On Wed, May 30, 2018 at 9:26 PM Robert Haas <robertmh...@gmail.com> wrote: > The FDW approach, of which I have been a supporter for some years now, > is really aiming at a different target, which is to allow efficient > analytics queries across a multi-node cluster. I think we're getting > pretty close to being able to do that -- IMHO, the last fundamental > building block that we need is asynchronous execution, which Andres is > working on. After that, it's a matter of adding other features that > people want (like cross-node MVCC) and improving the plans for queries > that still don't perform well (like joins that involve redistributing > one of the tables involved).
FWIW, Distributed analytical queries is the right market to be in. This is the field in which I work, and this is where the action is at. I am very, very, sure about this. My view is that many of the existing solutions to this problem (in particular hadoop class soltuions) have major architectural downsides that make them inappropriate in use cases that postgres really shines at; direct hookups to low latency applications for example. postgres is fundamentally a more capable 'node' with its multiple man-millennia of engineering behind it. Unlimited vertical scaling (RAC etc) is interesting too, but this is not the way the market is moving as hardware advancements have reduced or eliminated the need for that in many spheres. The direction of the project is sound and we are on the cusp of the point where multiple independent coalescing features (FDW, logical replication, parallel query, executor enhancements) will open new scaling avenues that will not require trading off the many other benefits of SQL that competing contemporary solutions might. The broader development market is starting to realize this and that is a major driver of the recent upswing in popularity. This is benefiting me tremendously personally due to having gone 'all-in' with postgres almost 20 years ago :-D. (Time sure flies) These are truly wonderful times for the community. merlin
