Adventures in Tech

In most database systems, the user defines the shape of the data that is stored and queried using concepts such as entities and relations. The database system takes care of translating that shape into physical storage, and managing its lifecycle. Most of the systems store data in the form of tuples, either in row format, or broken down into columns and stored in columnar format. The system also stores metadata associated with the data, that helps with speedy retrieval and processing. Defining the shape of the data a priori, and transforming it from the raw or ingestion format to the storage format is a cost that database systems incur to make queries faster. What if we can have fast queries without incurring that initial cost? In the paper " NoDB: Efficient Query Execution on Raw Data Files ", the authors examine that question, and advocate a system (NoDB) that answers it. The authors start with the motivation for such a system. With the recent explosion of data...

The recent data explosion, and the increase in appetite for fast results spurred a lot of interest in low-latency data processing systems. One such system is MillWheel, presented in the paper " MillWheel: Fault-Tolerant Stream Processing at Internet Scale ", which is widely used at Google. In MillWheel, the users specify a directed computation graph that describe what they would like to do, and write application code that runs on each individual node in the graph. The system takes care of managing the flow of data within the graph, persisting the state of the computation, and handling any failures that occur, relieving the users from that burden. MillWheel exposes an API for record processing, that handles each record in an idempotent fashion, with an exactly once delivery semantics. The system checkpoints progress with a fine granularity, removing the need to buffer data between external senders. The authors describe the system using the Zeitgeist produ...