Fix float inference to use DoubleParameter (64-bit) instead of FloatP…

[Shubhambhusate]

…arameter (32-bit)

What type of PR is this?

• Bug Fix

Description

dbsql_parameter_from_primitive() in src/databricks/sql/parameters/native.py currently maps Python's float type to FloatParameter, which binds values as Databricks SQL FLOAT (32-bit single-precision). However, Python's float is a 64-bit IEEE 754 double-precision number, so this mapping silently truncates precision.
This PR changes the inference to use DoubleParameter instead, which binds values as Databricks SQL DOUBLE (64-bit) and correctly preserves the full precision of Python floats.

Before (buggy):

cursor.execute("INSERT INTO t (val) VALUES (?)", [12345.678901234])
# Stored as 12345.0 — precision lost silently

After (fixed):

cursor.execute("INSERT INTO t (val) VALUES (?)", [12345.678901234])
# Stored as 12345.678901234 — full precision preserved

How is this tested?

• Unit tests
• Manually

All 55 unit tests in tests/unit/test_parameters.py pass, including test_inference[DoubleParameter-Primitive.FLOAT].

Manually verified that parameterized queries with raw Python floats now preserve full 64-bit precision when inserted into DOUBLE columns.

Related Tickets & Documents

[Shubhambhusate]

Issue link: #741

[Shubhambhusate]

Hi @msrathore-db Can you please review the PR

[msrathore-db]

Hi @msrathore-db Can you please review the PR

Adding @jprakash-db for review as he has more context on the same

[Shubhambhusate]

Hi @jprakash-db Can you please review this PR?

[gopalldb]

A minor comment on test, will also let Jothi also take a look

[jprakash-db]

Overall LGTM.
I think this is fine, even if customers define FLOAT table and insert DOUBLE from client the server auto truncates so not an issue.

[Shubhambhusate]

Hi @jprakash-db

Do we have any ETA for the PR to be merged, and the fix will be available?

[jprakash-db]

@Shubhambhusate Looks like the creds are not available in this forked branch to run the e2e. Let me run them manually and I will update the status here

[jprakash-db]

@Shubhambhusate We have merged it , thanks for the contribution

[Shubhambhusate]

Hi @jprakash-db

Thanks for merging it. Could you please let me know when the fix will be available to the user?

[jprakash-db]

@Shubhambhusate We are going to cut a release in this week tentatively, so you can expect by coming Monday

[Shubhambhusate]

Thanks for the update.