This is the Cassandra Summit 2014 presentation, talking about growing, scalability at Movile. Presentation live: https://www.youtube.com/watch?v=igmLnluGcDM Apache Cassandra was adopted by Movile in 2009, and became a fundamental piece within the robust and scalable architecture to support more than 50 products, impacted by over 200MM users in Latin America. In this case we present the architecture of our ring, configuration details, detailed tuning, hardware used to be able to achieve our performance requirements (order of a few milliseconds), information storage strategies for network and disk space optimization, and best practices, in addition to showing the evolution of the architecture of simple systems to become scalable and distributed platforms. We introduced our cluster with a relatively low number of nodes (6) using commodity hardware to support critical high-performance applications. After this talk, you'll understand how Apache Cassandra was essential to evolve our systems and leverage the growth of our business. Movile is the leading mobile content company in Latin America. Movile’s products include mobile content, mobile TV, mobile learning, mobile games, mobile payment, mobile marketing and mobile commerce. Every month, it publishes content and services to more than 20 million mobile costumers. It has grown substantially over the last few years (with a more than 25-fold increase in its revenue over the last five years) both organically and through an aggressive M&A strategy, including five acquisitions in the last five years. Movile is positioning itself as a kind of Silicon Valley company based in Brazil. For the last two years, Movile has been named in the “Great Place to Work” list for technology companies in Brazil. The company shareholders include the founders of the company plus Naspers, a South-African media conglomerate.

1. A Train of Thought About
Growing and Scalability
Bumping up Startup Business with Apache Cassandra
Eiti Kimura, Software Engineer @

2. Movile is the industry leader for development of mobile
content and commerce platforms in Latin America. With
products for mobile phones, smartphones and tablets.
Games, on-line education, entertainment apps for adults and
kids and many options for buying with confidence and
comfort. All of that comes to you through Movile.
For companies, Movile delivers complete products,
integrating transactions in M-Payments and content
distribution, fast and with quality.

4. Subscription and Billing Platform a.k.a SBS
• It is a service API
• responsible to manage users subscriptions
• charge users in carriers
• renew subscriptions
• “can not” stop anyway
• should be as performatic as possible

5. Some platform numbers
Renewal Engine: ~ 52,1
million of billing tries a
day
• about 603 request/s
• 1,5 billion billing tries
per month
50 million
subscriptions
~ 50 request/s
Operations:
★ subscribe
★ cancel
★ profile

6. Data Distribution
Subscriptions by Country
1%2%
4%
25%
68%
Others
Colombia
Argentina
Mexico
Brazil

7. Platform Architecture
“There isn’t just one way to state a system’s architecture; rather, there are
multiple architectures in a system, and the view of what is architecturally
significant is one that can change over a system’s lifetime.” - Patterns of
Enterprise Application Architecture
Martin Fowler

8. Basic Architecture Design

9. • scalabe
• high availability
• high performance

10. Very High Usage
• veryyyyy slow... system response
• overall throughput decreased
• low availability, single point of
failure
• Even worse than stopping is to only
work sometimes

11. Improved Distributed Design
A Cassandra Based Solution
• the operations are
distributed across the
nodes
• we achieved linear
scalability

12. Improved Distributed Design
A Cassandra Based Solution
• the performance issues
were solved
• the availability has improved
• there is no longer a single
point of failure

13. C* Data Modeling
• Dernormalization: Writes are cheap, reads are expensive, so insert data in every arrangement that
you need to read
• Don't be afraid of denormalization
• There are different ways to model your solution, there is no right or wrong way
• plan your queries and how you need to get the information before modeling. Use it as driver for
modeling decisions

14. Data Model V1
CREATE TABLE subscription (
subscription-id text PRIMARY KEY,
phone-number text,
config-id int,
…
enabled boolean,
creation-date timestamp
);
CREATE TABLE user_subscriptions (
phone-number text,
subscription-id text,
PRIMARY KEY (phone-number, subscription-id)
);

15. Data Model V1
user_subscriptions
phone-number subscription-id
551900001212 subs-093123
551911114567 subs-002202
551911114567 subs-002203
551911114567 subs-002204
subscriptions
subscription-id phone-number config-id . . . enabled creation-date
subs-093123 551900001212 342 . . . true 2013-08-01
subs-002202 551911114567 567 . . . false 2014-06-27
subs-002203 551911114567 678 . . . true 2014-07-05
subs-002204 551911114567 654 . . . true 2014-08-07

16. Data Model V1 – Quering Profile
user_subscriptions
phone-number subscription-id
551900001212 subs-093123
551911114567 subs-002202
551911114567 subs-002203
551911114567 subs-002204
#cql> _
1st step
• check the index table to get the ids of
subscriptions for a given user

17. Data Model V1 – Quering Profile
user_subscriptions
phone-number subscription-id
551900001212 subs-093123
551911114567 subs-002202
551911114567 subs-002203
551911114567 subs-002204
#cql> SELECT * FROM user_subscriptions WHERE phone-number = 551911114567;
551911114567 subs-002202
551911114567 subs-002203
551911114567 subs-002204

18. Data Model V1 – Quering Profile
#cql> _
2nd step
• query all the user’s subscriptions by its id
551911114567 subs-002202
551911114567 subs-002203
551911114567 subs-002204

19. Data Model V1 – Quering Profile
#cql> SELECT * FROM subscriptions WHERE subscription-id = ‘subs-002204’;
#cql> SELECT * FROM subscriptions WHERE subscription-id = ‘subs-002203’;
#cql> SELECT * FROM subscriptions WHERE subscription-id = ‘subs-002202’;
subscriptions
subscription-id phone-number config-id . . . enabled creation-date
subs-093123 551900001212 342 . . . true 2013-08-01
subs-002202 551911114567 567 . . . false 2014-06-27
subs-002203 551911114567 678 . . . true 2014-07-05
subs-002204 551911114567 654 . . . true 2014-08-07

20. Data Model V2
CREATE TABLE subscription (
phone-number text,
subscription-id text,
serialized blob,
PRIMARY KEY(phone-number, subscription-id)
);

21. Data Model V2
subscriptions
phone-number subscription-id serialized-data
551900001212 subs-093123 array [1,1,0,1,1,1,1,0,0,0,0,10,1,1,0,1,1,0,1,1,1,0,0,0,1,0]
551911114567 subs-002202 array [0,1,0,1,1,0,1,1,0,0,0,10,1,1,0,1,1,0,1,1,1,0,0,0,1,0]
551911114567 subs-002203 array [0,1,0,0,1,1,1,0,0,1,0,10,1,1,0,1,1,0,1,1,1,1,1,1,1,0]
551911114567 subs-002203 array [1,0,0,1,1,1,1,0,1,0,0,10,1,1,0,1,1,0,1,1,1,0,0,0,1,1]
542154231121 subs-320012 array [1,1,1,1,1,0,1,0,1,0,0,10,1,0,0,1,1,0,1,1,1,0,0,1,0,1]

22. Data Model V2 – Quering Profile
#cql> SELECT * FROM subscriptions WHERE phone-number = ‘551911114567’;
subscriptions
phone-number subscription-id serialized-data
551900001212 subs-093123 array [1,1,0,1,1,1,1,0,0,0,0,10,1,1,0,1,1,0,1,1,1,0,0,0,1,0]
551911114567 subs-002202 array [0,1,0,1,1,0,1,1,0,0,0,10,1,1,0,1,1,0,1,1,1,0,0,0,1,0]
551911114567 subs-002203 array [0,1,0,0,1,1,1,0,0,1,0,10,1,1,0,1,1,0,1,1,1,1,1,1,1,0]
551911114567 subs-002204 array [1,0,0,1,1,1,1,0,1,0,0,10,1,1,0,1,1,0,1,1,1,0,0,0,1,1]
542154231121 subs-320012 array [1,1,1,1,1,0,1,0,1,0,0,10,1,0,0,1,1,0,1,1,1,0,0,1,0,1]
551911114567 subs-002202 array [0,1,0,1,1,0,1,1,0,0,0,10,1,1,0,1,1,0,1,1,1,0,0,0,1,0]
551911114567 subs-002203 array [0,1,0,0,1,1,1,0,0,1,0,10,1,1,0,1,1,0,1,1,1,1,1,1,1,0]
551911114567 subs-002204 array [1,0,0,1,1,1,1,0,1,0,0,10,1,1,0,1,1,0,1,1,1,0,0,0,1,1]

23. Storage Strategy
• we tried some various ways to store
information
• it optimizes network traffic as well
0
500
1000
1500
2000
2500
1Objectsizeinbytes
Types of object representation
Object Representation
XML Java Byte Array JSON protobuff

24. Database volume
• the database size, decreases
considerably
• less data to handle, more
performance
0.00 20.00 40.00 60.00 80.00 100.00
XML
Java Byte Array
JSON
protobuff
Data Size in GB
StorageStrategy
Subscription Data Volume

25. C* New Data Model
• performance increased significantly
• reduced complexity: from 2 tables to 1, simpler, lighter
• reduced number of remote calls
• V1
• 1 query to the index table
• X queries (one per index returned)
• V2
• 1 query brings all data
• data volume reduced

26. Cassandra Cluster Configuration
• Geographically distributed
• 2 data centers in São Paulo Brazil

27. Ring Topology
Datacenter: DC1
===============
Status=Up/Down
|/ State=Normal/Leaving/Joining/Moving
-- Address Load Tokens Owns (effective) Host ID Rack
UN 200.xxx.xxx.73 29.58 GB 256 76,7% b9f890b6-6137-4359-90c2-74f87ce1676d RAC1
UN 200.xxx.xxx.72 29.8 GB 256 74,5% ec7fa873-edd9-4cb9-938d-60f1c9b8f742 RAC1
UN 200.xxx.xxx.71 30.76 GB 256 76,1% 1091799e-0617-42dd-a396-363f10c03295 RAC1
UN 200.xxx.xxx.74 26.68 GB 256 72,7% 984b848b-0ecb-4db3-a1fe-c9b088c295f6 RAC1
Datacenter: DC2
===============
Status=Up/Down
|/ State=Normal/Leaving/Joining/Moving
-- Address Load Tokens Owns (effective) Host ID Rack
UN 200.xxx.xxx.72 28.99 GB 256 100,0% f9b820d6-111f-4a3a-af6c-39d0e8e88084 RAC1
UN 200.xxx.xxx.71 30.36 GB 256 100,0% 120939bd-a6b4-4d88-b2cf-dbf79d93181c RAC1
UN 200.xxx.xxx.74 27.93 GB 256 100,0% c821b8f7-2224-4512-8a0e-0371460d900e RAC1
nodetool status

28. Hardware Infrastructure v1.0
• Centos 5.9
• 2x Intel(R) Xeon(R) CPU E5606 @ 2.13GHz (4 cores)
• 24GB / 32GB RAM
• 1x SATA 500gb (OS)
• 1x SSD CSSD-F120GB2 (data)
• Apache Cassandra v1.0.6
4 Servers

29. Hardware Infrastructure v2.0
• 2 Intel (R) Xeon (R) CPU @3.1GHz
• 128 GB of total RAM Memory per Server
• Running Cent OS 6.5
• 32 GB of RAM per VM
• 1 Intel (R) Xeon (R) CPU @3.1GHz
• 2 SSD Disks Model : CSSD-F120GBGT
• Configured as RAID0
• Apache Cassandra 1.2.13
6 Servers
6 Virtual Machines (one per physical server)
VMs

30. Keyspace
Keyspace: SBSPlatform:
Replication Strategy: org.apache.cassandra.locator.NetworkTopologyStrategy
Options: [DC2:3, DC1:3]
cassandra-cli : describe
Column Families:
ColumnFamily: subscription
ColumnFamily: delivery_ticket
ColumnFamily: hard_limit_control
ColumnFamily: hard_limit_rules
ColumnFamily: idx_config_subsc
ColumnFamily: user_directives

31. Column Family Status
Column Family: subscription
Space used (total): 13499012297
Number of Keys (estimate): 46.369.536
Read Count: 5599788263 / Read Latency: 0,497 ms.
Write Count: 5212858995 / Write Latency: 0,017
ms.
Compacted row mean size: 576
./nodetool cfstats SBSPlatform
Column Family: hard_limit_control
Space used (total): 7812531598
Number of Keys (estimate): 44.785.024
Read Count: 3987345295 / Read Latency: 0,509 ms.
Write Count: 11646786043 / Write Latency: 0,021 ms.
Compacted row mean size: 188

32. Overall cluster response time
Node 1 - : 200.xxx.xxx.71
load_avg: 0.39
write_latency(us): 900.8
read_latency(us): 553.6
Node 2 - : 200.xxx.xxx.72
load_avg: 0.51
write_latency(us): 874.1
read_latency(us): 620.5
Node 3 - : 200.xxx.xxx.74
load_avg: 0.35
write_latency(us): 834.87
read_latency(us): 515.6
Node 4 - : 200.xxx.xxx.73
load_avg: 0.35
write_latency(us): 900.87
read_latency(us): 700.6
Node 1 - : 200.xxx.xxx.71
load_avg: 0.63
write_latency(us): 806.3
read_latency(us): 882.3
Node 2 - : 200.xxx.xxx.72
load_avg: 0.37
write_latency(us): 802.8
read_latency(us): 969.0
Node 3 - : 200.xxx.xxx.74
load_avg: 0.62
write_latency(us): 965.7
read_latency(us): 887.43
Now: 2014-08-30 14:49:15
Total Reads/second: 13262
Total Writes/second: 9529
DATACENTER 1DATACENTER 2

33. O.S. and Software Customizations
• Java 1.7 + JNA
• Disable Swap
• NTP server in all servers
According to the Cassandra Docs the Recommended Settings for Production

34. O.S. - limits.conf
# number of open files
root soft nofile 100000
root hard nofile 100000
* soft nofile 100000
* hard nofile 100000
# allocated memory
root soft memlock unlimited
root hard memlock unlimited
* soft memlock unlimited
* hard memlock unlimited
# addressing (virtual memory)
root soft as unlimited
root hard as unlimited
* soft as unlimited
* hard as unlimited
# number of open processes
root soft nproc unlimited
root hard nproc unlimited
* soft nproc unlimited
* hard nproc unlimited

35. Daily Cluster Operations
Total Reads
Total Writes
> 1 billion

36. Conclusion: Why Cassandra?
• Good performance for Reads
• Excellent performance for Writes
• Read and Write throughput highly scalable (linear)
• Supports GEO distributed information
• Fault Tolerant
• Tunable consistency per client
• FOSS (Free and Open Source Software) + Support

37. Thank You!
Questions?
eiti.kimura@movile.com
eitikimura
facebook.com/eiti.kimura