Apache Drill is designed for interactive analysis of large-scale datasets, offering capabilities to process trillions of records at high speeds while supporting multiple data models. It features a flexible architecture that allows extensibility for different query languages and data formats, enabling efficient performance with both nested and flat records. The integration with Hadoop enhances its ability to handle massive data sources and formats, making it suitable for diverse applications across various industries.

1. Apache Drill
Interactive Analysis of Large-Scale Datasets
Jason Frantz
Architect, MapR

2. My Background
• Caltech
• Clustrix
• MapR
• Founding member of Apache Drill

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. Latency Matters
• Ad-hoc analysis with interactive tools
• Real-time dashboards
• Event/trend detection and analysis
– Network intrusions
– Fraud
– Failures

5. Big Data Processing
Batch processing Interactive analysis Stream processing
Query runtime Minutes to hours Milliseconds to Never-ending
minutes
Data volume TBs to PBs GBs to PBs Continuous stream
Programming MapReduce Queries DAG
model
Users Developers Analysts and Developers
developers
Google project MapReduce Dremel
Open source Hadoop Storm and S4
project MapReduce
Introducing Apache Drill…

6. GOOGLE DREMEL

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. 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

9. APACHE DRILL

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. Architecture (2)

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. Data Flow

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. 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. DrQL Example
SELECT DocId AS Id,
COUNT(Name.Language.Code) WITHIN Name AS
Cnt,
Name.Url + ',' + Name.Language.Code AS
Str
FROM t
WHERE REGEXP(Name.Url, '^http')
AND DocId < 20;
* Example from the Dremel paper

17. Query Components
• Query components:
– SELECT
– FROM
– WHERE
– GROUP BY
– HAVING
– (JOIN)
• Key logical operators:
– Scan
– Filter
– Aggregate
– (Join)

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. 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 schema
Operator Protocol Buffers JSON-like (MessagePack)
output
Supported ColumnIO (column-based protobuf/Dremel) JSON
data formats RecordIO (row-based protobuf) HBase
CSV
SELECT … ColumnIO(proto URI, data URI) Json(data URI)
FROM … RecordIO(proto URI, data URI) HBase(table name)

20. Design Principles
Flexible 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 sources
Dependable 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. 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. Get Involved!
• Download these slides
– http://www.mapr.com/company/events/bay-area-hug/9-19-2012
• Join the mailing list
– drill-dev-subscribe@incubator.apache.org
• Join MapR
– jobs@mapr.com