Chaining Snowflake Snowpark DataFrame Filters: How OR vs AND Precedence Causes Unexpected Rows, and Fixes

Learn why chained Snowflake Snowpark DataFrame filters misbehave when OR and AND mix. See precedence, query history, and fix with parentheses or expressions.

Consecutive filters in Snowpark: why your OR turns into unexpected rows

Chaining multiple filter calls on a Snowpark DataFrame can yield surprising results when OR and AND mix. A common pitfall is expecting the second filter to further narrow down the first, while the actual evaluation order produces a different predicate than intended.

Problem example

The following snippet builds a tiny DataFrame and applies two filters in sequence. Intuitively, you might expect no rows to survive, because the second condition enforces X = 0 after an OR:

import snowflake.snowpark

sp_sess = snowflake.snowpark.context.get_active_session()

with sp_sess.query_history(True) as qh:
    frame = sp_sess.sql("SELECT 1 AS X, 2 AS Y")

    frame.filter("X = 1 OR Y = 3") \
         .filter("X = 0") \
         .show()

qh.queries[1]

What actually happens

The resulting predicate is parsed as X = 1 OR Y = 3 AND X = 0. AND has precedence over OR, so the effective logic is X = 1 OR (Y = 3 AND X = 0). On top of that, chained filter conditions are connected with AND, which is why the second filter does not “wrap” the first OR; it is simply appended as another conjunct.

If you inspect the compiled SQL via snowflake.snowpark.Session.query_history, you will see the combined condition reflecting this operator precedence and the AND connection between chained filters.

The fix

Group the OR explicitly with parentheses so the combined filter reads as intended when the second condition is appended:

frame.filter("(X = 1 OR Y = 3)") \
     .filter("X = 0") \
     .show()

This ensures the final predicate is (X = 1 OR Y = 3) AND X = 0 rather than X = 1 OR (Y = 3 AND X = 0).

An alternative using expressions instead of raw SQL strings

You can build the same logic using column expressions, which makes grouping explicit without string parsing:

import snowflake.snowpark
from snowflake.snowpark.functions import col

sp_sess = snowflake.snowpark.context.get_active_session()

with sp_sess.query_history(True) as qh:
    ds = sp_sess.sql("SELECT 1 AS X, 2 AS Y")

    ds.filter((col("X") == 1) | (col("Y") == 3)) \
      .filter(col("X") == 0) \
      .show()

qh.queries[1]

Why this matters

In data pipelines and analytics code, subtle predicate grouping mistakes lead to incorrect results that are hard to detect on small samples and costly to debug later. Understanding that AND binds tighter than OR and that chained filters are combined with AND helps prevent logic drift between what you intend and what the engine executes. Query inspection via Session.query_history makes this behavior transparent when troubleshooting.

Takeaways

When chaining filters that include OR, always add parentheses to enforce the grouping you expect. If you prefer more explicit construction, use column expressions to make operator precedence and grouping clear at the code level. And when in doubt, review the compiled SQL via query history to verify the final predicate.