Apache Drill is a low latency distributed query engine for large-scale datasets, including structured and semi-structured/nested data. Inspired by Google’s Dremel, Drill is designed to scale to several thousands of nodes and query petabytes of data at interactive speeds that BI/Analytics environments require.
Drill is also useful for short, interactive ad-hoc queries on large-scale data sets. Drill is capable of querying nested data in formats like JSON and Parquet and performing dynamic schema discovery. Drill does not require a centralized metadata repository.
Drill includes a distributed execution environment, purpose built for large- scale data processing. At the core of Apache Drill is the “Drillbit” service, which is responsible for accepting requests from the client, processing the queries, and returning results to the client.
A Drillbit service can be installed and run on all of the required nodes in a Hadoop cluster to form a distributed cluster environment. When a Drillbit runs on each data node in the cluster, Drill can maximize data locality during query execution without moving data over the network or between nodes. Drill uses ZooKeeper to maintain cluster membership and health-check information.
Though Drill works in a Hadoop cluster environment, Drill is not tied to Hadoop and can run in any distributed cluster environment. The only pre-requisite for Drill is ZooKeeper.
You can access Drill through the following interfaces:
Dynamic schema discovery
Drill does not require schema or type specification for data in order to start the query execution process. Drill starts data processing in record-batches and discovers the schema during processing. Self-describing data formats such as Parquet, JSON, AVRO, and NoSQL databases have schema specified as part of the data itself, which Drill leverages dynamically at query time. Because the schema can change over the course of a Drill query, many Drill operators are designed to reconfigure themselves when schemas change.
Flexible data model
Drill allows access to nested data attributes, as if they were SQL columns, and provides intuitive extensions to easily operate on them. From an architectural point of view, Drill provides a flexible hierarchical columnar data model that can represent complex, highly dynamic and evolving data models. Relational data in Drill is treated as a special or simplified case of complex/multi-structured data.
No centralized metadata
Drill does not have a centralized metadata requirement. You do not need to create and manage tables and views in a metadata repository, or rely on a database administrator group for such a function. Drill metadata is derived through the storage plugins that correspond to data sources. Storage plugins provide a spectrum of metadata ranging from full metadata (Hive), partial metadata (HBase), or no central metadata (files). De-centralized metadata means that Drill is NOT tied to a single Hive repository. You can query multiple Hive repositories at once and then combine the data with information from HBase tables or with a file in a distributed file system. You can also use SQL DDL statements to create metadata within Drill, which gets organized just like a traditional database. Drill metadata is accessible through the ANSI standard INFORMATION_SCHEMA database.
Drill provides an extensible architecture at all layers, including the storage plugin, query, query optimization/execution, and client API layers. You can customize any layer for the specific needs of an organization or you can extend the layer to a broader array of use cases. Drill uses classpath scanning to find and load plugins, and to add additional storage plugins, functions, and operators with minimal configuration.