Resilient Distributed Datasets

Kevin Feasel

2016-05-30

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.

Related Posts

Handling Missing Data In Spark

Igor Sorokin explains how to implement DataFrameNaFunctions: Unfortunately, C&P comes in to play, therefore, if at some point in time a default value for ‘trackLength’ is also required, you may end up changing both of these methods. Another disadvantage is that if another similar method, which requires the same default values, is added, code duplication […]

Read More

Diving Into Spark’s Cost-Based Optimizer

Ron Hu, et al, explain how Spark’s cost-based optimizer works: At its core, Spark’s Catalyst optimizer is a general library for representing query plans as trees and sequentially applying a number of optimization rules to manipulate them. A majority of these optimization rules are based on heuristics, i.e., they only account for a query’s structure and ignore […]

Read More

Categories

May 2016
MTWTFSS
« Apr Jun »
 1
2345678
9101112131415
16171819202122
23242526272829
3031