Case study
Logstash Pipeline Optimization for a Canadian Bank
How SquareShift audited and refactored Logstash pipelines processing 50M+ daily events for a Canadian multinational bank.
One of Canada's largest multinational financial institutions, serving more than 12 million customers across personal banking, corporate banking, capital markets, and digital infrastructure.
Its Logstash pipelines were inconsistently parsing over 50 million system events a day, undermining the reliability of its Elasticsearch analytics.
Structured observations delivered, from quick wins to strategic fixes. Daily system events flowing through the audited Logstash pipelines. Grok patterns, field naming, and plugin use reviewed and aligned.
Key servicesHow SquareShift delivered it.
The challenge
The bank’s Logstash pipelines were inconsistently parsing more than 50 million system events a day, undermining ingestion rates, processing times, and the reliability of its Elasticsearch analytics. Complex conditional logic, regex issues, inconsistent field mappings, schema drift, and duplicated pipeline logic had built up with no standardization.
What we delivered
SquareShift engineers audited the pipeline line by line — reviewing grok patterns, field naming, and plugin use — and delivered 30 structured observations ranging from quick wins to strategic fixes. The team collaborated directly with the bank’s platform group to roll the fixes into production.
The payoff
The Logstash pipeline structure is now standardized, with consistent field mappings and de-duplicated logic aligned to how Elasticsearch and Kibana consume the data. Parsing accuracy is higher and memory usage is lower across the more than 50 million events the pipeline processes daily.
High-volume event streams surface every shortcut in a Logstash pipeline. The fix wasn't a bigger box, it was auditing every grok pattern and killing duplication underneath.
Platform & Software Engineering Practice Lead, SquareShift
Where this work sits