Recovery algorithms for in-memory OLTP databases
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Samuel Madden and Michael Stonebraker.
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Fine-grained, record-oriented write-ahead logging, as exemplified by systems like ARIES, has been the gold standard for relational database recovery. In this thesis, we show that in modern high-throughput transaction processing systems, this is no longer the optimal way to recover a database system. In particular, as transaction throughputs get higher, ARIES-style logging starts to represent a non-trivial fraction of the overall transaction execution time. We propose a lighter weight, coarse-grained command logging technique which only records the transactions that were executed on the database. It then does recovery by starting from a transactionally consistent checkpoint and replaying the commands in the log as if they were new transactions. By avoiding the overhead of fine-grained, page-level logging of before and after images (and substantial associated I/O), command logging can yield significantly higher throughput at run-time. Recovery times for command logging are higher compared to ARIES, but especially with the advent of high-availability techniques that can mask the outage of a recovering node, recovery speeds have become secondary in importance to run-time performance for most applications. We evaluated our approach on an implementation of TPC-C in a main memory database system (VoltDB), and found that command logging can offer 1.5x higher throughput than a main-memory optimized implementation of ARIES.
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 63-66).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.