Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

By Andrew Pavlo, Carlo Curino, S. Zdonik. SIGMOD Conference, 2012

343 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/4590d46d0ed400740be39f3f4a7f48046ee0280c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Andrew Pavlo
2. M. Stonebraker
3. Beng Chin Ooi
4. Samuel Madden
5. Gang Chen
6. Meihui Zhang
7. Kian-Lee Tan
8. Hao Zhang

Citations in the corpus

• Staring into the Abyss: An Evaluation of Concurrency Control with One Thousand Cores (2014, background): “Second, the DBMS’s throughput degrades as the number of multi-partition transactions in the workload increases because they reduce the amount of parallelism in the system [34, 42].”
• In-Memory Big Data Management and Processing: A Survey (2015, background): “aware partitioning model—Horticulture [140]—to automati-”
• Speedy transactions in multicore in-memory databases (2013, methodology): “For context, the database literature for highperformance in-memory OLTP systems cites per-core TPC-C throughput at least several times lower than Silo’s [16, 25, 27, 28], and benchmarks on our hardware with a commercial main-memory database system perform at most 3,000 transactions per second per co”