Apache Spark 2.0 Technical Preview Announced

Databricks announce the availability of the Apache Spark 2.0 technical preview package which is based on the upstream 2.0.0-preview release. According to the company, the upcoming version focuses on three major themes: easier, faster and smarter.

The final Apache Spark 2.0 release is still a few weeks away, this technical preview is intended to provide early access to the features in Spark 2.0 based on the upstream codebase. This way, you can satisfy your curiosity to try the shiny new toy.

Now, let’s take a look at the new developments.

Spark 2.0 continues this tradition, with focus on two areas: (1) standard SQL support and (2) unifying DataFrame/Dataset API.

On the SQL side, developer significantly expanded the SQL capabilities of Spark, with the introduction of a new ANSI SQL parser and support for subqueries. Spark 2.0 can run all the 99 TPC-DS queries, which require many of the SQL:2003 features. Because SQL has been one of the primary interfaces Spark applications use, this extended SQL capabilities drastically reduce the porting effort of legacy applications over to Spark.

Spark 2.0 ships with the second generation Tungsten engine. This engine builds upon ideas from modern compilers and MPP databases and applies them to data processing. The main idea is to emit optimized bytecode at runtime that collapses the entire query into a single function, eliminating virtual function calls and leveraging CPU registers for intermediate data. A company calls this technique “whole-stage code generation.”

Spark Streaming has long led the big data space as one of the first attempts at unifying batch and streaming computation. As a first streaming API called DStream and introduced in Spark 0.7, it offered developers with several powerful properties: exactly-once semantics, fault-tolerance at scale, and high throughput.

Spark 2.0’s Structured Streaming APIs is a novel way to approach streaming. It stems from the realization that the simplest way to compute answers on streams of data is to not having to reason about the fact that it is a stream. This realization came from our experience with programmers who already know how to program static datasets (aka batch) using Spark’s powerful DataFrame/Dataset API. The vision of Structured Streaming is to utilize the Catalyst optimizer to discover when it is possible to transparently turn a static program into an incremental execution that works on dynamic, infinite data (aka a stream). When viewed through this structured lens of data—as a discrete table or an infinite table—you simplify streaming.

As the first step towards realizing this vision, Spark 2.0 ships with an initial version of the Structured Streaming API, a (surprisingly small!) extension to the DataFrame/Dataset API. This unification should make adoption easy for existing Spark users, allowing them to leverage their knowledge of Spark batch API to answer new questions in real-time. Key features here will include support for event-time based processing, out-of-order/delayed data, sessionization and tight integration with non-streaming data sources and sinks.

Streaming is clearly a pretty broad topic, so stay tuned to this blog for more details on Structured Streaming in Spark 2.0, including details on what is possible in this release and what is on the roadmap for the near future.