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The proliferation of modern data processing tools has given rise toopen-source columnar data formats. The advantage of these formats is that theyhelp organizations avoid repeatedly converting data to a new format for eachapplication. These formats, however, are read-only, and organizations must usea heavy-weight transformation process to load data from on-line transactionalprocessing (OLTP) systems. We aim to reduce or even eliminate this process bydeveloping a storage architecture for in-memory database management systems(DBMSs) that is aware of the eventual usage of its data and emits columnarstorage blocks in a universal open-source format. We introduce relaxations tocommon analytical data formats to efficiently update records and rely on alightweight transformation process to convert blocks to a read-optimized layoutwhen they are cold. We also describe how to access data from third-partyanalytical tools with minimal serialization overhead. To evaluate our work, weimplemented our storage engine based on the Apache Arrow format and integratedit into the DB-X DBMS. Our experiments show that our approach achievescomparable performance with dedicated OLTP DBMSs while enablingorders-of-magnitude faster data exports to external data science and machinelearning tools than existing methods.

Mainlining Databases: Supporting Fast Transactional Workloads on Universal Columnar Data File Formats