter’s Apache‑licensed Scala library providing abstract algebra data structures and algorithms, especially for online/streaming aggregation. It includes Monoid, Approximate, HyperLogLog, CMS, BloomFilter, Min/Max, Averaged Value types, supporting efficient distributed aggregation and approximate analytics.
    Downloads: 1 This Week
    Last Update:
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  • 16
    Alpakka Kafka

    Alpakka Kafka

    Alpakka is a Reactive Enterprise Integration library for Java

    The Alpakka project is an open source initiative to implement stream-aware and reactive integration pipelines for Java and Scala. It is built on top of Akka Streams and has been designed from the ground up to understand streaming natively and provide a DSL for reactive and stream-oriented programming, with built-in support for backpressure. Akka Streams is a Reactive Stream and JDK 9+ java.util.concurrent.Flow-compliant implementation and therefore fully interoperable with other implementations. As Kafka’s client protocol negotiates the version to use with the Kafka broker, you may use a Kafka client version that is different than the Kafka broker’s version.
    Downloads: 1 This Week
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  • 17
    Binding.scala

    Binding.scala

    Reactive data-binding for Scala

    Binding.scala is a data-binding library for Scala, running on both JVM and Scala.js. Binding.scala can be used as the basis of UI frameworks, however latest Binding.scala 12.x does not contain any build-in UI frameworks anymore. For creating reactive HTML UI, you may want to check out html.scala, which is a UI framework based on Binding.scala, and it is also the successor of the previously built-in dom library. See also React / Binding.scala / html.scala Interoperability for using existing React components with Binding.scala. Regular HTML does not compile unless developers manually replace class and for attributes to className and htmlFor, and manually convert inline styles from CSS syntax to JSON syntax.
    Downloads: 1 This Week
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  • 18
    Caliban

    Caliban

    Functional GraphQL library for Scala

    Caliban is a purely functional library for building GraphQL servers and clients in Scala. The design principles behind the library are the following. Minimal amount of boilerplate: no need to manually define a schema for every type in your API. Pure interface: errors and effects are returned explicitly (no exceptions thrown), all returned types are referentially transparent (no Future). Clean separation between schema definition and implementation: schema is defined and validated at compile time using Scala standard types, resolver (RootResolver) is a simple value provided at runtime. All interfaces are pure and types are referentially transparent. Schemas are type safe and derived at compile time. No need to manually define a schema for every type in your API. Let the compiler do the boring work. Out-of-the-box support for major HTTP server libraries, effect types, Json libraries and more.
    Downloads: 1 This Week
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  • 19
    Cats

    Cats

    Lightweight, modular, extensible library for functional programming

    Cats is a library which provides abstractions for functional programming in the Scala programming language. The name is a playful shortening of the word category. Scala supports both object-oriented and functional programming, and this is reflected in the hybrid approach of the standard library. Cats strives to provide functional programming abstractions that are core, binary compatible, modular, approachable and efficient. A broader goal of Cats is to provide a foundation for an ecosystem of pure, typeful libraries to support functional programming in Scala applications. Cats strives to provide a solid and stable foundation for an ecosystem of FP libraries. Thus, we treat backward binary compatibility maintenance with a high priority. In semantic versioning, backward breaking change is only allowed between major versions.
    Downloads: 1 This Week
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  • 20
    Cats Effect

    Cats Effect

    The pure asynchronous runtime for Scala

    Cats-Effect is a high-quality functional programming library for Scala that provides a principled way to represent and manage side effects, particularly asynchronous and concurrent computations. It is part of the broader Typelevel ecosystem and builds on the abstractions from Cats (such as Functor, Monad, etc.). The core abstraction is the IO type (or effect types more generally), which encodes effectful computations in a pure, referentially transparent way. Cats-Effect offers capabilities like deferred execution, cancellation, resource safety (Resource), fiber concurrency (lightweight threads), and interoperation with underlying runtime platforms (JVM, Java concurrency, etc.). It enables developers to write effectful code while preserving composability, purity, and modular reasoning about side effects.
    Downloads: 1 This Week
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  • 21
    Chipyard

    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.
    Downloads: 1 This Week
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  • 22
    CoolplaySpark

    CoolplaySpark

    Spark Cool Play: Spark source code analysis, Spark class library, etc.

    CoolplaySpark is a learning and practice repository designed to help users understand and work with Apache Spark. It serves as a companion resource for the book 深入理解Spark核心思想与源码分析 (In-Depth Understanding of Spark’s Core Concepts and Source Code Analysis). The project contains annotated examples, explanations, and exercises that guide learners through Spark’s architecture, execution model, and source code internals. It is particularly valuable for developers who want to strengthen their understanding of Spark by not only using it as a data processing engine but also exploring how its internals function. Through code analysis and commentary, CoolplaySpark helps readers connect theoretical concepts with practical implementation details. By combining book study with this repository, learners can develop both conceptual clarity and hands-on expertise in Spark’s core components.
    Downloads: 1 This Week
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  • 23
    Deequ

    Deequ

    Deequ is a library built on top of Apache Spark

    Deequ is a library built atop Apache Spark that enables defining “unit tests for data” — that is, formal constraints or checks on datasets to ensure data quality along dimensions such as completeness, uniqueness, value ranges, correlations, etc. It can scale to large datasets (billions of rows) by translating those data checks into Spark jobs. Deequ supports advanced features like a metrics repository for storing computed statistics over time, anomaly detection of data quality metrics, and the suggestion of likely constraints automatically for new datasets. It also includes a little domain-specific language called DQDL (Data Quality Definition Language) which allows declarative specification of quality rules. Users typically run Deequ before feeding data downstream (to ML pipelines, analytics, or production systems), enabling early detection and isolation of data errors. There is also a Python wrapper, PyDeequ, for users who prefer working from Python environments.
    Downloads: 1 This Week
    Last Update:
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  • 24
    Diffy

    Diffy

    Find potential bugs in your services with Diffy

    Diffy is a traffic-shadowing and response-diffing tool that helps you validate a new version of a service against a trusted baseline before a full cutover. It acts as a proxy that fans out real production requests to three backends: the current “primary,” a “candidate” (new build), and a “shadow” baseline, then compares responses to detect behavioral differences. By using live traffic rather than synthetic tests, Diffy surfaces edge cases and data-dependent discrepancies that unit tests often miss. It provides mechanisms to normalize noise (timestamps, IDs, ordering) so diffs reflect meaningful regressions rather than incidental variance. Results are aggregated and visualized so teams can triage by endpoint, response field, or error type, turning release validation into a data-driven process. Built on Twitter’s Finagle stack, Diffy demonstrates a practical pattern for continuous verification and safer deployments in microservice environments.
    Downloads: 1 This Week
    Last Update:
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  • 25
    GitBucket

    GitBucket

    A Git platform powered by Scala

    A Git platform powered by Scala with easy installation, high extensibility & GitHub API compatibility. GitBucket is a Git web platform powered by Scala offering, easy installation, intuitive UI, high extensibility by plugins, API compatibility with GitHub. You can also deploy gitbucket.war to a servlet container which supports Servlet 3.0 (like Jetty, Tomcat, JBoss, etc). To upgrade GitBucket, replace gitbucket.war with the new version, after stopping GitBucket. All GitBucket data is stored in HOME/.gitbucket by default. So if you want to back up GitBucket's data, copy the directory to the backup location. If you want to try the development version of GitBucket, or want to contribute to the project, please see the Developer's Guide. It provides instructions on building from source and on setting up an IDE for debugging. It also contains documentation of the core concepts used within the project.
    Downloads: 1 This Week
    Last Update:
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