Analyzing Real-World Data with Apache Drill

®© 2014 MapR Technologies 1

®

© 2014 MapR Technologies

Analyzing Real-World Data with Apache Drill Tomer Shiran VP Product Management, MapR Technologies Co-Founder, PMC Member and Committer, Apache Drill November 20, 2014


Data is doubling in size every two years


44 ZETTABYTES

4.4 ZETTABYTES

2011 2013

1.8 ZETTABYTES

IDC estimates that in 2020, there will be 44 zettabytes

of data in the world

2020

Source: IDC Digital Universe


UNSTRUCTURED DATA

STRUCTURED DATA

1980 2000 2010 1990 2020

Unstructured data will account for more than 80% of the data

collected by organizations

Source: Human-Computer Interaction & Knowledge Discovery in Complex Unstructured, Big Data

Total Data S

tored

®© 2014 MapR Technologies 5 1980 2000 2010 1990 2020

Fixed schema

DBA controls structure

Dynamic schema (schema-free)

Application controls structure

“NOSCHEMA” DATASTORES RELATIONAL DATABASES

MBs-GBs TBs-PBs Volume

Database

NoSchema Datastores are Capturing this Data

Structure

Development

Structured Structured, semi-structured and unstructured

Planned (release cycle = months-years) Iterative (release cycle = days-weeks)


SQL in the Big Data World

•  SQL •  BI (Tableau, MicroStrategy, etc.) •  Low latency •  Scalability

•  Create and maintain schemas on: –  HDFS (Parquet, JSON, etc.) –  HBase –  MongoDB

•  Transform or copy data

2 DON’T WANT WANT

We want SQL and BI support without compromising the flexibility and agility of NoSchema datastores


• Schema-free scale-out query engine for Hadoop and NoSQL • Point-and-query vs. schema-first • Low latency • Extreme ease of use • Industry-standard APIs: ANSI SQL, ODBC/JDBC, RESTful APIs

APACHE DRILL

40+ contributors 150+ years of experience building databases and distributed systems


Evolution Towards Self-Service Data Exploration

Data Modeling and Transformation

Data Visualization

IT-driven

IT-driven

IT-driven

Self-service

IT-driven

Self-service

Not needed

Self-service

Traditional BI w/ RDBMS

Self-Service BI w/ RDBMS SQL-on-Hadoop

Self-Service Data Exploration

Zero-day analytics



Drill’s Data Model is Flexible

HBase

JSON BSON

CSV TSV

Parquet Avro

Schema-less Fixed schema

Flat

Complex

Flexibility

Flexibility

Name ! Gender ! Age !Michael ! M ! 6 !Jennifer ! F ! 3 !

{ ! name: { ! first: Michael, ! last: Smith ! }, ! hobbies: [ski, soccer], ! district: Los Altos !} !{ ! name: { ! first: Jennifer, ! last: Gates ! }, ! hobbies: [sing], ! preschool: CCLC !} !

RDBMS/SQL-on-Hadoop table

Apache Drill table


Drill Supports Schema Discovery On-The-Fly

•  Fixed schema •  Leverage schema in centralized

repository (Hive Metastore)

•  Fixed schema, evolving schema or schema-less

•  Leverage schema in centralized repository or self-describing data

2 Schema Discovered On-The-Fly Schema Declared In Advance

SCHEMA ON WRITE

SCHEMA BEFORE READ

SCHEMA ON THE FLY


Native JSON

SELECT json_value(po_document, '$.AllowPartialShipment’ RETURNING NUMBER) FROM j_purchaseorder;

SELECT po_document.AllowPartialShipment FROM j_purchaseorder;

JSON query with Oracle:

JSON query with Drill:

Relational databases cannot provide true schema-free JSON support.

®© 2014 MapR Technologies 13 © 2014 MapR Technologies ®

Architecture


High Level Architecture •  Cluster of commodity servers

–  Daemon (drillbit) on each node

•  No dependency on other execution engines (MapReduce, Spark, Tez) –  Better performance and manageability

•  ZooKeeper maintains ephemeral cluster membership information –  drillbit uses ZooKeeper to find other drillbits in the cluster –  Client uses ZooKeeper to find drillbits

•  Data processing unit is columnar record batches –  Enables schema flexibility with negligible performance impact


Drill Maximizes Data Locality

Data Source Best Practice HDFS or MapR-FS drillbit on each DataNode HBase or MapR-DB drillbit on each RegionServer MongoDB drillbit on each mongod node (when using replicas, run it on the replica node)

drillbit

DataNode/RegionServer/

mongod

drillbit


mongod

drillbit


mongod

ZooKeeper ZooKeeper

ZooKeeper …


SELECT* Query Execution

drillbit ZooKeeper

Client (JDBC, ODBC,

REST)

1.  Find drillbits (once per session)

3.  Create logical and physical execution plans 4.  Farm out execution of fragments to cluster

(completely distributed execution)

ZooKeeper ZooKeeper

drillbit drillbit

2.  Submit query to drillbit

5.  Return results to client

* CTAS (CREATE TABLE AS SELECT) queries include steps 1-4


Core Modules within drillbit

SQL Parser Hive

HBase

Distributed Cache

Sto

rage

Plu

gins

MongoDB

DFS

Phy

sica

l Pla

n

Execution Lo

gica

l Pla

n Optimizer

RPC Endpoint


Example: Analyzing Real-World Data


Demo Plan 1.  Run Drill 2.  Configure DFS and MongoDB storage plugins 3.  Explore the data

–  Basics –  Complex data –  Views


Run Drill


Run Drill in Embedded Mode (sqlline) $ tar xf apache-‐drill-‐0.7.0.tar.gz $ cd apache-‐drill-‐0.7.0 $ bin/sqlline -‐u jdbc:drill:zk=local > SELECT * FROM dfs.root.`/Users/tshiran/Development/demo/data/yelp/user.json` LIMIT 1; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | yelping_since | votes | review_count | name | user_id | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | 2012-‐02 | {"funny":1,"useful":5,"cool":0} | 6 | Lee | qtrmBGNqCvupHMHL_bKFgQ |

•  drillbit (Drill daemon) starts automatically in embedded mode •  No ZooKeeper in embedded mode (hence zk=local) •  Can’t use BI clients (JDBC/ODBC) in embedded mode

You can now access the Web UI: http://localhost:8047


Or Run Drill in Distributed Mode…

$ zkServer start •  Make sure ZooKeeper (zkServer) is running:

•  Access the Web UI: http://localhost:8047 •  Connect a client to the cluster (eg, sqlline):

•  Clients (like sqlline) connect to ZooKeeper to discover the cluster nodes •  If you have multiple Drill clusters registered in one ZooKeeper ensemble, specify the desired

cluster in the JDBC connection string: jdbc:drill:zk=localhost:2181/drill/<clustername>

•  Not sure if ZooKeeper is running? Run telnet localhost 2181 and make sure it connects

•  Define the Drill cluster name and ZooKeeper nodes in conf/drill-‐override.conf •  Start drillbit: $ bin/drillbit.sh start

$ bin/sqlline -‐u jdbc:drill:zk=localhost:2181


Configure Storage Plugins


Enable MongoDB Storage Plugin


Define Workspaces in the DFS Storage Plugin •  d


Explore the Data: Basics


Inventory: DFS Files

{ "votes": {"funny": 0, "useful": 2, "cool": 1}, "user_id": "Xqd0DzHaiyRqVH3WRG7hzg", "review_id": "15SdjuK7DmYqUAj6rjGowg", "stars": 5, "date": "2007-‐05-‐17", "text": "dr. goldberg offers everything ...", "type": "review", "business_id": "vcNAWiLM4dR7D2nwwJ7nCA" }


Inventory: MongoDB Collections $ mongo MongoDB shell version: 2.6.5 > show databases; admin (empty) local 0.078GB yelp 0.453GB > use yelp > db.users.findOne() {

"_id" : ObjectId("54566cdf3237149de181a92a"), "yelping_since" : "2012-‐02", "votes" : { "funny" : 1, "useful" : 5, "cool" : 0 }, "review_count" : 6, "name" : "Lee", "user_id" : "qtrmBGNqCvupHMHL_bKFgQ", "friends" : [ ]

}


Let’s Go! > SELECT * FROM dfs.root.`/Users/tshiran/Development/demo/data/yelp/review.json` WHERE stars = 1 LIMIT 1; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | votes | user_id | review_id | stars | date | text | type | business_id | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | {"funny":0,"useful":0,"cool":0} | Qrs3EICADUKNFoUq2iHStA | _ePLBPrkrf4bhyiKWEn4Qg | 1 | 2013-‐04-‐19 | I don't know what Dr. Goldberg was like before moving to Arizona, but let me tell you, STAY AWAY from this doctor and this office. | review | vcNAWiLM4dR7D2nwwJ7nCA | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


Using Storage Plugins and Workspaces

> SELECT * FROM dfs.root.`/Users/tshiran/Development/demo/data/yelp/review.json` LIMIT 1; > SELECT * FROM dfs.demo.`yelp/review.json` LIMIT 1; > SELECT * FROM mongo.yelp.users LIMIT 1; > USE mongo.yelp; > SELECT * FROM users LIMIT 1;

Storage plugin Workspace

Path relative to workspace

Storage Plugin Workspace Table dfs Path Path relative to workspace mongo Database Collection hive Database Table hbase Namespace Table


Most Common User Names (MongoDB) > SELECT name, count(*) AS users FROM mongo.yelp.users GROUP BY name ORDER BY users DESC LIMIT 10; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | name | users | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | David | 2453 | | John | 2378 | | Michael | 2322 | | Chris | 2202 | | Mike | 2037 | | Jennifer | 1867 | | Jessica | 1463 | | Jason | 1457 | | Michelle | 1439 | | Brian | 1436 | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


Cities with the Most Businesses > SELECT state, city, count(*) AS businesses FROM dfs.demo.`/yelp/business.json` GROUP BY state, city ORDER BY businesses DESC LIMIT 10; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | state | city | businesses | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | NV | Las Vegas | 12021 | | AZ | Phoenix | 7499 | | AZ | Scottsdale | 3605 | | EDH | Edinburgh | 2804 | | AZ | Mesa | 2041 | | AZ | Tempe | 2025 | | NV | Henderson | 1914 | | AZ | Chandler | 1637 | | WI | Madison | 1630 | | AZ | Glendale | 1196 | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


Explore the Data: Complex Data


business.json (1) {

"business_id": "4bEjOyTaDG24SY5TxsaUNQ", "full_address": "3655 Las Vegas Blvd S\nThe Strip\nLas Vegas, NV 89109", "hours": { "Monday": {"close": "23:00", "open": "07:00"}, "Tuesday": {"close": "23:00", "open": "07:00"}, "Friday": {"close": "00:00", "open": "07:00"}, "Wednesday": {"close": "23:00", "open": "07:00"}, "Thursday": {"close": "23:00", "open": "07:00"}, "Sunday": {"close": "23:00", "open": "07:00"}, "Saturday": {"close": "00:00", "open": "07:00"} }, "open": true, "categories": ["Breakfast & Brunch", "Steakhouses", "French", "Restaurants"], "city": "Las Vegas", "review_count": 4084, "name": "Mon Ami Gabi", "neighborhoods": ["The Strip"], "longitude": -‐115.172588519464,


business.json (2) "state": "NV", "stars": 4.0,

"attributes": { "Alcohol": "full_bar”,

"Noise Level": "average", "Has TV": false, "Attire": "casual", "Ambience": { "romantic": true, "intimate": false, "touristy": false, "hipster": false,

"classy": true, "trendy": false,

"casual": false }, "Good For": {"dessert": false, "latenight": false, "lunch": false,

"dinner": true, "breakfast": false, "brunch": false}, }

}


Which Places Are Open Right Now (22:00)? > SELECT name, b.hours FROM dfs.demo.`yelp/business.json` b WHERE b.hours.Saturday.`open` < '22:00' AND b.hours.Saturday.`close` > '22:00' LIMIT 2; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | name | hours | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | Chang Jiang Chinese Kitchen | {"Tuesday":{"close":"22:00","open":"11:00"},"Friday":{"close":"22:30","open":"11:00"},"Monday":{"close":"22:00","open":"11:00"},"Wednesday":{"close":"22:00","open":"11:00"},"Thursday":{"close":"22:00","open":"11:00"},"Sunday":{"close":"21:00","open":"16:00"},"Saturday":{"close":"22:30","open":"11:00"}} | | Grand China Restaurant | {"Tuesday":{"close":"22:00","open":"11:00"},"Friday":{"close":"23:00","open":"11:00"},"Monday":{"close":"22:00","open":"11:00"},"Wednesday":{"close":"22:00","open":"11:00"},"Thursday":{"close":"22:00","open":"11:00"},"Sunday":{"close":"22:00","open":"12:00"},"Saturday":{"close":"23:00","open":"11:00"}} | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


It’s 10pm in Vegas and I Want Good Hummus! > SELECT name, stars, b.hours.Friday, categories FROM dfs.demo.`yelp/business.json` b WHERE b.hours.Friday.`open` < '22:00' AND b.hours.Friday.`close` > '22:00' AND REPEATED_CONTAINS(categories, 'Mediterranean') AND city = 'Las Vegas' ORDER BY stars DESC LIMIT 2; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | name | stars | EXPR$2 | categories | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | Olives | 4.0 | {"close":"22:30","open":"11:00"} | ["Mediterranean","Restaurants"] | | Marrakech Moroccan Restaurant | 4.0 | {"close":"23:00","open":"17:30"} | ["Mediterranean","Middle Eastern","Moroccan","Restaurants"] | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


Flatten Repeated Values > SELECT name, categories FROM dfs.demo.`yelp/business.json` LIMIT 3; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | name | categories | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | Eric Goldberg, MD | ["Doctors","Health & Medical"] | | Pine Cone Restaurant | ["Restaurants"] | | Deforest Family Restaurant | ["American (Traditional)","Restaurants"] | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+

> SELECT name, FLATTEN(categories) AS categories FROM dfs.demo.`yelp/business.json` LIMIT 5; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | name | categories | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | Eric Goldberg, MD | Doctors | | Eric Goldberg, MD | Health & Medical | | Pine Cone Restaurant | Restaurants | | Deforest Family Restaurant | American (Traditional) | | Deforest Family Restaurant | Restaurants | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


Most and Least Common Business Categories > SELECT category, count(*) AS businesses FROM (SELECT name, FLATTEN(categories) AS category FROM dfs.demo.`yelp/business.json`) c GROUP BY category ORDER BY businesses DESC; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | category | businesses | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | Restaurants | 14303 | … | Australian | 1 | | Boat Dealers | 1 | | Firewood | 1 | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ 715 rows selected (3.439 seconds)

> SELECT name, categories FROM dfs.demo.`yelp/business.json` WHERE true and REPEATED_CONTAINS(categories, 'Australian'); +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | name | categories | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | The Australian AZ | ["Bars","Burgers","Nightlife","Australian","Sports Bars","Restaurants"] | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


Explore the Data: Views


Create a View for Name-Gender Mapping

> CREATE VIEW dfs.tmp.`names` AS SELECT columns[0] AS name, columns[4] AS gender FROM dfs.demo.`names.csv`; > USE dfs.tmp; > CREATE VIEW names1 ASSELECT columns[0] AS name, columns[4] AS gender FROM dfs.demo.`names.csv`; > SELECT * FROM dfs.tmp.names WHERE name = 'John'; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | name | gender | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | John | Male | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+

columns[0] columns[4]

names.csv:


Most Common Names (and their Genders) on Yelp > SELECT u.name, n.gender, count(*) AS number FROM mongo.yelp.users u, dfs.tmp.names n WHERE u.name = n.name GROUP BY u.name, n.gender ORDER BY number DESC LIMIT 10; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | name | gender | number | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | David | Male | 2453 | | John | Male | 2378 | | Michael | Male | 2322 | | Chris | Unknown | 2202 | | Mike | Male | 2037 | | Jennifer | Female | 1867 | | Jessica | Female | 1463 | | Jason | Male | 1457 | | Michelle | Female | 1439 | | Brian | Male | 1436 | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


Who Rates Higher – Men or Women? > SELECT n.gender, count(*) AS users, round(avg(average_stars), 2) stars FROM mongo.yelp.users u, dfs.tmp.names n WHERE u.name = n.name GROUP BY n.gender; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | gender | users | stars | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | Female | 103684 | 3.77 | | Male | 97430 | 3.696 | | Unknown | 18409 | 3.727 | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+


Who Writes More – Men or Women?

> SELECT n.gender, round(avg(length(r.text))) AS review_length FROM dfs.demo.`yelp/review.json` r, mongo.yelp.users u, dfs.tmp.names n WHERE u.name = n.name AND r.user_id = u.user_id GROUP BY n.gender; +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | gender | review_length | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+ | Male | 665 | | Female | 730 | | Unknown | 711 | +-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+

It takes a 3-way join to find out…


Drill Tweets (@ApacheDrill)


Thank You •  Learn: incubator.apache.org/drill/

•  Download: incubator.apache.org/drill/download/

•  Ask questions: [email protected]

•  Contact me: [email protected]


Thank You

@mapr maprtech

[email protected]

Tomer Shiran, VP Product Management

MapRTechnologies

maprtech

mapr-technologies

Data & Analytics

Analyzing Real-World Data with Apache Drill