Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Approach**: Time-based join—for each ticket_date, get max rate_timestamp <= ticket_date. CROSS APPLY / LATERAL with ORDER BY rate_timestamp DESC LIMIT 1. Spark: asof join (merge_asof in Pandas); or join with condition + window for latest....
Approach: Time-based join—for each ticket_date, get max rate_timestamp <= ticket_date. CROSS APPLY / LATERAL with ORDER BY rate_timestamp DESC LIMIT 1. Spark: asof join (merge_asof in Pandas); or join with condition + window for latest. Key: 'latest' = max timestamp <= ticket date.
Pro-Move: 'We use Spark's asof join pattern for FX; broadcast small rate table. Common in travel/finance.'
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