Introduction to Apache Spark and Its Core Components
Apache Spark, an open‑source unified analytics engine from UC Berkeley’s AMP Lab, is the leading platform for large‑scale batch and streaming data processing, featuring components such as Spark SQL, Streaming, GraphX, MLlib, and core modules like DAGScheduler, TaskScheduler and BlockManager.
Apache Spark is an open‑source general‑purpose parallel framework from UC Berkeley AMP Lab, designed as a fast, unified engine for large‑scale data processing.
Spark is currently the most popular unified batch‑and‑stream big‑data processing platform. Since the release of version 1.2 in 2014, it has become an indispensable component in the big‑data field, with rapid development and an active community. Spark’s ecosystem includes Spark SQL for batch and interactive queries, Spark Streaming for stream processing, and GraphX and MLlib for graph computation and machine learning.
As of now, the latest released version of Spark is 2.4.3.
This article is based on a recent internal Spark sharing, covering a detailed introduction to Spark RDDs and explanations of core modules such as DAGScheduler, TaskScheduler, and BlockManager.
Spark overview and overall workflow
Implementation of Spark core modules
Spark application libraries
Differences and connections between Spark and Hadoop
Spark applications
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