From the “Temporal Analytics on Big Data for Web Advertising” paper:

TiMR is a framework that transparently combines a map-reduce (M-R) system with a temporal DSMS¹. Users express time-oriented analytics using a temporal (DSMS) query lan- guage such as StreamSQL or LINQ. Streaming queries are declarative and easy to write/debug, real-time-ready, and often several orders of magnitude smaller than equivalent custom code for time-oriented applications. TiMR allows the temporal queries to transparently scale on offline temporal data in a cluster by leveraging existing M-R infrastructure.

Broadly speaking, TiMR’s architecture of compiling higher level queries into M-R stages is similar to that of Pig/SCOPE. However, TiMR specializes in time-oriented queries and data, with several new features such as: (1) the use of an unmodified DSMS as part of compilation, parallelization, and execution; and (2) the exploitation of new temporal parallelization opportunities unique to our setting. In addition, we leverage the temporal algebra underlying the DSMS in order to guarantee repeatability across runs in TiMR within M-R (when handling failures), as well as over live data.

According to the paper, DSMS work well for real-time data, but are not massively scalable. On the other hand, Map-Reduce is extremely scalable, but computation is performed on offline data. TiMR proposes a solution that is getting closer to a real-time map-reduce.

Read or download the paper after the break.