Simple and familiar. Build apps out of composable, easy-to-write blocks using languages you already know.
Super fast, rock solid. Compile-time static analysis ensures the browser does no more work than it needs to.
In Svelte, an application is composed from one or more components. A component is a reusable self-contained block of code that encapsulates markup, styles and behaviours that belong together.
Like Ractive and Vue, Svelte promotes the concept of single-file components: a component is just an .html file. Here's a simple example:
MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below:
Classification and regression
Feature extraction and transformation
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface centered on the RDD abstraction This interface mirrors a functional/higher-order model of programming: a "driver" program invokes parallel operations such as map, filter or reduce on an RDD by passing a function to Spark, which then schedules the function's execution in parallel on the cluster.
These operations, and additional ones such as joins, take RDDs as input and produce new RDDs. RDDs are immutable and their operations are lazy; fault-tolerance is achieved by keeping track of the "lineage" of each RDD so that it can be reconstructed in the case of data loss. RDDs can contain any type of Python, Java, or Scala objects.