How to handle complex SQL WHERE conditions with joins

Data and Databases
1

I’m working on converting a SQL query to work with Apache Pig on Hadoop but I’m stuck on something tricky. Usually converting queries is straightforward but this one has me confused.

Here’s what I’m dealing with:

SELECT x.col1, y.col2, y.col3
FROM orders x, products y  
WHERE(
    (
        x.col1 = y.col2
        AND
        (
            x.col2 = y.col1
            OR
            (
                (x.col2 = 'value1' OR x.col2 = 'value2')
                AND
                (y.col1 is null OR y.col1 = 'value3' OR y.col1 = 'value4')
            )
        )
    )
    OR
    (
        x.col3 = y.col4
        AND
        (
            x.col4 = y.col3
            OR
            (
                (x.col4 = 'value5' OR x.col4 = 'value6')
                AND
                (y.col3 is null OR y.col3 = 'value7' OR y.col3 = 'value8')
            )
        )
    )
)
GROUP BY -- additional stuff here

My main questions are:

  • How can I break down these complex WHERE conditions with OR logic?
  • What’s the best way to handle this kind of join pattern in Pig?
  • Would this be easier to solve using Hive instead?

I know Pig doesn’t handle OR conditions well with joins. I’ve checked the Pig documentation but haven’t found a clear solution yet. Any ideas on how to approach this would be really helpful.

2

Had the exact same problem migrating complex SQL to Pig. Don’t try to translate the WHERE clause directly - you’ll go crazy. Instead, break it into separate JOINs and UNION the results together. Handle (x.col1 = y.col2 AND x.col2 = y.col1) as one join first. Then create separate datasets for each OR branch using FILTER before joining. For those nested OR conditions, I always create intermediate filtered datasets - way easier to maintain. Honestly, Hive handles this stuff much better since it supports complex WHERE conditions out of the box. But if you’re stuck with Pig, try COGROUP instead of JOIN. Gives you more control over the join logic and handles nulls better.

3

hey, i’ve seen this question b4! try cross joins with filters - they’re often easier than fighting pig’s join quirks. what’s keeping you from switching to hive for this?

4

totally agree, it gets super messy lol. best to split it into small bites, do a join for each main part, then UNION them. that way, it’s way clearer and less error-prone. pig can struggle with all that complexity, so saavy filtering upfront helps a lot.