PushdownDB: Accelerating a DBMS using S3 Computation

02/14/2020
by   Xiangyao Yu, et al.
0

This paper studies the effectiveness of pushing parts of DBMS analytics queries into the Simple Storage Service (S3) engine of Amazon Web Services (AWS), using a recently released capability called S3 Select. We show that some DBMS primitives (filter, projection, aggregation) can always be cost-effectively moved into S3. Other more complex operations (join, top-K, group-by) require reimplementation to take advantage of S3 Select and are often candidates for pushdown. We demonstrate these capabilities through experimentation using a new DBMS that we developed, PushdownDB. Experimentation with a collection of queries including TPC-H queries shows that PushdownDB is on average 30 Select.

READ FULL TEXT

page 5

page 10

research
08/05/2019

Data Aggregation In The Astroparticle Physics Distributed Data Storage

German-Russian Astroparticle Data Life Cycle Initiative is an internatio...
research
10/02/2020

All You Need Is CONSTRUCT

In SPARQL, the query forms SELECT and CONSTRUCT have been the subject of...
research
02/13/2019

SaGe: Web Preemption for Public SPARQL Query Services

To provide stable and responsive public SPARQL query services, data prov...
research
01/22/2022

Comparison of 6 different approaches to outclass Top-k queries and Skyline queries

Topk queries and skyline queries have well explored limitations which re...
research
04/07/2021

Correlation Sketches for Approximate Join-Correlation Queries

The increasing availability of structured datasets, from Web tables and ...
research
09/08/2019

What is the value of experimentation measurement?

Experimentation and Measurement (E M) capabilities allow organizations...

Please sign up or login with your details

Forgot password? Click here to reset