Pacific-Design.com

    
Home Index

1. Spark

2. Scala

Spark / Scala /

Apache Spark popular libraries:

  • Spark SQL:
    • Spark SQL provides the capability to expose the Spark datasets over JDBC API and allow running the SQL like queries on Spark data using traditional BI and visualization tools. Spark SQL allows the users to ETL their data from different formats it’s currently in (like JSON, Parquet, a Database), transform it, and expose it for ad-hoc querying.
  • Spark MLlib:

    • 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.
  • Spark GraphX:

    • GraphX is the new (alpha) Spark API for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD by introducing the Resilient Distributed Property Graph: a directed multi-graph with properties attached to each vertex and edge. To support graph computation, GraphX exposes a set of fundamental operators (e.g., subgraph, joinVertices, and aggregateMessages) as well as an optimized variant of the Pregel API. In addition, GraphX includes a growing collection of graph algorithms and builders to simplify graph analytics tasks.
  • Spark Streaming:

    • Spark Streaming can be used for processing the real-time streaming data. This is based on micro batch style of computing and processing. It uses the DStream which is basically a series of RDDs, to process the real-time data.
  • Spark Cassandra Connector:

    • There are also integration adapters with other products like Cassandra (Spark Cassandra Connector) and R (SparkR). With Cassandra Connector, you can use Spark to access data stored in a Cassandra database and perform data analytics on that data.i

Spark - Scala vs. Python Programming


The above chart compares the runtime performance of running group-by-aggregation on 10 million integer pairs on a single machine (source code). Since both Scala and Python DataFrame operations are compiled into JVM bytecode for execution, there is little difference between the two languages, and both outperform the vanilla Python RDD variant by a factor of 5 and Scala RDD variant by a factor of 2.

DataFrames were inspired by previous distributed data frame efforts, including Adatao’s DDF and Ayasdi’s BigDF. However, the main difference from these projects is that DataFrames go through the Catalyst optimizer, enabling optimized execution similar to that of Spark SQL queries. As we improve the Catalyst optimizer, the engine also becomes smarter, making applications faster with each new release of Spark.

References: