The architectural styles for implementing a cloud service. (Continued)

 

Let’s compare the architectural style described in the previous post with the N-Tier architectural style of building services. This involves many logical layers and physical tiers. It comprises of WebTier, Messaging and Middle-tier and it may or may not involve a FrontEnd. In the closed style, a layer can only call the next layer immediately down and in the open style, a layer can call any of the layers below it.

Some of the benefits of this application include the following: There is portability between cloud and on-premises, and between cloud platforms. There is less learning curve for most developers. There is a natural evolution from the traditional application mode, and it is open to heterogeneous environment (Window/Linux)

Some of the challenges faced with this architectural style include: The middle tier degenerates to a data access layer that just does CRUD operations on the database which introduces unnecessary latency. There is a monolithic design that prevents independent deployment of features. Managing an IaaS application is more work than an application that uses only managed services. It can be difficult to manage network security in a large system.

Some of the best practices faced with this architectural style include changes in load can easily be accomplished by scaling out. Asynchronous messaging can decouple tiers. Semi static data can be cached. The database tier can be configured for high availability, using a solution such as SQL Server which is always on availability groups. It places a web application firewall (WAF) between the front end and the Internet. It places each tier in its own subnet and use subnets as a security boundary. The access is restricted to the data tier.

Some examples include a simple web application, or an application that migrates an on-premises application to Azure with minimal refactoring, and a unified development of on-premises and cloud applications.

Conclusion: Both these styles serve the purpose of a cloud service very well.

The architectural styles for implementing a cloud service.

 

Introduction:

A web service for the cloud must be well suited for the business purpose its serves not only in its functionality but also in the non-functional aspects which are recorded in the Service-Level Agreements. The choice of architecture for a web service has a significant contribution to this effect. We review the choices between Web-Queue architectural style and the N-Service architectural style.

The web-queue can absorb the latencies from the events and user actions are translated to events. It decouples the frontend and the API layer so that they become more responsive to the users. All the actions taken by the user can be mapped to one or other form of messages that are sent to the message queue which is usually the service bus. The Web Queue can handle plenty of messages and can even scale out to catch up with the items in the queue. Each message is handled by a different handler and there is one-to-one mapping which makes it easy to view.

Some of the benefits of this architecture include the following: 1) It is relatively simple architecture that is easy to understand.2) It is Easy to deploy and manage. 3) There is a clear separation of concerns. 4) The front end is decoupled from the worker using asynchronous messaging and 5) the front end and the worker can be scaled independently.

Some of the challenges faced with this architecture include the following: The front end and the worker can both become arbitrarily large, monolithic components which increase the maintenance costs. It may also hide dependencies, if the front end and worker share data schemas or code modules.

Some of the best practices demonstrated by this code include It exposes a well-designed API to the client. It can auto scale to handle changes in the load. It caches semi-static data. It uses a CDN to host static content. It uses a polyglot persistence when appropriate. It partitions data to improve scalability, it reduces contention, and optimizes performance.

Some of the examples with this architectural style include applications with a relatively simple domain, those with some long-running workflows or batch operations or when there are managed services rather than infrastructure as a service (IaaS).

Comparisons between web-queue-worker and event driven architecture.

Introduction:

This article is a comparison of two architectural styles in building services for the public cloud.

Description:

The two architectural styles correspond to:

1.       Event-Driven architecture style: Event producers that generate a stream of events and event consumers that listen for events

2.       A microservices architecture that consists of a collection of small, autonomous services. 

This is a comparison of the features and their relative price comparisons as low or high:

Feature/Subsystem	Event-Driven Architecture	Microservices
Organization	Events are produced in near real-time, so consumers can respond immediately to events as they occur.	Each Service is self-contained and implements a single business functionality encapsulating a domain model.
Management	Subscribers can be added as necessary without impacting existing ones.	Services can be added as necessary without impacting existing ones
Benefits	The publishers and subscribers are decoupled. There are no point-to-point integrations. It's easy to add new consumers to the system. Consumers can respond to events immediately as they arrive. They are highly scalable and distributed. There are subsystems that have independent views of the event stream.	This is a simple architecture that focuses on end-to-end addition of business capabilities. They are easy to deploy and manage. There is a clear separation of concerns. The front end is decoupled from the worker using asynchronous messaging. The front end and the worker can be scaled independently.
Challenges	Event loss is tolerated so if there needs to be guaranteed delivery, this poses a challenge. Some IoT traffic mandate a guaranteed delivery Events are processed in exactly the order they arrive. Each consumer type typically runs in multiple instances, for resiliency and scalability. This can pose a challenge if the processing logic is not idempotent or the events must be processed in order.	Care must be taken to ensure that  the front end and the worker do not become large, monolithic components that are difficult to maintain and update. It hides unnecessary dependencies when the front end and worker share data schemas or code modules.
Best practices	Events should be lean and mean and not bloated. Services should share only IDs and/or a timestamp. Large data transfer between services in this case is an antipattern. Loosely coupled event driven systems are best.	Expose a well-designed API to the client Autoscale to handle changes to load Cache semi-static data Use a CDN to host static content Use polyglot persistence when appropriate Partition data to improve scalability
Examples	The event driven architecture is suitable for edge computing including IoT traffic Works great for automations that rely heavily on asynchronous backend processing Useful to maintain order, retries and dead letter queues	The microservices are best suited for expanding the backend service portfolio such as for eCommerce Works great for transactional processing and deep separation of data access Useful to work with application gateway, load balancer and ingress.

Conclusion:

These are some comparisons between the two styles.

This is a continuation of an article that describes operational considerations for hosting solutions on the Azure public cloud.  

There are several references to best practices throughout the series of articles we wrote from the documentation for the Azure Public Cloud. The previous article focused on the antipatterns to avoid, specifically the noisy neighbor antipattern. This article focuses on transient fault handling.

Transient errors can occur anywhere in the cloud. When the services are hosted on-premises, the performance and availability are provided via redundant and often underutilized hardware, but the components are located close to each other. This reduces the failures from networking though not from power failures or other faults. The cloud provides unparalleled availability, but it might involve network latency and there can be any number of errors resulting from an unreachable network. Other forms of transient failures may come from:

1) many shared resources that are subject to throttling in order to protect the resource. Some services will refuse connections when the load rises to a specific level, or a maximum throughput rate may be reached. This helps to provide uniform quality of service for neighbors and other tenants using the shared resource.

2) commodity hardware units that make up the cloud where the performance is delivered by dynamically distributing the load across multiple computing units and infrastructure components. In this case, the faults are handled by dynamically recycling the affected components.

3)  hardware components including network infrastructure such as routers and load balancers, between the application and the resources and the services it uses. 

4) Clients when the conditions affect it such that the reachability to the service is affected due to the intermittent Internet disconnections.

Cloud-based services, applications, and solutions must work around these transient failures because they are hard to eliminate.

First, they must have a built-in retry mechanism although they can use varying scope from the level of an individual system call to the API implementations. 

Second, they should determine if the operation is suitable for retrying. Retry operations where the faults are transient and there is at least some likelihood that the operation will succeed when reattempted. These are easily known from the error codes for calls where the transient errors originate from. 

Third, the retry count and interval must be decided for this to work. Some strategies include exponential backoff, Incremental intervals, and regular intervals, immediate retry, and randomization.

Finally, retry storm antipatterns must be avoided.

Continued from previous post

 

The next step would require an increase in the resource units (RU) pertaining to this operation. When the RU is quadrupled, the throughput increases from 19 requests/second to 23 requests per second, and the average latency drops from 669ms to 569 ms. Notice that the maximum throughput is not significantly higher, but it eliminates all the 429 errors that were encountered. This is still a significant win.

The number of RUs provisioned still had sufficient headroom between provisioned and consumption. At this point, we could increase the RU per partition but let us review another angle where we plot the number of calls to the database per successful operation.  The number of calls reduces from 11 to 9 but it should match the actual query plan. This implies that the database call was for a cross-partition query that targeted all nine partitions. The client must fan out the query to all the partitions and collect the results. The queries however were completed one after the other. The operation takes as long as the sum of all the queries and the problem will only get worse as the size of the data grows and more physical partitions are added.

If the queries were executed in parallel, the latency would decrease, and the throughput would increase. In fact, the gains would be so much that the throughput would keep pace with the load. One of the side effects of increasing the throughput is that the resource unit consumption would increase and the headroom between the provisioned and the consumption would shrink. This would entail a database scale-out of the operation, but an alternative might be to optimize the query. The cross-partition query is a concern especially given that it is being run every time instead of selectively. The query is trying to filter the data based on the owner and the time of the call. Switching the collection to the new partition key where the owner ID is the partition helps mitigate the cross-partition querying. This will dramatically improve the throughput and keep it more regular just like the other calls noticed from the monitoring data. A consequence of the improved performance is that the node CPU utilization is also improved. When this happens, we know that the bottleneck has been eliminated.

 

 This is a continuation of an article that describes operational considerations for hosting solutions on the Azure public cloud. 

There are several references to best practices throughout the series of articles we wrote from the documentation for the Azure Public Cloud. The previous article focused on the antipatterns to avoid, specifically the noisy neighbor antipattern. This article focuses on performance tuning for multiple backend services. 

An example of an application using multiple backend services is a drone delivery application that runs on Azure Kubernetes Service.  Customers use a web application to schedule deliveries by drone. The backend services include a delivery service manager that manages deliveries, a drone scheduler that schedules drones for pickup, and a package service manager that manages packages. The orders are not processed synchronously.  An ingestion service puts the orders on a queue for processing and a workflow service coordinates the steps in the workflow.  Clients call REST API to get their latest invoice which includes a summary of deliveries, packages, and total drone utilization. The information is retrieved from multiple backend services and then the results are aggregated for the user. The clients do not call the backend services directly. Instead, the application implements a Gateway Aggregation pattern. 

Performance tuning begins with a baseline usually established with a load test. In this case, a six node AKS cluster with three replicas for each microservice was deployed for a step load test where the number of simulated users was stepped up from two to forty over a total duration of 8 minutes. It is observed that as the user load increases, the throughput average requests per second does not keep up. While there are no errors returned to the user, the throughput peaks halfway through the test and then drops off for the remainder. Resource contention, transient errors, and an increase in the rate of exceptions can contribute to this pattern. 

One of the ways to tackle this bottleneck is to review the monitoring data. The average duration of the HTTP calls from the gateway to the backend services is noted. When the chart for the duration of different backend calls is plotted, it shows that the GetDroneUtilization takes much longer on average by an order of magnitude. The Gateway makes the calls to the backends in parallel, so the slowest operation determines how long it takes for the entire request to complete. 

As the investigation narrows down to the GetDroneUtilization operation, the Azure Monitor for Containers is leveraged to pull up the resource consumption data for the CPU or memory utilization. Both the average and the maximum values are needed because the average will hide the spikes in utilization. If the overall utilization remains under 80%, this is not likely to be the issue. 

Another chart that shows the response code from the Delivery services’ backend database shows that a considerable number of 429 error codes are returned from the calls made to the database. Cosmos DB which is the backend database in this case would throw this error only when it is temporarily throttling requests and usually when the caller is consuming more resource units than provisioned. 

Fortunately, this level of focus comes with specific tools to assist with inferences. The Application Insight tool provides end-to-end telemetry for a representative sample of requests. The call to the GetDroneUtilization operation is analyzed for external dependencies. It shows that the Cosmos DB returns the 429-error code and waits 672 ms before retrying the operation.  This means most of the delay is coming from waits without any corresponding activity. Another chart for resource unit consumption per partition versus provisioned resource units per partition will help with the original cause for the 429-error preceding the wait. It turns out that there are nine partitions that were provisioned with 100 resource units each and while the database spreads the operations across the partitions, the resource unit consumption has exceeded the provisioned resource units

 

This is a summary of the book titled “13 things mentally strong people don’t do” by Amy Morin. The author is a licensed, clinical, social worker, college psychology instructor and psychotherapist and is dedicated to all those who strive to become better today than they were yesterday. She cuts to the chase with clear and precise instructions. Some excerpts follow in this summary:

Thoughts, behaviors and feelings are intertwined.  When used together, the “think positive” approach propels us forward otherwise they can even create a downward spiral. The points mentioned below are manifestations that are associated with people who understand this intertwining and become mentally strong. They need not appear tough or ignore their emotions, but they are resilient, more satisfied and demonstrate enhanced performance.

1.       They don’t waste time feeling sorry for themselves. Self-pity is the classic symptom of the weak and to gain strength, they must avoid this self-destructive behavior by behaving in a manner that makes it hard to feel sorry for themselves. One way to do this is to exchange self-pity for gratitude. The more they journal their gratitude, the stronger they become.

2.       They don’t give away their power.  There is always a buffer between the stimulus and their response. They do not let others offend them, turn them or trigger a knee-jerk reaction. Retaining their power is about being confident about who they are and the choices they make. Identifying the people who have taken their power and reframing their language helps them in this regard.

3.       They don’t shy away from change. Managing change can be daunting but the successful create a success plan for the change. They behave like the person they want to become. Balancing emotions and rational thoughts help make it easier.

4.       They don’t focus on things they can’t control. They develop a balanced sense of control. They identify their fears. They focus on what they can do which includes influencing people even without controlling them. Insisting on doing everything by themselves goes against their practice.

5.       They don’t worry about pleasing everyone. They identify their values and behave accordingly. They make a note of who they want to please, and it does not include everybody. They practice tolerating uncomfortable emotions.

6.       They don’t fear taking calculated risks. They are aware of the emotional reactions to risk taking and they identify the types of risks that are particularly challenging. They analyze risks before they decide. They also monitor the results so they can learn from each risk.

7.       They don’t dwell on the past. They reflect on the past just enough to learn from it. They move forward even if it is painful. Working through the grief lets them focus on the present and plan. They also find ways to make peace with the past, but they never pretend that it did not happen. They don’t try to undo the past or make up for past mistakes.

8.       They don’t make the same mistakes repeatedly. They acknowledge their personal responsibility for each mistake and even create a written plan to avoid repeating it. They identify the triggers and the warning signs for old behavior patterns and practice self-discipline strategies. They never make excuses or respond impulsively. They never put themselves in situations where they are likely to fail. Resisting temptation is one way to avoid repeating mistakes.

9.       They don’t resent other people’s success. They replace negative thoughts that breed resentment. They celebrate accomplishments, focus on strengths and co-operate rather than compete with everyone. They do not compare themselves to everyone around them or treat them as direct competition.

10.   They don’t give up after the first failure. They view failure as a learning opportunity, and they resolve to try again.  They identify and replace irrational thoughts and they focus on improving their skill rather than showing them off. They do not quit or assume that future attempts will be the same as the past.

11.   They don’t fear alone time. They learn how to appreciate silence and to be alone with their thoughts. They schedule a date with themselves at least once a month and practice mindfulness and meditation regularly. They do not indulge in beliefs that limit them and they do not always keep background noise on.

12.   They don’t feel the world owes them anything. They develop healthy amounts of self-esteem, and they recognize areas of life where they believe they are superior. They focus on what they must give rather than what they must take. They think about other people’s feelings. They are certainly not selfish or egoist.

13.   They don’t expect immediate results. Instead, they create realistic expectations, find accurate ways to measure progress, and celebrate milestones along the way. They don’t limit themselves to believing that if it is not working for them now, they are not making progress. They don’t look for shortcuts.

And finally, a conclusion on maintaining mental strength. This is a continuous process where they monitor their behavior, regulate their emotions and think about their thoughts.