Presentation on integrating Cassandra in PHP projects. 12 november 2013 - PHPMeetup Amersfoort

1. Cassandra
Integrating Cassandra into your project
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2. Maurits Lawende
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Work at Dutch Open Projects (DOP) since 2007
Development and technical design for challenging Drupal sites
Development of SaaS solutions in PHP & NodeJS

3. ToDoToDay
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Data versus information
History and usage of Cassandra
How to use Cassandra
Developments

4. Data versus information
Celko, J. (1999). Data and databases
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5. SQL is designed for information
DBMS knows how to use your data
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6. SQL is designed for flexibility
Not even a single line on scalability
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7. SQL
nearly 40 years of experience
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8. SQL
Never designed for scalability
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9. Alexa top 10
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Google
Facebook
YouTube
Yahoo
Baidu
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Wikipedia
QQ.com
LinkedIn
Live.com
Twitter

10. Alexa top 10
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Google (BigTable)
Facebook (MySQL)
YouTube (MySQL)
Yahoo
Baidu (HyperTable)
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Wikipedia (MySQL)
QQ.com
LinkedIn (Voldemort)
Live.com
Twitter (MySQL)

11. Cassandra users
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Facebook (+ Redis & HBase & MySQL)
Twitter (+ MySQL)
Reddit (+ Postgres)
Digg (+ Redis)
Bit.ly (+ MongoDB)
Netflix

12. Cassandra users
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Facebook (+ Redis & HBase & MySQL)
Twitter (+ MySQL)
Reddit (+ Postgres)
Digg (+ Redis)
Bit.ly (+ MongoDB)
Netflix
Jeff Hammerbacher

13. Cassandra users
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Facebook (+ Redis & HBase & MySQL)
Twitter (+ MySQL)
Reddit (+ Postgres)
Digg (+ Redis)
Bit.ly (+ MongoDB)
Netflix
Jeff Hammerbacher
left Facebook in 2008

14. Back to basic
Don’t think SQL
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15. Key/value store
Evolved towards tables
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16. Just data
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No joins
Limited sorting capabilities
No aggregation, grouping, subqueries whatsoever

17. Schemaless
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Fixed <strike>tables</strike> column families, but;
Dynamic column names

18. Operations in Cassandra 1.0
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CREATE KEYSPACE name
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USE name
CREATE COLUMN FAMILY name
DROP KEYSPACE name
DROP COLUMN FAMILY name

19. Operations in Cassandra 1.0
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SET columnfamily[‘row’][‘column’] = ‘value’;
GET columnfamily[‘row’]
LIST columnfamily
DEL columnfamily[‘row’]
DEL columnfamily[‘row’][‘column’]

20. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘lastname’] = ‘Lawende’;

21. Operations in Cassandra 1.0
post
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post[‘uuid’][‘title’] = ‘First post!’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘lastname’] = ‘Lawende’;
title
uuid First post!
user
firstname
mau Maurits
lastname
Lawende

22. Operations in Cassandra 1.0
sorted by rowkey, columnname (all ascending)
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post[‘uuid’][‘title’] = ‘First post!’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘lastname’] = ‘Lawende’;

23. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘mau’;
user[‘mau’][‘firstname’] = ‘Maurits’;

24. Operations in Cassandra 1.0
How to get a list
of blogs by “mau”?
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘mau’;
user[‘mau’][‘firstname’] = ‘Maurits’;

25. Operations in Cassandra 1.0
How to get a list
of blogs by “mau”?
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘mau’;
user[‘mau’][‘firstname’] = ‘Maurits’;
WHERE user = ‘mau’

26. Operations in Cassandra 1.0
How to get a list
of blogs by “mau”?
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WHERE user = ‘mau’
post[‘uuid’][‘title’] = ‘First post!’;
Bad Request:
No indexed columns present in
post[‘uuid’][‘user’] = ‘mau’;
by-columns clause with
user[‘mau’][‘firstname’] = ‘Maurits’;
Equal operator

27. Operations in Cassandra 1.0
How to get a list
of blogs by “mau”?
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WHERE user = ‘mau’
post[‘uuid’][‘title’] = ‘First post!’;
Bad Request:
No indexed columns present in
post[‘uuid’][‘user’] = ‘mau’;
by-columns clause with
user[‘mau’][‘firstname’] = ‘Maurits’;
Equal operator
sequal scans
are rejected
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28. Operations in Cassandra 1.0
How to get a list
of blogs by “mau”?
WHERE user = ‘mau’
post[‘uuid’][‘title’] = ‘First post!’;
Bad Request:
No indexed columns present in
post[‘uuid’][‘user’] = ‘mau’;
by-columns clause with
user[‘mau’][‘firstname’] = ‘Maurits’;
Equal operator
Bad Request: Order by is currently only supported
on the clustered columns of the PRIMARY KEY
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29. Operations in Cassandra 1.0
How to get a list
of blogs by “mau”?
WHERE user = ‘mau’
post[‘uuid’][‘title’] = ‘First post!’;
Bad Request:
No indexed columns present in
post[‘uuid’][‘user’] = ‘mau’;
by-columns clause with
user[‘mau’][‘firstname’] = ‘Maurits’;
Equal operator
Bad Request: Order by is currently only supported
on the clustered columns of the PRIMARY KEY
Bad Request: ORDER BY is only supported when the partition key is
restricted by an EQ or an IN.
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30. Operations in Cassandra 1.0
How to get a list
of blogs by “mau”?
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘mau’;
user[‘mau’][‘firstname’] = ‘Maurits’;
WHERE user = ‘mau’
ORDER BY date DESC
LIMIT 10

31. Operations in Cassandra 1.0
How to get a list
of blogs by “mau”?
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘mau’;
user[‘mau’][‘firstname’] = ‘Maurits’;
WHERE user = ‘mau’
ORDER BY date DESC
LIMIT 10
only possible when user and
date is in primary key

32. Predictable performance
No performance degradation after data growth
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33. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘mau’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘post001’] = ‘uuid’;
user[‘mau’][‘post002’] = ‘uuid’;

34. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘mau’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘post001’] = ‘uuid’;
user[‘mau’][‘post002’] = ‘uuid’;
any order and limit

35. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘uuid’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘post001’] = ‘uuid’;
user[‘mau’][‘post002’] = ‘uuid’;
join

36. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
post[‘uuid’][‘user’] = ‘uuid’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘post001’] = ‘uuid’;
user[‘mau’][‘post002’] = ‘uuid’;
join
no uuid IN (...) or OR’s

37. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘post001:uuid’] = ‘First post!’;
user[‘mau’][‘post002:uuid’] = ‘Second post!’;

38. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
user[‘mau’][‘firstname’] = ‘Maurits’;
only one query required
to get user profile
with latest posts
user[‘mau’][‘post001:uuid’] = ‘First post!’;
user[‘mau’][‘post002:uuid’] = ‘Second post!’;

39. Operations in Cassandra 1.0
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post[‘uuid’][‘title’] = ‘First post!’;
user[‘mau’][‘firstname’] = ‘Maurits’;
user[‘mau’][‘post001:uuid’] = ‘First post!’;
user[‘mau’][‘post002:uuid’] = ‘Second post!’;
64 KB
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2 billion cells
64 KB
2 GB

40. Beauty?
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Dirty in the SQL world, but;
It’s a best practice in Big Data
Don’t think of it as a relational database
No strict rules on how to use it, just push it to the limits

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42. Each row is a snapshot of data
meant to satisfy a given query, sort
of like a materialized view.
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43. Storage in a cluster
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44. Cluster structures
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45. Master-slave
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46. Master-master
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47. Sharding
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48. HDFS / GlusterFS
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49. HyperTable
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50. Dynamo
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51. No master or single point of failure
Every node is (nearly) identical
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52. Distribution and replication
2^127 0
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53. Distribution and replication
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54. Distribution and replication
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55. Distribution and replication
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56. Distribution and replication
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57. Distribution and replication
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58. Client can connect to any node
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59. Seed nodes
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Required for bootstrapping nodes
Define 2 or 3 seed nodes per cluster

60. Extending the ring
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Assign a token for new node
Configure seed node host
Start Cassandra on new node

61. Extending the ring
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Assign a token for new node
Configure seed node host
Start Cassandra on new node

62. Consistency
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63. Writing data
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Hinted handoff
Write to commit log
Write in memory
Write to disk (together with timestamp)

64. Write consistency
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Choose from ANY, ONE, TWO, THREE, QUORUM, ALL
QUORUM = floor((replication factor / 2) + 1)

65. Read consistency
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Choose from ONE, TWO, THREE, QUORUM, ALL
Most recent copy is returned

66. Read repair
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Compares data with 2 other replica’s in the background
Fixes inconsistent and missing data
At 10% of all reads

67. Node repair
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Gradually compares all data in nodes with replica’s
Required in conjunction with read repair to fix ‘forgotten deletes’

68. ACID theorem
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Atomic; completed successfully or entirely rolled back
Consistent; transations never invalidates the database state
Isolated; transactions are processed sequential
Durable; completed actions are persistent

69. CAP theorem
Impossible to achieve all three:
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Consistency
Availability
Partition tolerance

70. Eventual consistency
Not guaranteed to be consistent, but becomes consistent later
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71. Eventual consistency
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Best effort
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Configurable consistency level, but no transaction support
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Consistency is not always more important than speed and scalability
(doesn’t require locking)

72. Surrogate keys
Say bye to sequences
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73. Surrogate keys
Say bye to sequences
ss cluster
istent acro
not cons
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74. Surrogate keys
Say bye to sequences
ss cluster
istent acro
not cons
counters a
re for cou
n
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ting

75. Native support for uuid’s
f47ac10b-58cc-4372-a567-0e02b2c3d479
Surrogate keys
Say bye to sequences
ss cluster
istent acro
not cons
counters a
re for cou
n
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ting

76. Cassandra 1.2
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77. Cassandra 1.2
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Not longer schemaless
Introduced CQL3
No wide tables anymore

78. Collections
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Lists
Maps
Sets

79. Lists
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user[‘mau’][‘posts’] = ‘uuid’;
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UPDATE user SET posts = posts + [‘uuid’]
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CREATE TABLE user (
username text PRIMARY KEY,
posts list<uuid>
);
UPDATE user SET posts = [‘uuid’] + posts

80. Set
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CREATE TABLE user (
username text PRIMARY KEY,
email set<text>
);
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UPDATE user SET emails = emails + {‘mail@example.com’}
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81. Maps
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CREATE TABLE user (
username text PRIMARY KEY,
attending map<timestamp,text>
);
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UPDATE user SET attending[‘2013-11-12’] = ‘PHPMeetup’
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DELETE attending[‘2013-12-05’] FROM user

82. Limits on collections
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64K
Whole collection loaded in memory when reading / writing
Not an alternative to wide tables!

83. Limits on collections
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64K
No size check in CQL
SET list = list + [‘...’]
Whole collection loaded in memory when reading / writing
Not an alternative to wide tables!

84. Wide tables in CQL3
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CREATE TABLE tweets (
tweet_id uuid PRIMARY KEY,
author varchar,
body varchar
);
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CREATE TABLE timeline (
user_id varchar,
tweet_id uuid,
author varchar,
body varchar,
PRIMARY KEY (user_id, tweet_id)
)
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85. Wide tables in CQL3
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CREATE TABLE tweets (
tweet_id uuid PRIMARY KEY,
author varchar,
body varchar
);
CREATE TABLE timeline (
user_id varchar,
tweet_id uuid,
author varchar,
body varchar,
PRIMARY KEY (user_id, tweet_id)
)
user_id
mau
user_id
mike
uuid:author
anne
uuid:author
david
uuid:body
Tweet from Anne
uuid:body
Tweet from David

86. Wide tables in CQL3
For schemaless lovers:
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CREATE TABLE tweets (
tweet_id uuid PRIMARY KEY,
author varchar,
body varchar
);
CREATE TABLE timeline (
user_id varchar,
tweet_id uuid,
author varchar,
body varchar,
PRIMARY KEY (user_id, tweet_id)
)
user_id
mau
user_id
mike
CREATE TABLE name (
rowkey varchar,
columnname varchar,
value blob,
PRIMARY KEY (rowkey, columnname)
);
uuid:author uuid:body
anne
Tweet from Anne
uuid:author uuid:body
david
Tweet from David

87. Secondary index
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CREATE INDEX name ON table (column);
High memory usage when used with high cardinality

88. Iteration
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SELECT * FROM users

89. Iteration
unpredictable performance
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SELECT * FROM users LIMIT 10 OFFSET 100

90. Iteration
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SELECT * FROM users
SELECT token(username), username, country, age FROM user

91. Iteration
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SELECT * FROM users
SELECT token(username), username, country, age FROM user
WHERE token(username) > 23947239 LIMIT 10

92. Queries are always controlled by
one node
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93. Queries are always controlled by
one node
Even if data from 100 nodes is involved
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94. MapReduce
Or just ‘MapRed’
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95. MapReduce
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array_map
array_reduce

96. map()
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Processes a subset of the data
array_map(function($v) { return strtoupper($v); }, array('a', 'b'))

97. reduce()
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Merge results from the mapping function
array_reduce(array(1, 2, 3), function($a, $b) { return $a + $b; });

98. MapReduce
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99. MapReduce
map()
map()
map()
map()
map()
map()
map()
map()
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map()
map()
map()
map()

100. MapReduce
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101. MapReduce
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102. MapReduce
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103. MapReduce
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104. MapReduce
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105. MapReduce
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106. MapReduce
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107. MapReduce
result
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108. Wordcount
$data = array(‘red green blue’, ‘orange blue’, ‘purple green’);
$data = array_map(function($v) {
$words = array();
foreach (explode(' ', $v) as $word)
$words[$word] = isset($words[$word]) ? $words[$word] + 1 : 1;
return $words;
}, $data);
$data = array_reduce($data, function($a, $b) {
foreach ($a as $word => $count)
$b[$word] = isset($b[$word]) ? $b[$word] + $count : $count;
return $b;
}, array());
array(‘red’ => 1, ‘green’ => 2, ‘blue’ => 2, ‘orange’ => 1, ‘purple’ => 1)
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109. ORDER BY value LIMIT 5
$data = array(array(4,5,2), array(62,35,1), array(74,56,2,34));
$data = array_map(function($v) {
sort($v);
return array_slice($v, 0, 5);
}, $data);
$data = array_reduce($data, function($a, $b) {
$v = array_merge($a, $b);
sort($v);
return array_slice($v, 0, 5);
}, array());
array(1, 2, 2, 4, 5)
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110. Remember
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Getting information is a bumpy road in big data
Use MapRed to transform data into information

111. MapReduce
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No native support in Cassandra
MapReduce possible with Hadoop (requires Java programming)

112. Pig
input_lines = LOAD '/tmp/my-copy-of-all-pages-on-internet' AS (line:chararray);
words = FOREACH input_lines GENERATE FLATTEN(TOKENIZE(line)) AS word;
filtered_words = FILTER words BY word MATCHES 'w+';
word_groups = GROUP filtered_words BY word;
word_count = FOREACH word_groups GENERATE COUNT(filtered_words) AS
count, group AS word;
ordered_word_count = ORDER word_count BY count DESC;
STORE ordered_word_count INTO '/tmp/number-of-words-on-internet';
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113. Hive
SELECT v['ip'], COUNT(1) AS cnt FROM www_access
GROUP BY v['ip']
ORDER BY cnt DESC LIMIT 30
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114. Pig and Hive
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Using MapReduce
No(t very) predictable performance
Good for analysis

115. Hack your own
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Not too difficult
Data can be split into subsets by filtering on tokens
Application must run on all MapRed nodes
Probably better performance than Pig / Hive

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117. Interfaces / protocols
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Thrift
Binary protocol (1.2+)
Gossip (internode communication)

118. Thrift
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Something like SOAP in a binary format
Tool which generates libraries based on definition files
Supports many languages (incl. PHP, JS, NodeJS, c, java, python, ruby.....)
Also used by HyperTable, HBase, Accumulo and ElasticSearch
Sole interface before 1.2

119. Thrift
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No support for collections

120. Binary protocol
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Recommended protocol for Cassandra 1.2
Few client libraries available
No binary connectors were available for PHP
https://github.com/mauritsl/php-cassandra

121. php-cassandra
require('lib/cassandra/Cassandra.php');
use CassandraConnection as Cassandra;
$connection = new Cassandra('localhost', 'keyspace');
$rows = $connection->query('SELECT * FROM user');
foreach ($rows as $row) {
print $row->firstname;
print $row->listfield[0];
}
$rows->count();
$rows->getColumns();
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122. Scaling applications
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123. Rule 1:
Don’t ask for NoSQL drivers for a CMS
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124. Cassandra does not fit all
(same story for every NoSQL solution)
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125. Every page (or API call) should only
require a few (if not one) query
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126. Static versus Dynamic data
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Static: information that doesn’t change very often
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I.e.: translations
May go in a RDBMS or local storage (files?)
Dynamic: many changes
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Changes must be visible on all nodes
Use Cassandra

127. Local versus Global data
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Logging
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Separate logs per node
Cache
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Sometimes no need to share cache between nodes
Statistics
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Can be kept local for a limited time

128. Local versus Global data
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Sessions
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Dependent on session stickiness

129. Caching
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Memcache is recommended for local cache
Cassandra can be used for global cache
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Has a TTL feature
INSERT INTO ... (...) VALUES (...) USING TTL 86400

130. What about files?
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Use Hadoop Distributed File System (HDFS) or GlusterFS

131. What about files?
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Use Hadoop Distributed File System (HDFS) or GlusterFS
Or use Cassandra

132. What about files?
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Split files in chunks to avoid hotspots and save the heap
Not uncommon to have files in Cassandra
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github.com/Netflix/astyanax
GB’s are ok, but do not store TB’s

133. Maximum size of cluster?
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No satisfactory answer
Probably more dependent on network equipment
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Rack awareness helps here
Facebook: 150 node cluster, 50TB data (2010)
Easou: 400 node cluster, 300TB data (300 million images)

134. Minimum size of a cluster?
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Can run on a single node
4GB RAM recommended
Runs fine on 1GB RAM

135. Minimum size of a cluster?
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•
•
dinsdag 12 november 13
Can run on a single node
4GB RAM recommended
Runs fine on 1GB RAM
“hot data” should fit in RAM

136. Installing Cassandra
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Install JDK
Oracle Java recommended but OpenJDK works ok
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Add Cassandra repository
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apt-get install cassandra
Set listen and seed address (IP address of node and seed)
(Re)start Cassandra

137. Last words...
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138. Data versus information
Data structure is naturally responsive for information
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139. Data versus information
Data structure is naturally responsive for information
predictable performance
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140. History and usage
Jeff Hammerbacher
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141. How to use it
Schema design, CQL3 and limits
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142. Developments
CQL3 and binary protocol
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143. Thank you!
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144. Questions?
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