Travellers are demanding more exhaustive, accurate, and relevant results when they search for flights, and they want these results instantly – even when there can be 100 Billion travel options for a single trip. Amadeus’s “Instant Search” feature was built to meet those requirements. These searches are not trivial. Several terabytes of constantly evolving data is needed to reply instantly to questions like, “I live in Frankfurt, where can I go this weekend for €200?” “What’s the cheapest and most convenient flight for a MongoDB Europe attendee?” This technical session will show you how Amadeus integrated MongoDB into its system, and how it allowed us to handle huge numbers of updates and searches in a high-volume system, to deliver the next generation of flight search products. It will cover topics such as how we discovered which extra indexes were needed, how we were able to get the balancer to meet our needs, and how we modelled our data for optimal performance.

1. { “Amadeus”: ”Instant Search for Travel” }
©2016AmadeusS.A.S.
Breaking the boundaries of
travel inspiration
Loic CRETIN
Pierre PETITPREZ
November 15th
MongoDB Europe - London

2. About us…
©2015AmadeusITGroupSA
Pierre PETITPREZ
Software Engineer,
Middleware
Loic CRETIN
Manager Software
Development

3. Page 3
©2016AmadeusS.A.S.
Agenda
{
“1”: “Who are we?”,
“2”: “Data modelling”,
“3”: “Sharding”,
“4”: “Advanced queries”,
“5”: “3.2 Connections”,
“6”: “MongoDB @ Amadeus”,
“7”: “What is coming…”
}

4. { “Topic”: “Who are we?” }1
©2016AmadeusS.A.S.

5. Amadeus is a technology company dedicated to the
global travel industry.
We are present in 195 countries
with a worldwide team of more than 13,000 people.
Our solutions help improve the
business performance
of travel agencies, corporations, airlines,
airports, hotels, railways and more.
Amadeus in a few words
©2015AmadeusITGroupSA

6. Robust global operations
©2015AmadeusITGroupSA
1.6+
billion
data requests
processed per day
566+
million
travel agency
bookings
processed in 2015
747+
million
Passengers
Boarded (PBs)
in 2015
95%of the world’s
scheduled
network airline
seats

7. Page 7
Amadeus Search, Shopping and Pricing solutions
Airlines Travel Agency
Online Web Sites
Search
ShoppingPricing
Re-Pricing

8. Page 8
to complex infrastructure
©2016AmadeusS.A.S.
Consumer relevance
& natural search
Content
exhaustiveness
Instant response
Time
25% travelers are not
decided on destination
42% travelers are not
decided on the date
76% travelers have no clear
idea on budget to spent
From simple idea…

9. Page 9
Use case description
Leaving from London, where
can I go with 1200€ from 17th
to 21th of December?
NYC
1038 €
SAO
1170 €
CPT
1006 €
SAO
1170 €
CPT
1006 €

10. Page 10
Use case description
Leaving from London, where
can I go with 1200€ from 17th
to 21th of December?
NYC
1038 €
SAO
1170 €
CPT
1006 €
SAO
1170 €
CPT
1006 €
LON NYC 12/17 12/21
Travel.com
Search1 adult economy
View deal
1038€ 10:50 LGW -> 18:45 JFK 12h 55m 1 stop (MAD)
22:10 JFK -> 10:00 LHR 6h 50m nonstop
View deal
1041€ 14:05 LHR -> 19:30 JFK 10h 25m 1 stop (AMS)
17:55 JFK -> 08:10 LHR 9h 15m 1 stop (AMS)
View deal
1084€ 11:20 LHR -> 19:02 JFK 12h 44m 1 stop (BOS)
11:25 LGA -> 06:50 LHR 10h 25m 1 stop (RDV)

11. Page 11
Deep dive into the architecture
©2016AmadeusS.A.S.
Cache
Computation
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Indexing
Web Service

12. Page 12
In-house NoSQL Store
©2016AmadeusS.A.S.
In-house technology
• Functional data partitioning
• Non blocking updates, eventual consistency
• Indexing for multi-dimensional range queries
• In-memory and/or disk
Scalability limit
• Use case with more than 1000
recommendations
• Atomic data update for high cache
quality
Move to Standard NoSQL technology

13. { “Topic”: “data modeling” }2
©2016AmadeusS.A.S.

14. Page 14
Data modeling
Principle & goals
©2015AmadeusITGroupSA
Why?
Limit index entries,
document fetch,
operation
What?
Find the best
data model
and query
strategy
How?
Go deeper into use case

15. Page 15
Data modeling
Focus on search use cases
©2015AmadeusITGroupSA
Description
• Get all price results of a cell
• Inputs: Client, Origin, Destination,
Departure and return date
Service Level
Agreement
• Response time O(100)ms
• No down time

16. Page 16
Data modeling
• 30 stay durations per departure date
• 360 departure dates per city pair
• 250 price results per date
combination
• Thousands of city pairs per client
• Hundreds of clients
• Total >100,000,000,000 results
• bookable solution
Focus on data domain
©2015AmadeusITGroupSA
LAX
NYC
Stay Duration
Date
LAX
BOS
Stay Duration
Date
NYC
LAX
Stay Duration
Date
BOS
LAX
Stay Duration
Date
BOS
NYC
Stay Duration
Date
NYC
BOS
Stay Duration
Date
30
360
O(1000)
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
Travel Solution
250

17. Page 17
Data modeling
• Updates come per group of cell(s)
• Add/remove one date every day
• Update could be done several time a
day
• Capacity to update all data in a day
Focus on update use cases
©2015AmadeusITGroupSA
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
LAX
NYC
Stay Duration
Date
LAX
NYC
Stay Duration
Date

18. Page 18
Data model: 1 document = 1 Cell
©2015AmadeusITGroupSA
Document format:
{
"_id" : ObjectId("570…2d6"),
"Orig" : "NYC",
"Dest" : "LAX",
"DepDate" : ISODate("2016-06-27…"),
"Stay" : 2,
"PriceResultList" : [
{…Price result 1…},
{…Price result 2…},
…
]
}
Highlights
• Duplicate the key fields
• Divide by 250 the index
entries and operations
• 468kB documents

19. Page 19
Bulk.find(
{
AX
).update(
Queries
©2015AmadeusITGroupSA
db.CLI.Update(
{"Orig" : "NYC",
"Dest" : "LAX",
"DepDate" :
ISODate("2016-06-27…"),
"Stay" : 2},
{$set:{
PriceResultList:[{…},…]
}},
{upsert:1}
)
db.CLI.find(
{"Orig" : "NYC",
"Dest" : "LAX",
"DepDate" :
ISODate("2016-06-27…"),
"Stay" : 2},
{_id:0,PriceResultList:1}
)

20. Page 20
Indexes and Shard key
©2015AmadeusITGroupSA
{
"Orig" : 1,
"Dest" : 1,
"DepDate" : 1,
"Stay" : 1
}
{
"v" : 1,
"key" : {
"Orig" : 1,
"Dest" : 1,
"DepDate" : 1,
"Stay" : 1
},
"name" : "KeyIdx"
}
{
"v" : 1,
"key" : {
"DepDate" : 1
},
"name" : "expireAt",
"expireAfterSeconds" : 172800
}
Data Access index Expiration index Shard key

21. { “Topic”: “Sharding - solution for
limitations with consequences” }
3
©2015AmadeusITGroupSA

22. Page 22
Why sharding help you
©2015AmadeusITGroupSA
Replication throughput limitation
Initial Sync speed
Update performance (index size reduction)
Replication lag

23. Page 23
Sharding consequence for us
Split/Migration chunks problems
©2015AmadeusITGroupSA
Split & Balancing leads to peak of response time
Shard key important (Date into shard key, Empty chunk)
Split capacity decrease with number of chunks
Slow balancing SplitAt loop
3 days for 200k
Add of 4 shards
1 week of balancing

24. Page 24
Split/Migration chunks
Solution
©2015AmadeusITGroupSA
Shard 1
OldCol Chunks
Chunk1
Shard 2
Chunk2
NewCol
Chunks
Chunk1
Chunk2
Feed Search
Free
Space
Deactivate autosplit & balancing
Shard key: Hashkey
Manual dispatch (double feed/search)

25. { “Topic”: “Advanced queries” }5
©2015AmadeusITGroupSA

26. Page 26
Use case description
Leaving from London, where
can I go with 1200€ from 17th
to 21th of December?
NYC
1038 €
SAO
1170 €
CPT
1006 €
SAO
1170 €
CPT
1006 €

27. Page 27
NYC
LON
Stay Duration
Date
ORI DEST OutDate Stay
Fares/Flights 3
NYC
PAR
Stay Duration
Date
ORI DEST OutDate Stay
Fares/Flights 3
NYC
LAX
Stay Duration
Date
LON-???
©2015AmadeusITGroupSA
Open destination
ORI DEST OutDate Stay
Fares/Flights 3
ORI DEST OutDate Stay
Fares/Flights 3
ORI DEST OutDate Stay
Fares/Flights 3
ORI DEST OutDate Stay
Fares/Flights 3
ORI DEST OutDate Stay
Fares/Flights 3
ORI DEST OutDate Stay
Fares/Flights 3
• Fixed Value:
ü Client
ü Origin
ü Destination
• Range:
ü Departure date
ü Return date
ü Budget
Return best travel solutions
(lowest price) per destinations
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250
ORI DEST OutDate Stay
Fares/Flights 1
ORI DEST OutDate Stay
Fares/Flights 2
ORI DEST OutDate Stay
Fares/Flights 3
…
ORI DEST OutDate Stay
Fares/Flights 249
ORI DEST OutDate Stay
Fares/Flights 250

28. Page 28
LON - ???
©2015AmadeusITGroupSA
Open destination
db.IFL.aggregate(
[{"$match": {
"Ori": "NYC",
"DepDate": {"$gte": ISODate("2016-06-20T…"),
"$lte": ISODate("2016-06-30T…")},
"Stay": {"$gte": 4, "$lte": 10},
"PriceResultList.amount": {"$lte": 900}
}},
{"$unwind": "$PriceResultList"},
{"$project": {
"Dest": "$Dest",
"matcher": {
"prices": "$PriceResultList.amount",
"doc": "$PriceResultList"
}
}},
{"$group": {
"_id": {
"Dest": "$Dest",
},
"price": {
"$min": "$matcher"
}}}]);
Using MongoDB aggregation
framework
Performance issue taking
minutes

29. Page 29
Data model: 1 document = 1 Cell = “best” price
©2015AmadeusITGroupSA
{
"_id" : ObjectId("570…2d6"),
"Orig" : "NYC",
"Dest" : "LAX",
"DepDate" : ISODate("2016-06-27…"),
"Stay" : 2,
"PR" : {
"type" : "CHP",
"amount" : 215.12,
…price result fields…
}
}
}
additional index:
{
"v" : 1,
"key" : {
"Orig" : 1,
"PR.type" : 1,
"Dest" :1,
"PR.amount" : 1,
"Stay" : 1,
"DepDate" : 1,
},
"name" : "Cheapest"
},
New Collection (only cheapest),
new index
Using MongoDB aggregation
framework
Performance issue still above
the second
equality
sort
range

30. Page 30
Query strategy: Open destination
©2015AmadeusITGroupSA
Two collections
• Destinations candidate
• City pair prices
Don’t use MongoDB aggregation framework (combination of find)
Grouping with Aggregation framework took 350 ms
Better performance

31. { “Topic”: “3.2 Connections” }5
©2015AmadeusITGroupSA

32. Page 32
3.2 Connection pools
Problems
©2015AmadeusITGroupSA
_ Open search activation on 3.2
_ Migration from 3.0 to 3.2
_ Search query response time
around 5 second (several peaks
per minutes)
_ Open/Close connections
_ Peaks disappear with higher
search traffic
Max response time
Average response time
Connection activity per shard (ops-manage)

33. Page 33
3.2 Connection pools
Investigation on-going
©2015AmadeusITGroupSA
_ Connection pooling enhancement
• 3.0: One Global connection pool
• 3.2: Tunable smaller connection pool
_ taskExecutorPoolSize parameter set by default to 0
• Automatically set depending the number of available cores
• 64 cores on machine hosted mongos -> many connections idle
_ SERVER-25027 Server ticket

34. Page 34
3.2 Connection pools
Investigation on-going
©2015AmadeusITGroupSA
_ taskExecutorPoolsize set to
• 4 on mongod side
• 16 on mongos side
_ Connections parameters tunable into 3.4 (3.4.0-rc3)
• kDefaultMinConns
• kDefaultRefreshTimeout
• kDefaultRefreshRequirement
• kDefaultHostTimeout
Max response time
Average response time

35. { “Topic”: “MongoDB @ Amadeus” }6
©2015AmadeusITGroupSA

36. Page 36
MongoDB Enterprise
©2015AmadeusITGroupSA
Automation
Monitoring
Backup /
restore
Training /
consulting
Commercial
support
Security
MongoDB
Enterprise

37. Page 37
Topology DBOps Manager
Operations
©2015AmadeusITGroupSA
Internal tooling
Monitoring
Alerting
Operability
Configuration deployment

38. Page 38
Middleware
©2015AmadeusITGroupSA
Enterprise-wide standard
Client side monitoring
Built-in best practices
Resiliency (Mongos HA)

39. Page 39
Deployment
©2015AmadeusITGroupSA
Monitoring / Alerting
MongoDB Cluster
Server 1
Shard 1
Shard 2
Shard 3
…
Shard n
Server 2
Shard 1
Shard 2
Shard 3
…
Shard n
Server 3
Shard 1
Shard 2
Shard 3
…
Shard n
Ops Manager
Corporate tooling
Application servers
BE
S S
BE
S S
BE
S
S

40. Page 40
Deployment
©2015AmadeusITGroupSA
_ Hardware
• 24 physical machines
• 1152 CPU
• 6 TB of RAM
• 72 TB Fusion IO
• 3 members replica set
• 56 shards in total
• 2 Clusters
_ Traffic
• 14.6 Billions Price results
• 40 TB Data uncompressed
• 5.3 TB Data compressed on disk
• 12 Millions transaction Search / day
• Mongo response time ~0.15ms/PR
• 120 Millions transaction Update / day
• 18.6 Billions PR / day
• Mongo response time ~0.10ms/PR
Add
shards
Add
shards Add
shards
Size per shard

41. { “Topic”: “What’s coming…” }7
©2015AmadeusITGroupSA

42. Page 42
What’s coming… Scaling up
©2016AmadeusS.A.S.
Handle huge
amount of data
Manageable and Multi
cluster environment

43. Page 43
What’s coming… Data Store into Cloud Services
©2016AmadeusS.A.S.
_ Closer to our customers
• Reduce the latency
_ Remote operations
• Install appliances within customer premises when
appropriate
_ Multiple Cloud/data-center capability

44. Page 44
Takeaways
Next step
_ Follow MongoDB
evolution (3.2, 3.4++)
_ Scaling up
infrastructure
_ Multi Cloud/data-center
©2016AmadeusS.A.S.
Production
_ Volume ramp up
_ 24 Physical machines
_ Increase to > 100
Shards
Performance
_ Benchmark for
performance
optimization
_ Find, sort, limit vs
Aggregate Framework
_ Lessons learned
Collaboration
_ MongoDB JIRA
contributor
_ Amadeus MongoDB
community

45. You can follow us on:
AmadeusITGroup
amadeus.com/blog
amadeus.com
Thank you
©2015AmadeusITGroupSA