What is recorded here?
Introduced resilient distributed datasets, using immutable partitions and lineage-based recomputation for fault-tolerant in-memory cluster computations, and evaluated the abstraction in Spark.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact nine-author NSDI 2012 paper and its reported Spark implementation. Later Spark releases, APIs, contributors and deployments remain separate; benchmark gains are limited to the tested workloads and systems.
Read the original work
- USENIX proceedings record for Resilient Distributed Datasets
- USENIX full paper for Resilient Distributed Datasets
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Matei Zaharia; Mosharaf Chowdhury; Tathagata Das; Ankur Dave; Justin Ma; Murphy McCauley; Michael J. Franklin; Scott Shenker; Ion Stoica
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
Documented mapping · Included in country comparison
Documented countries / regions: United States
Documented research locations or paper affiliations for this contribution. Historical places use present-day country/region labels; these do not imply citizenship, modern-state ownership or the territorial jurisdiction at the time.
English catalogue narrative
Zaharia and colleagues: resilient distributed datasets for in-memory cluster computing
Introduced resilient distributed datasets, using immutable partitions and lineage-based recomputation for fault-tolerant in-memory cluster computations, and evaluated the abstraction in Spark.
Scientific scope
The record is the exact nine-author NSDI 2012 paper and its reported Spark implementation. Later Spark releases, APIs, contributors and deployments remain separate; benchmark gains are limited to the tested workloads and systems.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Matei Zaharia; Mosharaf Chowdhury; Tathagata Das; Ankur Dave; Justin Ma; Murphy McCauley; Michael J. Franklin; Scott Shenker; Ion Stoica
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.