flakiness a month ago

The parser.py [1] has only 1.6k lines. And it is hand-written parser. This size is amazing if it's really capable, but I intuitively doubt it. For example, duckdb's select.y [2] has 3700 lines, and this is only for SELECT. ZetaSQL's grammar file [3] is almost 10k lines.

The SQL is a monstrous language. Is there any trick that keeps the code simple?

[1] https://github.com/tobymao/sqlglot/blob/main/sqlglot/parser.... [2] https://github.com/duckdb/duckdb/blob/master/third_party/lib... [3] https://github.com/google/zetasql/blob/master/zetasql/parser...

captaintobs a month ago

Author here, feel free to ask me any questions!

Something that I'm working on is a pure python SQL engine https://github.com/tobymao/sqlglot/blob/main/sqlglot/executo.... It does the whole shebang, parsing, optimizations, logical planning, physical execution.

  • gavinray a month ago

    Holy smokes, this is super impressive!

    I have a personal question if you don't mind -- I do some SQL query generation and transpilation for both work and hobby. One headache I've run into recently is generating nested EXISTS() subqueries.

    Imagine you have something like this:

        // WHERE Name = 'Audioslave'
            type: "binary_op",
            operator: "equal",
            column: { path: [], name: "Name" },
            value: { type: "scalar", value: "Audioslave" },
    This is all fine, but what if you want to say "A binary operation on related entities":

            type: "binary_op",
            operator: "equal",
            column: { path: ["Albums", "Tracks"], name: "AlbumId" },
            value: { type: "scalar", value: 40 },
    Which you want to generate something like:

      WHERE EXISTS(SELECT 1 FROM Albums WHERE Albums.ForeignKey = t.PrimaryKey
      AND EXISTS(SELECT 1 FROM Tracks WHERE Tracks.ForeignKey = Albums.PrimaryKey AND AlbumId = 40))
    How to do this is giving me a headache for a lot of reasons and I can't seem to come up with a good way. Any tips, references, or search terms to google?

    Thank you, look forward to digging in more + gave your repo a star!

    • captaintobs a month ago

      i rarely ever use exists and prefer to do left joins or left semi joins (in spark)

      i'm not exactly sure what you're asking though, in terms of sql generation, it's not difficult for me because i just take in sql and output sql from the ast

      • zasdffaa a month ago

        Exists can be very efficient as it allows execution to stop immediately when something is found.

  • ramraj07 a month ago

    Any plans on supporting snowflake? May I submit a PR?

    • captaintobs a month ago

      Id love to support more engines. PRs are very welcome!

  • contravariant a month ago

    Wow this looks neat. I've been looking for an easy to use sql parser (I've tried sqlparse).

    How permissive is the parser? Do you need to know the exact dialect up front or can it make some intelligent guesses for unknown UDFs etc?

    • captaintobs a month ago

      The parser is very permissive. You don’t need the exact dialect upfront, it handles unknown udfs internally as anonymous funcs.

  • zasdffaa a month ago

    Optimisation is a tricky one as you need the various cardinalities, how do you handle that?

    • captaintobs a month ago

      I don’t do join order optimizations because that relies on cardinality estimation, I leave that up to the physical plan. But there’s plenty more to optimize like predicate and projection pushdown.

  • diehunde a month ago

    Hey, great work! Can you talk a bit about the use case that inspire you to write the tool?

    • captaintobs a month ago

      At the large tech companies I've been working at, there are many different big data engines that speak different dialects of sql (presto / spark). People write SQL queries in one language and want to run it in another, but it doesn't just work, there are many parts of the query that need to be manually changed in order for it to run which is tedious and error prone.

      • diehunde a month ago

        Got it. I'm asking because I worked on something similar. The idea was to unit test some Airflow workflows locally. The production workflows were using Hive, but having a local Hive container was too slow for tests, so we wrote a small parser to translate the Hive queries into SQLite queries at runtime. In the end, we had a decent PoC but couldn't complete it because of all the Hive features, but it was super fun.

        • captaintobs a month ago

          yep, we do the same but using duckdb, it's got a lot more analytical functions and closer to hive than sqlite

      • travisjungroth a month ago

        Where I work, we handle it by having Data Scientists running experiments on our platform commit their queries as Python code to a repository of metrics.

        • captaintobs a month ago

          I actually built the system you work on :). It was the main inspiration for this project because PyPika is not a great experience for data scientists.

          • travisjungroth a month ago

            Thought you might catch that! I've actually helped swap a few things to SQLGlot from pypika.

RobinL a month ago

SQLGlot is great. We've used it to extend our FOSS probabilistic data linking library[1] so that it is now capable of executing against a variety of SQL backends (Spark, Presto, DuckDB, Sqlite), significantly widening our potential user base.

We implement the core statistical model in SQL, and then use SQLGlot to transpile to the target execution engine. One big motivation was to futureproof our work - we're no longer tied down to Spark, and so when the 'next big thing' (GPU accelerated SQL for analytics?) comes along, it should be relatively straightforward to support it by writing another adaptor.

Working on this has highlighted some of the really tricky problems associated with translating between SQL engines, and we haven't hit any major problems, so kudos to the author!

[1] https://github.com/moj-analytical-services/splink/tree/splin...

  • contravariant a month ago

    Any particular reason you chose to use a sql transpiler rather than using e.g. sqlalchemy to generate sql directly?

    • RobinL a month ago

      Good question.

      First, the SQL involves complex analytical queries on large datasets that need careful pipelining, caching and optimisation. I wasn't sure the extent to which this was possible in sqlalchemy.

      Second, it was important that our implementation of the em algorithm (a iterative numerical approach for maximising a likelihood function) was readable/understandable and I felt that readers of the code were more likely to know SQL than sqlalchemy. Certainly i was more comfortable expressing it in SQL than another (i.e. sqlalchemy's) API.

      Third, our API allows the user to inject SQL to customise their data linking models and it felt more natural for this to be directly executed rather than go through an abstraction layer.

      I'm not a sqlalchemy expert, but my sense is thats it's more appropriate for transaction/atomic SQL than for complex analytical queries

      • contravariant a month ago

        Well you're not wrong per se, sqlalchemy is primarily an ORM system. However one of its components is a module purely for sql generation, which you could (not saying you should) use to generate (complex) sql queries. Obviously their main concern is querying and updating rows, but I think pretty much all common sql constructs are supported (and if not it is fairly easy to add your own).

        I can't say that I ever had to implement an EM algorithm that way though, so I can't say how complicated that would be. Certainly it'll take some more effort than writing it in a familiar SQL dialect. The main reason to do it would be that sqlalchemy has pretty good support for quite a lot of databases.

eatonphil a month ago

Neat! I did an exploration of sql parsers in different languages [0] and couldn't find much for python. But between this project itself and the couple it lists in the benchmarks I have a few more to look at.

[0] https://datastation.multiprocess.io/blog/2022-04-11-sql-pars...

  • captaintobs a month ago

    Awesome, let me know if you have any questions or suggestions if you check it out!

  • contravariant a month ago

    You were a bit too quick in dismissing sqlparse. I had the same initial reaction but it does actually build a tree, more or less, by grouping together tokens. It doesn't build a full syntax tree, though. For instance, it doesn't attempt to identify the FROM or JOINs from a select statement for instance, but it does make sure to group things such that a FROM keyword following a SELECT is part of the same SELECT statement. This makes it flexible but does leave you with slightly more work (though it worked well enough for my purposes).

Pandabob a month ago

Could this be used in VSCode as plugin to autoformat/lint my .sql files?

xiaodai a month ago

nice one. do you feel that having it in pure python leaves some performance on the table? or is performance not so critical in this use case?

  • captaintobs a month ago

    performance is not that important, usually the time to run a sql query takes 100ms > 1 hour, and so it doesn't matter that parsing it takes 0.5 ms.