31–40 of 49 posts
The only query languages that do this "right" are datalog (Datomic) and Q/K (KDB+, Shakti).
I was invited to work at Google in 2013 on an internal project using their Knowledge Graph. I had just written two books on RDF/SPARQL/linked-data and at first I was taken aback by the query language used at the time. However, when I realized how well the KG scaled and how fast the queries were, I became a fan. BTW, I think this paper is very well written. Graph queries is not an easy topic and their examples and tex…
What are your thoughts on just using plain datalog/prolog?
Datalog solves most of that :) My read of the paper is their semantics largely reduces to datalog. The most interesting semantic exception is their bounded recursion, which suggests they're doing optimizations normal datalog wouldn't. Likewise, graph workloads often have weird phenomena to optimize for in terms of expected queries & data, where I'm guessing datalog might fit semantically but not how people would normally implement an engine. The PathQuery paper carefully side-stepped all such discussion, so I've been curious.
At Graphistry, we do end-to-end GPU stuff, so I've been thinking about this particular problem, and came to a similar conclusion of a datalog-ish subset being the most straightforward way to get more performance/$ for this kind of task.
Earlier quoted context omitted.
What are your thoughts on just using plain datalog/prolog?
Having written a chunk of my phd in prolog and even a simplified implementation of one, I'm definitely a fan. At the same time, our startup (graphistry) works w/ teams needing to look at all sorts of graph data, including knowledge graphs like the topic here, and I'd be nervous about meeting the performance & scale needs with out-of-the-box prolog systems. Likewise, a lot of prolog's power comes from ideas like autom…
https://opensource.googleblog.com/2021/04/logica-organizing-...
(Datalog/Prolog family language compiled to SQL)
I was invited to work at Google in 2013 on an internal project using their Knowledge Graph. I had just written two books on RDF/SPARQL/linked-data and at first I was taken aback by the query language used at the time. However, when I realized how well the KG scaled and how fast the queries were, I became a fan. BTW, I think this paper is very well written. Graph queries is not an easy topic and their examples and tex…
I'm interested in industry history, so I wonder if you can comment on how much of this, if any, came into Google from Metaweb. There are no Metaweb-connected people among the authors (that I can tell) but Warren Harris is acknowledged. Was it a language that was in development before the acquisition, or was it needed mainly after?
I was invited to work at Google in 2013 on an internal project using their Knowledge Graph. I had just written two books on RDF/SPARQL/linked-data and at first I was taken aback by the query language used at the time. However, when I realized how well the KG scaled and how fast the queries were, I became a fan. BTW, I think this paper is very well written. Graph queries is not an easy topic and their examples and tex…
I'm interested in industry history, so I wonder if you can comment on how much of this, if any, came into Google from Metaweb. There are no Metaweb-connected people among the authors (that I can tell) but Warren Harris is acknowledged. Was it a language that was in development before the acquisition, or was it needed mainly after?
Warren started working on PathQuery as a replacement for MQL. PathQuery was originally executed on Pregel and was far from a realtime query language.
A realtime query engine was later developed for PathQuery in the search stack so interesting search queries could be answered a la minute from the Knowledge Graph, rather than just pre-generating results for a limited class of queries.
This was all done in the 2012-2013 timeframe. Most of the people involved are long gone.
Earlier quoted context omitted.
I'm interested in industry history, so I wonder if you can comment on how much of this, if any, came into Google from Metaweb. There are no Metaweb-connected people among the authors (that I can tell) but Warren Harris is acknowledged. Was it a language that was in development before the acquisition, or was it needed mainly after?
Mainly after. Metaweb brought MQL with them, which ran on their own single server graphd, which was eventually shutdown. Warren started working on PathQuery as a replacement for MQL. PathQuery was originally executed on Pregel and was far from a realtime query language. A realtime query engine was later developed for PathQuery in the search stack so interesting search queries could be answered a la minute from the Kn…
Earlier quoted context omitted.
Mainly after. Metaweb brought MQL with them, which ran on their own single server graphd, which was eventually shutdown. Warren started working on PathQuery as a replacement for MQL. PathQuery was originally executed on Pregel and was far from a realtime query language. A realtime query engine was later developed for PathQuery in the search stack so interesting search queries could be answered a la minute from the Kn…
I've forgotten the details: by real-time query engine are you referring to DGraph, Livegraph or something else?
Reminds me of GQL [2]. Anybody know the differences and or similarities? [2] https://www.gqlstandards.org/
Earlier quoted context omitted.
Having written a chunk of my phd in prolog and even a simplified implementation of one, I'm definitely a fan. At the same time, our startup (graphistry) works w/ teams needing to look at all sorts of graph data, including knowledge graphs like the topic here, and I'd be nervous about meeting the performance & scale needs with out-of-the-box prolog systems. Likewise, a lot of prolog's power comes from ideas like autom…
You might like Google's Logica https://opensource.googleblog.com/2021/04/logica-organizing-... (Datalog/Prolog family language compiled to SQL)
I was invited to work at Google in 2013 on an internal project using their Knowledge Graph. I had just written two books on RDF/SPARQL/linked-data and at first I was taken aback by the query language used at the time. However, when I realized how well the KG scaled and how fast the queries were, I became a fan. BTW, I think this paper is very well written. Graph queries is not an easy topic and their examples and tex…
I'm interested in industry history, so I wonder if you can comment on how much of this, if any, came into Google from Metaweb. There are no Metaweb-connected people among the authors (that I can tell) but Warren Harris is acknowledged. Was it a language that was in development before the acquisition, or was it needed mainly after?
I think what differentiates PathQuery from many "query languages" is that it takes on the task of transforming that data into somebody else's schema (aka "turning protos into other protos"). Having a DSL for this is particularly attractive in a company that will otherwise expect you to do string formatting in C++. But even under normal circumstances there are wins from having data transformation vertically integrated with your query language.