Rethinking Main Memory OLTP Recovery Nirmesh Malviya #1, Ariel Weisberg .2, Samuel Madden #3, Michael Stonebraker #4 # MIT CSAIL * VoltDB Inc.
[email protected] [email protected] [email protected] [email protected] Abstract-Fine-grained, record-oriented write-ahead logging, procedure's name) along with the query parameters; doing so as exemplified by systems like ARIES, has been the gold standard also keeps the log entries small. Such a command log captures for relational database recovery. In this paper, we show that updates performed on the database implicitly in the commands in modern high-throughput transaction processing systems, this (or transactions) themselves, with only one log record entry per is no longer the optimal way to recover a database system. In particular, as transaction throughputs get higher, ARIEs-style command. After a crash, if we can bring up the database using logging starts to represent a non-trivial fraction of the overall a pre-crash transactionally-consistent snapshot (which may or transaction execution time. may not reflect all of the committed transactions from before We propose a lighter weight, coarse-grained command logging the crash), the database can recover by simply re-executing the technique which only records the transactions that were executed transactions stored in the command log in serial order instead on the database. It then does recovery by starting from a trans actionally consistent checkpoint and replaying the commands of replaying individual writes as in ARIES-style physiological in the log as if they were new transactions. By avoiding the logging. overhead of fine-grained logging of before and after images (both Compared to physiological logging, command logging oper CPU complexity as well as substantial associated 110), command ates at a much coarser granularity, and this leads to important logging can yield significantly higher throughput at run-time.