TLDR Tech / Data
How We Cut BigQuery Slot Usage by 90% On One Of Our Most Resource Hungry Service After a Production Outage
Teads dramatically cut BigQuery slot usage by 90%+ on their Audience Planning service through application fixes (request coalescing with Redis distributed locks to eliminate duplicate queries, fail-fast validation for huge filters, and rewriting large IN clauses as semi-joins) combined with data model optimizations (compressing data types, precomputing repeated work, and an improved partitioning strategy), reducing the effective table footprint by ~95%.
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