datastax: old dogs, new tricks. teaching your relational dba to fetch
TRANSCRIPT
©2013 DataStax Confidential. Do not distribute without consent.
Old dog, new tricks
1
@PatrickMcFadin
Patrick McFadinChief Evangelist for Apache Cassandra at DataStax
Oracle Architecture
More CPU
More memory
More disk
DBRM
Clusterware
FAN
RAC
Data GuardGolden Gate
Cache Fusion
Active? Standby?
TimesTen
Scale
Hadoop
Scale
Scale
UptimeUptime
Uptime
ASM
• Think a YouTube competitor – Users add videos, rate them, comment on them, etc. – Can search for videos by tag
CREATE TABLE users ( id number(12) NOT NULL , firstname nvarchar2(25) NOT NULL , lastname nvarchar2(25) NOT NULL, email nvarchar2(50) NOT NULL, password nvarchar2(255) NOT NULL, created_date timestamp(6), PRIMARY KEY (id), CONSTRAINT email_uq UNIQUE (email) );
-- Users by email address indexCREATE INDEX idx_users_email ON users (email);
• Create entity table • Add constraints • Index fields • Foreign Key relationships
CREATE TABLE videos ( id number(12), userid number(12) NOT NULL, name nvarchar2(255), description nvarchar2(500), location nvarchar2(255), location_type int, added_date timestamp, CONSTRAINT users_userid_fk FOREIGN KEY (userid) REFERENCES users (Id) ON DELETE CASCADE, PRIMARY KEY (id) );
“Static” Table
CREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, added_date timestamp, PRIMARY KEY (videoid) );
Table Name
Column NameColumn CQL Type
Primary Key Designation Partition Key
Row
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Partition
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Table Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Column 1
Column 2
Column 3
Column 4
Column 1
Column 2
Column 3
Column 4
Column 1
Column 2
Column 3
Column 4
Partition Key 2
Partition Key 2
Partition Key 2
Keyspace
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 1
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Column 1
Partition Key 2
Column 2
Column 3
Column 4
Table 1 Table 2Keyspace 1
Insert
INSERT INTO videos (videoid, name, userid, description, location, location_type, preview_thumbnails, tags, added_date, metadata) VALUES (06049cbb-dfed-421f-b889-5f649a0de1ed,'The data model is dead. Long live the data model.',9761d3d7-7fbd-4269-9988-6cfd4e188678, 'First in a three part series for Cassandra Data Modeling','http://www.youtube.com/watch?v=px6U2n74q3g',1, {'YouTube':'http://www.youtube.com/watch?v=px6U2n74q3g'},{'cassandra','data model','relational','instruction'}, '2013-05-02 12:30:29');
Table Name Fields
Values
Partition Key: Required
Partition keys
06049cbb-dfed-421f-b889-5f649a0de1ed Murmur3 Hash Token = 7224631062609997448
873ff430-9c23-4e60-be5f-278ea2bb21bd Murmur3 Hash Token = -6804302034103043898
Consistent hash. 128 bit number between 2-63 and 264
INSERT INTO videos (videoid, name, userid, description) VALUES (06049cbb-dfed-421f-b889-5f649a0de1ed,'The data model is dead. Long live the data model.’, 9761d3d7-7fbd-4269-9988-6cfd4e188678, 'First in a three part series for Cassandra Data Modeling');
INSERT INTO videos (videoid, name, userid, description) VALUES (873ff430-9c23-4e60-be5f-278ea2bb21bd,'Become a Super Modeler’, 9761d3d7-7fbd-4269-9988-6cfd4e188678, 'Second in a three part series for Cassandra Data Modeling');
“Dynamic” Table
CREATE TABLE videos_by_tag ( tag text, videoid uuid, added_date timestamp, name text, preview_image_location text, tagged_date timestamp, PRIMARY KEY (tag, videoid) );
Partition Key Clustering Column
Primary key relationship
PRIMARY KEY (tag,videoid)
Primary key relationship
Partition Key
PRIMARY KEY (tag,videoid)
Primary key relationship
Partition Key Clustering Column
PRIMARY KEY (tag,videoid)
Primary key relationship
Partition Key
data model
PRIMARY KEY (tag,videoid)
Clustering Column
-5.6
06049cbb-dfed-421f-b889-5f649a0de1ed
Primary key relationship
Partition Key
2013-05-16 16:50:002013-05-02 12:30:29
873ff430-9c23-4e60-be5f-278ea2bb21bd
PRIMARY KEY (tag,videoid)
Clustering Column
data model49f64d40-7d89-4890-b910-dbf923563a33
2013-06-11 11:00:00
Select
name | description | added_date---------------------------------------------------+----------------------------------------------------------+--------------------------The data model is dead. Long live the data model. | First in a three part series for Cassandra Data Modeling | 2013-05-02 12:30:29-0700
SELECT name, description, added_dateFROM videosWHERE videoid = 06049cbb-dfed-421f-b889-5f649a0de1ed;
FieldsTable Name
Primary Key: Partition Key Required
Controlling OrderCREATE TABLE raw_weather_data ( wsid text, year int, month int, day int, hour int, temperature double, PRIMARY KEY ((wsid), year, month, day, hour) ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘10010:99999’,2005,12,1,10,-5.6);
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘10010:99999’,2005,12,1,9,-5.1);
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘10010:99999’,2005,12,1,8,-4.9);
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘10010:99999’,2005,12,1,7,-5.3);
Clustering
200510010:99999 12 1 10
200510010:99999 12 1 9
raw_weather_data
-5.6
-5.1
200510010:99999 12 1 8
200510010:99999 12 1 7
-4.9
-5.3
Order By
DESC
• Think a YouTube competitor – Users add videos, rate them, comment on them, etc. – Can search for videos by tag
CREATE TABLE users ( id number(12) NOT NULL , firstname nvarchar2(25) NOT NULL , lastname nvarchar2(25) NOT NULL, email nvarchar2(50) NOT NULL, password nvarchar2(255) NOT NULL, created_date timestamp(6), PRIMARY KEY (id), CONSTRAINT email_uq UNIQUE (email) );
-- Users by email address indexCREATE INDEX idx_users_email ON users (email);
• Create entity table • Add constraints • Index fields • Foreign Key relationships
CREATE TABLE videos ( id number(12), userid number(12) NOT NULL, name nvarchar2(255), description nvarchar2(500), location nvarchar2(255), location_type int, added_date timestamp, CONSTRAINT users_userid_fk FOREIGN KEY (userid) REFERENCES users (Id) ON DELETE CASCADE, PRIMARY KEY (id) );
Some Application Workflows in KillrVideo
27
User Logs into site
Show basic information about user
Show videos added by a
user
Show comments posted by a
user
Search for a video by tag
Show latest videos
added to the site
Show comments for a video
Show ratings for a
video
Show video and its details
28
Show video and its details
Find video by idShow videos added by a
user
Find videos by user (latest first)
CREATE TABLE videos ( videoid uuid, userid uuid, name text, description text, location text, location_type int, preview_image_location text, tags set<text>, added_date timestamp, PRIMARY KEY (videoid) );
CREATE TABLE user_videos ( userid uuid, added_date timestamp, videoid uuid, name text, preview_image_location text, PRIMARY KEY (userid, added_date, videoid) ) WITH CLUSTERING ORDER BY (added_date DESC, videoid ASC);
Views or indexes?
Denormalized data
Opps!
29
Table A
Client
Opps!
30
Table A
Client
Table B
Opps!
31
Table A
Client
Table B
Opps!
32
Table A
Client
Table B
Opps!
33
Client
Table B
Thank you!
Bring the questions
Follow me on twitter @PatrickMcFadin