Cassandra Summit 2014: A Train of Thoughts About Growing and Scalability — Bumping up Startup...

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.

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

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

Data Distribution Subscriptions by Country

1% 2%

4%

25%

68%

Others

Colombia

Argentina

Mexico

Brazil

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

Basic Architecture Design

•  scalabe •  high availability •  high performance

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

Improved Distributed Design A Cassandra Based Solution

•  the operations are distributed across the nodes

•  we achieved linear

scalability

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

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

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)

);

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

Data Model V1 – Quering Profile

user_subscriptions


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


user_subscriptions


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


#cql> _

2nd step •  query all the user’s subscriptions by its id

551911114567 subs-002202

551911114567 subs-002203

551911114567 subs-002204

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

Data Model V2

CREATE TABLE subscription (

phone-number text, subscription-id text, serialized blob,

PRIMARY KEY(phone-number, subscription-id) );

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]

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]

Storage Strategy

•  we tried some various ways to store information

•  it optimizes network traffic as well

0

500

1000

1500

2000

2500

1 O

bjec

t siz

e in

byt

es

Types of object representation

Object Representation

XML Java Byte Array JSON protobuff

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

Stor

age

Stra

tegy

Subscription Data Volume

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

Cassandra Cluster Configuration

•  Geographically distributed

•  2 data centers in São Paulo Brazil

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

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 and commit logs)

•  Apache Cassandra v1.0.6

4 Servers

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

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

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

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


Node 4 -‐ : 200.xxx.xxx.73



Node 2 -‐ : 200.xxx.xxx.72



Now: 2014-‐08-‐30 14:49:15 Total Reads/second: 13262 Total Writes/second: 9529

DATACENTER 1 DATACENTER 2

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

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

Daily Cluster Operations

Total Reads

Total Writes

> 1 billion

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

Thank You!

Questions?

[email protected]

eitikimura

facebook.com/eiti.kimura

Cassandra Summit 2014: A Train of Thoughts About Growing and Scalability — Bumping up Startup...

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