SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
Physical integrity in a large segmented database
ACM Transactions on Database Systems (TODS)
The implementation and performance of compressed databases
ACM SIGMOD Record
The working set model for program behavior
Communications of the ACM
Query optimization in compressed database systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
What Happens During a Join? Dissecting CPU and Memory Optimization Effects
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Understanding the Linux Virtual Memory Manager
Understanding the Linux Virtual Memory Manager
Super-Scalar RAM-CPU Cache Compression
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Integrating compression and execution in column-oriented database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
How to barter bits for chronons: compression and bandwidth trade offs for database scans
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
H-store: a high-performance, distributed main memory transaction processing system
Proceedings of the VLDB Endowment
Constant-Time Query Processing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Dictionary-based order-preserving string compression for main memory column stores
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Positional update handling in column stores
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
The mixed workload CH-benCHmark
Proceedings of the Fourth International Workshop on Testing Database Systems
Fast checkpoint recovery algorithms for frequently consistent applications
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
How to efficiently snapshot transactional data: hardware or software controlled?
Proceedings of the Seventh International Workshop on Data Management on New Hardware
Efficiently compiling efficient query plans for modern hardware
Proceedings of the VLDB Endowment
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
Fast updates on read-optimized databases using multi-core CPUs
Proceedings of the VLDB Endowment
SAP HANA database: data management for modern business applications
ACM SIGMOD Record
Optimizing write performance for read optimized databases
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Enabling efficient OS paging for main-memory OLTP databases
Proceedings of the Ninth International Workshop on Data Management on New Hardware
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Growing main memory sizes have facilitated database management systems that keep the entire database in main memory. The drastic performance improvements that came along with these in-memory systems have made it possible to reunite the two areas of online transaction processing (OLTP) and online analytical processing (OLAP): An emerging class of hybrid OLTP and OLAP database systems allows to process analytical queries directly on the transactional data. By offering arbitrarily current snapshots of the transactional data for OLAP, these systems enable real-time business intelligence. Despite memory sizes of several Terabytes in a single commodity server, RAM is still a precious resource: Since free memory can be used for intermediate results in query processing, the amount of memory determines query performance to a large extent. Consequently, we propose the compaction of memory-resident databases. Compaction consists of two tasks: First, separating the mutable working set from the immutable "frozen" data. Second, compressing the immutable data and optimizing it for efficient, memory-consumption-friendly snapshotting. Our approach reorganizes and compresses transactional data online and yet hardly affects the mission-critical OLTP throughput. This is achieved by unburdening the OLTP threads from all additional processing and performing these tasks asynchronously.