Open Source Scala Software - Page 2
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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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Get full visibility and control over your tasks and projects with Wrike.Wrike offers world-class features that empower cross-functional, distributed, or growing teams take their projects from the initial request stage all the way to tracking work progress and reporting results.
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Supercharge Your Manufacturing with Easy MRP and MES SoftwareFlowlens eliminates stock-outs, shortage and overstocks, avoiding costly production delays. Stay in control of inventory levels and keep production running smoothly with real-time visibility and easy-to-use stock management. Import bulk data with ease.
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FlockDB
A distributed, fault-tolerant graph database
FlockDB is a specialized graph / adjacency-list storage system designed for high performance in large-scale, low-latency, real-time environments. It was developed at Twitter to store social graph data (followers, following, blocks, etc.) and secondary indexes. FlockDB emphasizes horizontal scalability, replication, and support for high rates of writes and updates, as well as efficient paging through very large result sets. It is not a general graph database in the sense of supporting complex multi-hop traversal queries or sophisticated graph algorithms; instead it focuses on the core problem of storing and querying directed edges with attributes such as sort order, state (normal, archived, removed), and position. Edges are stored both in forward and backward directions to facilitate queries in both directions. The project is now archived and in read-only mode, meaning it's no longer actively maintained by Twitter. -
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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. -
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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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Spark NLP
State of the Art Natural Language Processing
Experience the power of large language models like never before, unleashing the full potential of Natural Language Processing (NLP) with Spark NLP, the open source library that delivers scalable LLMs. The full code base is open under the Apache 2.0 license, including pre-trained models and pipelines. The only NLP library built natively on Apache Spark. The most widely used NLP library in the enterprise. Spark ML provides a set of machine learning applications that can be built using two main components, estimators and transformers. The estimators have a method that secures and trains a piece of data to such an application. The transformer is generally the result of a fitting process and applies changes to the target dataset. These components have been embedded to be applicable to Spark NLP. Pipelines are a mechanism for combining multiple estimators and transformers in a single workflow. They allow multiple chained transformations along a machine-learning task. -
Empower Your Contact Center with Human-Like AI ConversationsEnterprise Bot, based in Switzerland, is a pioneer in Conversational AI, Process Automation, and Generative AI. With the trust of esteemed enterprise giants across industries like Generali, SIX, SBB, DHL, and SWICA, Enterprise Bot is revolutionizing both customer and employee experiences. Through its advanced integration with Large Language Models (LLM) such as ChatGPT and Llama 2, and its unique patent-pending DocBrain technology, the company delivers unparalleled personalization, active engagement, and omnichannel solutions across platforms like email, voice, and chat. Furthermore, Enterprise Bot integrates with existing core systems, such as SAP, CRMs, Confluence and more, and with its proprietary middleware, Blitzico, enables the AI to not only respond to queries but also take action to resolve them. This dedication to innovation in four main use case areas, Customer Support, Sales and Marketing, Knowledge Management and Digital Coworker, elevates both CX and employee productivity.
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almond
A Scala kernel for Jupyter
Almond already supports code navigation in dependencies via meta browse, paving the way for more IDE-like features and closer integration with the Scalameta ecosystem. Ammonite is a modern and user-friendly Scala shell. Almond wraps it in a Jupyter kernel, giving you all its features and niceties, including customizable pretty-printing, magic imports, advanced dependency handling, and its API, right from Jupyter. This also makes it easy to copy some code from notebooks to Ammonite scripts, and vice versa. Almond exposes APIs to interact with Jupyter front-ends. Call them from notebooks… or from your own libraries. Several plotting libraries are already available to plot things from notebooks, such as plotly-scala or Vegas. Load the Spark version of your choice, create a Spark session, and start using it from your notebooks. -
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circe
Yet another JSON library for Scala
circe is a JSON library for Scala (and Scala.js). -
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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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Docspell
Assist in organizing your piles of documents
Docspell is a personal document organizer. Or sometimes called a "Document Management System" (DMS). You'll need a scanner to convert your papers into files. Docspell can then assist in organizing the resulting mess. It can unify your files from scanners, emails, and other sources. It is targeted for home use, i.e. families, households, and also for smaller groups/companies. You can associate tags, set correspondent,s and lots of other predefined and custom metadata. If your documents are associated with such metadata, you can quickly find them later using the search feature. However adding this manually is a tedious task. Docspell can help by suggesting correspondents, guessing tags or finding dates using machine learning. It can learn metadata from existing documents and find things using NLP. This makes adding metadata to your documents a lot easier. For machine learning, it relies on the free (GPL) Stanford Core NLP library. -
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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. -
MaintainX is the world-leading mobile-first workflow management platform for industrial and frontline workers.Your day-to-day maintenance tasks, simplified. MaintainX eliminates the paperwork, so you can spend less time on your clipboard and more time getting things done.
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Jawn
Jawn is for parsing jay-sawn (JSON)
The term "jawn" comes from the Philadelphia area. It conveys about as much information as "thing" does. I chose the name because I had moved to Montreal so I remembered Philly fondly. Also, there isn't a better way to describe objects encoded in JSON than "things". Finally, we get a catchy slogan. Jawn was designed to parse JSON into an AST as quickly as possible. Currently, Jawn is competitive with the fastest Java JSON libraries (GSON and Jackson) and in the author's benchmarks, it often wins. It seems to be faster than any other Scala parser that exists (as of July 2014). -
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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. -
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Scio
A Scala API for Apache Beam and Google Cloud Dataflow
Scio is a Scala API developed by Spotify that builds on Apache Beam to enable expressive batch and streaming data pipelines, optimized for running on Google Cloud Dataflow. Inspired by Spark and Scalding, it provides scalable, type‑safe, and production-grade data processing, with built-in support for BigQuery, Pub/Sub, Cassandra, Elasticsearch, Redis, TensorFlow IO, and more. -
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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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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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ZIO JSON
Fast, secure JSON library with tight ZIO integration
ZIO Json is a fast and secure JSON library with tight ZIO integration. The goal of this project is to create the best all-round JSON library for Scala. Extreme performance is achieved by decoding JSON directly from the input source into business objects (docs/inspired by plokhotnyuk). Although not a requirement, the latest advances in Java Loom can be used to support arbitrarily large payloads with near-zero overhead. Best-in-class security is achieved with an aggressive early exit strategy that avoids costly stack traces, even when parsing malformed numbers. Malicious (and badly formed) payloads are rejected before finishing reading. -
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ZIO Quill
Compile-time Language Integrated Queries for Scala
ZIO Quill is a compile-time language-integrated query library for Scala, enabling the construction of type-safe and efficient database queries. It integrates with the ZIO ecosystem, providing asynchronous and composable database interactions. -
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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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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. -
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Akka HTTP
The Streaming-first HTTP server/module of Akka
The Akka HTTP modules implement a full server- and client-side HTTP stack on top of akka-actor and akka-stream. It’s not a web framework but rather a more general toolkit for providing and consuming HTTP-based services. While interaction with a browser is of course also in scope it is not the primary focus of Akka HTTP. Akka HTTP follows a rather open design and many times offers several different API levels for “doing the same thing”. You get to pick the API level of abstraction that is most suitable for your application. This means that, if you have trouble achieving something using a high-level API, there’s a good chance that you can get it done with a low-level API, which offers more flexibility but might require you to write more application code. Akka HTTP has been driven with a clear focus on providing tools for building integration layers rather than application cores. As such it regards itself as a suite of libraries rather than a framework. -
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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 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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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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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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Metarank
A low code Machine Learning service that personalizes articles
Metarank is a service that can personalize any type of content: product listings, articles, recommendations and search results in 3 easy steps with a few lines of code. It’s often considered "too risky" to spend 6+ months on an in-house moonshot project to reinvent the wheel without an experienced team and no existing open-source tools. Metarank makes it easy not only for Amazon to do personalization but for everyone else. Ingest historical item listings, clicks and item metadata so Metarank can find hidden dependencies in the data using our simple JSON format.No Machine Learning experience is required, run our CLI tool with a set of features in a YAML configuration. Run Metarank API service, feed it with real-time events and receive a personalized ranking for your items that will boost conversion, click-through rate or any other business-critical metric you define.
