Intro to Microservices
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Transcript of Intro to Microservices
Microservices
2 / 50
Microservices
What are the micro services?
Where did they come from?
Core principles
Why should I bother?
Challenges and pitfalls
Good practices
Pro-tips
3 / 50
Microservices
What are the micro services?
Where did they come from?
Core principles
Why should I bother?
Challenges and pitfalls
Good practices
Pro-tips
3 / 50
Microservices
What are the micro services?
Where did they come from?
Core principles
Why should I bother?
Challenges and pitfalls
Good practices
Pro-tips
3 / 50
Microservices
What are the micro services?
Where did they come from?
Core principles
Why should I bother?
Challenges and pitfalls
Good practices
Pro-tips
3 / 50
Microservices
What are the micro services?
Where did they come from?
Core principles
Why should I bother?
Challenges and pitfalls
Good practices
Pro-tips
3 / 50
Microservices
What are the micro services?
Where did they come from?
Core principles
Why should I bother?
Challenges and pitfalls
Good practices
Pro-tips
3 / 50
Microservices
What are the micro services?
Where did they come from?
Core principles
Why should I bother?
Challenges and pitfalls
Good practices
Pro-tips
3 / 50
Microservices : what’s that?
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Microservices : what’s that?
Post continuous delivery SOA
Small independently releasable services, that work together,modeled around a business domain.
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Microservices : what’s that?
Post continuous delivery SOA
Small independently releasable services, that work together,modeled around a business domain.
5 / 50
Prehistoric technology
6 / 50
Prehistoric technology
SOA
Domain driven design
Continous delivery
7 / 50
Prehistoric technology
SOA
Domain driven design
Continous delivery
7 / 50
Prehistoric technology
SOA
Domain driven design
Continous delivery
7 / 50
Case study
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Case study
9 / 50
Case study
Catalog
Recommendations / Promotions
Shopping Cart
Search
Orders
RMA
Customer Support
10 / 50
Application Composition : Monolith
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Application Composition : Monolith
Works most of the time
Somewhat scalable
Manageable up to a point
All or nothing updates
Tight coupling
12 / 50
SOA
Break up application into set of independent services
Add extra layers
Integration Layer (ESB)Service Layer
Rename existing layers
Frontend → Consumer Interface LayerDatabase → Operational SystemMiddleware → Business Process Layer
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SOA
Break up application into set of independent services
Add extra layers
Integration Layer (ESB)Service Layer
Rename existing layers
Frontend → Consumer Interface LayerDatabase → Operational SystemMiddleware → Business Process Layer
13 / 50
SOA
Break up application into set of independent services
Add extra layers
Integration Layer (ESB)Service Layer
Rename existing layers
Frontend → Consumer Interface LayerDatabase → Operational SystemMiddleware → Business Process Layer
13 / 50
Application Composition : SOA
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Application Composition : SOA
Did well in some organizations
Not so well in others
Reasons for failure
Conway’s LawIgnoring CAP theoremIgnoring Fallacies of distributedcomputing
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Conway’s Law
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Conway’s Law
organizations which design systems ... are constrained to producedesigns which are copies of the communication structures of theseorganizations
– M. Conway
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Conway’s Law
organizations which design systems ... are constrained to producedesigns which are copies of the communication structures of theseorganizations
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Fallacies of distributedcomputing
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Fallacies of distributed computing
The network is reliable
Latency is zero
Bandwidth is infinite
The network is secure
Topology doesn’t change
There is one administrator
Transport cost is zero
The network is homogeneous
20 / 50
CAP Theorem
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CAP Theorem
Consistency
Availability
Partitioning tolerance
22 / 50
Domain Driven Design
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Domain Driven Design
Bounded contexts
Draw boundaries around business domains
Do not partition system based on technology
24 / 50
Domain Driven Design
Bounded contexts
Draw boundaries around business domains
Do not partition system based on technology
24 / 50
Domain Driven Design
Bounded contexts
Draw boundaries around business domains
Do not partition system based on technology
24 / 50
Application Composition :Microservices
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Application Composition : Microservices
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Application Composition : Microservices
Modeled around business domain
Autonomous
Hide implementation detail
Decentralized
Isolate the failure
Highly observable
Culture of automation
27 / 50
Microservices : Good Parts
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Microservices : Good Parts
Small and focused
Independent
Loosely coupled
Allow try and pilot new tech
Firm module boundaries
Decentralized data
29 / 50
Microservices : Good Parts
Small and focused
Independent
Loosely coupled
Allow try and pilot new tech
Firm module boundaries
Decentralized data
29 / 50
Microservices : Good Parts
Small and focused
Independent
Loosely coupled
Allow try and pilot new tech
Firm module boundaries
Decentralized data
29 / 50
Microservices : Good Parts
Small and focused
Independent
Loosely coupled
Allow try and pilot new tech
Firm module boundaries
Decentralized data
29 / 50
Microservices : Good Parts
Small and focused
Independent
Loosely coupled
Allow try and pilot new tech
Firm module boundaries
Decentralized data
29 / 50
Microservices : Good Parts
Small and focused
Independent
Loosely coupled
Allow try and pilot new tech
Firm module boundaries
Decentralized data
29 / 50
Why bother?
30 / 50
Why bother?
Latest Buzzword
All the cool kids are doing it
Probably, not good reasons
31 / 50
Why bother?
Latest Buzzword
All the cool kids are doing it
Probably, not good reasons
31 / 50
Why bother?
Latest Buzzword
All the cool kids are doing it
Probably, not good reasons
31 / 50
Why bother?
Faster dev cycles
Team autonomy
Smoother scaling
32 / 50
Why bother?
Faster dev cycles
Team autonomy
Smoother scaling
32 / 50
Why bother?
Faster dev cycles
Team autonomy
Smoother scaling
32 / 50
Challenges
Complex runtime
Distributed deployment
Extensive communication
Handling failures
33 / 50
Challenges
Complex runtime
Distributed deployment
Extensive communication
Handling failures
33 / 50
Challenges
Complex runtime
Distributed deployment
Extensive communication
Handling failures
33 / 50
Challenges
Complex runtime
Distributed deployment
Extensive communication
Handling failures
33 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fast
Bulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Good practices
Humane registries
Team composition
Isolate failures
Fail fastBulkheading
Standardize communications
REST or Message Queues
Robust Service Discovery
Consider something like Consul / Zookeeper / ETCd
Monitor everything
34 / 50
Deployments
Avoid huge build jobs
One service per host
Host is either physical host, VM or container
Test
Contract TestsConsumer driven development
35 / 50
Deployments
Avoid huge build jobs
One service per host
Host is either physical host, VM or container
Test
Contract TestsConsumer driven development
35 / 50
Deployments
Avoid huge build jobs
One service per host
Host is either physical host, VM or container
Test
Contract TestsConsumer driven development
35 / 50
Deployments
Avoid huge build jobs
One service per host
Host is either physical host, VM or container
Test
Contract TestsConsumer driven development
35 / 50
Deployments
Avoid huge build jobs
One service per host
Host is either physical host, VM or container
Test
Contract Tests
Consumer driven development
35 / 50
Deployments
Avoid huge build jobs
One service per host
Host is either physical host, VM or container
Test
Contract TestsConsumer driven development
35 / 50
Potential pitfalls
Incorrect partitioning
Distributed point of failure
Misunderstanding your network
36 / 50
Potential pitfalls
Incorrect partitioning
Distributed point of failure
Misunderstanding your network
36 / 50
Potential pitfalls
Incorrect partitioning
Distributed point of failure
Misunderstanding your network
36 / 50
Refactoring to Microservices
Strangler pattern
Use static analysis tool to find natural boundaries
Shadow systems
37 / 50
Refactoring to Microservices
Strangler pattern
Use static analysis tool to find natural boundaries
Shadow systems
37 / 50
Refactoring to Microservices
Strangler pattern
Use static analysis tool to find natural boundaries
Shadow systems
37 / 50
Personal Experiences
38 / 50
Monitor Everything
Exception catchers
Centralized log collections
Synthetic transactions
39 / 50
Monitor Everything
Exception catchers
Centralized log collections
Synthetic transactions
39 / 50
Monitor Everything
Exception catchers
Centralized log collections
Synthetic transactions
39 / 50
Monitor Everything
Exception catchers
Centralized log collections
Synthetic transactions
39 / 50
Understand your networking
Microservices increase network chatter
More failures with UDP based protocols (DNS, discovery)
More hiccups with TCP based protocols
Run out of sockets
Decrease TCP timeouts, seconds instead of minutes
Batch the transactions.
But can’t have batching service
40 / 50
Understand your networking
Microservices increase network chatter
More failures with UDP based protocols (DNS, discovery)
More hiccups with TCP based protocols
Run out of sockets
Decrease TCP timeouts, seconds instead of minutes
Batch the transactions.
But can’t have batching service
40 / 50
Understand your networking
Microservices increase network chatter
More failures with UDP based protocols (DNS, discovery)
More hiccups with TCP based protocols
Run out of sockets
Decrease TCP timeouts, seconds instead of minutes
Batch the transactions.
But can’t have batching service
40 / 50
Understand your networking
Microservices increase network chatter
More failures with UDP based protocols (DNS, discovery)
More hiccups with TCP based protocols
Run out of sockets
Decrease TCP timeouts, seconds instead of minutes
Batch the transactions.
But can’t have batching service
40 / 50
Understand your networking
Microservices increase network chatter
More failures with UDP based protocols (DNS, discovery)
More hiccups with TCP based protocols
Run out of sockets
Decrease TCP timeouts, seconds instead of minutes
Batch the transactions.
But can’t have batching service
40 / 50
Robust CI/CD pipeline
1 Service = 1 Repo = 1 Build Job = 1 Artifact
Instantaneous deployments
Pre-build AMis, Containers
Test coverage
Consumer Driven Tests
Avoid integration tests written by service devshttps://github.com/realestate-com-au/pact
41 / 50
Robust CI/CD pipeline
1 Service = 1 Repo = 1 Build Job = 1 Artifact
Instantaneous deployments
Pre-build AMis, Containers
Test coverage
Consumer Driven Tests
Avoid integration tests written by service devshttps://github.com/realestate-com-au/pact
41 / 50
Robust CI/CD pipeline
1 Service = 1 Repo = 1 Build Job = 1 Artifact
Instantaneous deployments
Pre-build AMis, Containers
Test coverage
Consumer Driven Tests
Avoid integration tests written by service devshttps://github.com/realestate-com-au/pact
41 / 50
Robust CI/CD pipeline
1 Service = 1 Repo = 1 Build Job = 1 Artifact
Instantaneous deployments
Pre-build AMis, Containers
Test coverage
Consumer Driven Tests
Avoid integration tests written by service devshttps://github.com/realestate-com-au/pact
41 / 50
Robust CI/CD pipeline
1 Service = 1 Repo = 1 Build Job = 1 Artifact
Instantaneous deployments
Pre-build AMis, Containers
Test coverage
Consumer Driven Tests
Avoid integration tests written by service devshttps://github.com/realestate-com-au/pact
41 / 50
Robust CI/CD pipeline
1 Service = 1 Repo = 1 Build Job = 1 Artifact
Instantaneous deployments
Pre-build AMis, Containers
Test coverage
Consumer Driven Tests
Avoid integration tests written by service devs
https://github.com/realestate-com-au/pact
41 / 50
Robust CI/CD pipeline
1 Service = 1 Repo = 1 Build Job = 1 Artifact
Instantaneous deployments
Pre-build AMis, Containers
Test coverage
Consumer Driven Tests
Avoid integration tests written by service devshttps://github.com/realestate-com-au/pact
41 / 50
Consider using client side load balancing
Service side load balancers create point of failure
Hard to deploy and manage HW load balancers
Problem of running out of sockets
Netflix Ribbon Libraryhttps://github.com/Netflix/ribbon/wiki/Working-with-load-balancers
42 / 50
Consider using client side load balancing
Service side load balancers create point of failure
Hard to deploy and manage HW load balancers
Problem of running out of sockets
Netflix Ribbon Libraryhttps://github.com/Netflix/ribbon/wiki/Working-with-load-balancers
42 / 50
Consider using client side load balancing
Service side load balancers create point of failure
Hard to deploy and manage HW load balancers
Problem of running out of sockets
Netflix Ribbon Libraryhttps://github.com/Netflix/ribbon/wiki/Working-with-load-balancers
42 / 50
Consider using client side load balancing
Service side load balancers create point of failure
Hard to deploy and manage HW load balancers
Problem of running out of sockets
Netflix Ribbon Libraryhttps://github.com/Netflix/ribbon/wiki/Working-with-load-balancers
42 / 50
Never make MapReduce pipeline talk to service outside ofyour cluster
43 / 50
Never make MapReduce pipeline talk to service outside ofyour cluster
44 / 50
Further resources
45 / 50
Further resources
46 / 50
Further resources
47 / 50
Further resources
Toughtworks Youtube channel
48 / 50
???
49 / 50
Thank you
50 / 50
