ACM SIGMOD Record
Differential files: their application to the maintenance of large databases
ACM Transactions on Database Systems (TODS)
The SDSS skyserver: public access to the sloan digital sky server data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Rethinking Database System Architecture: Towards a Self-Tuning RISC-Style Database System
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
MIL primitives for querying a fragmented world
The VLDB Journal — The International Journal on Very Large Data Bases
Database tuning advisor for microsoft SQL server 2005: demo
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An adaptive packed-memory array
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Hexastore: sextuple indexing for semantic web data management
Proceedings of the VLDB Endowment
Self-organizing tuple reconstruction in column-stores
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Workload-aware indexing of continuously moving objects
Proceedings of the VLDB Endowment
Self-selecting, self-tuning, incrementally optimized indexes
Proceedings of the 13th International Conference on Extending Database Technology
Saving space and time using index merging
Data & Knowledge Engineering
Benchmarking adaptive indexing
TPCTC'10 Proceedings of the Second TPC technology conference on Performance evaluation, measurement and characterization of complex systems
Transactions on large-scale data- and knowledge-centered systems II
Transactions on large-scale data- and knowledge-centered systems II
Merging what's cracked, cracking what's merged: adaptive indexing in main-memory column-stores
Proceedings of the VLDB Endowment
The database architectures research group at CWI
ACM SIGMOD Record
Stochastic database cracking: towards robust adaptive indexing in main-memory column-stores
Proceedings of the VLDB Endowment
Concurrency control for adaptive indexing
Proceedings of the VLDB Endowment
Holistic indexing: offline, online and adaptive indexing in the same kernel
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
NoDB: efficient query execution on raw data files
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Adaptive indexing in modern database kernels
Proceedings of the 15th International Conference on Extending Database Technology
Incrementally maintaining run-length encoded attributes in column stores
Proceedings of the 16th International Database Engineering & Applications Sysmposium
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A cracked database is a datastore continuously reorganized based on operations being executed. For each query, the data of interest is physically reclustered to speed-up future access to the same, overlapping or even disjoint data. This way, a cracking DBMS self-organizes and adapts itself to the workload. So far, cracking has been considered for static databases only. In this paper, we introduce several novel algorithms for high-volume insertions, deletions and updates against a cracked database. We show that the nice performance properties of a cracked database can be maintained in a dynamic environment where updates interleave with queries. Our algorithms comply with the cracking philosophy, i.e., a table is informed on pending insertions and deletions, but only when the relevant data is needed for query processing just enough pending update actions are applied. We discuss details of our implementation in the context of an open-source DBMS and we show through a detailed experimental evaluation that our algorithms always manage to keep the cost of querying a cracked datastore with pending updates lower than the non-cracked case.