Introspective sorting and selection algorithms
Software—Practice & Experience
Making B+- trees cache conscious in main memory
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Automating physical database design in a parallel database
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Dynamic page placement to improve locality in CC-NUMA multiprocessors for TPC-C
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Data page layouts for relational databases on deep memory hierarchies
The VLDB Journal — The International Journal on Very Large Data Bases
Data placement in shared-nothing parallel database systems
The VLDB Journal — The International Journal on Very Large Data Bases
Serving Datacube Tuples from Main Memory
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Performance tradeoffs in read-optimized databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Data morphing: an adaptive, cache-conscious storage technique
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Column-stores vs. row-stores: how different are they really?
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Breaking the memory wall in MonetDB
Communications of the ACM - Surviving the data deluge
Read-optimized databases, in depth
Proceedings of the VLDB Endowment
Constant-Time Query Processing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Sort vs. Hash revisited: fast join implementation on modern multi-core CPUs
Proceedings of the VLDB Endowment
HYRISE: a main memory hybrid storage engine
Proceedings of the VLDB Endowment
Design and evaluation of main memory hash join algorithms for multi-core CPUs
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Distributed cube materialization on holistic measures
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
SAP HANA database: data management for modern business applications
ACM SIGMOD Record
Efficient transaction processing in SAP HANA database: the end of a column store myth
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Vectorwise: A Vectorized Analytical DBMS
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Massively parallel sort-merge joins in main memory multi-core database systems
Proceedings of the VLDB Endowment
The vertica analytic database: C-store 7 years later
Proceedings of the VLDB Endowment
Hekaton: SQL server's memory-optimized OLTP engine
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Main-memory hash joins on multi-core CPUs: Tuning to the underlying hardware
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Existing main memory data processing systems employ a variety of storage organizations and make a number of storage-related design choices. The focus of this paper is on systematically evaluating a number of these key storage design choices for main memory analytical (i.e. read-optimized) database settings. Our evaluation produces a number of key insights: First, it is always beneficial to organize data into self-contained memory blocks rather than large files. Second, both column-stores and row-stores display performance advantages for different types of queries, and for high performance both should be implemented as options for the tuple-storage layout. Third, cache-sensitive B+-tree indices can play a major role in accelerating query performance, especially when used in a block-oriented organization. Finally, compression can also play a role in accelerating query performance depending on data distribution and query selectivity.