About the Event
An emerging class of distributed database management systems (DBMS), known as
NewSQL, provide the same scalable performance of NoSQL systems while
maintaining the consistency guarantees of a traditional, single-node DBMS.
These NewSQL systems achieve high throughput rates for data-intensive
applications by storing their databases in a cluster of main memory
partitions. This partitioning enables them to eschew much of the legacy, disk-
oriented architecture that slows down traditional systems, such as heavy-
weight concurrency control algorithms, thereby allowing for the efficient
execution of single-node transactions. But many applications cannot be
partitioned such that all of their transactions execute in this manner; these
multi-node transactions require expensive coordination that inhibits
performance. Thus, without intelligent methods to overcome these impediments,
a NewSQL DBMS will scale no better than a traditional DBMS.
In this talk, I will present our research on integrating machine learning
techniques to improve the performance of fast database systems that is
inspired by my adventures at greyhound racing tracks. In particular, I will
discuss my work on the H-Store parallel, main memory transaction processing
system. I will first describe the Houdini framework that uses Markov models to
predict transactions’ behaviors to allow a DBMS to selectively enable runtime
optimizations. I will then present Hermes, a method for the deterministic
execution of speculative transactions whenever a DBMS stalls because of
distributed transactions. Together, these projects enable H-Store to support
transactional workloads that are beyond what single-node systems can handle.