DiNoDB: an Interactive-speed Query Engine for Ad-hoc Queries on Temporary Data

TL;DR

DiNoDB is a query engine designed for fast ad-hoc analytics on temporary data, leveraging metadata from batch processing to avoid costly data loading and transformation, thus enabling efficient iterative workflows.

Contribution

DiNoDB introduces a novel approach that piggybacks metadata creation during batch processing to enable instant interactive queries on temporary data.

Findings

Achieves high performance for ad-hoc queries

Outperforms Hive, Stado, SparkSQL, and Impala in experiments

Reduces data loading overhead in iterative workflows

Abstract

As data sets grow in size, analytics applications struggle to get instant insight into large datasets. Modern applications involve heavy batch processing jobs over large volumes of data and at the same time require efficient ad-hoc interactive analytics on temporary data. Existing solutions, however, typically focus on one of these two aspects, largely ignoring the need for synergy between the two. Consequently, interactive queries need to re-iterate costly passes through the entire dataset (e.g., data loading) that may provide meaningful return on investment only when data is queried over a long period of time. In this paper, we propose DiNoDB, an interactive-speed query engine for ad-hoc queries on temporary data. DiNoDB avoids the expensive loading and transformation phase that characterizes both traditional RDBMSs and current interactive analytics solutions. It is tailored to modern workflows found in machine learning and data exploration use cases, which often involve iterations of cycles of batch and interactive analytics on data that is typically useful for a narrow processing window. The key innovation of DiNoDB is to piggyback on the batch processing phase the creation of metadata that DiNoDB exploits to expedite the interactive queries. Our experimental analysis demonstrates that DiNoDB achieves very good performance for a wide range of ad-hoc queries compared to alternatives %such as Hive, Stado, SparkSQL and Impala.