I recommend anyone who works with databases to write a simple engine. It's a lot simpler than you may think and it's a great exercise. If using python, sqlglot (https://github.com/tobymao/sqlglot) let's you skip all the parsing and it even does some simple optimizations. From the parsed query tree it's pretty straightforward to build a logical plan and execute that. You can even use python's builtin ast module to convert sql expressions into python ones (so no need for a custom interpreter!)
In a larger system we are building we need a text-to-sql capability for some structured data retrieval.
Is there a way one could utilize this library (sqlglot) to build a multi-dialect sql generator -- that is not currently solved by directly relying on a LLM that is better at code generation in general?
another digression here... sorry... i see you're trying to diy text-to-sql—at some point you're gonna hit a bunch of hiccups. like, the model writes a query that “almost” works but joins the wrong tables, or it assumes column names that don’t exist, or it returns the wrong agg because it misread the intent. and retries won’t always save you—it’ll just confidently hallucinate again.
we’ve been through all of that at wobby.ai we ended up building a system where the data team defines guardrails and reusable query templates, so the agent doesn’t just make stuff up. it can still handle user prompts, but within a safe structure. if you want to save yourself from debugging this stuff endlessly, might be worth checking out wobby.ai.
And then it will render dialect-specific SQL. It has very advanced emulation functionality for things like JSON aggregations and working around quirks of dialects.
Alternatively, you can ask an LLM to generate a specific dialect of SQL, and then use jOOQ to parse it to an AST, and then render it as a different dialect, like:
val parser= DSL.using(SQLDialect.POSTGRES).parser()
val parsedQuery = parser.parseQuery(postgresQuery)
val renderedMySQL = DSL.using(SQLDialect.MYSQL).renderInlined(parsedQuery)
println(renderedMySQL)
Unsure if functionality like this exists in other Query Builder libraries for other languages.
Second this - building even a simple engine gives real insight into query planning and execution. Once parsing is handled, the core ideas are a lot more approachable than they seem.
This is a great write up about a pull-style volcano SQL engine.
The IR I've used is the Calcite implementation, this looks very concept adjacent enough that it makes sense on the first read.
> tmp2/test-branch> explain plan select count() from xy join uv on x = u;
One of the helpful things we did was to build a graphviz dot export for the explains plans, which saved us days and years of work when trying to explain an optimization problem between the physical and logical layers.
My version would end up displayed as SVG like this
When I read SQL for Dummies almost thirty years ago, it made a point of distinguishing "sequel" as a historical predecessor to standard "SQL." As I recall, the author even asserted that SQL is not an acronym/initialism for structured query language. I felt funny saying sequel for the next decade or so, because I wasn't an old timer experienced with this pre-SQL technology.
Now I usually say sequel because everyone else does. That and it rolls off the tongue better than S-Q-L.
Many (most?) non-native English speakers do pronounce it as ess-queue-ell, especially in their own languages, so yes, the use of "a" instead of "an" does look off from that perspective.
Using dolthub's go-mysql-server for Grafana's upcoming SQL expressions feature (private preview in Grafana 12, but in the OSS version with a feature toggle).
GMS lets you provide your own table and database implementations, so we use GMS to perform SQL queries against Grafana's dataframes - so users can join or manipulate different data source queires, but we don't have to insert the data into SQL to do this thanks to GMS.
I recommend anyone who works with databases to write a simple engine. It's a lot simpler than you may think and it's a great exercise. If using python, sqlglot (https://github.com/tobymao/sqlglot) let's you skip all the parsing and it even does some simple optimizations. From the parsed query tree it's pretty straightforward to build a logical plan and execute that. You can even use python's builtin ast module to convert sql expressions into python ones (so no need for a custom interpreter!)
Sorry for slight digression.
In a larger system we are building we need a text-to-sql capability for some structured data retrieval.
Is there a way one could utilize this library (sqlglot) to build a multi-dialect sql generator -- that is not currently solved by directly relying on a LLM that is better at code generation in general?
another digression here... sorry... i see you're trying to diy text-to-sql—at some point you're gonna hit a bunch of hiccups. like, the model writes a query that “almost” works but joins the wrong tables, or it assumes column names that don’t exist, or it returns the wrong agg because it misread the intent. and retries won’t always save you—it’ll just confidently hallucinate again.
we’ve been through all of that at wobby.ai we ended up building a system where the data team defines guardrails and reusable query templates, so the agent doesn’t just make stuff up. it can still handle user prompts, but within a safe structure. if you want to save yourself from debugging this stuff endlessly, might be worth checking out wobby.ai.
You can use an LLM to generate query-builder expressions from popular libraries in whatever language.
For example, on the JVM there is jOOQ, which allows you to write something like:
And then it will render dialect-specific SQL. It has very advanced emulation functionality for things like JSON aggregations and working around quirks of dialects.Alternatively, you can ask an LLM to generate a specific dialect of SQL, and then use jOOQ to parse it to an AST, and then render it as a different dialect, like:
Unsure if functionality like this exists in other Query Builder libraries for other languages.This is a SQL to X library, though. I don’t think it’s what you need.
Second this - building even a simple engine gives real insight into query planning and execution. Once parsing is handled, the core ideas are a lot more approachable than they seem.
This is a great write up about a pull-style volcano SQL engine.
The IR I've used is the Calcite implementation, this looks very concept adjacent enough that it makes sense on the first read.
> tmp2/test-branch> explain plan select count() from xy join uv on x = u;
One of the helpful things we did was to build a graphviz dot export for the explains plans, which saved us days and years of work when trying to explain an optimization problem between the physical and logical layers.
My version would end up displayed as SVG like this
https://web.archive.org/web/20190724161156/http://people.apa...
But the calcite logical plans also have that dot export modes.
https://issues.apache.org/jira/browse/CALCITE-4197
Calcite also has a relatively-unknown web tool for plan visualization that lets you step through execution.
It's a method from "RuleMatchVisualizer":
https://github.com/apache/calcite/blob/36f6dddd894b8b79edeb5...
Here's a screenshot of what the webpage looks like, for anyone curious:
https://github.com/GavinRay97/GraphQLCalcite/blob/92b18a850d...
This is really great!!
Man, this title tripped me up for a minute because I pronounce it with the letters like Ess-Queue-Ell
So the "A" in "A ess-queue-ell" engine felt like it should have been an "An" until I realized it was meant to be pronounced like "sequel"
When I read SQL for Dummies almost thirty years ago, it made a point of distinguishing "sequel" as a historical predecessor to standard "SQL." As I recall, the author even asserted that SQL is not an acronym/initialism for structured query language. I felt funny saying sequel for the next decade or so, because I wasn't an old timer experienced with this pre-SQL technology.
Now I usually say sequel because everyone else does. That and it rolls off the tongue better than S-Q-L.
Many (most?) non-native English speakers do pronounce it as ess-queue-ell, especially in their own languages, so yes, the use of "a" instead of "an" does look off from that perspective.
I prefer "ess queue ell" these days, but the first DBA I ever worked with pronounced it "squirrel".
Not necessarily, I see native speakers completely ignore this a lot.
Have you ever considered pronouncing it as squirrel by the way?
Using dolthub's go-mysql-server for Grafana's upcoming SQL expressions feature (private preview in Grafana 12, but in the OSS version with a feature toggle).
GMS lets you provide your own table and database implementations, so we use GMS to perform SQL queries against Grafana's dataframes - so users can join or manipulate different data source queires, but we don't have to insert the data into SQL to do this thanks to GMS.
Very nice write up enumerating all the stages of SQL query execution. Interesting even if you don’t care about the DoIt database specifically.