BlinkDB is an approximate query engine built on top of Hive as well as Shark (Hive on Spark, the former Spark SQL). It allows users to trade-off query accuracy for response time, thus enabling interactive queries on big data. BlinkDB builds a couple of stratified samples on the original data and executes the queries on the samples instead of the original data to reduce query execution time. It has two major parts: one is the sample building engine that selects what stratified samples to build by considering historic workloads and the features of the table; the other part is a dynamic sample selection module that chooses appropriate sample files at runtime according to specific time/accuracy requirements.[01]
- Website
- http://blinkdb.org[01]
- Source Code
- https://github.com/sameeragarwal/blinkdb[02]
- Country of Origin
- US
- Start Year
- 2012
- End Year
- 2014
- Project Types
- Academic, Open Source
- Derived From
- Spark SQL
- Operating System
- All OS with Java VM
- License
- Apache v2
BlinkDB is an approximate query engine built on top of Hive as well as Shark (Hive on Spark, the former Spark SQL). It allows users to trade-off query accuracy for response time, thus enabling interactive queries on big data. BlinkDB builds a couple of stratified samples on the original data and executes the queries on the samples instead of the original data to reduce query execution time. It has two major parts: one is the sample building engine that selects what stratified samples to build by considering historic workloads and the features of the table; the other part is a dynamic sample selection module that chooses appropriate sample files at runtime according to specific time/accuracy requirements.[01]
History[03]
BlinkDB was proposed in BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data, which is the best paper of Eurosys 2013.
BlinkDB is no longer maintained. It is integrated into VerdictDB.
Query Interface[01]
The query interface of BlinkDB is SQL-based aggregation queries along with response time of error bound constraints. Like: SELECT avg(sessionTime) FROM Table WHERE city='San Francisco' WITHIN 2 SECONDS or SELECT avg(sessionTime) FROM Table WHERE city='San Francisco' ERROR 0.1 CONFIDENCE 95.0%.
