History[07][08][04]

In 2010, Google published a paper titled "Dremel: Interactive Analysis of Web-Scale Datasets" that described a scalable database system designed for "interactive analysis of nested data". The Dremel system is available today under Google's BigQuery system. Development of Apache Drill began in 2012, with the goal of replicating the capabilities of Dremel. Initial goals of the system included support for multiple storage systems, file formats, query languages, and data sources, as well as the ability to scale over 10,000 servers and process petabytes of data in seconds.

Checkpoints[09]

Drill adopts optimistic query execution, which assumes that failures occur rarely during queries. Therefore, it does not take checkpoints. With its pipelined query execution model, single queries are simply reran when they fail.

Concurrency Control[10]

Drill supports Optimistic Concurrency Control. It plans queries in fragments, assuming that all of the fragments can be completed in parallel without interfering with each other. Larger fragments are broken into smaller fragments, which are run in clusters until the whole fragment is complete.

Data Model[11][12][01]

Drill features a JSON self-describing data model that supports language independence and loosely defined, weak data typing. This data model uses on-the-fly schema discovery, also known as late binding, to begin the execution of queries without having to know the structure of the data. Through this data model, Drill can handle data with evolving schemas or even no schemas at all.

Drill's internal data representation is columnar and hierarchical, which allows for efficient SQL processing without the need to flatten data into rows. The data model supports queries on complex/nested data as well as evolving data structures.

Foreign Keys[13][14]

Drill supports the usage of foreign keys within the schemas of the datastores that it gathers data from.

Query Interface

System Architecture