Integrate Apache Spark and QuestDB for Time-Series Analytics

QuestDB, an open-source time-series database known for its high ingestion rate and SQL analytics capabilities, is particularly well-suited for processing market data such as tick data. This article explores the integration of QuestDB with Apache Spark, a distributed analytics engine, to enhance data processing efficiency. It details the steps involved in loading time-series data from QuestDB into Spark using JDBC, and highlights Spark's lazy evaluation and partitioning capabilities, which optimize resource usage during data analysis. It also discusses the importance of caching data within Spark to reduce database strain, customizing type mappings for data precision, and strategically using partitioning to align with QuestDB's partitions for improved performance. Additionally, the article emphasizes the necessity of managing data write-back to QuestDB, using appropriate saving modes to handle data overwrites and maintain database schema integrity. The integration highlights potential areas for future improvement, such as seamless partition handling and enhanced type mapping, along with the anticipation of QuestDB's evolution towards supporting distributed systems.