The Benefits Of Polybase

I take a look at running a Hadoop query against a big(gish) data set:

Nearly 12 minutes doesn’t sound fantastic, but let’s remember that this is running on a single-node sandbox hosted on my laptop.  That’s hardly a fair setup for a distributed processing system.  Also, I have done nothing to optimize the files; I’m using compressed, comma-separated text files, have not partitioned the data in any meaningful way, and have taken the easy way out whenever possible.  This means that an optimized file structure running on a real cluster with powerful servers behind it could return the data set a lot faster…but for our purposes, that’s not very important.  I’m using the same hardware in all three cases, so in that sense this is a fair comp.

Despite my hemming and hawing, Polybase still performed as well as Hive and kicked sand in the linked server’s face.  I have several ideas for how to tune and want to continue down this track, showing various ways to optimize Polybase and Hive queries.

Related Posts

Last-Click Attribution With Databricks Delta

Caryl Yuhas and Denny Lee give us an example of building a last-click digital marketing attribution model with Databricks Delta: The first thing we will need to do is to establish the impression and conversion data streams.   The impression data stream provides us a real-time view of the attributes associated with those customers who were served the […]

Read More

Working With Kafka At Scale

Tony Mancill has some tips for working with large-scale Kafka clusters: Unless you have architectural needs that require you to do otherwise, use random partitioning when writing to topics. When you’re operating at scale, uneven data rates among partitions can be difficult to manage. There are three main reasons for this: First, consumers of the “hot” […]

Read More

Categories

July 2016
MTWTFSS
« Jun Aug »
 123
45678910
11121314151617
18192021222324
25262728293031