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Applicare patterns di sviluppo con Azure

The document discusses various software design patterns that can be applied when developing applications for Azure. It begins by introducing design patterns and their use in solving common problems. It then covers specific patterns organized into categories like availability, data management, design and implementation, messaging, management and monitoring, performance and scalability, resiliency, and security. For each pattern, it provides a brief description of the problem it addresses and how it can improve qualities like performance, scalability, and availability. Examples of patterns discussed include cache-aside, command query responsibility segregation, and queue-based load leveling. The document emphasizes that understanding and applying these patterns is important for developing applications suited to cloud environments.

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1 Applicare patterns di sviluppo con Azure Marco Parenzan Solutions Sales Specialist @ Insight Azure MVP
@cloudgen_verona #CodeGen #dotnetconf
3 Main sponsor
4 Community sponsors
5
marco_parenzan marcoparenzan marcoparenzan Marco Parenzan Solutions Sales Specialist @ Insight Azure MVP
7 Applicare patterns di sviluppo con Azure Marco Parenzan Solutions Sales Specialist @ Insight Azure MVP
“Everything works... ...until it breaks the damage!
“I don’t have to convince you to move to the cloud...
“...I want to convince you what moving to the cloud means...
“...just to avoid wrong expectations...
“...saving money...
“...you can only increase business!
“You can have HA only with money and design (patterns)
“Azure: the best place where applying your GOF book
“Cloud is 66% Lift&Shift Don’t rewrite
17 IaaS and Lift & Shift: an anti-pattern? From Wikipedia [https://en.wikipedia.org/wiki/Anti-pattern]: An anti-pattern is a common response to a recurring problem that is usually ineffective and risks being highly counterproductive
18 IaaS and Lift & Shift an anti-pattern: why? Moving to the cloud is just a matter of costs It costs Then limits comes again And opportunities can become big limits You don’t avoid IaaS limits (infrastructural, hidden, unquoted costs)
19 IaaS is really evil? NO.... ...if, and only if,... ...a rewrite/evolution strategy is planned at day 1!
20 IaaS is not evil? IaaS is the toolbox to hybridize company and cloud Everything should be used with «care»
“PaaS if possible IaaS if (really really really) needed
22 [Software] Design pattern From Wikipedia [https://en.wikipedia.org/wiki/Software_design_pattern]: In software engineering, a software design pattern is a general, reusable solution to a commonly occurring problem within a given context in software design. It is not a finished design that can be transformed directly into source or machine code. It is a description or template for how to solve a problem that can be used in many different situations. Design patterns are formalized best practices that the programmer can use to solve common problems when designing an application or system.
“Lot of services are practice, not strictly «design»
“Azure PaaS Services are Pattern implementations
“Believe it or not.... This is the way
26 context Agile Alm Devops Ci/cd Microservices Elastic Geo redundancy
27 Just an example: Azure Virtual Machine It’s not a pattern It’s a tool in your toolbox TO RUN your pattern implementation Try to scale a VM in production....! (every PaaS service run over VMs)
28 Just an example: Azure Sql Database It’s just a Database It’s a pattern! Why? «It’s ONLY 99.9% compatible with SQL Server» It’s really very important? How many projects use SQL Server for its full power? It’s a lot if it use Views or Stored procedures, even Triggers! why a pattern? Because it’s just most developers do
29
30 Some useful links https://docs.microsoft.com/en- us/azure/architecture/patterns/ https://docs.microsoft.com/en-us/azure/architecture/ https://azureinteractives.azurewebsites.net/CloudDesignPatt erns/default.html
“Patterns
32 Categories Availability Data management Design and implementation Messaging Management and monitoring Performance and scalability Resiliency Security
33 Availability Availability defines the proportion of time that the system is functional and working... Health Endpoint Monitoring Queue-Based Load Leveling Throttling
34 Data Management Data management is the key element of cloud applications, and influences most of the quality attributes… Cache-Aside Command and Query Reponsibility Event Sourcing Index Table Index Table Materialized View Sharding Static Content Hosting Valet Key
35 Design and Implementation Good design encompasses factors… …Decisions made during the design and implementation phase have a huge impact on the quality and the total cost of ownership of cloud hosted applications and services. Ambassador Anti Corruption Layer Backends for Frontends Command And Query Responsibility Computer Resource Consolidation External Configuration Store Gateway Aggregation Gateway Offloading Gateway Routing Leader Election Pipes and Filters Sidecar Static Conten Hosting Strangler
36 Messaging The distributed nature of cloud applications requires a messaging infrastructure that connects the components and services, ideally in a loosely coupled manner in order to maximize scalability. … Compensating Consumers Pipes and Filters Priority Queue Queue-Based Load Leveling Scheduler Agent Supervisor
37 Management and Monitoring Cloud applications run in a remote datacenter where you do not have full control of the infrastructure or, in some cases, the operating system. … Ambassador Anti Corruption Layer External Configuration Store Gateway Aggregation Gateway Offloading Gateway Routing Healt Endpoint Monitoring Sidecar Strangler
38 Performance and Scalability Performance is an indication of the responsiveness of a system, while scalability is the ability to gracefully handle increases in load, perhaps through an increase in available resources… Cache-aside Command and Query Responsibility Compensating Consumers Index Table Materialized View Priority Queue Sharding Static Content Hosting Throttling
39 Resiliency Resiliency is the ability of a system to gracefully handle and recover from failures… Bulkhead Circuit Breaker Compensating Transaction Leader Election Retry Scheduler Agent Supervisor
40 Security Security is the capability of a system to prevent malicious or accidental actions outside of the designed usage, and to prevent disclosure or loss of information. … Federated Identity Gatekeeper Valet key
“A scenario
42 Room reservation Reserve a room from office See a panel on the room to see the daily reservations
43 Classic approach Client/server or three tier Db/centricone db for all Issues • Coupling • Integrity • Performances • Scalability • Availability • Single point of failure
44 Modern solution four apps Reservation Worker Handler Panel Four data stores requests Write Events read Cons: • Initial Higher cost Pro: • Long term lower cost • Flexible • Maintainable • Scalable • Available
“Our solution
46 Queue-Based Load Leveling From Availability and Performances Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
47 Compensating Consumers Enable multiple concurrent consumers to process messages received on the same messaging channel. This pattern enables a system to process multiple messages concurrently to optimize throughput, to improve scalability and availability, and to balance the workload.
“That’s why you have (lot of) worker roles, containers, actors and microservices...
49 Command and Query Responsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
50 Materialized View Generate pre-populated views over the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.
“That’s why there are a lot o data services (No SQL, in memory, graph, ...)
52 Event Sourcing Use an append-only store to record actions taken on data, rather than the current state, and use the store to materialize the domain objects. In complex domains this can avoid synchronizing the data model and the business domain; improve performance, scalability, and responsiveness; provide consistency; and provide audit history to enable compensating actions.
“That’s why you have event-based services like Functions, Logic Apps, Event Grid and Serverless!!!
55 Cache-aside Load data on demand into a cache from a data store. This pattern can improve performance and also helps to maintain consistency between data held in the cache and the data in the underlying data store.
“Conclusions
“Investing in patterns means being natural over cloud
“Find the patterns you are confortable
“Patterns change the way you think software development, not only cloud
Grazie Domande? marcoparenzan marco_parenzan marcoparenzan
“Patterns
“Availability
63 Availability Availability defines the proportion of time that the system is functional and working It will be affected by system errors, infrastructure problems, malicious attacks, and system load. It is usually measured as a percentage of uptime. Cloud applications typically provide users with a service level agreement (SLA), which means that applications must be designed and implemented in a way that maximizes availability. Health Endpoint Monitoring Queue-Based Load Leveling Throttling
64 Health Endpoint Monitoring Implement functional checks within an application that external tools can access through exposed endpoints at regular intervals. This pattern can help to verify that applications and services are performing correctly.
65 Queue-Based Load Leveling Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
66 Throttling Control the consumption of resources used by an instance of an application, an individual tenant, or an entire service. This pattern can allow the system to continue to function and meet service level agreements, even when an increase in demand places an extreme load on resources.
67 Materialized View Generate pre-populated views over the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.
“Data Management
69 Data Management Data management is the key element of cloud applications, and influences most of the quality attributes. Data is typically hosted in different locations and across multiple servers for reasons such as performance, scalability or availability, and this can present a range of challenges. For example, data consistency must be maintained, and data will typically need to be synchronized across different locations. Cache-Aside Command and Query Reponsibility Event Sourcing Index Table Index Table Materialized View Sharding Static Content Hosting Valet Key
70 Cache-aside Load data on demand into a cache from a data store. This pattern can improve performance and also helps to maintain consistency between data held in the cache and the data in the underlying data store.
71 Command and Query Responsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
72 Event Sourcing Use an append-only store to record actions taken on data, rather than the current state, and use the store to materialize the domain objects. In complex domains this can avoid synchronizing the data model and the business domain; improve performance, scalability, and responsiveness; provide consistency; and provide audit history to enable compensating actions.
73 Index Table Create indexes over the fields in data stores that are frequently referenced by query criteria. This pattern can improve query performance by allowing applications to more quickly locate the data to retrieve from a data store.
74 Materialized View Generate pre-populated views over the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.
75 Sharding Divide a data store into a set of horizontal partitions or shards. This pattern can improve scalability when storing and accessing large volumes of data.
76 Static Content Hosting
77 Valet key Use a token or key that provides clients with restricted direct access to a specific resource or service in order to offload data transfer operations from the application code. This pattern is particularly useful in applications that use cloud- hosted storage systems or queues, and can minimize cost and maximize scalability and performance.
“Design and Implementation
79 Design and Implementation Good design encompasses factors such as consistency and coherence in component design and deployment, maintainability to simplify administration and development, and reusability to allow components and subsystems to be used in other applications and in other scenarios. Decisions made during the design and implementation phase have a huge impact on the quality and the total cost of ownership of cloud hosted applications and services. Ambassador Anti Corruption Layer Backends for Frontends Command And Query Responsibility Computer Resource Consolidation External Configuration Store Gateway Aggregation Gateway Offloading Gateway Routing Leader Election Pipes and Filters Sidecar Static Conten Hosting Strangler
80 Ambassador Create helper services for making networking requests on behalf of a consumer service or application. An ambassador service can be thought of as an out of process proxy that is co-located with the client.
81 Anti Corruption Layer Ensure the design and structure of new apps and services are not limited by legacy resources. Implement a façade or adapter layer in between legacy systems and modern systems where features and capabilities are being migrated to. This façade or layer translates requests between modern systems and legacy applications using methods appropriate to each.
82 Backends for Frontends Develop targeted backend services for use with specific frontend applications or interfaces. This pattern is useful when you wish to avoid implementing customizations for multiple interfaces in a single backend.
83 Command and Query Responsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
84 Compute Resource Consolidation Consolidate multiple tasks or operations into a single computational unit. This pattern can increase compute resource utilization, and reduce the costs and management overhead associated with performing compute processing in cloud-hosted applications.
85 External Configuration Store Move configuration information out of the application deployment package to a centralized location. This pattern can provide opportunities for easier management and control of configuration data, and for sharing configuration data across applications and application instances.
86 Gateway Aggregation Aggregate multiple individual requests to a single request using the gateway aggregation pattern. This pattern is useful when a client must make multiple calls to different backend systems to perform an operation.
87 Gateway Offloading Offload shared or specialized service functionality to a gateway proxy. This pattern can simplify application development by moving shared service functionality, such as the use of SSL certificates, to a gateway proxy, simplifying application management.
88 Gateway Routing Route requests to multiple services using a single endpoint with the gateway routing pattern. This pattern is useful when you wish to expose multiple services on a single endpoint and route to the appropriate service based on the request.
89 Leader Election Coordinate the actions performed by a collection of collaborating task instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the other instances. This pattern can help to ensure that task instances do not conflict with each other, cause contention for shared resources, or inadvertently interfere with the work that other task instances are performing.
90 Pipes and Filters Decompose a task that performs complex processing into a series of discrete elements that can be reused. This pattern can improve performance, scalability, and reusability by allowing task elements that perform the processing to be deployed and scaled independently.
91 Sidecar Deploy components of an application into a separate process or container using the sidecar pattern to provide isolation, encapsulation, and to enable applications composed of heterogeneous components and technologies.
92 Static Content Hosting
93 Strangler Incrementally move features from legacy systems to more modern systems while allowing the new system to add features in the meantime. As features from the legacy system are replaced, the new system eventually replaces all of the old system’s features, strangling the old system and allowing you to decommission it.
“Messaging
95 Messaging The distributed nature of cloud applications requires a messaging infrastructure that connects the components and services, ideally in a loosely coupled manner in order to maximize scalability. Asynchronous messaging is widely used, and provides many benefits, but also brings challenges such as the ordering of messages, poison message management, idempotency, and more. Compensating Consumers Pipes and Filters Priority Queue Queue-Based Load Leveling Scheduler Agent Supervisor
96 Compensating Consumers Enable multiple concurrent consumers to process messages received on the same messaging channel. This pattern enables a system to process multiple messages concurrently to optimize throughput, to improve scalability and availability, and to balance the workload.
97 Pipes and Filters Decompose a task that performs complex processing into a series of discrete elements that can be reused. This pattern can improve performance, scalability, and reusability by allowing task elements that perform the processing to be deployed and scaled independently.
98 Priority Queue Prioritize requests sent to services so that requests with a higher priority are received and processed more quickly than those of a lower priority. This pattern is useful in applications that offer different service level guarantees to individual clients.
99 Queue-Based Load Leveling Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
100 Scheduler Agent Supervisor Coordinate a set of actions across a distributed set of services and other remote resources, attempt to transparently handle faults if any of these actions fail, or undo the effects of the work performed if the system cannot recover from a fault. This pattern can add resiliency to a distributed system by enabling it to recover and retry actions that fail due to transient exceptions, long- lasting faults, and process failures.
“Management and Monitoring
102 Management and Monitoring Cloud applications run in a remote datacenter where you do not have full control of the infrastructure or, in some cases, the operating system. This can make management and monitoring more difficult than an on-premises deployment. Applications must expose runtime information that administrators and operators can use to manage and monitor the system, as well as supporting changing business requirements without requiring the application to be stopped or redeployed. Ambassador Anti Corruption Layer External Configuration Store Gateway Aggregation Gateway Offloading Gateway Routing Healt Endpoint Monitoring Sidecar Strangler
103 Ambassador Create helper services for making networking requests on behalf of a consumer service or application. An ambassador service can be thought of as an out of process proxy that is co-located with the client.
104 Anti Corruption Layer Ensure the design and structure of new apps and services are not limited by legacy resources. Implement a façade or adapter layer in between legacy systems and modern systems where features and capabilities are being migrated to. This façade or layer translates requests between modern systems and legacy applications using methods appropriate to each.
105 External Configuration Store Move configuration information out of the application deployment package to a centralized location. This pattern can provide opportunities for easier management and control of configuration data, and for sharing configuration data across applications and application instances.
106 Gateway Aggregation Aggregate multiple individual requests to a single request using the gateway aggregation pattern. This pattern is useful when a client must make multiple calls to different backend systems to perform an operation.
107 Gateway Offloading Offload shared or specialized service functionality to a gateway proxy. This pattern can simplify application development by moving shared service functionality, such as the use of SSL certificates, to a gateway proxy, simplifying application management.
108 Gateway Routing Route requests to multiple services using a single endpoint with the gateway routing pattern. This pattern is useful when you wish to expose multiple services on a single endpoint and route to the appropriate service based on the request.
109 Health Endpoint Monitoring Implement functional checks within an application that external tools can access through exposed endpoints at regular intervals. This pattern can help to verify that applications and services are performing correctly.
110 Sidecar Deploy components of an application into a separate process or container using the sidecar pattern to provide isolation, encapsulation, and to enable applications composed of heterogeneous components and technologies.
111 Strangler Incrementally move features from legacy systems to more modern systems while allowing the new system to add features in the meantime. As features from the legacy system are replaced, the new system eventually replaces all of the old system’s features, strangling the old system and allowing you to decommission it.
“Performance and Scalability
113 Performance and Scalability Performance is an indication of the responsiveness of a system, while scalability is the ability to gracefully handle increases in load, perhaps through an increase in available resources. Cloud applications, especially in multi-tenant scenarios, typically encounter variable workloads and unpredictable activity peaks and should be able to scale out within limits to meet demand, and scale in when demand decreases. Scalability concerns not just compute instances, but other items such as data storage, messaging infrastructure, and more. Cache-aside Command and Query Responsibility Compensating Consumers Index Table Materialized View Priority Queue Sharding Static Content Hosting Throttling
114 Cache-aside Load data on demand into a cache from a data store. This pattern can improve performance and also helps to maintain consistency between data held in the cache and the data in the underlying data store.
115 Command and Query Responsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
116 Compensating Consumers Enable multiple concurrent consumers to process messages received on the same messaging channel. This pattern enables a system to process multiple messages concurrently to optimize throughput, to improve scalability and availability, and to balance the workload.
117 Index Table Create indexes over the fields in data stores that are frequently referenced by query criteria. This pattern can improve query performance by allowing applications to more quickly locate the data to retrieve from a data store.
118 Materialized View Generate pre-populated views over the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.
119 Priority Queue Prioritize requests sent to services so that requests with a higher priority are received and processed more quickly than those of a lower priority. This pattern is useful in applications that offer different service level guarantees to individual clients.
120 Sharding Divide a data store into a set of horizontal partitions or shards. This pattern can improve scalability when storing and accessing large volumes of data.
121 Static Content Hosting
122 Throttling Control the consumption of resources used by an instance of an application, an individual tenant, or an entire service. This pattern can allow the system to continue to function and meet service level agreements, even when an increase in demand places an extreme load on resources.
“Resiliency
124 Resiliency Resiliency is the ability of a system to gracefully handle and recover from failures. The nature of cloud hosting, where applications are often multi-tenant, use shared platform services, compete for resources and bandwidth, communicate over the Internet, and run on commodity hardware means there is an increased likelihood that both transient and more permanent faults will arise. Detecting failures, and recovering quickly and efficiently, is necessary to maintain resiliency. Bulkhead Circuit Breaker Compensating Transaction Leader Election Retry Scheduler Agent Supervisor
125 Bulkhead Isolate the elements of an application like the sectioned partitions of a ship's hull. If the hull of a ship that uses bulkheads is compromised, only the damaged section of the ship will fill with water, and the ship will not sink. Using the same concept in application design, this pattern isolates instances of a service or clients into pools so that if one fails, the others will continue to function.
126 Circuit Breaker Handle faults that may take a variable amount of time to rectify when connecting to a remote service or resource. This pattern can improve the stability and resiliency of an application.
127 Compensating Transaction Undo the work performed by a series of steps, which together define an eventually consistent operation, if one or more of the operations fails. Operations that follow the eventual consistency model are commonly found in cloud-hosted applications that implement complex business processes and workflows.
128 Leader Election Coordinate the actions performed by a collection of collaborating task instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the other instances. This pattern can help to ensure that task instances do not conflict with each other, cause contention for shared resources, or inadvertently interfere with the work that other task instances are performing.
129 Retry Enable an application to handle anticipated, temporary failures when it attempts to connect to a service or network resource by transparently retrying an operation that has previously failed in the expectation that the cause of the failure is transient. This pattern can improve the stability of the application.
130 Scheduler Agent Supervisor Coordinate a set of actions across a distributed set of services and other remote resources, attempt to transparently handle faults if any of these actions fail, or undo the effects of the work performed if the system cannot recover from a fault. This pattern can add resiliency to a distributed system by enabling it to recover and retry actions that fail due to transient exceptions, long- lasting faults, and process failures.
“Security
132 Security Security is the capability of a system to prevent malicious or accidental actions outside of the designed usage, and to prevent disclosure or loss of information. Cloud applications are exposed on the Internet outside trusted on-premises boundaries, are often open to the public, and may serve untrusted users. Applications must be designed and deployed in a way that protects them from malicious attacks, restricts access to only approved users, and protects sensitive data. Federated Identity Gatekeeper Valet key
133 Federated Identity Delegate authentication to an external identity provider. This pattern can simplify development, minimize the requirement for user administration, and improve the user experience of then application.
134 Gatekeeper Protect applications and services by using a dedicated host instance that acts as a broker between clients and the application or service, validates and sanitizes requests, and passes requests and data between them. This pattern can provide an additional layer of security, and limit the attack surface of the system.
135 Valet key Use a token or key that provides clients with restricted direct access to a specific resource or service in order to offload data transfer operations from the application code. This pattern is particularly useful in applications that use cloud- hosted storage systems or queues, and can minimize cost and maximize scalability and performance.
“Let’s restart from single patterns
137 Valet key Use a token or key that provides clients with restricted direct access to a specific resource or service in order to offload data transfer operations from the application code. This pattern is particularly useful in applications that use cloud- hosted storage systems or queues, and can minimize cost and maximize scalability and performance. Security Data Management
138 Queue-Based Load Leveling Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
139 Cache-aside Load data on demand into a cache from a data store. This pattern can improve performance and also helps to maintain consistency between data held in the cache and the data in the underlying data store.
140 Command and Query Responsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
141 Index Table Create indexes over the fields in data stores that are frequently referenced by query criteria. This pattern can improve query performance by allowing applications to more quickly locate the data to retrieve from a data store.
142 Materialized View Generate pre-populated views over the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.

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Applicare patterns di sviluppo con Azure

  • 1.
    1 Applicare patterns di sviluppocon Azure Marco Parenzan Solutions Sales Specialist @ Insight Azure MVP
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
    7 Applicare patterns di sviluppocon Azure Marco Parenzan Solutions Sales Specialist @ Insight Azure MVP
  • 8.
  • 9.
    “I don’t haveto convince you to move to the cloud...
  • 10.
    “...I want toconvince you what moving to the cloud means...
  • 11.
    “...just to avoidwrong expectations...
  • 12.
  • 13.
  • 14.
    “You can haveHA only with money and design (patterns)
  • 15.
    “Azure: the bestplace where applying your GOF book
  • 16.
    “Cloud is 66%Lift&Shift Don’t rewrite
  • 17.
    17 IaaS and Lift& Shift: an anti-pattern? From Wikipedia [https://en.wikipedia.org/wiki/Anti-pattern]: An anti-pattern is a common response to a recurring problem that is usually ineffective and risks being highly counterproductive
  • 18.
    18 IaaS and Lift& Shift an anti-pattern: why? Moving to the cloud is just a matter of costs It costs Then limits comes again And opportunities can become big limits You don’t avoid IaaS limits (infrastructural, hidden, unquoted costs)
  • 19.
    19 IaaS is reallyevil? NO.... ...if, and only if,... ...a rewrite/evolution strategy is planned at day 1!
  • 20.
    20 IaaS is notevil? IaaS is the toolbox to hybridize company and cloud Everything should be used with «care»
  • 21.
    “PaaS if possible IaaSif (really really really) needed
  • 22.
    22 [Software] Design pattern FromWikipedia [https://en.wikipedia.org/wiki/Software_design_pattern]: In software engineering, a software design pattern is a general, reusable solution to a commonly occurring problem within a given context in software design. It is not a finished design that can be transformed directly into source or machine code. It is a description or template for how to solve a problem that can be used in many different situations. Design patterns are formalized best practices that the programmer can use to solve common problems when designing an application or system.
  • 23.
    “Lot of servicesare practice, not strictly «design»
  • 24.
    “Azure PaaS Services arePattern implementations
  • 25.
    “Believe it ornot.... This is the way
  • 26.
  • 27.
    27 Just an example:Azure Virtual Machine It’s not a pattern It’s a tool in your toolbox TO RUN your pattern implementation Try to scale a VM in production....! (every PaaS service run over VMs)
  • 28.
    28 Just an example:Azure Sql Database It’s just a Database It’s a pattern! Why? «It’s ONLY 99.9% compatible with SQL Server» It’s really very important? How many projects use SQL Server for its full power? It’s a lot if it use Views or Stored procedures, even Triggers! why a pattern? Because it’s just most developers do
  • 29.
  • 30.
  • 31.
  • 32.
    32 Categories Availability Data management Design andimplementation Messaging Management and monitoring Performance and scalability Resiliency Security
  • 33.
    33 Availability Availability defines the proportionof time that the system is functional and working... Health Endpoint Monitoring Queue-Based Load Leveling Throttling
  • 34.
    34 Data Management Data managementis the key element of cloud applications, and influences most of the quality attributes… Cache-Aside Command and Query Reponsibility Event Sourcing Index Table Index Table Materialized View Sharding Static Content Hosting Valet Key
  • 35.
    35 Design and Implementation Gooddesign encompasses factors… …Decisions made during the design and implementation phase have a huge impact on the quality and the total cost of ownership of cloud hosted applications and services. Ambassador Anti Corruption Layer Backends for Frontends Command And Query Responsibility Computer Resource Consolidation External Configuration Store Gateway Aggregation Gateway Offloading Gateway Routing Leader Election Pipes and Filters Sidecar Static Conten Hosting Strangler
  • 36.
    36 Messaging The distributed natureof cloud applications requires a messaging infrastructure that connects the components and services, ideally in a loosely coupled manner in order to maximize scalability. … Compensating Consumers Pipes and Filters Priority Queue Queue-Based Load Leveling Scheduler Agent Supervisor
  • 37.
    37 Management and Monitoring Cloudapplications run in a remote datacenter where you do not have full control of the infrastructure or, in some cases, the operating system. … Ambassador Anti Corruption Layer External Configuration Store Gateway Aggregation Gateway Offloading Gateway Routing Healt Endpoint Monitoring Sidecar Strangler
  • 38.
    38 Performance and Scalability Performanceis an indication of the responsiveness of a system, while scalability is the ability to gracefully handle increases in load, perhaps through an increase in available resources… Cache-aside Command and Query Responsibility Compensating Consumers Index Table Materialized View Priority Queue Sharding Static Content Hosting Throttling
  • 39.
    39 Resiliency Resiliency is theability of a system to gracefully handle and recover from failures… Bulkhead Circuit Breaker Compensating Transaction Leader Election Retry Scheduler Agent Supervisor
  • 40.
    40 Security Security is thecapability of a system to prevent malicious or accidental actions outside of the designed usage, and to prevent disclosure or loss of information. … Federated Identity Gatekeeper Valet key
  • 41.
  • 42.
    42 Room reservation Reserve aroom from office See a panel on the room to see the daily reservations
  • 43.
    43 Classic approach Client/server orthree tier Db/centricone db for all Issues • Coupling • Integrity • Performances • Scalability • Availability • Single point of failure
  • 44.
    44 Modern solution four apps Reservation Worker Handler Panel Fourdata stores requests Write Events read Cons: • Initial Higher cost Pro: • Long term lower cost • Flexible • Maintainable • Scalable • Available
  • 45.
  • 46.
    46 Queue-Based Load Leveling FromAvailability and Performances Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
  • 47.
    47 Compensating Consumers Enable multipleconcurrent consumers to process messages received on the same messaging channel. This pattern enables a system to process multiple messages concurrently to optimize throughput, to improve scalability and availability, and to balance the workload.
  • 48.
    “That’s why youhave (lot of) worker roles, containers, actors and microservices...
  • 49.
    49 Command and QueryResponsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
  • 50.
    50 Materialized View Generate pre-populated viewsover the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.
  • 51.
    “That’s why thereare a lot o data services (No SQL, in memory, graph, ...)
  • 52.
    52 Event Sourcing Use anappend-only store to record actions taken on data, rather than the current state, and use the store to materialize the domain objects. In complex domains this can avoid synchronizing the data model and the business domain; improve performance, scalability, and responsiveness; provide consistency; and provide audit history to enable compensating actions.
  • 53.
    “That’s why youhave event-based services like Functions, Logic Apps, Event Grid and Serverless!!!
  • 55.
    55 Cache-aside Load data ondemand into a cache from a data store. This pattern can improve performance and also helps to maintain consistency between data held in the cache and the data in the underlying data store.
  • 56.
  • 57.
    “Investing in patterns meansbeing natural over cloud
  • 58.
    “Find the patternsyou are confortable
  • 59.
    “Patterns change the wayyou think software development, not only cloud
  • 60.
  • 61.
  • 62.
  • 63.
    63 Availability Availability defines the proportionof time that the system is functional and working It will be affected by system errors, infrastructure problems, malicious attacks, and system load. It is usually measured as a percentage of uptime. Cloud applications typically provide users with a service level agreement (SLA), which means that applications must be designed and implemented in a way that maximizes availability. Health Endpoint Monitoring Queue-Based Load Leveling Throttling
  • 64.
    64 Health Endpoint Monitoring Implementfunctional checks within an application that external tools can access through exposed endpoints at regular intervals. This pattern can help to verify that applications and services are performing correctly.
  • 65.
    65 Queue-Based Load Leveling Usea queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
  • 66.
    66 Throttling Control the consumptionof resources used by an instance of an application, an individual tenant, or an entire service. This pattern can allow the system to continue to function and meet service level agreements, even when an increase in demand places an extreme load on resources.
  • 67.
    67 Materialized View Generate pre-populated viewsover the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.
  • 68.
  • 69.
    69 Data Management Data managementis the key element of cloud applications, and influences most of the quality attributes. Data is typically hosted in different locations and across multiple servers for reasons such as performance, scalability or availability, and this can present a range of challenges. For example, data consistency must be maintained, and data will typically need to be synchronized across different locations. Cache-Aside Command and Query Reponsibility Event Sourcing Index Table Index Table Materialized View Sharding Static Content Hosting Valet Key
  • 70.
    70 Cache-aside Load data ondemand into a cache from a data store. This pattern can improve performance and also helps to maintain consistency between data held in the cache and the data in the underlying data store.
  • 71.
    71 Command and QueryResponsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
  • 72.
    72 Event Sourcing Use anappend-only store to record actions taken on data, rather than the current state, and use the store to materialize the domain objects. In complex domains this can avoid synchronizing the data model and the business domain; improve performance, scalability, and responsiveness; provide consistency; and provide audit history to enable compensating actions.
  • 73.
    73 Index Table Create indexesover the fields in data stores that are frequently referenced by query criteria. This pattern can improve query performance by allowing applications to more quickly locate the data to retrieve from a data store.
  • 74.
    74 Materialized View Generate pre-populated viewsover the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.
  • 75.
    75 Sharding Divide a datastore into a set of horizontal partitions or shards. This pattern can improve scalability when storing and accessing large volumes of data.
  • 76.
  • 77.
    77 Valet key Use atoken or key that provides clients with restricted direct access to a specific resource or service in order to offload data transfer operations from the application code. This pattern is particularly useful in applications that use cloud- hosted storage systems or queues, and can minimize cost and maximize scalability and performance.
  • 78.
  • 79.
    79 Design and Implementation Gooddesign encompasses factors such as consistency and coherence in component design and deployment, maintainability to simplify administration and development, and reusability to allow components and subsystems to be used in other applications and in other scenarios. Decisions made during the design and implementation phase have a huge impact on the quality and the total cost of ownership of cloud hosted applications and services. Ambassador Anti Corruption Layer Backends for Frontends Command And Query Responsibility Computer Resource Consolidation External Configuration Store Gateway Aggregation Gateway Offloading Gateway Routing Leader Election Pipes and Filters Sidecar Static Conten Hosting Strangler
  • 80.
    80 Ambassador Create helper servicesfor making networking requests on behalf of a consumer service or application. An ambassador service can be thought of as an out of process proxy that is co-located with the client.
  • 81.
    81 Anti Corruption Layer Ensurethe design and structure of new apps and services are not limited by legacy resources. Implement a façade or adapter layer in between legacy systems and modern systems where features and capabilities are being migrated to. This façade or layer translates requests between modern systems and legacy applications using methods appropriate to each.
  • 82.
    82 Backends for Frontends Developtargeted backend services for use with specific frontend applications or interfaces. This pattern is useful when you wish to avoid implementing customizations for multiple interfaces in a single backend.
  • 83.
    83 Command and QueryResponsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
  • 84.
    84 Compute Resource Consolidation Consolidatemultiple tasks or operations into a single computational unit. This pattern can increase compute resource utilization, and reduce the costs and management overhead associated with performing compute processing in cloud-hosted applications.
  • 85.
    85 External Configuration Store Moveconfiguration information out of the application deployment package to a centralized location. This pattern can provide opportunities for easier management and control of configuration data, and for sharing configuration data across applications and application instances.
  • 86.
    86 Gateway Aggregation Aggregate multiple individualrequests to a single request using the gateway aggregation pattern. This pattern is useful when a client must make multiple calls to different backend systems to perform an operation.
  • 87.
    87 Gateway Offloading Offload sharedor specialized service functionality to a gateway proxy. This pattern can simplify application development by moving shared service functionality, such as the use of SSL certificates, to a gateway proxy, simplifying application management.
  • 88.
    88 Gateway Routing Route requeststo multiple services using a single endpoint with the gateway routing pattern. This pattern is useful when you wish to expose multiple services on a single endpoint and route to the appropriate service based on the request.
  • 89.
    89 Leader Election Coordinate theactions performed by a collection of collaborating task instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the other instances. This pattern can help to ensure that task instances do not conflict with each other, cause contention for shared resources, or inadvertently interfere with the work that other task instances are performing.
  • 90.
    90 Pipes and Filters Decomposea task that performs complex processing into a series of discrete elements that can be reused. This pattern can improve performance, scalability, and reusability by allowing task elements that perform the processing to be deployed and scaled independently.
  • 91.
    91 Sidecar Deploy components ofan application into a separate process or container using the sidecar pattern to provide isolation, encapsulation, and to enable applications composed of heterogeneous components and technologies.
  • 92.
  • 93.
    93 Strangler Incrementally move features fromlegacy systems to more modern systems while allowing the new system to add features in the meantime. As features from the legacy system are replaced, the new system eventually replaces all of the old system’s features, strangling the old system and allowing you to decommission it.
  • 94.
  • 95.
    95 Messaging The distributed natureof cloud applications requires a messaging infrastructure that connects the components and services, ideally in a loosely coupled manner in order to maximize scalability. Asynchronous messaging is widely used, and provides many benefits, but also brings challenges such as the ordering of messages, poison message management, idempotency, and more. Compensating Consumers Pipes and Filters Priority Queue Queue-Based Load Leveling Scheduler Agent Supervisor
  • 96.
    96 Compensating Consumers Enable multipleconcurrent consumers to process messages received on the same messaging channel. This pattern enables a system to process multiple messages concurrently to optimize throughput, to improve scalability and availability, and to balance the workload.
  • 97.
    97 Pipes and Filters Decomposea task that performs complex processing into a series of discrete elements that can be reused. This pattern can improve performance, scalability, and reusability by allowing task elements that perform the processing to be deployed and scaled independently.
  • 98.
    98 Priority Queue Prioritize requestssent to services so that requests with a higher priority are received and processed more quickly than those of a lower priority. This pattern is useful in applications that offer different service level guarantees to individual clients.
  • 99.
    99 Queue-Based Load Leveling Usea queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
  • 100.
    100 Scheduler Agent Supervisor Coordinatea set of actions across a distributed set of services and other remote resources, attempt to transparently handle faults if any of these actions fail, or undo the effects of the work performed if the system cannot recover from a fault. This pattern can add resiliency to a distributed system by enabling it to recover and retry actions that fail due to transient exceptions, long- lasting faults, and process failures.
  • 101.
  • 102.
    102 Management and Monitoring Cloudapplications run in a remote datacenter where you do not have full control of the infrastructure or, in some cases, the operating system. This can make management and monitoring more difficult than an on-premises deployment. Applications must expose runtime information that administrators and operators can use to manage and monitor the system, as well as supporting changing business requirements without requiring the application to be stopped or redeployed. Ambassador Anti Corruption Layer External Configuration Store Gateway Aggregation Gateway Offloading Gateway Routing Healt Endpoint Monitoring Sidecar Strangler
  • 103.
    103 Ambassador Create helper servicesfor making networking requests on behalf of a consumer service or application. An ambassador service can be thought of as an out of process proxy that is co-located with the client.
  • 104.
    104 Anti Corruption Layer Ensurethe design and structure of new apps and services are not limited by legacy resources. Implement a façade or adapter layer in between legacy systems and modern systems where features and capabilities are being migrated to. This façade or layer translates requests between modern systems and legacy applications using methods appropriate to each.
  • 105.
    105 External Configuration Store Moveconfiguration information out of the application deployment package to a centralized location. This pattern can provide opportunities for easier management and control of configuration data, and for sharing configuration data across applications and application instances.
  • 106.
    106 Gateway Aggregation Aggregate multiple individualrequests to a single request using the gateway aggregation pattern. This pattern is useful when a client must make multiple calls to different backend systems to perform an operation.
  • 107.
    107 Gateway Offloading Offload sharedor specialized service functionality to a gateway proxy. This pattern can simplify application development by moving shared service functionality, such as the use of SSL certificates, to a gateway proxy, simplifying application management.
  • 108.
    108 Gateway Routing Route requeststo multiple services using a single endpoint with the gateway routing pattern. This pattern is useful when you wish to expose multiple services on a single endpoint and route to the appropriate service based on the request.
  • 109.
    109 Health Endpoint Monitoring Implementfunctional checks within an application that external tools can access through exposed endpoints at regular intervals. This pattern can help to verify that applications and services are performing correctly.
  • 110.
    110 Sidecar Deploy components ofan application into a separate process or container using the sidecar pattern to provide isolation, encapsulation, and to enable applications composed of heterogeneous components and technologies.
  • 111.
    111 Strangler Incrementally move features fromlegacy systems to more modern systems while allowing the new system to add features in the meantime. As features from the legacy system are replaced, the new system eventually replaces all of the old system’s features, strangling the old system and allowing you to decommission it.
  • 112.
  • 113.
    113 Performance and Scalability Performanceis an indication of the responsiveness of a system, while scalability is the ability to gracefully handle increases in load, perhaps through an increase in available resources. Cloud applications, especially in multi-tenant scenarios, typically encounter variable workloads and unpredictable activity peaks and should be able to scale out within limits to meet demand, and scale in when demand decreases. Scalability concerns not just compute instances, but other items such as data storage, messaging infrastructure, and more. Cache-aside Command and Query Responsibility Compensating Consumers Index Table Materialized View Priority Queue Sharding Static Content Hosting Throttling
  • 114.
    114 Cache-aside Load data ondemand into a cache from a data store. This pattern can improve performance and also helps to maintain consistency between data held in the cache and the data in the underlying data store.
  • 115.
    115 Command and QueryResponsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
  • 116.
    116 Compensating Consumers Enable multipleconcurrent consumers to process messages received on the same messaging channel. This pattern enables a system to process multiple messages concurrently to optimize throughput, to improve scalability and availability, and to balance the workload.
  • 117.
    117 Index Table Create indexesover the fields in data stores that are frequently referenced by query criteria. This pattern can improve query performance by allowing applications to more quickly locate the data to retrieve from a data store.
  • 118.
    118 Materialized View Generate pre-populated viewsover the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.
  • 119.
    119 Priority Queue Prioritize requestssent to services so that requests with a higher priority are received and processed more quickly than those of a lower priority. This pattern is useful in applications that offer different service level guarantees to individual clients.
  • 120.
    120 Sharding Divide a datastore into a set of horizontal partitions or shards. This pattern can improve scalability when storing and accessing large volumes of data.
  • 121.
  • 122.
    122 Throttling Control the consumptionof resources used by an instance of an application, an individual tenant, or an entire service. This pattern can allow the system to continue to function and meet service level agreements, even when an increase in demand places an extreme load on resources.
  • 123.
  • 124.
    124 Resiliency Resiliency is theability of a system to gracefully handle and recover from failures. The nature of cloud hosting, where applications are often multi-tenant, use shared platform services, compete for resources and bandwidth, communicate over the Internet, and run on commodity hardware means there is an increased likelihood that both transient and more permanent faults will arise. Detecting failures, and recovering quickly and efficiently, is necessary to maintain resiliency. Bulkhead Circuit Breaker Compensating Transaction Leader Election Retry Scheduler Agent Supervisor
  • 125.
    125 Bulkhead Isolate the elementsof an application like the sectioned partitions of a ship's hull. If the hull of a ship that uses bulkheads is compromised, only the damaged section of the ship will fill with water, and the ship will not sink. Using the same concept in application design, this pattern isolates instances of a service or clients into pools so that if one fails, the others will continue to function.
  • 126.
    126 Circuit Breaker Handle faultsthat may take a variable amount of time to rectify when connecting to a remote service or resource. This pattern can improve the stability and resiliency of an application.
  • 127.
    127 Compensating Transaction Undo thework performed by a series of steps, which together define an eventually consistent operation, if one or more of the operations fails. Operations that follow the eventual consistency model are commonly found in cloud-hosted applications that implement complex business processes and workflows.
  • 128.
    128 Leader Election Coordinate theactions performed by a collection of collaborating task instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the other instances. This pattern can help to ensure that task instances do not conflict with each other, cause contention for shared resources, or inadvertently interfere with the work that other task instances are performing.
  • 129.
    129 Retry Enable an applicationto handle anticipated, temporary failures when it attempts to connect to a service or network resource by transparently retrying an operation that has previously failed in the expectation that the cause of the failure is transient. This pattern can improve the stability of the application.
  • 130.
    130 Scheduler Agent Supervisor Coordinatea set of actions across a distributed set of services and other remote resources, attempt to transparently handle faults if any of these actions fail, or undo the effects of the work performed if the system cannot recover from a fault. This pattern can add resiliency to a distributed system by enabling it to recover and retry actions that fail due to transient exceptions, long- lasting faults, and process failures.
  • 131.
  • 132.
    132 Security Security is thecapability of a system to prevent malicious or accidental actions outside of the designed usage, and to prevent disclosure or loss of information. Cloud applications are exposed on the Internet outside trusted on-premises boundaries, are often open to the public, and may serve untrusted users. Applications must be designed and deployed in a way that protects them from malicious attacks, restricts access to only approved users, and protects sensitive data. Federated Identity Gatekeeper Valet key
  • 133.
    133 Federated Identity Delegate authenticationto an external identity provider. This pattern can simplify development, minimize the requirement for user administration, and improve the user experience of then application.
  • 134.
    134 Gatekeeper Protect applications and servicesby using a dedicated host instance that acts as a broker between clients and the application or service, validates and sanitizes requests, and passes requests and data between them. This pattern can provide an additional layer of security, and limit the attack surface of the system.
  • 135.
    135 Valet key Use atoken or key that provides clients with restricted direct access to a specific resource or service in order to offload data transfer operations from the application code. This pattern is particularly useful in applications that use cloud- hosted storage systems or queues, and can minimize cost and maximize scalability and performance.
  • 136.
  • 137.
    137 Valet key Use atoken or key that provides clients with restricted direct access to a specific resource or service in order to offload data transfer operations from the application code. This pattern is particularly useful in applications that use cloud- hosted storage systems or queues, and can minimize cost and maximize scalability and performance. Security Data Management
  • 138.
    138 Queue-Based Load Leveling Usea queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
  • 139.
    139 Cache-aside Load data ondemand into a cache from a data store. This pattern can improve performance and also helps to maintain consistency between data held in the cache and the data in the underlying data store.
  • 140.
    140 Command and QueryResponsibility Segregation (CQRS) Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.
  • 141.
    141 Index Table Create indexesover the fields in data stores that are frequently referenced by query criteria. This pattern can improve query performance by allowing applications to more quickly locate the data to retrieve from a data store.
  • 142.
    142 Materialized View Generate pre-populated viewsover the data in one or more data stores when the data is formatted in a way that does not favor the required query operations. This pattern can help to support efficient querying and data extraction, and improve performance.