Search Results for "interface"

...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. ...

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. ...

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.

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...

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...

Java-based scientific graphics with support for Java, Groovy, MATLAB, Python, the R statistical environment, Scala and SciLab.

Scalargs is a Scala library for parsing command line options as easily as possible.