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Matt Youill

dblp:259/1265 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Query processing and optimization · 49% Distributed and cloud data management · 34% Indexing and storage engines · 17%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization › query optimization › transformation-based optimization
query pushdown
0.922021
FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS · Proc. VLDB Endow. 2021
PushdownDB: Accelerating a DBMS Using S3 Computation · ICDE 2020
Indexing and storage engines › storage management › memory management
cache replacement policy
0.512021
FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS · Proc. VLDB Endow. 2021
Distributed and cloud data management
cloud database
0.512021
FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS · Proc. VLDB Endow. 2021
Distributed and cloud data management › distributed database architecture
disaggregated storage
0.512021
FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS · Proc. VLDB Endow. 2021
Query processing and optimization
query result caching
0.512021
FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS · Proc. VLDB Endow. 2021
Cloud and datacenter computing › cloud data management
cloud query execution
0.412020
PushdownDB: Accelerating a DBMS Using S3 Computation · ICDE 2020
Cloud and datacenter computing
cloud storage
0.412020
PushdownDB: Accelerating a DBMS Using S3 Computation · ICDE 2020

Methods — techniques the papers use, named apart from their topics

s3 select · 0.9separable operators · 0.5Weighted-LFU · 0.5
YearPublicationVenuePosition
2021 FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS
abstract
Modern cloud databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck: caching and computation pushdown. While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited. In this paper we present FlexPushdownDB (FPDB) , an OLAP cloud DBMS prototype that supports fine-grained hybrid query execution to combine the benefits of caching and computation pushdown in a storage-disaggregation architecture. We build a hybrid query executor based on a new concept called separable operators to combine the data from the cache and results from the pushdown processing. We also propose a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Our experimental evaluation on the Star Schema Benchmark shows that the hybrid execution outperforms both the conventional caching-only architecture and pushdown-only architecture by 2.2X. In the hybrid architecture, our experiments show that Weighted-LFU can outperform the baseline LFU by 37%.
Matt Youill, Matthew E. Woicik, Xiangyao Yu, Marco Serafini, Ashraf Aboulnaga, Michael Stonebraker
Proc. VLDB Endow.2
2020 PushdownDB: Accelerating a DBMS Using S3 Computation
abstract
This paper studies the effectiveness of pushing parts of DBMS analytics queries into the Simple Storage Service (S3) of Amazon Web Services (AWS), using a recently released capability called S3 Select. We show that some DBMS primitives (filter, projection, and aggregation) can always be cost-effectively moved into S3. Other more complex operations (join, top-K, and 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% cheaper and 6.7× faster than a baseline that does not use S3 Select.
Xiangyao Yu, Matt Youill, Matthew E. Woicik, Abdurrahman Ghanem, Marco Serafini, Ashraf Aboulnaga, Michael Stonebraker
ICDE2