Open Source Scala Libraries
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Scala Libraries
View 74 business solutionsBrowse free open source Scala Libraries and projects below. Use the toggles on the left to filter open source Scala Libraries 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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tapir
Declarative, type-safe web endpoints library
Declarative, type-safe web endpoints library. With tapir, you can describe HTTP API endpoints as immutable Scala values. Each endpoint can contain a number of input and output parameters. Compile-time guarantees, develop-time completions, read-time information. Separate the shape of the endpoint (the "what"), from the server logic (the "how"). Generate documentation from endpoint descriptions. Leverage the metadata to report rich metrics and tracing information. Re-use common endpoint definitions, as well as individual inputs/outputs. Library, not a framework, integrates with your stack. Is your company already using tapir? We're continually expanding the "adopters" section in the documentation; the more the merrier! It would be great to feature your company's logo, but in order to do that, we'll need to write permission to avoid any legal misunderstandings. -
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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. -
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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. -
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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. -
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Refined
Refinement types for Scala
Refined is a Scala library that enhances types with compile-time constraints using predicate-style refinement types. It allows embedding validation logic into types like Refined[Int, Positive], ensuring invalid values are rejected at compile or runtime, thereby increasing safety and reducing boilerplate in domain modeling. -
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Waterloo
Java-based scientific graphics
Java-based scientific graphics with support for Java, Groovy, MATLAB, Python, the R statistical environment, Scala and SciLab. -
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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. -
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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. -
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Ammonite
Scala Scripting
Ammonite is a modern Scala REPL and scripting tool designed to give Scala users a more interactive and flexible REPL experience and to free them from heavyweight project boilerplate. It provides syntax‐highlighting, multiline editing, auto‐completion, and dynamic importing of dependencies (using a magic import syntax like import $ivy…). Instead of having to set up an sbt project for many small tasks, one can write Scala scripts (with .sc extension) and run them directly, with Ammonite handling compilation and execution transparently. In the REPL, Ammonite can survive compiler errors (by restarting the compiler internally) and preserve session state, improving resilience compared to the default Scala REPL. It also integrates filesystem utilities and command-line abstractions (via Ammonite-Ops) so that common shell tasks become more Scala-native. -
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Assorted projects. General-purpose libraries for Python, C++, Scala, bash, and others. Meta-programming tools. System utilities. UI components. Web APIs. Configuration files. Benchmarks. Programming competition entries. And much more.
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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. -
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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. -
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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. -
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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. -
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FS2
Compositional, streaming I/O library for Scala
FS2 (“Functional Streams for Scala”) is a purely functional, effectful abstraction for stream processing on the JVM. Built on Cats Effect, it enables compositional resource-safe streaming workflows with robust error handling, back-pressure, pull/push semantics, and support for concurrent and interruptible pipelines. -
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Finch
Scala combinator library for building Finagle HTTP services
Finch is a thin layer of purely functional basic blocks atop of Finagle for building composable HTTP APIs. Its mission is to provide the developers with simple and robust HTTP primitives being as close as possible to the bare metal Finagle API. -
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Kamon Telemetry
Distributed Tracing, Metrics and Context Propagation for applications
Kamon Telemetry is a set of libraries for instrumenting applications running on the JVM. With Kamon Telemetry you can collect metrics, propagate context across threads and services, and get distributed traces automatically. The best way to get started is by following our installation guides and taking it from there. Have fun with Kamon. Monitor your backend applications, fix performance issues, and get alerted when problems happen. All without being a monitoring expert. Everybody starts monitoring with logs because they are there by default. Just connect to your server and start tailing. But logs have a hard time showing you the overall response times for your application, or whether certain calls to the database are happening in sequence or parallel (among a million other things). -
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Lagom
Reactive Microservices for the JVM
The opinionated microservices framework for moving away from the monolith. Lagom helps you decompose your legacy monolith and build, test, and deploy entire systems of Reactive microservices. Lagom is an open source framework for building systems of Reactive microservices in Java or Scala. Lagom builds on Akka and Play, proven technologies that are in production in some of the most demanding applications today. Lagom's integrated development environment allows you to focus on solving business problems instead of wiring services together. A single command builds the project, starts supporting components and your microservices, as well as the Lagom infrastructure. The build hot-reloads when it detects changes to source code. -
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Monix
Asynchronous, Reactive Programming for Scala and Scala.js
Monix is a high-performance, reactive, and asynchronous programming library for Scala and Scala.js. Built as a Typelevel project, it provides advanced abstractions like Task, Observable, Iterant, and Coeval, enabling compositional, back-pressure‑aware event-driven systems that integrate cleanly with Cats Effect and Reactive Streams. -
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Monocle
Optics library for Scala
Monocle is a pure functional, optics library for Scala providing immutable data access and transformation tools — including Lens, Prism, Iso, Optional, and Traversal. It enables composable, declarative modifications of deeply nested immutable structures in a concise and type-safe fashion. -
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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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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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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.
