Building Scalable, Real Time Applications for Financial Services with DataStax

1. Building Scalable, Real Time Applications for Financial Services
Simon Webster
SVP & General Manager of EMEA

3. © DataStax, All Rights Reserved.
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3

4. DataStax Enterprise
Foundations for Finance
Daniel Cohen
Solutions Engineer

5. © DataStax, All Rights Reserved.
But Enough About Me…
• Solutions Engineer at DataStax
• LA ➜ SF ➜ NYC ➜ SF ➜ London
• Previously at JP Morgan in London
• Morgan Stanley, UBS, Merrill Lynch…
5

6. © DataStax, All Rights Reserved.
But Enough About Me…
• Solutions Engineer at DataStax
• LA ➜ SF ➜ NYC ➜ SF ➜ London
• Previously at JP Morgan in London
• Morgan Stanley, UBS, Merrill Lynch…
5

7. © DataStax, All Rights Reserved.
But Enough About Me…
• Solutions Engineer at DataStax
• LA ➜ SF ➜ NYC ➜ SF ➜ London
• Previously at JP Morgan in London
• Morgan Stanley, UBS, Merrill Lynch…
5

8. Sandy

9. 7
Sandy

10. 8
Sandy

11. © DataStax, All Rights Reserved.
DataStax Enterprise
9
The database for
cloud applications

12. © DataStax, All Rights Reserved.
1 Introductions
2 Technology Overview
3 Architectures
4 Use Cases
5 Questions?
10

13. © DataStax, All Rights Reserved.
What is Apache Cassandra?
• A distributed NoSQL database
– Google BigTable ✖ Amazon Dynamo
• Scale out with linear performance
– Just add nodes
• Continuously available
– Disaster avoidance, not disaster recovery
• Run on commodity hardware
– In the cloud, on premise, or hybrid
11

14. © DataStax, All Rights Reserved.
Distributed Architecture
• Fully distributed
– Data spread over multiple nodes
– All nodes participate in a cluster
– Configurable data replication
• Masterless
– All nodes are equal
– Read from or write to any node
• No Single Point of Failure
12
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Node 3
3rd copy

15. © DataStax, All Rights Reserved.
Scale Out Linearly
• Need more storage?
– Add more nodes.
• Need greater throughput?
– Add more nodes.
• Predictable, linear performance gains
13

16. © DataStax, All Rights Reserved.
Scale Out Linearly
• Need more storage?
– Add more nodes.
• Need greater throughput?
– Add more nodes.
• Predictable, linear performance gains
13

17. © DataStax, All Rights Reserved.
Scale Out Linearly
• Need more storage?
– Add more nodes.
• Need greater throughput?
– Add more nodes.
• Predictable, linear performance gains
13

18. © DataStax, All Rights Reserved.
Linear Scale Out at Netflix
14
Source:
The Netflix Tech Blog
http://techblog.netflix.com/
2011/11/benchmarking-
cassandra-scalability-on.html

19. © DataStax, All Rights Reserved.
A Quick Vocabulary Lesson
15

20. © DataStax, All Rights Reserved.
A Quick Vocabulary Lesson
15
Replication Factor – RF
• Specifies how many copies of a datum C* distributes across nodes
• Highly configurable for SLA & network topology (racks, data centers)
CREATE KEYSPACE "Excalibur" WITH REPLICATION =
{'class' : 'NetworkTopologyStrategy', 'CPH1' : 3, 'CPH2' : 3};

21. © DataStax, All Rights Reserved.
A Quick Vocabulary Lesson
15
Replication Factor – RF
• Specifies how many copies of a datum C* distributes across nodes
• Highly configurable for SLA & network topology (racks, data centers)
CREATE KEYSPACE "Excalibur" WITH REPLICATION =
{'class' : 'NetworkTopologyStrategy', 'CPH1' : 3, 'CPH2' : 3};
• Specifies how synchronized a row of data must be across C* nodes
• Tunable consistency: client application decides CL for each operation
… ONE … LOCAL_QUORUM … QUORUM … EACH_QUORUM …
Consistency Level – CL

22. © DataStax, All Rights Reserved.
A Quick Vocabulary Lesson
15
Replication Factor – RF
• Specifies how many copies of a datum C* distributes across nodes
• Highly configurable for SLA & network topology (racks, data centers)
CREATE KEYSPACE "Excalibur" WITH REPLICATION =
{'class' : 'NetworkTopologyStrategy', 'CPH1' : 3, 'CPH2' : 3};
• Specifies how synchronized a row of data must be across C* nodes
• Tunable consistency: client application decides CL for each operation
… ONE … LOCAL_QUORUM … QUORUM … EACH_QUORUM …
Consistency Level – CL

23. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

24. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

25. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Write
Consistency Level = QUORUM
Replication Factor = 3
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

26. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Parallel
Write
Write
Consistency Level = QUORUM
Replication Factor = 3
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

27. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Parallel
Write
Write
Consistency Level = QUORUM
Replication Factor = 3
5 μs ack
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

28. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Parallel
Write
Write
Consistency Level = QUORUM
Replication Factor = 3
5 μs ack
12 μs ack
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

29. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Parallel
Write
Write
Consistency Level = QUORUM
Replication Factor = 3
5 μs ack
12 μs ack
12 μs ack
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

30. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Parallel
Write
Write
Consistency Level = QUORUM
Replication Factor = 3
5 μs ack
12 μs ack
500 μs ack
12 μs ack
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

31. © DataStax, All Rights Reserved.
Node 1
1st copy
Tunable Consistency
16
Parallel
Write
Write
Consistency Level = QUORUM
Replication Factor = 3
5 μs ack
12 μs ack
500 μs ack
12 μs ack
Node 2
2nd copy
Node 3
3rd copy
Node 4
Node 5

32. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 3
3rd copy
Node 3
3rd copy
Node 4
Node 5

33. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 3
3rd copy
Node 3
3rd copy
Node 4
Node 5

34. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Read
Consistency Level = QUORUM
Replication Factor = 3
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 3
3rd copy
Node 3
3rd copy
Node 4
Node 5

35. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Read
Consistency Level = QUORUM
Replication Factor = 3
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 4
Node 5

36. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Parallel
Read
Read
Consistency Level = QUORUM
Replication Factor = 3
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 4
Node 5

37. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Parallel
Read
Read
Consistency Level = QUORUM
Replication Factor = 3
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 4
Node 5

38. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Parallel
Read
Read
Consistency Level = QUORUM
Replication Factor = 3
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 4
Node 5

39. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Parallel
Read
Read
Consistency Level = QUORUM
Replication Factor = 3
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 4
Node 5

40. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Parallel
Read
Read
Consistency Level = QUORUM
Replication Factor = 3
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 3
3rd copy
Node 3
3rd copy
Node 4
Node 5

41. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Parallel
Read
Read
Consistency Level = QUORUM
Replication Factor = 3
Hints
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 3
3rd copy
Node 3
3rd copy
Node 4
Node 5

42. © DataStax, All Rights Reserved.
Continuous Availability
17
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Parallel
Read
Read
Consistency Level = QUORUM
Replication Factor = 3
Hints
Node 4
Node 2
2nd copy
Node 1
1st copy
Node 3
3rd copy
Node 3
3rd copy
Node 4
Node 5

43. © DataStax, All Rights Reserved.
Multiple Data Centers
18
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Node 3
3rd copy
Node 1
1st copy
Node 4
Node 5
Node 2
2nd copy
Node 3
London DCNew York DC

44. © DataStax, All Rights Reserved.
Disaster Avoidance
19
New York
London
Singapore

45. © DataStax, All Rights Reserved.
Disaster Avoidance
19
New York
London
Singapore

46. © DataStax, All Rights Reserved.
Disaster Avoidance
19
New York
London
Singapore

47. © DataStax, All Rights Reserved.
Disaster Avoidance
19
New York
London
Singapore

48. © DataStax, All Rights Reserved.
Mixed Workload DSE Cluster
Cassandra
Only DC
Transactions
Workload Isolation
20
Cassandra
+ Spark DC
Analytics
Analytical
Application
Operational
Application

49. © DataStax, All Rights Reserved.
• Syntax similar to RDBMS SQL
• Create objects via DDL
– e.g. CREATE…
• INSERT, UPDATE, DELETE
• GRANT, REVOKE
• SELECT…WHERE
• Command line and GUI tools
CQL: Cassandra Query Language
21
CQL Example
CREATE TABLE market_prices (
symbol TEXT,
date TIMESTAMP,
price DECIMAL,
side INT,
PRIMARY KEY (symbol, date)
) WITH CLUSTERING ORDER BY
(date DESC);

50. © DataStax, All Rights Reserved.
Drivers & Connectors
• Certified by DataStax:
– Java, C#
– Node.js, Python
– Ruby, C / C++
– PHP, ODBC
– Connector for Apache Spark
• Many community drivers
– Go, Clojure
– Erlang, Rust
22

51. © DataStax, All Rights Reserved.
1 Introductions
2 Technology Overview
3 Architectures
4 Use Cases
5 Questions?
23

52. © DataStax, All Rights Reserved.
Application Tier Resilience
• Stateless ➜ fault tolerant
• Horizontally scalable
• Store sessions in Cassandra
– Sessions replicate naturally
– Recover on disaster / failure
• UI ➜ intelligently stateful
– Active failover to app server
24
App
Server
Cache
C* C*
App
Server
Cache
DC LDN1 DC LDN2

53. © DataStax, All Rights Reserved.
Application Tier Resilience
• Stateless ➜ fault tolerant
• Horizontally scalable
• Store sessions in Cassandra
– Sessions replicate naturally
– Recover on disaster / failure
• UI ➜ intelligently stateful
– Active failover to app server
24
App
Server
Cache
C* C*
App
Server
Cache
DC LDN1 DC LDN2

54. © DataStax, All Rights Reserved.
Application Tier Resilience
• Stateless ➜ fault tolerant
• Horizontally scalable
• Store sessions in Cassandra
– Sessions replicate naturally
– Recover on disaster / failure
• UI ➜ intelligently stateful
– Active failover to app server
24
App
Server
Cache
C* C*
App
Server
Cache
DC LDN1 DC LDN2

55. © DataStax, All Rights Reserved.
Application Tier Resilience
• Stateless ➜ fault tolerant
• Horizontally scalable
• Store sessions in Cassandra
– Sessions replicate naturally
– Recover on disaster / failure
• UI ➜ intelligently stateful
– Active failover to app server
24
App
Server
Cache
C* C*
App
Server
Cache
session
DC LDN1 DC LDN2

56. © DataStax, All Rights Reserved.
Application Tier Resilience
• Stateless ➜ fault tolerant
• Horizontally scalable
• Store sessions in Cassandra
– Sessions replicate naturally
– Recover on disaster / failure
• UI ➜ intelligently stateful
– Active failover to app server
24
C* C*
App
Server
Cache
session
DC LDN1 DC LDN2

57. © DataStax, All Rights Reserved.
Application Tier Resilience
• Stateless ➜ fault tolerant
• Horizontally scalable
• Store sessions in Cassandra
– Sessions replicate naturally
– Recover on disaster / failure
• UI ➜ intelligently stateful
– Active failover to app server
24
C* C*
App
Server
Cache
session
DC LDN1 DC LDN2

58. © DataStax, All Rights Reserved.
Application Tier Resilience
• Stateless ➜ fault tolerant
• Horizontally scalable
• Store sessions in Cassandra
– Sessions replicate naturally
– Recover on disaster / failure
• UI ➜ intelligently stateful
– Active failover to app server
24
C* C*
App
Server
Cache
session
DC LDN1 DC LDN2

59. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25

60. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25

61. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25

62. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25

63. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25

64. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25

65. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25
❓

66. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25
❌

67. © DataStax, All Rights Reserved.
…
A Resilient Enterprise Architecture
DC LDN2
App App
App App
DC NY1
App App
App App
DC LDN2
C*
DC LDN1
C*
DC NY2
C*DC NY1
C*
25
❌

68. © DataStax, All Rights Reserved.
Unify & Scale Legacy Infrastructure
26
…USA
Equities
UK
FX
UK
Bonds
Global
Users
Legacy
Systems
USA
FX
DataStax Enterprise ClusterC*
User Interface / Application Services

69. © DataStax, All Rights Reserved.
Microservices on Legacy
27
Users µServices
DC NY1
A E
F T
DC LDN1
A B
E F
Messages
DC NY1
DC LDN1
DC NY1
DC LDN1
C*
C*
USA
Equities
DSE
UK
FX
Legacy

70. © DataStax, All Rights Reserved.
DSE Real-time Analytics Reference Architecture
HTTP Application Message Queue
Streaming
Analytics
Batch
Analytics
Real-time
28

71. © DataStax, All Rights Reserved.
1 Introductions
2 Technology Overview
3 Architectures
4 Use Cases
5 Questions?
29

72. © DataStax, All Rights Reserved.
DataStax Use Cases
• Customer 360°
• Master data management
• Customer profile management
• Authentication & identity management
• Product personalization
• Anti-fraud & money laundering
• Payments & transactions
• Risk reporting / capital adequacy
• Market data capture / replay
30

73. © DataStax, All Rights Reserved.
Customer 360° / User Profile
• Consolidate data, reduce complexity
• Alleviate strain on legacy systems
• Integrated data sovereignty
• Eliminate downtime
• Recommend higher margin products
• Customer personalization
• Audit and regulatory compliance
• Full text indexing and document search
31
“DataStax has allowed us to break the
complexities of our legacy systems, empowering
us to leverage data to innovate and make more
informed decisions so we can provide a truly
personalized and premium experience to our
customers.”

74. © DataStax, All Rights Reserved.
Payments & Transactions
• Transaction history tagging, search and
budgeting, cash flow prediction
• Product recommendation based on
purchasing history
• Data safe against loss of node, rack, or
data center
• Shed load from legacy systems to reduce
cost and provide better user experience
“We had reached the limits of our scalability.
We wanted to remove all single points of
failure. We had to be active-active. That's
why we chose Cassandra.”
32

75. © DataStax, All Rights Reserved.
Regulatory Compliance
“The platform enables traders to capture vast
amounts of versioned market data which is then
used for risk management purposes in real time.”
33
• Capture and analyze every trade,
every position for every trader
• Real-time risk analysis
• Capital adequacy
• MiFID II
• Globally replicated, 100% uptime
• Low latency, high ingest

76. © DataStax, All Rights Reserved.
“DataStax Enterprise gives us the power to
change information in our supporting
systems into actionable insights… we can
not only scale but give our users a real-
time, engaging customer experience.”
• Financial product recommendation
• Provide the “small bank” feel through
personalization
• Real-time recommendation using transaction
data, search, and browsing history
• Measure engagement and satisfaction to
prevent flight
• Website and mobile metrics
• Prevent customer churn
Recommendations & Engagement
34

77. © DataStax, All Rights Reserved.
Market Tick & Time Series Data
• Capture high fidelity time series
information at high ingest rates and
low latencies
• 100% uptime even in the event of
data center loss
35
“We primarily use Cassandra as a distributed data store
for Financial Instrument and Time-Series data, where
the ‘master’ data stores cannot scale globally and
provide the high availability needed to support a global
application with heavy user demand.”

78. © DataStax, All Rights Reserved.
Mission Critical Applications
36
“DSE is a perfect match… easing availability
challenges by being active-active and
having an always-on architecture”
Source: Exploiting Hotel Cassandra ➜ http://bit.ly/1mf0KRE

79. Thank you!
Daniel Cohen
Solutions Engineer
daniel.cohen@datastax.com
@CodaAzzurra