Data stream processing and micro service architecture. English version

1. Data Stream processing
and Micro-service
architecture
© 2015 BY VYACHESLAV BENEDICHUK

2. Vyacheslav Benedichuk
Software development professional
Overall IT experience spanning 15 years.
Project management experience 9+ years.
Biggest team managed - 25 members.
Biggest project developed – over 15 man-years
https://ru.linkedin.com/in/vbenedichuk

3. Presentation plan
History
Today
Challenges
Solutions Advantages Disadvantages
Application
samples
Implementation
suggestions
Related design
patterns
Questions

4. History

5. History
Application Business logic DTO
DAL
UI
Service
Service
Service
Service
Service
History
Today
Challenges
Solutions Advantages Disadvantages
Application
samples
Implementation
suggestions
Related design
patterns
Questions

6. Today Challenges
Application complexity grows
•Complexity of interactions between layers is growing
•Layers interaction complexity growing
•Intensity of communications between developers is growing
•More complex test cases
Data size growth
•Classic applications require a lot of resources in a single physical node. Such applications are not intended for horizontal scalability.
•Single issue can break the whole application and may lead to data loss
Instability of data flows
•Levels of user activity in a social network very susceptible to the time of day
•Buyer activity pm an online shopping site very susceptible to the time of day and season
•Hardware purchased for peak workload is under-used outside peak times
•Hardware purchased for medium workload is inadequate for peak workload
History
Today
Challenges
Solutions Advantages Disadvantages
Application
samples
Implementation
suggestions
Related design
patterns
Questions

7. Solution
Build the application as a set of small (50-300 lines
of code) loosely coupled modules.
• Each micro-service works independently
• Data exchange between layers is made with standard messages
• Services linked with message queues. Other data interaction
protocols may be used (for example REST), however message
queues increase overall system stability.
History
Today
Challenges
Solution Advantages Disadvantages
Application
samples
Implementation
suggestions
Related design
patterns
Questions

8. Advantages
overview
Fault tolerance Scalability
Better performance
evaluation and
optimization of the
cost of ownership
Ease of
development
Updates do not
require downtime
Simplified A/B and
regression testing
History
Today
Challenges
Solution Advantages Disadvantages
Application
samples
Implementation
suggestions
Related design
patterns
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9. Advantages
Fault tolerance
The number of single points of
failure is minimal or none. Failure
of a single service instance, or
one of the nodes does not lead to
failure of the entire system. It
only affects performance in a
short term.
Service 1
SaaS Queue
Service 2
Service 3 Service 3
SaaS Queue
Service 2 Service 2
Service 1 Service 1
Service 3
SaaS Data Storage
Fault tolerance

10. Advantages
Scalability
Scalability
Local load spikes (seconds) are
flattered by message queues.
Response time grows but no
requests are denied.
Global load spikes (tens of seconds
and more) are handled by starting
new worker instances.
Fault tolerance in case of DDoS
attack is limited by finances only. If
provided in the cloud, it is much
cheaper than buying in-house
infrastructure designed for
excessive load.
Service 1
SaaS queue
Service 2
Service 3 Service 3
SaaS queue
Service 1 Service 1
Service 3
SaaS Data Storage
5 messages in the
queue
100500 messages in
the queue
Service 3
(запускается)
Service 2 (stopping)
Service 3
(запускается)Service 3
(Starting)

11. Advantages
Ease performance evaluation and optimization of the cost of ownership
Ease performance evaluation
Maximum load capacity of each particular service
can be evaluated independently.
On the basis of the "laboratory" measurement of
performance and the projected load, optimal
network topology can be constructed. It allows the
minimum cost of ownership to be determined.
Different combinations of infrastructure solutions
can be compared on the basis of actual
performance indexes rather than "by eye".
Infrastructure investments are rising in proportion
with the growing business without spikes.
You can freely distribute the components of the
system between in-house and cloud datacenters.
SaaS
queue
Service 2
Measured load capacity is 300
messages per second.
SaaS
queue
Test data stream
100500000 messages accumulated
1080000 after the 1 hour of
processing
With a
minimum
flow of 450
messages per
second with
hour peaks of
up to 1,500
messages per
second, we
need 2
instances of
pre-paid (or
in house) and
up to 3-4 on-
demand
services.

12. Advantages
Ease of development
Ease of development
Micro-services code can be easily
maintained due to its size.
Micro-service independence
allows services to be created
using different technology stacks
and with minimal or no
integration overhead.
Service 1 (php)
SaaS queue
Service 2 (c#)
Service 3 (Java) Service 3 (Java)
SaaS queue
Service 2 (c#)
Service 1 (php)
SaaS Data Storage
100 lines of code
vs
1000 lines of code

13. Advantages
Update does not require the downtime
New version can be launched by
sequential launch of new micro-
services. Accumulated requests
will be processed by the old
infrastructure. New requests will
be processed by the new
infrastructure.
Old and new versions can work
simultaneously.
SaaS queue
(accumulated data)
Service 2 V1
Service 3 V1
SaaS queue
Service 2 V1 Service 2 V2
Service 1 V2
SaaS Data Storage
Updates do not require downtime
Service 3 V1
SaaS queue
Service 3 V1

14. Advantages
Simplified A/B and regression testing
A/B testing and regress testing
can be implemented by running 2
micro service instances in
parallel.
Service 1 V1
Service 2 V1 Service 2 V2
Service 1 V2Simplified A/B and regression
testing
Comparator
SaaS queue
SaaS queue
Duplicator

15. Disadvantages
The complexity of the initial deployment
• Each micro-service has a separate installer. Manual deployment of large systems requires
considerable time. It should be solved by automated deployment. For example using the
Docker.
The complexity of the manual update
• Many small services distributed across different physical devices or datacenters require
automated upgrades.
Additional efforts required for monitoring
• By default, a single event log / error is not available. Industrial solutions such as Zabbix
should be used. This may require additional programming effort to transmit service state.
History
Today
Challenges
Solution Advantages Disadvantages
Application
samples
Implementation
suggestions
Related design
patterns
Questions

16. Application Samples
Financial Industry. Debtor reliability evaluation.
High flow of applications due to business size.
Multi-stage evaluation process.
The system consists of several independent jobs.
The system makes a large number of long-running queries to external systems (BCI).
The evaluation process is changed regularly.
The system can operate several parallel processes.
History
Today
Challenges
Solution Advantages Disadvantages
Application
Samples
Implementation
suggestions
Related design
patterns
Questions

17. Application Samples
SMM. Audience analysis.
Huge data flow. (billions of messages per day).
Users activity varies during the day. It has several high spikes.
Each message affects several parameters which should be counted independently.
Each message should be counted.
Statistics should be calculated in real time.
Additional long-running requests to the external systems are required.
History
Today
Challenges
Solution Advantages Disadvantages
Application
Samples
Implementation
suggestions
Related design
patterns
Questions

18. Application Samples
Online ticket sales. Order processing.
Large number of users.
Users activity varies during the day.
The number of users is large, but the number of tickets is limited, you need to avoid overbooking.
There are a lot of independent sales channels, so only the service provider has an accurate information on
ticket availability.
History
Today
Challenges
Solution Advantages Disadvantages
Application
Samples
Implementation
suggestions
Related design
patterns
Questions

19. Implementation Suggestions
It is very useful to define a single message format for all the services even if each service will use
only part of the data. It permits new services to be added easily.
Data processing and data persistence should .separate into different services. It gives more
flexibility and simplifies refactoring in the case where data storage is inadequate for the growth
in load.
In the case where you have a workplace that requires user interaction, it makes sense to
minimize data storage.
In the case where you are planning to use XaaS infrastructure do not forget to estimate costs of
additional services (queues, storages, etc.). Architecture is a representation of the needs of the
business in the form of technological solutions and the cost of ownership is one of the most
important business parameters.
History
Today
Challenges
Solution Advantages Disadvantages
Application
Samples
Implementation
Suggestions
Related design
patterns
Questions

20. Related architectural templates
CQRS (Command and Query Responsibility Separation)
Event Sourcing
Key-Value storage
NoSQL
Memory Cache
History
Today
Challenges
Solution Advantages Disadvantages
Application
Samples
Implementation
Suggestions
Related Design
Patterns
Questions

21. Questions?
If you have any questions feel free to contact me.
Email: vbenedichuk@gmail.com
LinkedIn: https://ru.linkedin.com/in/vbenedichuk
History
Today
Challenges
Solution Advantages Disadvantages
Application
Samples
Implementation
Suggestions
Related Design
Patterns
Questions