Tweet
Tweet

Slide 1

Slide 1 text

Introduction to Apache Drill Big Data Bellevue Meetup @tnachen Timothy Chen

Slide 2

Slide 2 text

Motivation Key Facts Architecture Overview

Slide 3

Slide 3 text

About me

Slide 4

Slide 4 text

No content

Slide 5

Slide 5 text

No content

Slide 6

Slide 6 text

No content

Slide 7

Slide 7 text

No content

Slide 8

Slide 8 text

No content

Slide 9

Slide 9 text

I Open Source

Slide 10

Slide 10 text

No content

Slide 11

Slide 11 text

No content

Slide 12

Slide 12 text

No content

Slide 13

Slide 13 text

No content

Slide 14

Slide 14 text

No content

Slide 15

Slide 15 text

No content

Slide 16

Slide 16 text

No content

Slide 17

Slide 17 text

No content

Slide 18

Slide 18 text

No content

Slide 19

Slide 19 text

No content

Slide 20

Slide 20 text

No content

Slide 21

Slide 21 text

No content

Slide 22

Slide 22 text

Use Case: Marketing Campaign Jane, a marketing analyst Determine target segments Data from different sources

Slide 23

Slide 23 text

Use Case: Crime Detection • Online purchases • Fraud, billing, etc. • Batch-generated overview • Modes – Explorative – Alerts

Slide 24

Slide 24 text

Requirements • Support for different data sources • Support for different query interfaces • Low-latency/real-time • Ad-hoc queries • Scalable, reliable

Slide 25

Slide 25 text

No content

Slide 26

Slide 26 text

Google’s Dremel http://research.google.com/pubs/pub36632.html Sergey Melnik, Andrey Gubarev, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, Theo Vassilakis, Proc. of the 36th Int'l Conf on Very Large Data Bases (2010), pp. 330-339 Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system scales to thousands of CPUs and petabytes of data, and has thousands of users at Google. … “ “ Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system scales to thousands of CPUs and petabytes of data, and has thousands of users at Google. …

Slide 27

Slide 27 text

Google’s Dremel multi-level execution trees columnar data layout

Slide 28

Slide 28 text

Google’s Dremel nested data + schema column-striped representation map nested data to tables

Slide 29

Slide 29 text

Google’s Dremel experiments: datasets & query performance

Slide 30

Slide 30 text

Apache Drill–key facts Inspired by Google’s Dremel Standard SQL 2003 support Plug-able data sources Nested data is a first-class citizen Schema is optional Community driven, open, 100’s involved

Slide 31

Slide 31 text

No content

Slide 32

Slide 32 text

No content

Slide 33

Slide 33 text

High-level Architecture

Slide 34

Slide 34 text

Principled Query Execution Source query—what we want to do (analyst friendly) Logical Plan— what we want to do (language agnostic, computer friendly) Physical Plan—how we want to do it (the best way we can tell) Execution Plan—where we want to do it

Slide 35

Slide 35 text

Principled Query Execution Sourc e Query Parser Logica l Plan Optimizer Physical Plan Execution SQL 2003 DrQL MongoQL DSL scanner API Topology CF etc. query: [ { @id: "log", op: "sequence", do: [ { op: "scan", source: “logs” }, { op: "filter", condition: "x > 3” }, parser API

Slide 36

Slide 36 text

Wire-level Architecture Each node: Drillbit - maximize data locality Co-ordination, query planning, execution, etc, are distributed Any node can act as endpoint for a query—foreman Storage Process Drillbit node Storage Process Drillbit node Storage Process Drillbit node Storage Process Drillbit node

Slide 37

Slide 37 text

Wire-level Architecture Curator/Zookeeper for ephemeral cluster membership info Distributed cache (Hazelcast) for metadata, locality information, etc. Curator/Zk Distributed Cache Storage Process Drillbit node Storage Process Drillbit node Storage Process Drillbit node Storage Process Drillbit node Distributed Cache Distributed Cache Distributed Cache

Slide 38

Slide 38 text

Wire-level Architecture Originating Drillbit acts as foreman: manages query execution, scheduling, locality information, etc. Streaming data communication avoiding SerDe Curator/Zk Distributed Cache Storage Process Drillbit node Storage Process Drillbit node Storage Process Drillbit node Storage Process Drillbit node Distributed Cache Distributed Cache Distributed Cache

Slide 39

Slide 39 text

Wire-level Architecture Foreman turns into root of the multi-level execution tree, leafs activate their storage engine interface. node node node Curator/Zk

Slide 40

Slide 40 text

No content

Slide 41

Slide 41 text

On the shoulders of giants … Jackson for JSON SerDe for metadata Typesafe HOCON for configuration and module management Netty4 as core RPC engine, protobuf for communication Vanilla Java, LArray and Netty ByteBuf for off-heap large data structures Hazelcast for distributed cache Netflix Curator on top of Zookeeper for service registry Optiq for SQL parsing and cost optimization Parquet (http://parquet.io)/ ORC Janino for expression compilation ASM for ByteCode manipulation Yammer Metrics for metrics Guava extensively Carrot HPC for primitive collections

Slide 42

Slide 42 text

Key features Full SQL – ANSI SQL 2003 Nested Data as first class citizen Optional Schema Extensibility Points …

Slide 43

Slide 43 text

Extensibility Points Source query  parser API Custom operators, UDF  logical plan Serving tree, CF, topology  physical plan/optimizer Data sources &formats  scanner API Sourc e Query Parser Logica l Plan Optimizer Physical Plan Execution

Slide 44

Slide 44 text

User Interfaces API—DrillClient Encapsulates endpoint discovery Supports logical and physical plan submission, query cancellation, query status Supports streaming return results JDBC driver, converting JDBC into DrillClient communication. REST proxy for DrillClient

Slide 45

Slide 45 text

User Interfaces

Slide 46

Slide 46 text

Let’s get our hands dirty…

Slide 47

Slide 47 text

Demo Install Preparation Usage $ wget http://people.apache.org/~jacques/apache-drill-1.0.0- m1.rc3/apache-drill-1.0.0-m1-binary-release.tar.gz $ tar -zxf apache-drill-1.0.0-m1-binary-release.tar.gz $ export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.7.0_11.jdk/Content s/Home $ export DRILL_LOG_DIR=$PWD $ ./bin/drillbit.sh start $ ./bin/sqlline -u jdbc:drill:schema=parquet-local -n admin -p admin

Slide 48

Slide 48 text

Useful Resources Getting Started guide https://github.com/vrtx/incubator- drill/blob/getting_started/docs/getting_started.rst Demo HowTo https://cwiki.apache.org/confluence/display/DRILL/ Demo+HowTo How to build/install Apache Drill on Ubuntu 13.04 http://www.confusedcoders.com/bigdata/apache- drill/how-to-build-apache-drill-on-ubuntu-13-04

Slide 49

Slide 49 text

Be a part of it!

Slide 50

Slide 50 text

Status Heavy development by multiple organizations (MapR, Pentaho, Microsoft, Thoughtworks, XingCloud, etc.) Currently more than 100k LOC Alpha available via http://people.apache.org/~jacques/apache-drill- 1.0.0-m1.rc3/

Slide 51

Slide 51 text

Kudos to … Julian Hyde, Pentaho Lisen Mu, XingCloud Tim Chen, Microsoft Chris Merrick, RJMetrics David Alves, UT Austin Sree Vaadi, SSS Srihari Srinivasan, ThoughtWorks Alexandre Beche, CERN Jason Altekruse, MapR http://incubator.apache.org/drill/team.html • Ben Becker, MapR • Jacques Nadeau, MapR • Ted Dunning, MapR • Keys Botzum, MapR • Jason Frantz • Ellen Friedman • Chris Wensel, Concurrent • Gera Shegalov, Oracle • Ryan Rawson, Ohm Data

Slide 52

Slide 52 text

Contributing Contributions appreciated—not only code drops … Test data & test queries Use case scenarios (textual/SQL queries) Documentation

Slide 53

Slide 53 text

Engage! Follow @ApacheDrill on Twitter Sign up at mailing lists (user | dev) http://incubator.apache.org/drill/mailing-lists.html Standing G+ hangouts every Tuesday at 18:00 CET http://j.mp/apache-drill-hangouts Keep an eye on http://drill-user.org/

Slide 54

Slide 54 text

Twitter: @tnachen Email: tnachen@gmail.com