Part-2

 The problem might not be the cluster nodes but the containers or pods which might be resource-constrained. If the pods also appear healthy, then adding more pods will not solve the problem.

Application Insight might show that the duration of the workflow service’s process operation is 246 ms. The query can even request a breakout of the processing time per each of the calls to the three backend services. The individual processing times for each of these services might also appear reasonable leaving the shortfall in request processing unexplained. One of the key observations here is that the overall processing time of about 250 ms might indicate a fixed cost that puts an upper bound on how fast the messages can be processed in serial. The key to increasing throughput is to facilitate the parallel processing of messages. The delays in processing appear to come from network RTT for I/O Completions. Fortunately, orders in the request queue are independent of each other. These two factors enable us to increase parallelism which we demonstrate by setting the MaxConcurrentCalls to 20 from the initial value of 1 and the Prefetch Count to increase to 3000 from the local cache from the initial value of 0. The best practices for performance improvement using Service bus messaging indicate looking at dead letter queue. Service bus atomically retrieves and locks a message as it is processed so that it is not delivered to other receivers. When the lock expires, the messages can be delivered to other receivers. After a maximum number of delivery attempts which is configurable, Service Bus puts the messages in the dead letter queue for examining later. The Workflow service is prefetching a large batch of 3000 messages where the total time to process each message is longer and results in messages timing out, going back into the queue, and eventually reaching the dead-letter queue. This behavior can also be tracked via the MessageLostLockException. This symptom is mitigated with the lock duration set to 5 minutes to prevent lock timeouts. The plot for incoming and outgoing messages confirms that the system is now keeping up with the rate of incoming messages. The results from the performance load test show that over the total duration of 8 minutes, the application completed 25,000 operations, with a peak throughput of 72 operations per second, representing a 400% increase in maximum throughput.

While this solution clearly works, repeating the experiment over a much longer period shows that the application cannot sustain this rate. The container metrics show that the maximum CPU utilization was close to 100% At this point, the application appears to be CPU bound. So, scaling the cluster now might increase performance unlike earlier. The new setting for the cluster now includes 12 cluster nodes with 3 replicas for Ingestion service, 6 replicas for workflow service, and 9 replicas for package delivery and drone scheduler. 

To recap, the bottlenecks identified include out-of-memory exceptions for Azure Cache for Redis, Lack of parallelism in message processing, insufficient message lock duration, leading to locking timeouts, and messages being placed in the dead letter queue and CPU exhaustion. The metrics used to detect these bottlenecks include the rate of incoming and outgoing Service Bus messages, the application map in application insights, errors and exceptions, custom log analytics queries, and CPU and memory utilization for containers. 

Part-1

 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 distributed business transactions.

An example of an application using distributed transactions 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.

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.

Since the users get back a response the moment their request is put on a queue, the processing of requests is not useful to study but when the backend cannot keep up with the request rate as the users increase, then it becomes useful to make performance improvements. A plot of incoming and outgoing messages will serve this purpose. When the outgoing messages fall severely behind the incoming messages, a few actions need to be taken which depend on the errors encountered at the time this occurs and indicates ongoing systematic issues. For example, the workflow service might be getting errors from the Delivery service. Let us say the errors indicate that an exception is being thrown due to memory limits in Azure Cache for Redis.

When the cache is added, it resolves a lot of the internal errors seen from the log, but the outbound responses still lag the incoming requests by an order of magnitude. A Kusto query on the logs indicates that the throughput of completed messages based on data points at 5-second samples indicates that the backend is a bottleneck. This can be alleviated by scaling out the backend services - package, delivery, and drone scheduler to see if throughput increases. The number of replicas is increased from 3 to 6. The load test shows only modest improvement. Outgoing messages are still not keeping up with incoming messages. The Azure Monitor for containers indicates that the problem is not resource-exhaustion because the CPU is underutilized at less than 40% even in the 95^th percentile and memory utilization is under 20%.  The problem might not be the cluster nodes but the containers or pods which might be resource-constrained. If the pods also appear healthy, then adding more pods will not solve the problem.

This is a continuation of an article that describes operational considerations for hosting solutions on 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 improper instantiation antipattern. This one focuses on busy database antipattern. 

Database stores data but frequently some code is frequently used with calculations for that data. These are stored in the database as stored procedures and triggers. There is a lot of advantages to running code local to the data which avoids the transmission to a client application for processing. But the overuse of this feature can hurt performance due to the server spending more time processing, rather than accepting new client requests and fetching data. A database is also a shared resource, and it might deny resources to other requests when one of them is using a lot for computations. Runtime costs might shoot up if the database is metered. A database may have finite capacity to scale up. Compute resources are better suitable for hosting complicated logic while storage products are more customized for large disk space. The antipattern occurs when the database is used to host a service rather than a repository or it is used to format the data, manipulate data, or perform complex calculations.  Developers trying to overcompensate for the extraneous fetching antipattern often write complex queries that take significantly longer to run but produce a small amount of data. Stored procedures are used to encapsulate business logic because they are considered easier to maintain and update. They lead to this antipattern. 

This antipattern can be fixed in one of several ways. First the processing can be moved out of the database into an Azure Function or some application tier. As long as the database is confined to data access operations using only the capabilities that the database is optimized and will not manifest this antipattern. Queries can be simplified to fetching the data with a proper select statement that merely retrieves the data with the help of joins. The application then uses the .NET framework APIs to run standard query operators. 

Database tuning is an important routine for many organizations. The introduction of long running queries and stored procedures often goes against the benefits of a tuned database. If the processing is already under the control of the database tuning techniques, then they should not be moved.  

Avoiding unnecessary data transfer solves both this antipattern as well as chatty I/O antipattern. When the processing is moved to the application tier, it provides the opportunity to scale out rather than require the database to scale up. 

Detection of this antipattern is easier with the monitoring tools and the built-in supportability features of the database. If the database activity reveals significant processing and very low data emission, it is likely that this antipattern is manifesting. 

Examine the work performed by the database in terms of transaction units, number of queries processed and the data throughput which can be narrowed down by callers and this may reveal just the database objects that are likely to be causing this antipattern 

Finally, periodic assessments must be performed with the database. 

 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 retry storm antipattern. This one focuses on the noisy neighbor antipattern. 

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 monolithic persistence antipattern. This one focuses on synchronous I/O antipattern.  

This antipattern occurs when there are many tenants that can starve other tenants as they hold up a disproportionate set of critical resources from a shared and reserved pool of resources meant for all tenants.  The noisy neighbor problem occurs when one tenant causes problem for another. Some common examples of resource intensive operations include, retrieving or persisting data to a database, sending a request to a web service, posting a message or retrieving a message from a queue, and writing or reading from a file in a blocking manner. There is a lot of advantages to running dedicated calls especially from debugging and troubleshooting purposes because the calls do not have interference, but multi-tenancy enables reuse of the same components. The overuse of this feature can hurt performance due to the tenants' consuming resources that can starve other tenants. It appears notably when there are components or I/O requiring synchronous I/O. The application uses library that only uses synchronous methods or I/O in this case. The base tier may have finite capacity to scale up. Compute resources are better suitable for scale out rather than scale up and one of the primary advantages of a clean separation of layers with asynchronous processing is that they can be hosted even independently. Container orchestration frameworks facilitate this very well. As an example, the frontend can issue a request and wait for a response without having to delay the user experience. It can use the model-view-controller paradigms so that they are not only fast but can also be hosted such that tenants using one view model do not affect the other. 

This antipattern can be fixed in one of several ways. First the processing can be moved out of the application tier into an Azure Function or some background api layer. Tenants are given promises and are actively monitored. If the application frontend is confined to data input and output display operations using only the capabilities that the frontend is optimized for, then it will not manifest this antipattern. APIs and Queries can articulate the business layer interactions such that the tenants find it responsive while the system reserves the right to perform. Many libraries and components provide both synchronous and asynchronous interfaces. These can then be used judiciously with the asynchronous pattern working for most API calls. Finally, limits and throttling can be applied. Application gateway and firewall rules can handle restrictions to specific tenants

The introduction of long running queries and stored procedures, blocking I/O and network waits often goes against the benefits of a responsive multi-tenant service. If the processing is already under the control of the service, then it can be optimized further. 

There are several ways to fix this antipattern. They are about detection and remedy. The remedies include capping the number of tenant attempts and preventing retrying for a long period of time. The tenant calls could include an exponential backoff strategy that increases the duration between successive calls exponentially, handle errors gracefully, use the circuit breaker pattern which is specifically designed to break the retry storm. Official SDKs for communicating to Azure Services already include sample implementations of retry logic. When the number of I/O requests is many, they can be batched into coarse requests. The database can be read with one query substituting many queries. It also provides an opportunity for the database to execute it better and faster. Web APIs can be designed with the REST best practices. Instead of separate GET methods for different properties, there can be a single GET method for the resource representing the object. Even if the response body is large, it will likely be a single request. File I/O can be improved with buffering and using cache. Files may need not be opened or closed repeatedly. This helps to reduce fragmentation of the file on disk. 

When more information is retrieved via fewer I/O calls and fewer retries, the operational necessary evil becomes less risky but there is also a risk of falling into the extraneous fetching antipattern. The right tradeoff depends on the usages. It is also important to read-only as much as necessary to avoid both the size and the frequency of calls and their retries for tenants. Sometimes, data can also be partitioned into two chunks, frequently accessed data that accounts for most requests and less frequently accessed data that is used rarely. When data is written, resources need not be locked at too large a scope or for a longer duration. Tenant calls, limits and throttling can also be prioritized so that only the higher priority tenant calls go through.

 

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 monolithic persistence antipattern. This one focuses on the Retry storm antipattern.

When I/O requests fail due to transient errors, services must retry their calls. It helps overcome errors, throttle and rate limits and avoid surfacing and requiring user intervention for operational errors. But when the number of retries or the duration of retries is not governed, the retries are frequent and numerous, which can have a significant impact on performance and responsiveness. Network calls and other I/O operations are much slower compared to compute tasks. Each I/O request has a significant overhead as it travels up and down the networking stack on local and remote and includes the round trip time, and the cumulative effect of numerous I/O operations can slow down the system. There are some manifestations of the retry storm.

Reading and writing individual records to a database as distinct requests – When records are often fetched one at a time, then a series of queries are run one after the other to get the information. It is exacerbated when the Object-Relational Mapping hides the behavior underneath the business logic and each entity is retrieved over several queries. The same might happen on writing for an entity. When each of these queries is wrapped in their own retry, they can cause severe errors. 

Implementing a single logical operation as a series of HTTP requests. This occurs when objects residing on a remote server are represented as a proxy in the memory of the local system. The code appears as if an object is modified locally when in fact every modification is coming with at least the cost of the RTT. When there are many networks round trips, the cost is cumulative and even prohibitive. It is easily observable when a proxy object has many properties, and each property get/set requires a relay to the remote object. In such a case, there is also the requirement to perform validation after every access.

Reading and writing to a file on disk – File I/O also hides the distributed nature of interconnected file systems.  Every byte written to a file on amount must be relayed to the original on the remote server. When the writes are several, the cost accumulates quickly. It is even more noticeable when the writes are only a few bytes and frequent. When individual requests are wrapped in a retry, the number of calls can rise dramatically.

There are several ways to fix the problem. They are about detection and remedy. The remedies include capping the number of retry attempts and preventing retrying for a long period of time. The retries could include an exponential backoff strategy that increases the duration between successive calls exponentially, handle errors gracefully, use the circuit breaker pattern which is specifically designed to break the retry storm. Official SDKs for communicating to Azure Services already include sample implementations of retry logic. When the number of I/O requests is many, they can be batched into coarse requests. The database can be read with one query substituting many queries. It also provides an opportunity for the database to execute it better and faster. Web APIs can be designed with the REST best practices. Instead of separate GET methods for different properties, there can be a single GET method for the resource representing the object. Even if the response body is large, it will likely be a single request. File I/O can be improved with buffering and using cache. Files may need not be opened or closed repeatedly. This helps to reduce fragmentation of the file on disk.

When more information is retrieved via fewer I/O calls and fewer retries, the operational necessary evil becomes less risky but there is also a risk of falling into the extraneous fetching antipattern. The right tradeoff depends on the usages. It is also important to read-only as much as necessary to avoid both the size and the frequency of calls and their retries. Sometimes, data can also be partitioned into two chunks, frequently accessed data that accounts for most requests and less frequently accessed data that is used rarely. When data is written, resources need not be locked at too large a scope or for a longer duration. Retries can also be prioritized so that only the lower scope retries are issued for idempotent workflows.

This is a continuation of an article that describes operational considerations for hosting solutions on 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 busy frontend antipattern. This one focuses on monolithic persistence antipattern.

This antipattern occurs when a single data store hurts performance due to resource contention. Additionally, the use of multiple data sources can help with virtualization of data and query.

A specific example of this antipattern is when applications save transactional records, logs, metrics and events to the same database. The online transaction processing benefits from a relational store but logs and metrics can be moved to a log index store and time-series database respectively. Usually, a single datastore works well for transactional data but this does not mean documents need to be stored in the same data store. An object storage or document database can be used in addition to a regular transactional database to allow individual documents to be shared without any impact to the business operations. Each document can then have its own web accessible address.

This antipattern can be fixed in one of several ways. First, the data types must be listed, and their corresponding data stores must be assigned. Many data types can be bound to the same database but when they are different, they must be passed to the data stores that handles them best. Second, the data access patterns for each data type must be analyzed.  If the data type is a document, a CosmosDB instance is a good choice. Third, if the database instance is not suitable for all the data access patterns of the given data type, it must be scaled up. A premium sku will likely benefit this case.

Detection of this antipattern is easier with the monitoring tools and the built-in supportability features of the database layer. If the database activity reveals significant processing, contention and very low data rate, it is likely that this antipattern is manifesting.

Examine the work performed by the database in terms of data types which can be narrowed down by callers and scenarios, may reveal just the culprits that are likely to be causing this antipattern

Finally, periodic assessments must be performed on the data storage tier.

 

 

This is a continuation of an article that describes operational considerations for hosting solutions on 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 busy database antipattern. This one focuses on busy frontend antipattern.

This antipattern occurs when there are many background threads that can starve foreground tasks of their resources which decreases response times to unacceptable levels. There is a lot of advantages to running background jobs which avoids the interactivity for processing and can be scheduled asynchronously. But the overuse of this feature can hurt performance due to the tasks consuming resources that foreground workers need for interactivity with the user, leading to a spinning wait and frustrations for the user. It appears notably when the foreground is monolithic compressing the business tier with the application frontend. Runtime costs might shoot up if this tier is metered. An application tier may have finite capacity to scale up. Compute resources are better suitable for scale out rather than scale up and one of the primary advantages of a clean separation of layers and components is that they can be hosted even independently. Container orchestration frameworks facilitate this very well. The Frontend can be as lightweight as possible and built on model-view-controller or other such paradigms so that they are not only fast but also hosted on separate containers that can scale out.

This antipattern can be fixed in one of several ways. First the processing can be moved out of the application tier into an Azure Function or some background api layer. If the application frontend is confined to data input and output display operations using only the capabilities that the frontend is optimized for, then it will not manifest this antipattern. APIs and Queries can articulate the business layer interactions. The application then uses the .NET framework APIs to run standard query operators on the data for display purposes.

UI interface is designed for purposes specific to the application. The introduction of long running queries and stored procedures often goes against the benefits of a responsive application. If the processing is already under the control of the application techniques, then they should not be moved. 

Avoiding unnecessary data transfer solves both this antipattern as well as chatty I/O antipattern. When the processing is moved to the business tier, it provides the opportunity to scale out rather than require the frontend to scale up.

Detection of this antipattern is easier with the monitoring tools and the built-in supportability features of the application layer. If the frontend activity reveals significant processing and very low data emission, it is likely that this antipattern is manifesting.

Examine the work performed by the Frontend in terms of latency and page load times which can be narrowed down by callers and scenarios, may reveal just the view models that are likely to be causing this antipattern

Finally, periodic assessments must be performed on the application tier.