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Nice to see Datalog being validated by a big name, though I don't see what's modern about Logica in particular, or why one should use it over plain Datalog (as

Nice to see Datalog being validated by a big name, though I don't see what's modern about Logica in particular, or why one should use it over plain Datalog (as syntactical Prolog subset) when the available backends are restricted to SQL rewriters or locked-in to BigQuery. Will have to look into the model for aggregate queries which I guess is a selling point for Logica (as is modularization/composition with optimization), and a weak point since typically neither in Datalog (the decidable logic fragment) nor portable. Edit: also I find the title a bit grandiose since this isn't about Logic Programming in general, but only database querying

devit 6y ago
It seems that it's open source (Apache 2.0) and can generate SQL for PostgreSQL and SQLite in addition to BigQuery.
- anentropic 6y ago
  Oh that's good, there was no mention of anything other than BigQuery in the front-matter but looks like it's in there: https://github.com/EvgSkv/logica/blob/main/compiler/dialects.py https://github.com/EvgSkv/logica/blob/main/compiler/dialects...
  - deleted 6y ago
    [deleted]
- tannhaeuser 6y ago
  Yeah but that reduces Logica outside Google to SQL rewriting. When the weak point of SQL isn't so much the syntax but the scalability and expressiveness limitations for big data and document data (fixation on strong ACID/CAP guarantees, schemas). SQL syntax has strong points, too; one being that it's not just a query but also update/batch language with ACID semantics; another being that it's standardized with a range of mature options available. Consider also the practical side: using Datalog as merely "prettier SQL" still doesn't allow you to dynamically define data properties or go schema-less as in RDF or other logic/deductive graph databases. Whenever you want a new column, you must execute DDLs (ALTER TABLE ADD COLUMN) also leading to forced commits, overly broad permissions, chaotic backup procedures and/or code artefacts containing the dreaded SELECT * syntax. Also, parsing Datalog queries, reformulating into SQL, then re-parsing SQL in the DB engine isn't the most efficient thing. Basically, the workflows and use cases for SQL RDBMSs and Datalog/graph databases are not the same, and if you're using one on top of the other, you're getting the intersection of possibilities but the union of problems, as is well known from O/R mappers );
  - YeGoblynQueenne 6y ago
    >> Whenever you want a new column, you must execute DDLs (ALTER TABLE ADD COLUMN) also leading to forced commits, overly broad permissions, chaotic backup procedures and/or code artefacts containing the dreaded SELECT * syntax. I don't understand what you mean here. With datalog, if you have a predicate person(Name, Age, Height) and you want to add an argument (a "column") for income, you can simply create a new predicate person(Name, Age, Height, Income). Or, if you want to avoid duplication, you can write a rule to combine the information in two (or more) predicates: person(Name, Age, Height, Income):- person(Name, Age, Height) ,person(Name, Income). You don't need to remove the old predicate. That's actually one case where Datalog works better than SQL, that only allows "rows" i.e. "facts" (in Datalog parlance) but not "rules" that establish relations _between tables_. That's true for Datalog, based on what I know about Prolog (not a Datalog expert!). I don't know how it works in Logica, but from reading the article above I think the semantics would be similar. >> Basically, the workflows and use cases for SQL RDBMSs and Datalog/graph databases are not the same, and if you're using one on top of the other, you're getting the intersection of possibilities but the union of problems, as is well known from O/R mappers ); That's funny. But I don't think it applies here. SQL and datalog are both relational. The difference is that Datalog lets you define relations over tables ("rules"), not just relations over data ("facts"/"rows"). Essentially, SQL is one half of datalog's relational semantics - only information without reasoning. Datalog adds reasoning on top, but the reasoning is still, well, relational (facts, rules and queries are all relations). There's no impedence mismatch here, as in trying to fit relational data into a non-relational program.
    - juki 6y ago
      > That's actually one case where Datalog works better than SQL, that only allows "rows" i.e. "facts" (in Datalog parlance) but not "rules" that establish relations _between tables_. What is the difference between Datalog rules and SQL views?
      - YeGoblynQueenne 6y ago
        It's been a while since I used SQL and I'm a bit rusty in it, but views would probably be the equivalent of Datalog rules, yes. The difference, as in my other comment to OP, is that Datalog rules are part of the Datalog program, which also stores the actual "tables" i.e. the facts. Whreas in SQL, views are only sort of ... virtual? Like I say I'm a bit rusty- but from my understanding, SQL vies don't live in the same space as tables. Funny thing. It used to be my day to day work was 80% SQL. Nowadays it's 99% Prolog maybe with a little bit of bash and powershell scripting (gotta automate those experiments!). I kiiind of miss SQL? But not quite. Personally I don't 100% get the grumbling about SQL's syntax. It's unintuitive and it works very hard to hide the actual semantics behind it, but, eh, at least it has clean semantics. I recently found this free book on databases that goes over both SQL and Datalog. It's a bit thick with obtuse terminology but it actually goes in depth over many useful topics: http://webdam.inria.fr/Alice/ http://webdam.inria.fr/Alice/ I also recommend that to OP, if they're reading.
cpdean 6y ago
> ...or why one should use it over plain Datalog... I have been looking for examples of how to use a practical implementation of Datalog for years and the closest I've come to is actually miniKanren instead. Could you point me to codebases that productively use Datalog internally?
- RBerenguel 6y ago
  What example with miniKanren have you found? This is an area I have a passing interest but never find the time to delve deep enough to find anything shiny enough
  - cpdean 6y ago
    The most interesting open source example I have found is a hy-lang minikanren that implements a tree-rewriter which lets you encode equivalent implementations of code snippets. It's a code-linter that will automatically simplify code you write via higher-level rules written in mk https://github.com/algernon/hydiomatic https://github.com/algernon/hydiomatic I've heard informally there are many companies relying on clojure/core.logic to rewrite complicated business rules for the sake of filtering and constraint solving problems in applications, but I do not know of any open source examples to reference. edit: i accidentally linked to a dependency of the project i meant to link to. originally linked to the mk implementation: https://github.com/algernon/adderall https://github.com/algernon/adderall
    - RBerenguel 6y ago
      Thanks!
- hcarvalhoalves 6y ago
  Datomic: https://docs.datomic.com/cloud/query/query-data-reference.html https://docs.datomic.com/cloud/query/query-data-reference.ht... Datascript: https://github.com/tonsky/datascript https://github.com/tonsky/datascript Crux: https://opencrux.com/main/index.html https://opencrux.com/main/index.html
samuell 6y ago
My hope would be mainly that this can get datalog into mainstream use, and soon get more (and more mature) libraries created by the community. That is in itself very exciting to me though. Would be pretty awesome if we could have logica (or something similar) for dataframes (including pandas), and so could build pipelines of transformations-via-queries on those. (If there is anything like this already implemented, I'm all ears!).

devit 6y ago

It seems that it's open source (Apache 2.0) and can generate SQL for PostgreSQL and SQLite in addition to BigQuery.

anentropic 6y ago
Oh that's good, there was no mention of anything other than BigQuery in the front-matter but looks like it's in there: https://github.com/EvgSkv/logica/blob/main/compiler/dialects.py https://github.com/EvgSkv/logica/blob/main/compiler/dialects...
- deleted 6y ago
  [deleted]
tannhaeuser 6y ago
Yeah but that reduces Logica outside Google to SQL rewriting. When the weak point of SQL isn't so much the syntax but the scalability and expressiveness limitations for big data and document data (fixation on strong ACID/CAP guarantees, schemas). SQL syntax has strong points, too; one being that it's not just a query but also update/batch language with ACID semantics; another being that it's standardized with a range of mature options available. Consider also the practical side: using Datalog as merely "prettier SQL" still doesn't allow you to dynamically define data properties or go schema-less as in RDF or other logic/deductive graph databases. Whenever you want a new column, you must execute DDLs (ALTER TABLE ADD COLUMN) also leading to forced commits, overly broad permissions, chaotic backup procedures and/or code artefacts containing the dreaded SELECT * syntax. Also, parsing Datalog queries, reformulating into SQL, then re-parsing SQL in the DB engine isn't the most efficient thing. Basically, the workflows and use cases for SQL RDBMSs and Datalog/graph databases are not the same, and if you're using one on top of the other, you're getting the intersection of possibilities but the union of problems, as is well known from O/R mappers );
- YeGoblynQueenne 6y ago
  >> Whenever you want a new column, you must execute DDLs (ALTER TABLE ADD COLUMN) also leading to forced commits, overly broad permissions, chaotic backup procedures and/or code artefacts containing the dreaded SELECT * syntax. I don't understand what you mean here. With datalog, if you have a predicate person(Name, Age, Height) and you want to add an argument (a "column") for income, you can simply create a new predicate person(Name, Age, Height, Income). Or, if you want to avoid duplication, you can write a rule to combine the information in two (or more) predicates: person(Name, Age, Height, Income):- person(Name, Age, Height) ,person(Name, Income). You don't need to remove the old predicate. That's actually one case where Datalog works better than SQL, that only allows "rows" i.e. "facts" (in Datalog parlance) but not "rules" that establish relations _between tables_. That's true for Datalog, based on what I know about Prolog (not a Datalog expert!). I don't know how it works in Logica, but from reading the article above I think the semantics would be similar. >> Basically, the workflows and use cases for SQL RDBMSs and Datalog/graph databases are not the same, and if you're using one on top of the other, you're getting the intersection of possibilities but the union of problems, as is well known from O/R mappers ); That's funny. But I don't think it applies here. SQL and datalog are both relational. The difference is that Datalog lets you define relations over tables ("rules"), not just relations over data ("facts"/"rows"). Essentially, SQL is one half of datalog's relational semantics - only information without reasoning. Datalog adds reasoning on top, but the reasoning is still, well, relational (facts, rules and queries are all relations). There's no impedence mismatch here, as in trying to fit relational data into a non-relational program.
  - juki 6y ago
    > That's actually one case where Datalog works better than SQL, that only allows "rows" i.e. "facts" (in Datalog parlance) but not "rules" that establish relations _between tables_. What is the difference between Datalog rules and SQL views?
    - YeGoblynQueenne 6y ago
      It's been a while since I used SQL and I'm a bit rusty in it, but views would probably be the equivalent of Datalog rules, yes. The difference, as in my other comment to OP, is that Datalog rules are part of the Datalog program, which also stores the actual "tables" i.e. the facts. Whreas in SQL, views are only sort of ... virtual? Like I say I'm a bit rusty- but from my understanding, SQL vies don't live in the same space as tables. Funny thing. It used to be my day to day work was 80% SQL. Nowadays it's 99% Prolog maybe with a little bit of bash and powershell scripting (gotta automate those experiments!). I kiiind of miss SQL? But not quite. Personally I don't 100% get the grumbling about SQL's syntax. It's unintuitive and it works very hard to hide the actual semantics behind it, but, eh, at least it has clean semantics. I recently found this free book on databases that goes over both SQL and Datalog. It's a bit thick with obtuse terminology but it actually goes in depth over many useful topics: http://webdam.inria.fr/Alice/ http://webdam.inria.fr/Alice/ I also recommend that to OP, if they're reading.

cpdean 6y ago

> ...or why one should use it over plain Datalog... I have been looking for examples of how to use a practical implementation of Datalog for years and the closest I've come to is actually miniKanren instead. Could you point me to codebases that productively use Datalog internally?

RBerenguel 6y ago
What example with miniKanren have you found? This is an area I have a passing interest but never find the time to delve deep enough to find anything shiny enough
- cpdean 6y ago
  The most interesting open source example I have found is a hy-lang minikanren that implements a tree-rewriter which lets you encode equivalent implementations of code snippets. It's a code-linter that will automatically simplify code you write via higher-level rules written in mk https://github.com/algernon/hydiomatic https://github.com/algernon/hydiomatic I've heard informally there are many companies relying on clojure/core.logic to rewrite complicated business rules for the sake of filtering and constraint solving problems in applications, but I do not know of any open source examples to reference. edit: i accidentally linked to a dependency of the project i meant to link to. originally linked to the mk implementation: https://github.com/algernon/adderall https://github.com/algernon/adderall
  - RBerenguel 6y ago
    Thanks!
hcarvalhoalves 6y ago
Datomic: https://docs.datomic.com/cloud/query/query-data-reference.html https://docs.datomic.com/cloud/query/query-data-reference.ht... Datascript: https://github.com/tonsky/datascript https://github.com/tonsky/datascript Crux: https://opencrux.com/main/index.html https://opencrux.com/main/index.html

samuell 6y ago

My hope would be mainly that this can get datalog into mainstream use, and soon get more (and more mature) libraries created by the community. That is in itself very exciting to me though. Would be pretty awesome if we could have logica (or something similar) for dataframes (including pandas), and so could build pipelines of transformations-via-queries on those. (If there is anything like this already implemented, I'm all ears!).