Sep 2012 HUG: Apache Drill for Interactive Analysis


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Apache Drill is a new open source Apache Incubator project for interactive analysis of large-scale datasets, inspired by Google's Dremel. It enables users to query terabytes of data in seconds. Apache Drill supports a broad range of data formats, including Protocol Buffers, Avro and JSON, and leverages Hadoop and HBase as data sources. Drill's primary query language, DrQL, is compatible with Google BigQuery. In this talk we provide an overview of the Drill project, including its design goals and architecture.

Presenter: Jason Frantz, Software Architect, MapR Technologies

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Sep 2012 HUG: Apache Drill for Interactive Analysis

  1. 1. Apache DrillInteractive Analysis of Large-Scale Datasets Jason Frantz Architect, MapR
  2. 2. My Background• Caltech• Clustrix• MapR• Founding member of Apache Drill
  3. 3. MapR Technologies• The open enterprise-grade distribution for Hadoop – Easy, dependable and fast – Open source with standards-based extensions• MapR is deployed at 1000’s of companies – From small Internet startups to the world’s largest enterprises• MapR customers analyze massive amounts of data: – Hundreds of billions of events daily – 90% of the world’s Internet population monthly – $1 trillion in retail purchases annually• MapR has partnered with Google to provide Hadoop on Google Compute Engine
  4. 4. Latency Matters• Ad-hoc analysis with interactive tools• Real-time dashboards• Event/trend detection and analysis – Network intrusions – Fraud – Failures
  5. 5. Big Data Processing Batch processing Interactive analysis Stream processingQuery runtime Minutes to hours Milliseconds to Never-ending minutesData volume TBs to PBs GBs to PBs Continuous streamProgramming MapReduce Queries DAGmodelUsers Developers Analysts and Developers developersGoogle project MapReduce DremelOpen source Hadoop Storm and S4project MapReduce Introducing Apache Drill…
  7. 7. Google Dremel• Interactive analysis of large-scale datasets – Trillion records at interactive speeds – Complementary to MapReduce – Used by thousands of Google employees – Paper published at VLDB 2010 • Authors: Sergey Melnik, Andrey Gubarev, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, Theo Vassilakis• Model – Nested data model with schema • Most data at Google is stored/transferred in Protocol Buffers • Normalization (to relational) is prohibitive – SQL-like query language with nested data support• Implementation – Column-based storage and processing – In-situ data access (GFS and Bigtable) – Tree architecture as in Web search (and databases)
  8. 8. Google BigQuery• Hosted Dremel (Dremel as a Service)• CLI (bq) and Web UI• Import data from Google Cloud Storage or local files – Files must be in CSV format • Nested data not supported [yet] except built-in datasets – Schema definition required
  10. 10. Architecture• Only the execution engine knows the physical attributes of the cluster – # nodes, hardware, file locations, …• Public interfaces enable extensibility – Developers can build parsers for new query languages – Developers can provide an execution plan directly• Each level of the plan has a human readable representation – Facilitates debugging and unit testing
  11. 11. Architecture (2)
  12. 12. Execution Engine Layers• Drill execution engine has two layers – Operator layer is serialization-aware • Processes individual records – Execution layer is not serialization-aware • Processes batches of records (blobs) • Responsible for communication, dependencies and fault tolerance
  13. 13. Data Flow
  14. 14. Nested Query Languages• DrQL – SQL-like query language for nested data – Compatible with Google BigQuery/Dremel • BigQuery applications should work with Drill – Designed to support efficient column-based processing • No record assembly during query processing• Mongo Query Language – {$query: {x: 3, y: "abc"}, $orderby: {x: 1}}• Other languages/programming models can plug in
  15. 15. Nested Data Model• The data model in Dremel is Protocol Buffers – Nested – Schema• Apache Drill is designed to support multiple data models – Schema: Protocol Buffers, Apache Avro, … – Schema-less: JSON, BSON, …• Flat records are supported as a special case of nested data – CSV, TSV, … Avro IDL JSON enum Gender { { MALE, FEMALE "name": "Srivas", } "gender": "Male", "followers": 100 record User { } string name; { Gender gender; "name": "Raina", long followers; "gender": "Female", } "followers": 200, "zip": "94305" }
  16. 16. DrQL ExampleSELECT DocId AS Id, COUNT(Name.Language.Code) WITHIN Name ASCnt, Name.Url + , + Name.Language.Code ASStrFROM tWHERE REGEXP(Name.Url, ^http) AND DocId < 20; * Example from the Dremel paper
  17. 17. Query Components• Query components: – SELECT – FROM – WHERE – GROUP BY – HAVING – (JOIN)• Key logical operators: – Scan – Filter – Aggregate – (Join)
  18. 18. Extensibility• Nested query languages – Pluggable model – DrQL – Mongo Query Language – Cascading• Distributed execution engine – Extensible model (eg, Dryad) – Low-latency – Fault tolerant• Nested data formats – Pluggable model – Column-based (ColumnIO/Dremel, Trevni, RCFile) and row-based (RecordIO, Avro, JSON, CSV) – Schema (Protocol Buffers, Avro, CSV) and schema-less (JSON, BSON)• Scalable data sources – Pluggable model – Hadoop – HBase
  19. 19. Scan Operators• Drill supports multiple data formats by having per-format scan operators • Queries involving multiple data formats/sources are supported• Fields and predicates can be pushed down into the scan operator• Scan operators may have adaptive side-effects (database cracking) • Produce ColumnIO from RecordIO • Google PowerDrill stores materialized expressions with the data Scan with schema Scan without schemaOperator Protocol Buffers JSON-like (MessagePack)outputSupported ColumnIO (column-based protobuf/Dremel) JSONdata formats RecordIO (row-based protobuf) HBase CSVSELECT … ColumnIO(proto URI, data URI) Json(data URI)FROM … RecordIO(proto URI, data URI) HBase(table name)
  20. 20. Design PrinciplesFlexible Easy• Pluggable query languages • Unzip and run• Extensible execution engine • Zero configuration• Pluggable data formats • Reverse DNS not needed • Column-based and row-based • IP addresses can change • Schema and schema-less • Clear and concise log messages• Pluggable data sourcesDependable Fast• No SPOF • C/C++ core with Java support• Instant recovery from crashes • Google C++ style guide • Min latency and max throughput (limited only by hardware)
  21. 21. Hadoop Integration• Hadoop data sources – Hadoop FileSystem API (HDFS/MapR-FS) – HBase• Hadoop data formats – Apache Avro – RCFile• MapReduce-based tools to create column-based formats• Table registry in HCatalog• Run long-running services in YARN
  22. 22. Get Involved!• Download these slides –• Join the mailing list –• Join MapR –