Search Results for "live-graph"

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.

...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. ...

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.

...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.

...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. ...

This repository hosts a major part of The Guardian’s web application stack, historically the Play Framework–based code that serves the newspaper’s content at scale. It orchestrates rendering of articles, live blogs, and interactive pieces while integrating advertising, analytics, identity, and paywall-adjacent features. The codebase coordinates with upstream content APIs, image services, and media platforms to compose pages dynamically with caching and edge-friendly layouts. Operationally, it’s engineered for high traffic spikes—breaking news or live sports—through aggressive caching strategies, feature switches, and robust fault isolation between services. ...

Asteroid has been merged with Scampi, to give rise to OScar. Follow us on https://bitbucket.org/oscarlib/oscar/wiki/Home Asteroid offers a powerful framework for developing constraint-based local search solution to combinatorial problems. This technique provides good scalability to real-world problems. It includes a library of standard constraints and invariants to declaratively define the problem you want to solve, and it also provides powerful search mechanisms.