TY - GEN
T1 - Wander join
T2 - 2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
AU - Li, Feifei
AU - Wu, Bin
AU - Yi, Ke
AU - Zhao, Zhuoyue
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/6/26
Y1 - 2016/6/26
N2 - Joins are expensive, and online aggregation over joins was proposed to mitigate the cost, which offers a nice and flexible tradeoff between query effciency and accuracy in a continuous, online fashion. However, the state-of-the-art approach, in both internal and external memory, is based on ripple join, which is still very expensive and may also need very restrictive assumptions (e.g., tuples in a table are stored in random order). We introduce a new approach, wander join, to the online aggregation problem by performing random walks over the underlying join graph. We have also implemented and tested wander join in the latest PostgreSQL.
AB - Joins are expensive, and online aggregation over joins was proposed to mitigate the cost, which offers a nice and flexible tradeoff between query effciency and accuracy in a continuous, online fashion. However, the state-of-the-art approach, in both internal and external memory, is based on ripple join, which is still very expensive and may also need very restrictive assumptions (e.g., tuples in a table are stored in random order). We introduce a new approach, wander join, to the online aggregation problem by performing random walks over the underlying join graph. We have also implemented and tested wander join in the latest PostgreSQL.
UR - https://www.scopus.com/pages/publications/84979697403
U2 - 10.1145/2882903.2899413
DO - 10.1145/2882903.2899413
M3 - Conference contribution
AN - SCOPUS:84979697403
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 2121
EP - 2124
BT - SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PB - Association for Computing Machinery
Y2 - 26 June 2016 through 1 July 2016
ER -