Ripple joins for online aggregation

Peter J. Haas, Joseph M. Hellerstein
1999 Proceedings of the 1999 ACM SIGMOD international conference on Management of data - SIGMOD '99  
We present a new family of join algorithms, called ripple joins, for online processing of multi-table aggregation queries in a relational database management system (DBMS). Such queries arise naturally in interactive exploratory decision-support applications. Traditional offline join algorithms are designed to minimize the time to completion of the query. In contrast, ripple joins are designed to minimize the time until an acceptably precise estimate of the query result is available, as
more » ... by the length of a confidence interval. Ripple joins are adaptive, adjusting their behavior during processing in accordance with the statistical properties of the data. Ripple joins also permit the user to dynamically trade off the two key performance factors of online aggregation: the time between successive updates of the running aggregate, and the amount by which the confidence-interval length decreases at each update. We show how ripple joins can be
doi:10.1145/304182.304208 dblp:conf/sigmod/HaasH99 fatcat:y6lf25wvzvapfkchkoz5o3pbf4