Yes, Magic sets is a very important and popular optimization as well. I guess once we can get a basic notion of recursion as a construct in Calcite, and get it to work correctly, we can start cracking optimizations. One thing to note is that the convergence/fixed point depend on the data, and there is no way to know beforehand what the (complete) plan will look like (i.e., how many joins). It seems that there must be some sort of awareness in the host engine of the fact that the query is recursive, and it should keep iterating till fixed point, or at least tell Calcite if it converged or not, and if not, Calcite will ask it to keep trying, so every iteration Calcite sends a traditional (non-recursive) RA plan, or ask the engine to stop. Do you agree?
On Fri, Dec 15, 2017 at 12:06 PM, Julian Hyde <[email protected]> wrote: > (Moving Carl, Shrikanth, Vasanth to bcc.) > > Regarding optimizations. One one hand, it is daunting that there so > many optimizations are required to make graph queries run efficiently, > but on the other hand, it is good news for the project if those can be > expressed in relational algebra. > > Looking at the previous research, some of the optimizations applied > are genuinely only possible at run-time, but others should be thought > of as logical rewrites. Semi-naive evaluation, which Walaa mentions, > can be expressed as a logical operation (very similar to incremental > view maintenance and streaming, by the way). > > (Untangling the capabilities of a particular engine from algebraic > rewrites is Calcite's gift to the world!) > > Another very important logical rewrite is "magic sets"[1], which can > be modeled as semi-join push-down and done entirely at planning > time[2] or (if the runtime supports it) as side-ways information > passing of bloom filters or similar. Magic sets are important for > graph queries but also very useful for star-schema queries with a > fixed number of joins. > > Julian > > [1] http://db.cs.berkeley.edu/papers/sigmod96-magic.pdf > > [2] https://issues.apache.org/jira/browse/CALCITE-468 > > > On Fri, Dec 15, 2017 at 11:21 AM, Edmon Begoli <[email protected]> wrote: > > Great initiative. > > > > I will also share some comparative performance studies we did at ORNL on > > different graph processing engines. Could be useful. > > > > On Fri, Dec 15, 2017 at 14:11 Walaa Eldin Moustafa < > [email protected]> > > wrote: > > > >> Hi Julian, > >> > >> Thanks for referencing our Datalog query processing paper [5]. I have > been > >> thinking about the same idea for a while now too :) I think Calcite is > very > >> well positioned as a generic query optimizer to add Datalog/recursive > query > >> support. Also, it makes a lot of sense since it opens a completely new > >> dimension for the kind of logic and queries that Calcite can handle, > >> including but not limited to graph queries, and that can be immediately > >> available to engines talking to Calcite. > >> > >> To answer your questions, we probably need to add a transitive closure > >> operator. This 1988 paper <http://ieeexplore.ieee.org/document/42731/> > by > >> Rakesh Agrawal proposes the notion of alpha relations, and defines an > alpha > >> operator on top of them which computes the transitive closure of alpha > >> relations. The operator fits well with the rest of Cod's relational > algebra > >> operators. > >> > >> For query optimizations, one of the commonly used Datalog optimizations > is > >> Semi-naive evaluation, where instead of re-evaluating the recursive > program > >> using all existing facts, only new facts inferred since last iteration > are > >> used. Datalog optimizations become much more interesting when > introducing > >> aggregation and negation, and it is still an open research question, but > >> there is already some tangible progress. Otherwise, as you mentioned > >> transitive closure is repeated joins, so pretty much many of the join > >> optimizations apply such as predicate pushdown, and aggregation > >> pushdown/pull up. > >> > >> Regarding the effort, we can always start from basic features and expand > >> from there. I have already started working on the parser, AST and > logical > >> plan builder for basic Datalog without recursion. I am happy to create a > >> JIRA ticket to track this effort there. > >> > >> Thanks, > >> Walaa. > >> > >> > >> On Fri, Dec 15, 2017 at 10:26 AM, Julian Hyde <[email protected]> wrote: > >> > >> > I've been thinking about Datalog front end to Calcite. How much effort > >> > would it be? Would there be an audience who would find it useful? > >> > > >> > The genesis of the idea is talks by Frank McSherry[1] and Vasia > >> > Kalavri[2] about graph queries and in particular Timely > >> > Dataflow[3][4], and a paper about using Datalog for graph processing > >> > [5]. > >> > > >> > A few observations: > >> > * Graph queries require repeated (unbounded) joins, and so are beyond > >> > SQL:92. > >> > * Datalog allows recursion, and can therefore compute things like > >> > transitive closure, and is therefore powerful enough for graph > >> > queries. > >> > * SQL:99 added 'WITH RECURSIVE' so can handle a pretty useful class of > >> > queries. However, for a variety of reasons, people tend not to use SQL > >> > for querying graph databases. > >> > * Datalog is more than just recursive queries. For instance, it is > >> > popular with academics analyzing the power/complexity of languages. > >> > And it can do deductive queries. > >> > * There are different "strengths" of Datalog. Some variants support > >> > only linearizable recursion; most variants only support queries whose > >> > results are stratified (i.e. can be defined using well-founded > >> > induction, necessary when negations are involved). > >> > * The "big data" languages (Hadoop, Spark, Flink, even Pig) have all > >> > discussed how they can handle graph queries and iterative computation. > >> > However they have mainly focused on improvements to their engine and > >> > physical algebra, not looked at logical algebra or query language. > >> > * If Calcite's algebra could express deductive query / recursive query > >> > / iteration, then not only would Datalog be possible, but also SQL's > >> > WITH RECURSIVE, and also SPARQL. > >> > > >> > So, questions on my mind: > >> > * What new operator(s) would we add to Calcite's algebra to enable > >> > recursive query? > >> > * What optimization rules are possible/necessary for graph queries? > >> > * How much effort would it be to add a Datalog parser to Calcite and > >> > translate to Calcite's algebra? > >> > > >> > Julian > >> > > >> > [1] http://www.dataengconf.com/scalability-but-at-what-cost > >> > > >> > [2] https://qconsf.com/sf2017/speakers/vasia-kalavri > >> > > >> > [3] https://github.com/frankmcsherry/timely-dataflow > >> > > >> > [4] http://sigops.org/sosp/sosp13/papers/p439-murray.pdf > >> > > >> > [5] http://www.sysnet.ucsd.edu/sysnet/miscpapers/datalog-icbd16.pdf > >> > > >> >
