Hive: compile to MR, Aster: external tables in MPP, Oracle/MySQL: export MR results to RDBMSDrill, Impala, CitusDB: real-time
Suppose a marketing analyst trying to experiment with ways to do targeting of user segments for next campaign. Needs access to web logs stored in Hadoop, and also needs to access user profiles stored in MongoDB as well as access to transaction data stored in a conventional database.
Re ad-hoc:You might not know ahead of time what queries you will want to make. You may need to react to changing circumstances.
Two innovations: handle nested-data column style (column-striped representation) and query push-down
Source query is parsed and transformed to produce the logical planTypically, the logical plan lives in memory in the form of Java objects, but it also has a textual form. The logical query is then transformed and optimized into the physical plan.The physical plan represents the actual structure of computation as it is done by the system. One of the most important things the optimizer does is the introduction of parallel computation (other: columnar data to improve processing speed)
Introduction to Apache Drill - interactive query and analysis at scale
Introduction to Apache Drill –interactive query and analysis at scale Michael Hausenblas, MapR EMEA 2013-02-22, HUG Munich
About Michael• Background in large-scale data integration• Chief Data Engineer EMEA, MapR• Apache Drill contributor
Apache Drill Overview• Inspired by Google Dremel• Standard SQL2003 support• …. other QL (DSL, etc.) possible• Plug-able data sources• Support for nested data (JSON, etc.)• Schema is optional• Community driven, open, 100’s involved
Full SQL – ANSI SQL2003• SQL-like is often not enough• Integration with existing tools – Tableau, Excel, SAP Crystal Reports – Use standard ODBC/JDBC driver
Nested Data• Nested data becoming prevalent – JSON/BSON, XML, ProtoBuf, Avro – Some data sources support it natively (MongoDB, etc.) – Innovation through Dremel• Flattening nested data is error-prone• Apache Drill supports nested data, extension to ANSI SQL2003
Optional Schema• Many data sources don’t have rigid schemas – Schema changes rapidly – Different schema per record (e.g. HBase)• Apache Drill supports queries against unknown schema• user can define schema or via discovery
Extensibility Points • Query language (parser) - UDFs • Data sources/formats (scanner) • Optimizer • Custom operators (logical plan)Source Logical PhysicalQuery Parser Plan Optimizer Plan Execution
Engage!• Follow @ApacheDrill on Twitter• Sign up at mailing lists (user|dev) http://incubator.apache.org/drill/mailing-lists.html• Keep an eye on http://drill-user.org/• Ping me: firstname.lastname@example.org