The COMFORT automatic tuning project
Information Systems
Network and cpu co-allocation in high throughput computing environments
Network and cpu co-allocation in high throughput computing environments
Toward a progress indicator for database queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Estimating progress of execution for SQL queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Increasing the Accuracy and Coverage of SQL Progress Indicators
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
When can we trust progress estimators for SQL queries?
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Stop-and-restart style execution for long running decision support queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Dynamic workload management for very large data warehouses: juggling feathers and bowling balls
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Multi-query SQL progress indicators
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
A testbed for managing dynamic mixed workloads
Proceedings of the VLDB Endowment
Adaptive query scheduling for mixed database workloads with multiple objectives
Proceedings of the Third International Workshop on Testing Database Systems
Proceedings of the VLDB Endowment
Predicting completion times of batch query workloads using interaction-aware models and simulation
Proceedings of the 14th International Conference on Extending Database Technology
Managing dynamic mixed workloads for operational business intelligence
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
Workload management for big data analytics
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Business Intelligence query workloads that run against very large data warehouses contain queries whose execution times range, sometimes unpredictably, from seconds to hours. The presence of even a handful of long-running queries can significantly slow down a workload consisting of thousands of queries, creating havoc for queries that require a quick response. Long-running queries are a known problem in all commercial database products. However, we have not seen a thorough classification of long-running queries nor a systematic study of the most effective corrective actions. We present here a systematic study of workload management policies, including many implemented by commercial database vendors. Our goal is to enable a system to: (1) recognize long-running queries and categorize them in terms of their impact on performance and (2) determine and take (automatically!) the most effective control actions to remedy the situation. To this end, we identify common workload management scenarios involving long-running queries, and create a taxonomy of long-running queries. We carry out an extensive set of experiments to evaluate different management policies and the relative and absolute thresholds that they may use. We find that in some scenarios, the right combination of policies can reduce the runtime of a workload by a factor of two, but that in other scenarios, any action taken increases runtime. One surprising result was that relative thresholds for execution control can compensate for inaccurate cost estimates, so that Kill&Requeue actions perform as well as Suspend&Resume.