timsaucer commented on issue #513:
URL:
https://github.com/apache/datafusion-python/issues/513#issuecomment-3361133274
I've been using Claude to assist me in trying to understand the conventions,
so take this with a grain of salt.
- DuckDB: `result = duckdb.sql("SELECT * FROM df", df=pandas_df)` (but we've
seen they can also pull directly from scope)
- Spark: `spark.sql("SELECT * FROM users WHERE age > :age", age=25)`
- Daft: `daft.sql("SELECT * FROM df", catalog={"df": df_customer})`
- Pandas: `query = "SELECT * FROM df_customer"` (direct injection it looks
like)
- Postgresql: `cursor.execute("SELECT * FROM users WHERE age > %s", (25,))`
or with named parameters `cursor.execute("SELECT * FROM users WHERE age >
%(age)s", {"age": 25})`
One potential problem with the proposed `ctx.sql("select c_custkey, c_name
from {df}", df=df_customer)` is that if the user also uses f-string replacement
it gets messy. For example suppose they did `ctx.sql(f"select
{key_of_interest}, c_name from {df}", df=df_customer)` then I expect this would
go very poorly. It would try to coerce `df` to a string because of the `f" "`.
I'm a bit torn on the PostgreSQL approach. On the one hand `datafusion`
upstream tries to stick closely to PostgreSQL. On the other hand the non-named
parameters I find to be nasty. It reminds me of old `fprint` statements where
you had to closely watch your parameter ordering.
From this preliminary look it doesn't appear that there is a strong
consensus in approach.
If I had to pick from one of these, I would probably lean towards the Spark
approach. I think the f-string replacement argument is a very valid one and
would just lead to headaches down the road for our users.
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