Open Source Scala Software - Page 3
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Scala Software
Browse free open source Scala Software and projects below. Use the toggles on the left to filter open source Scala Software by OS, license, language, programming language, and project status.
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The full-stack observability platform that protects your dataLayer, tags and conversion dataCode-Cube.io detects issues instantly, alerts you in real time and helps you resolve them fast. No manual QA. No unreliable data. Just data you can trust and act on.
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Outbound sales softwareAdversus is an outbound dialing solution that helps you streamline your call strategies, automate manual processes, and provide valuable insights to improve your outbound workflows and efficiency.
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elastic4s
Elasticsearch Scala Client - Reactive, Non Blocking, Type Safe, HTTP
Elastic4s is a concise, idiomatic, reactive, type safe Scala client for Elasticsearch. The official Elasticsearch Java client can of course be used in Scala, but due to Java's syntax it is more verbose and it naturally doesn't support classes in the core Scala core library nor Scala idioms such as typeclass support. Elastic4s's DSL allows you to construct your requests programatically, with syntactic and semantic errors manifested at compile time, and uses standard Scala futures to enable you to easily integrate into an asynchronous workflow. The aim of the DSL is that requests are written in a builder-like way, while staying broadly similar to the Java API or Rest API. Each request is an immutable object, so you can create requests and safely reuse them, or further copy them for derived requests. Because each request is strongly typed your IDE or editor can use the type information to show you what operations are available for any request type. -
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scalajs-react
Facebook's React on Scala.JS
Scala.js React wraps Facebook React for Scala.js with a strong emphasis on type safety and functional idioms. It provides a typed virtual DOM interface, reusable components, hooks, and utilities for routing, testing, SSR, and performance profiling, all aligned with Cats, Cats Effect, and Monocle ecosystems. -
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DataNucleus
Java persistence using JDO, JPA or REST
DataNucleus provides Java data persistence to a range of datastores using JDO/JPA/REST APIs. *** Note that code development is no longer on SourceForge (code on SourceForge is for versions up to 3.3.5 only) *** -
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Airframe
Essential Building Blocks for Scala
Airframe is an essential building block for developing applications in Scala, including logging, object serialization using JSON or MessagePack, dependency injection, HTTP server/client with RPC support, functional testing with AirSpec, etc. Airframe RPC supports seamless integration of servers and clients using Scala as RPC interfaces. AirSpec is a simple unit testing framework for Scala and Scala.js. You can use public methods in your classes as test cases. There is no need to remember complex DSLs for writing tests in Scala. Retrying HTTP requests for API calls is an essential technique for connecting microservices. airframe-control will provide essential tools for making your requests reliable with exponential backoff retry, jitter, circuit-breaker, rate control, etc. airframe-fluentd supports logging your metrics to fluentd in a type-safe manner. You just need to send your case classes as metrics for fluentd. -
MicroStation by Bentley Systems is the trusted computer-aided design (CAD) software built specifically for infrastructure design.MicroStation is the only computer-aided design software for infrastructure design, helping architects and engineers like you bring their vision to life, present their designs to their clients, and deliver their projects to the community.
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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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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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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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FiloDB
Distributed Prometheus time series database
FiloDB is an open-source distributed, real-time, in-memory, massively scalable, multi-schema time series / event / operational database with Prometheus query support and some Spark support as well. The normal configuration for real-time ingestion is deployment as stand-alone processes in a cluster, ingesting directly from Apache Kafka. The processes form a cluster using peer-to-peer Akka Cluster technology. Designed to ingest many millions of entities, sharded across multiple processes, with distributed querying built in. Support for indexing and fast querying over flexible tags for each time series/partition, just like Prometheus. Holds a huge amount of data in-memory thanks to columnar compression techniques. Designed for highly concurrent, low-latency workloads such as dashboards and alerting. Data immediately available for querying once ingested. -
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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. -
Rezku Point of SaleRezku is an all-inclusive ordering platform and management solution for all types of restaurant and bar concepts. You can now get a fully custom branded downloadable smartphone ordering app for your restaurant exclusively from Rezku.
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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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Kestrel
Simple, distributed message queue system (inactive)
Kestrel is a simple, distributed message queue system built originally by Twitter. Its design is relatively lightweight and is engineered for speed and simplicity. Kestrel supports queuing patterns such as enqueue, dequeue, and delayed re-enqueue (for example, when a consumer fails to process a message). It stores messages persistently on disk with a memory-backed cache, allowing recovery in case of failures. Because it is intended for relatively simple use cases, it does not provide the full feature set of some enterprise messaging systems, but is often sufficient for many asynchronous or buffered workloads. Over time, the project became inactive and is now archived. Its minimalism and ease of integration made it appealing for smaller or more controlled message-queueing needs. -
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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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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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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. -
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spray-json
A lightweight, clean and simple JSON implementation in Scala
spray-json is a lightweight, clean and efficient JSON implementation in Scala. -
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Akka
Build concurrent, distributed, and resilient message-driven apps
Build powerful reactive, concurrent, and distributed applications more easily. Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications for Java and Scala. Actors and Streams let you build systems that scale up, using the resources of a server more efficiently, and out, using multiple servers. Building on the principles of The Reactive Manifesto Akka allows you to write systems that self-heal and stay responsive in the face of failures. Up to 50 million msg/sec on a single machine. Small memory footprint; ~2.5 million actors per GB of heap. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure. -
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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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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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Feathr
A scalable, unified data and AI engineering platform for enterprise
Feathr is a data and AI engineering platform that is widely used in production at LinkedIn for many years and was open sourced in 2022. It is currently a project under LF AI & Data Foundation. Define data and feature transformations based on raw data sources (batch and streaming) using Pythonic APIs. Register transformations by names and get transformed data(features) for various use cases including AI modeling, compliance, go-to-market and more. Share transformations and data(features) across team and company. Feathr is particularly useful in AI modeling where it automatically computes your feature transformations and joins them to your training data, using point-in-time-correct semantics to avoid data leakage, and supports materializing and deploying your features for use online in production. -
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Finagle
A fault tolerant, protocol-agnostic RPC system
Finagle is an extensible RPC system for the JVM, used to construct high-concurrency servers. Finagle implements uniform client and server APIs for several protocols, and is designed for high performance and concurrency. Most of Finagle’s code is protocol agnostic, simplifying the implementation of new protocols. Finagle is written in Scala, but provides both Scala and Java idiomatic APIs. Finagle is a protocol-agnostic, asynchronous RPC system for the JVM that makes it easy to build robust clients and servers in Java, Scala, or any JVM-hosted language. We set out to develop a single implementation of the basic components of network servers and clients that could be used for all of our protocols. Finagle is a protocol-agnostic, asynchronous Remote Procedure Call (RPC) system for the Java Virtual Machine (JVM) that makes it easy to build robust clients and servers in Java, Scala, or any JVM-hosted language. -
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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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akka-http-json
Integrate some of the best JSON libs in Scala with Akka HTTP
akka-http-json provides JSON (un)marshalling support for Akka HTTP. Use respective support trait or object, e.g. ArgonautSupport, FailFastCirceSupport, etc. into your Akka HTTP code which is supposed to (un)marshal from/to JSON. Don't forget to provide the type class instances for the respective JSON libraries, if needed. -
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JobScheduler
JobScheduler workload automation to execute jobs and workflows
JS7 JobScheduler runs executables, scripts and database procedures and can be operated with e.g. MySQL, PostgreSQL, SQL Server, Oracle databases. JS7 JobScheduler provides workflow patterns such as sequential and parallel task execution, forking, synchronization, cross-platform scheduling, job load sharing, fail-over and a real REST API. JS7 includes the components: * JOC Cockpit (user interface for inventory management and control) * Controller (orchestration of Agents) * Agents: platform independent Agents for job execution Product Knowledge Base at https://kb.sos-berlin.com/display/JS7 Change Management System at https://change.sos-berlin.com Release Archive at: https://www.sos-berlin.com/jobscheduler-release-archive -
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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.
