# OLAP database query optimizer and performance --- ## OLAP workloads > Online analytical processing, or OLAP, is an approach to answer multi-dimensional analytical queries swiftly in computing. https://en.wikipedia.org/wiki/Online_analytical_processing --- ## OLAP VS. OLTP: THE DIFFERENCES > OLAP is used for complex data analysis, while OLTP is used for real-time processing of online transactions at scale. https://www.snowflake.com/guides/olap-vs-oltp/ --- ## Luft: OLAP database for Airbridge https://engineering.ab180.co/stories/introducing-luft --- ## Airbridge dashboard demo Let's take a look at what you'd do if you implemented. 1. Retention 2. Funnel --- ## TrailDB to Ziegel (C++ to GO) https://marsettler.com/go/c++-to-go/ --- ## Revisiting TODO 1. Remove TrailDB compatibility 2. More efficient memory management 3. Support very fast autoscale 4. Download partial column only if needed - Pre requirment for the very fast autoscale 5. User level filter --- ## 오늘 할 이야기 - Luft 성능 리포트 2: 더 많은 코호트에 대한 리텐션 집계 - 제안서: Luft 의 대형 쿼리 처리에 관하여 - AWS Lambda 를 활용한 Luft 스케일링 --- ## Luft 성능 리포트 2 ## 더 많은 코호트에 대한 리텐션 집계 https://engineering.ab180.co/stories/luft-performance-report-2 --- ## 제안서: Luft 의 대형 쿼리 ## 처리에 관하여 https://engineering.ab180.co/stories/luft-query-optimizer-and-scale --- ## AWS Lambda 를 활용한 ## Luft 스케일링 https://engineering.ab180.co/stories/luft-task-executor-with-aws-lambda --- ## Q & A --- ## Reference - [Luft: 유저 행동 분석에 최적화된 OLAP 데이터베이스](https://engineering.ab180.co/stories/introducing-luft) - [C++ to Go: Introducing Ziegel](https://marsettler.com/go/c++-to-go/) - [Luft 성능 리포트 2: 더 많은 코호트에 대한 리텐션 집계](https://engineering.ab180.co/stories/luft-performance-report-2) - [제안서: Luft 의 대형 쿼리 처리에 관하여](https://engineering.ab180.co/stories/luft-query-optimizer-and-scale) - [AWS Lambda 를 활용한 Luft 스케일링](https://engineering.ab180.co/stories/luft-task-executor-with-aws-lambda)