Category: Spark

Spark is built around the concept of Resilient Distributed Datasets.  If you have not read Matei Zaharia, et al’s paper on the topic, I highly recommend it:

Spark exposes RDDs through a language-integrated API similar to DryadLINQ [31] and FlumeJava [8], where each dataset is represented as an object and transformations are invoked using methods on these objects.

Programmers start by defining one or more RDDs through transformations on data in stable storage (e.g., map and filter). They can then use these RDDs in actions, which are operations that return a value to the application or export data to a storage system. Examples of actions include count (which returns the number of elements in the dataset), collect (which returns the elements themselves), and save (which outputs the dataset to a storage system). Like DryadLINQ, Spark computes RDDs lazily the first time they are used in an action, so that it can pipeline transformations.

In addition, programmers can call a persist method to indicate which RDDs they want to reuse in future operations. Spark keeps persistent RDDs in memory by default, but it can spill them to disk if there is not enough RAM. Users can also request other persistence strategies, such as storing the RDD only on disk or replicating it across machines, through flags to persist. Finally, users can set a persistence priority on each RDD to specify which in-memory data should spill to disk first.

The link also has a video of their initial presentation at NSDI.  Check it out.

Ben Snively discusses using Spark SQL as part of an ETL process:

Now interact with SparkSQL through a Zeppelin UI, but re-use the table definitions you created in the Hive metadata store.   You’ll create another table in SparkSQL later in this post to show how that would have been done there.

Connect to the Zeppelin UI and create a new notebook under the Notebook tab. Query to show the tables. You can see that the two tables you created in Hive are also available in SparkSQL.

There are a bunch of tools in here, but for me, the moral of the story is that SQL is a great language for data processing.  Spark SQL has gaps, but has filled many of those gaps over the past year or so, and I recommend giving it a shot.

Sameer Agarwal, Davies Liu, and Reynold Xin show off major Spark engine improvements:

From the above observation, a natural next step for us was to explore the possibility of automatically generating this handwritten code at runtime, which we are calling “whole-stage code generation.” This idea is inspired by Thomas Neumann’s seminal VLDB 2011 paper onEfficiently Compiling Efficient Query Plans for Modern Hardware. For more details on the paper, Adrian Colyer has coordinated with us to publish a review on The Morning Paper blog today.

The goal is to leverage whole-stage code generation so the engine can achieve the performance of hand-written code, yet provide the functionality of a general purpose engine. Rather than relying on operators for processing data at runtime, these operators together generate code at runtime and collapse each fragment of the query, where possible, into a single function and execute that generated code instead.

The possibility of getting an order of magnitude better performance is certainly enticing.