2. 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…
4. 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)
6. 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
7. 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
8. 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
9. 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)
10. Get Involved!
• Download (a longer version of) these slides
– http://www.mapr.com/company/events/bay-area-hug/9-19-2012
• Join the project
– drill-dev-subscribe@incubator.apache.org #apachedrill
• Contact me:
– tdunning@maprtech.com
– tdunning@apache.org
– ted.dunning@maprtech.com
– @ted_dunning
• Join MapR
– jobs@mapr.com
Notas do Editor
Drill Remove schema requirementIn-situ for real since we’ll support multiple formatsNote: MR needed for big joins so to speak
Likely to support theseCould add HiveQL and more as well. Could even be clever and support HiveQL to MR or Drill based upon queryPig as wellPluggabilityData formatQuery languageSomething 6-9 months alpha qualityCommunity driven, I can’t speak for projectMapRFS gives better chunk size controlNFS support may make small test drivers easierUnified namespace will allow multi-cluster accessMight even have drill component that autoformats dataRead only model
Example query that Drill should supportNeed to talk more here about what Dremel does