fix: alias column when fetching values

[betodealmeida]

SUMMARY

The endpoint that returns values for a given column, which powers filter dropdowns, is failing for some databases (Snowflake, Clickhouse) because the query generated has an unusual alias for the column.

For example, for Clickhouse, when selecting the values of a column called name we have the following query:

SELECT DISTINCT name AS name_12345
FROM ...

Clickhouse adds the stochastic alias name_#####, and reading the values from the Pandas dataframe fails because the column name is not present.

For Snowflake we have a different issue:

SELECT DISTINCT NAME AS NAME
FROM ...

Here, the resulting dataframe will have NAME but not name, since for Snowflake these are identical.

This PR fixes the problem by adding an alias to the projection. This way we know the alias is present in the dataframe.

BEFORE/AFTER SCREENSHOTS OR ANIMATED GIF

TESTING INSTRUCTIONS

ADDITIONAL INFORMATION

• Has associated issue:
• Required feature flags:
• Changes UI
• Includes DB Migration (follow approval process in SIP-59)
  • Migration is atomic, supports rollback & is backwards-compatible
  • Confirm DB migration upgrade and downgrade tested
  • Runtime estimates and downtime expectations provided
• Introduces new feature or API
• Removes existing feature or API

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Comparison is base (849ca64) 69.10% compared to head (bd26ad0) 69.10%.
Report is 3 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master   #26120   +/-   ##
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  Coverage   69.10%   69.10%           
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  Files        1940     1940           
  Lines       75869    75869           
  Branches     8445     8445           
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  Hits        52428    52428           
  Misses      21266    21266           
  Partials     2175     2175           

Flag	Coverage Δ
hive	53.68% <0.00%> (ø)
javascript	56.28% <ø> (ø)
mysql	78.16% <100.00%> (ø)
postgres	78.25% <100.00%> (ø)
presto	53.63% <0.00%> (ø)
python	82.95% <100.00%> (ø)
sqlite	76.91% <100.00%> (ø)
unit	55.79% <0.00%> (ø)

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[sadpandajoe]

🏷️ preset:2023.47

[michael-s-molina]

Thanks for adding a comment explaining the context! It's always helpful.