I have the best readers. One sent me email expressing a hope that I’d write about Martin Fowler’s LMAX Architecture. I’d be glad to. In fact I had already thought of doing so, but the fact that at least one reader has already expressed an interest makes it even more fun. The architecture seems to incorporate three basic ideas.

  • Event sourcing, or the idea of using a sequentially written log as the “system of record” with the written-in-place copy as a cache – an almost direct inversion of roles compared to standard journaling.
  • The “disruptor” data/control structure.
  • Fitting everything in memory.

I don’t really have all that much to say about fitting everything in memory. I’m a storage guy, which almost by definition means I don’t get interested until there’s more data than will fit in memory. Application programmers should IMO strive to use storage only as a system of record, not as an extension of memory or networking (“sending” data from one machine to another through a disk is a pet peeve). If they want to cache storage contents in memory that’s great, and if they can do that intelligently enough to keep their entire working set in memory that’s better still, but if their locality of reference doesn’t allow that then LMAX’s prescription just won’t work for them and that’s that. The main thing that’s interesting about the “fit in memory” part is that it’s a strict prerequisite for the disruptor part. LMAX’s “one writer many readers” rule makes sense because of how cache invalidation and so forth work, but disks don’t work that way so the disruptor’s advantage over queues is lost.

With regard to the disruptor structure, I’ll also keep my comments fairly brief. It seems pretty cool, not that dissimilar to structures I’ve used and seen used elsewhere; some of the interfaces to the SiCortex chip’s built-in interconnect hardware come to mind quickly. I think it’s a mistake to contrast it with Actors or SEDA, though. I see them as complementary, with Actors and SEDA as high-level paradigms and the disruptor as an implementation alternative to the queues they often use internally. The idea of running these other models on top of disruptors doesn’t seem strange at all, and the familiar nature of disruptors doesn’t even make the combination seem all that innovative to me. It’s rather disappointing to see useful ideas dismissed because of a false premise that they’re alternatives to another instead of being complementary.

The really interesting part for me, as a storage guy, is the event-sourcing part. Again, this has some pretty strong antecedents. This time it recalls Seltzer et al’s work on log-structured filesystems, which is even based on may of the same observations e.g. about locality of reference and relative costs of random vs. sequential access. That work’s twenty years old, by the way. Because event sourcing is so similar to log-structured file systems, it runs into some of the same problems. Chief among these is the potentially high cost of reads that aren’t absorbed by the cache, and the complexity involved with pruning no-longer-relevant logs. Having to scan logs to find the most recent copy of a block/record/whatever can be extremely expensive, and building indices carries its own expense in terms of both resources and complexity. It’s not a big issue if your system has very high locality of reference, which time-oriented systems such as LMAX or several others types of systems tend to, but it can be a serious problem in the general case. Similarly, the cleanup problem doesn’t matter if you can simply drop records from the tail or at least stage them to somewhere else, but it’s a big issue for files that need to stay online – with online rather than near-line performance characteristics – indefinitely.

In conclusion, then, the approach Fowler describes seems like a good one if your data characteristics are similar to LMAX’s, but probably not otherwise. Is it an innovative approach? Maybe in some ways. Two out of the three main features seem strongly reminiscent of technology that already existed, and combinations of existing ideas are so common in this business that this particular combination doesn’t seem all that special. On the other hand, there might be more innovation in the low-level details than one would even expect to find in Fowler’s high-level overview. It’s interesting, well worth reading, but I don’t think people who have dealt with high data volumes already will find much inspiration there.