Mongo DB in gaming industry

MongoDB in KIXEYE Analytics
Ben Goswami

Game Play
BP Video clips ( http://www.youtube.com/watch?v=uUHzppVn8WM)

©2011 Kixeye, Private and Confidential

Agenda

• Data scaling problems at KIXEYE analytics and
what technologies we use
• Example application: Cheater Detection
Engine
• How MongoDB helped us to solve it


Game Play
BP Attack Video (https://www.youtube.com/watch?v=qud_-18D2MU)


Game Play and Data
• Games generate lots of click data

• Click = event
o time of the event
o what happened before or after
o how the events are correlated


Attack: {
attacker : {
user:123
player_level: 31
fleet:
{
1:{
Weapons: {
name:“Ripper Cannon IV”
damage:306
accuracy:55
}
armors:{
}
…
}
2: {
}
_id" : ObjectId("506e23001d4524122c00004b"), "ip" : "10.54.72.206", "dt" :{ "_id" :
ObjectId("506e23001d4524dd2b0000bd"), "ip" : "10.54.72.206",
}
"&attacker_level=42&defender=0&defender_ext=salvage&defender_level=25&attack_type=PVE&attacking_
}
units=%7b%224071%22%3a%5b5%2c3%2c0%5d%7d&defending_units=%7b%221040%22%3a%5b4%2c
defender: {
0%2c4%5d%2c%221060%22%3a%5b1%2c0%2c1%5d%7d&attacking_shipconfig=%7b%22ships%22%3a
}
%5b%7b%22armors%22%3a%5b333%2c333%2c333%2c333%5d%2c%22weapons%22%3a%5b134%2c1
attack_time: 100354563
34%2c193%2c134%2c134%2c134%2c134%2c134%5d%2c%22specials%22%3a%5b542%2c561%2c571
attack_duration:290
%2c501%5d%2c%22hullID%22%3a4071%2c%22tacticalModules%22%3a%5b%5d%2c%22actives%22%3
sector:7
a%7b%22hp%22%3a9317.68457%2c%22rank%22%3a4%2c%22fltp%22%3a5%2c%22bid%22%3a54834
…
79%2c%22f%22%3a0%2c%22id%22%3a110%2c%22flt%22%3a5%2c%22vxp%22%3a10.858466%7d%7
d%2c%7b%22armors%22%3a%5b333%2c333%2c333%2c333.. "stage" : "new" }
}

How much data?

•Multi TBs / month (4-5x in next 6 months)

•Critical : Volume reads with speed

•< 1% data has actionable intelligence


MongoDB in KIXEYE

•Why:
– fast read + writes for high volume
– Document based architecture, schema less
– Indexing , auto failover, auto sharding
•Where:
– Games , KIXEYE Platform
– Analytics, Logging


Data technologies at Analytics

Technology Data Volume Query Type I/O speed

MySQL Small Adhoc + Read fast, only
< 300 GB Pre defined Aggregated data
Mongo Mid Predefined Read + Write speed
< 2 TB fast

Hadoop X-Large Adhoc Read slow
(Hive+HDFS) 2 TB+ writes: only insert
no update


An Example Application

Cheater Detection engine for games:
How mongoDB helped us


A simple backyard at start in BYM

Currency is time or $ or both


Sample record

• 4kb nested jason block
• base = {
base_name: “sweet_mango_db”
resources {
1: { name: "storage silo"
level: 5,
mode:“built"
time:123456902
2: { …
..}
...}
...} ..}

How people cheat?

• Each upgrade or repair
takes pre defined time

• if time+payment < predefined time = cheater
• victims chain create snowball effect


..and why they cheat...


Data volume for cheater detection

• 16Mil * 50 * 10 * 3
*....
• multiple time based
snapshots
• 0.01 % cheaters
• Detect < 5 mins
• That's searching
through lot of data
fast


.. and boss told

• Catch and auto
ban cheaters

• Don't label a good
guy a cheater


..team was ready for the challenge

• Speed+Accuracy of
application

• Technology evalauation


MongoDB fits the requirement

• read writes are fast because memory mapped
files compared to mysql+memcache or hbase
• Unstructured data => need schema less
• indexing on a nested data element


.. more on why mongo

• Auto purging thru TTL
• Auto sharding during traffic spikes
• Binary data support, fast uncompression
• Capped collection: writes faster


Development decision points

• Old map-reduce (prior to 2.2) vs New
Aggregation framework
• Global write locks removal in 2.2
• Lazy and Non lazy inserts


Result

• 100+ cheaters caught
per hour

• Better game balancing

• $$ saved


Wrap up : A good partnership

•Good partnership with KIXEYE and 10gen
•Consulting/Training and MMS service was very
helpful
•More mongoDB usages in future at KIXEYE


{Status: “The End”}

•{ next_action: “Q&A” }

• Like this jason? meet us outside – KIXEYE recruiting
{ Company : “KIXEYE”,
“Engineers/Total _employee” : “65%”,
Culture : [“Fun”, “Innovative”, “Passionate”]
Analytics_engg : { technology : “mongo, hadoop , mysql”,
tag : “big data, geek” }
}


Usage of data

• Data mining and aggregation to
o Optimize User Acquisition
o Optimize game analyzing game play
behavior and
Algo +
Data Optimize
r

Feedback

Mongo - pain points

• Diskspace handling
• No sql syntax support
• Capped collection - no deletes allowed
• No good client GUI based tool


Data related to player

• building has 5-10 levels
• building can have 3 states [built, damaged, repair in progress)
• an average base can have 50+ buildings
• 16 million such bases
• 45k people at any second [i.e base gets updated]
• Each level up or repair has time delay. People pay to reduce delay


Map-reduce in cheater detection

function mapf()
{
emit(this.userid, {userid:this.userid, total_time:this.length, bldg_id:1} ;
}

function reducef(key, values)
{
var r = {userid:key, total_time:0, count:0, avg_time:0};
values.forEach(function(v)
{r.total_time += v.total_time; }); return r;
}

function finalizef(key, value) { ... }


The End

•Q&A

•Love numbers and big data? We have a job for
you at analytics engg at KIXEYE. Talk to our
recruiting team


To scale data handling

• Write speed for large volume data
• Read speed + volume [aggregate+find]
• MongoDB provides good balance compared to other sql+nosql solutions
for certain use cases


Games+Platforms
devices

mysql mongo BI Mongo Cluster

ETL

non Real time >= 1 hr real time 5
API
mins

mySQL Mongo Hadoop mySQL
/mongo

Admin console
Data access tools

Dashboards Hive sql query API Scheduler,

Analytics Components - Birds' Eye View

Mongo DB in gaming industry

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