Azure Function App Interviewer Questions : ChatGPT Answers
Manmohan J Mundhra
Architect @ Wissen Infotech | Apps Integration, Tech Upgrade, Cloud Migration
What is durable function in azure ?
Azure Durable Functions is an extension of Azure Functions that allows developers to write stateful serverless functions in a simpler and more efficient way. Durable Functions makes it easier to build complex workflows, such as those found in long-running business processes, IoT telemetry processing, and human workflow automation.
With Durable Functions, developers can write stateful functions using familiar programming models, such as C#, JavaScript, and Python. These functions can be triggered by events or messages, and can persist their state across multiple function invocations. The state of a Durable Function is managed by an underlying orchestrator function, which coordinates the execution of the function and maintains its state across multiple invocations.
Durable Functions provides a range of benefits, including improved scalability, fault tolerance, and long-running execution capabilities. It also allows developers to build complex workflows in a more modular and manageable way, by breaking down the workflow into smaller, more focused functions that can be composed together using Durable Functions’ orchestration capabilities.
Why durable function is called stateful serverless functions ?
Durable Functions are called stateful serverless functions because they allow developers to write serverless functions that maintain state across multiple function invocations. Traditionally, serverless functions are stateless, meaning that they do not maintain any state between invocations. Each invocation of a stateless function is independent of the previous one, and the function does not retain any information about previous invocations.
In contrast, Durable Functions provide a way for developers to write serverless functions that can maintain state across multiple invocations. This is achieved by using an underlying orchestrator function that coordinates the execution of the stateful functions and maintains their state across multiple invocations.
The state of a Durable Function is persisted in a durable storage provider such as Azure Blob Storage, Azure Table Storage, or Azure Cosmos DB. The orchestrator function uses this durable storage provider to store and retrieve the state of the stateful functions.
By allowing serverless functions to maintain state, Durable Functions enable developers to write more complex and long-running workflows that require stateful behavior. This makes it easier to build complex, event-driven applications that can handle a variety of business scenarios, such as workflow automation, IoT telemetry processing, and more.
Can we make normal function stateful ?
In general, normal functions are stateless, meaning that they do not maintain state between calls. However, there are techniques and patterns that can be used to make a normal function stateful.
One way to make a normal function stateful is to use closures. A closure is a function that has access to variables in its parent function’s scope, even after the parent function has returned. By using closures, you can create a function that maintains state between calls by keeping track of the values of its parent function’s variables.
Another way to make a normal function stateful is to use global variables. By using global variables, you can create a function that maintains state between calls by keeping track of the values of its global variables.
However, using closures or global variables to make a normal function stateful can make the function harder to understand and test. It can also make the function less scalable and harder to maintain. Therefore, it is generally recommended to use a stateful serverless function framework, such as Azure Durable Functions, if you need to write serverless functions that maintain state between calls.
What is orchestrator function ? Give and example.
In Azure Durable Functions, an orchestrator function is a type of function that is responsible for coordinating the execution of a workflow. The orchestrator function manages the state of the workflow and determines which actions should be taken next, based on the results of previous actions.
An example of an orchestrator function might be a workflow for processing an order in an e-commerce application. The workflow might include several steps, such as validating the order, checking inventory, processing payment, and shipping the order. The orchestrator function would be responsible for coordinating these steps and managing the state of the workflow.
Here is an example of an orchestrator function in C#:
[FunctionName("OrderProcessingOrchestrator")]
public static async Task<List<string>> Run(
[OrchestrationTrigger] IDurableOrchestrationContext context)
{
var order = context.GetInput<Order>();
var outputs = new List<string>();
// Step 1: Validate order
var validationResult = await context.CallActivityAsync<string>("ValidateOrderActivity", order);
if (validationResult != "Success")
{
// If the order is invalid, terminate the workflow
return outputs;
}
// Step 2: Check inventory
var inventoryResult = await context.CallActivityAsync<string>("CheckInventoryActivity", order);
outputs.Add(inventoryResult);
// Step 3: Process payment
var paymentResult = await context.CallActivityAsync<string>("ProcessPaymentActivity", order);
outputs.Add(paymentResult);
// Step 4: Ship order
var shippingResult = await context.CallActivityAsync<string>("ShipOrderActivity", order);
outputs.Add(shippingResult);
// Return the results of each step
return outputs;
}
In this example, the orchestrator function is triggered by an event and receives an input object representing an order. The function then executes several activities, such as validating the order, checking inventory, processing payment, and shipping the order. The results of each activity are collected and returned as a list. If any activity fails, the workflow is terminated and no further actions are taken.
How to handle and raise exceptions from orchestrator and activity error. Can we propagate exception to orchestrator function so that exception can be handled in one place ?
In Azure Durable Functions, exceptions can be raised from both activity functions and orchestrator functions. When an exception is raised in an activity function, it is automatically caught by the Durable Functions runtime, and the function instance is marked as failed. When an exception is raised in an orchestrator function, it can be caught using try-catch blocks.
To handle exceptions in a centralized way, you can use the Durable Functions exception handling features, such as the?Task.WhenAll?method and the?Task.WhenAny?method. These methods allow you to wait for the completion of multiple function calls and catch any exceptions that are thrown during the execution of those calls.
Here’s an example of how to use the?Task.WhenAll?method to catch exceptions from multiple activity function calls in an orchestrator function:
[FunctionName("OrderProcessingOrchestrator")]
public static async Task<List<string>> Run(
[OrchestrationTrigger] IDurableOrchestrationContext context)
{
var order = context.GetInput<Order>();
var outputs = new List<string>();
try
{
// Call multiple activity functions in parallel and wait for all of them to complete
var tasks = new List<Task<string>>
{
context.CallActivityAsync<string>("ValidateOrderActivity", order),
context.CallActivityAsync<string>("CheckInventoryActivity", order),
context.CallActivityAsync<string>("ProcessPaymentActivity", order),
context.CallActivityAsync<string>("ShipOrderActivity", order)
};
var results = await Task.WhenAll(tasks);
// Add the results of each activity to the output list
outputs.AddRange(results);
}
catch (Exception ex)
{
// Handle the exception in a centralized way
// Log the exception, notify the user, or take other appropriate action
}
// Return the results of each step
return outputs;
}
In this example, the orchestrator function calls multiple activity functions in parallel using the?Task.WhenAll?method. If any of the activity functions raise an exception, the?Task.WhenAll?method catches the exception and returns a faulted task. The?try-catch?block in the orchestrator function catches the exception and allows you to handle it in a centralized way, such as logging the exception, notifying the user, or taking other appropriate action.
What is Durable Functions runtime ? What it does ? Is this runtime only loaded when using durable function ?
Azure Durable Functions runtime is a component of Azure Functions that provides the infrastructure to support the execution of Durable Functions. The runtime provides the following functionalities:
The Durable Functions runtime is responsible for maintaining the state of a durable function instance and executing its activities in the correct order. It also provides automatic checkpointing and recovery in case of failures, which ensures that the workflow can continue from where it left off when it is restarted.
The Azure Functions runtime is loaded whenever an Azure Function is triggered, including Durable Functions. The Durable Functions runtime is then responsible for managing the execution of the function and coordinating with other instances of the function as needed.
Explain internal of how durable function able to retain state ? Is durable function run on ondemand instance or on VM ?
Azure Durable Functions are able to retain state using a combination of orchestration and durable entities.
When an instance of a durable function is started, the state is stored in a durable storage account. This allows the runtime to persist the state of the function instance even if the underlying compute instance is recycled or scaled down. The state is then reloaded when the function is resumed.
The runtime uses a combination of checkpoints and message queues to ensure that the function state is retained. As the function executes, the runtime checkpoints the function state and saves it to the durable storage account. This checkpointing process allows the runtime to restart the function from where it left off if there is a failure or if the function is scaled down.
In addition to checkpointing, the Durable Functions runtime uses message queues to ensure that the function state is retained even if the underlying compute instance is recycled. Whenever an activity function is invoked, the runtime places a message on a durable message queue. This message contains the state of the activity function and any input parameters. If the underlying compute instance is recycled, the message queue is used to resume the activity function and ensure that the function state is retained.
Durable Functions can run on either on-demand instances or on VMs. When running on an on-demand instance, the Azure Functions runtime creates and manages the compute resources needed to run the function. When running on a VM, the Azure Functions runtime deploys the function to the VM and manages the resources needed to run the function. In either case, the Durable Functions runtime is responsible for managing the state of the function and ensuring that it is retained across instances.
Is durable storage account stores state in VM ?
Azure Durable Functions uses a durable storage account to persist the state of a durable function instance, but the storage account itself does not run on a VM.
The durable storage account is a durable and highly available storage service provided by Azure. It stores the state data of durable function instances and other metadata related to the execution of durable functions. The data stored in the storage account is replicated across multiple Azure data centers to ensure durability and high availability.
The storage account is a separate service from the Azure Functions runtime and is not tied to any specific VM. The durable storage account is accessed via the Azure Storage API, which provides a RESTful interface to interact with the data stored in the storage account. When a durable function instance needs to store or retrieve data, it communicates with the storage account over the network using the Storage API.
In summary, the durable storage account used by Azure Durable Functions does not run on a VM, but is a separate Azure service that provides durable and highly available storage for the state and metadata of durable function instances.
Is it possible for normal function to use durable storage account and act as orchestrator function ?
Yes, it is possible for a normal Azure Function to use the same durable storage account that is used by Azure Durable Functions to store and retrieve state information. However, to act as an orchestrator function, the function must be written using the Durable Functions extension and follow certain conventions.
To use the durable storage account, the normal Azure Function can use the Azure Storage API to interact with the durable storage account. The function can read and write state information, as well as other metadata related to the execution of durable functions.
To act as an orchestrator function, the normal Azure Function must be written using the Durable Functions extension and follow the conventions of an orchestrator function. This includes using the?yield?keyword to call child activities, and using the?Task.all?method to wait for all activities to complete.
While it is possible for a normal Azure Function to use the durable storage account and act as an orchestrator function, it is generally recommended to use the built-in features and APIs provided by Azure Durable Functions for writing orchestrator functions. This ensures compatibility with the Durable Functions runtime and makes it easier to write and manage durable function workflows.
Can durable function able to replace logic app ? If no, under which criteria one can choose over each other ?
Azure Durable Functions and Logic Apps are both workflow orchestration solutions offered by Azure, but they have different strengths and use cases.
Durable Functions is a serverless compute service that allows you to write stateful workflows in code using a programming language of your choice. It is a more low-level solution compared to Logic Apps and provides more fine-grained control over the workflow logic.
Logic Apps, on the other hand, is a fully-managed, serverless workflow orchestration service that allows you to create workflows using a visual designer. It provides a more high-level and declarative approach to workflow orchestration, making it easier to build and maintain complex workflows without writing code.
While Durable Functions and Logic Apps have some overlapping features, such as the ability to call APIs and execute code, there are some key differences between the two.
Durable Functions is generally better suited for scenarios where you need more fine-grained control over the workflow logic, such as scenarios that require complex branching or decision-making logic, or scenarios where you need to integrate with custom code or services that are not available as connectors in Logic Apps.
Logic Apps is generally better suited for scenarios where you want to create and manage workflows in a more visual and declarative way, without writing code. It also provides a rich set of built-in connectors that make it easy to integrate with a wide variety of services and systems.
In summary, while Durable Functions and Logic Apps have some similarities, they are designed for different use cases. The choice between the two depends on the specific requirements of your workflow orchestration scenario.
How we can call logic app from function and vice versa ? How request and response data handled in such scanario ?
You can call a Logic App from an Azure Function using the Logic Apps HTTP or trigger-based API. The Logic Apps HTTP API allows you to trigger a Logic App by sending an HTTP request to a specific URL, while the trigger-based API allows you to trigger a Logic App by sending a message to an Azure Service Bus queue or topic.
To call a Logic App using the HTTP API, you can use the HttpClient class in your Azure Function code to send an HTTP POST request to the Logic App’s trigger URL. The request payload can include any data that you want to pass to the Logic App.
The Logic App can then process the data and perform any necessary actions, such as calling external services or executing business logic. Once the Logic App completes its processing, it can return a response to the Azure Function, which can then continue with its own processing.
To call an Azure Function from a Logic App, you can use the Azure Functions connector in the Logic App designer. The connector allows you to specify the Azure Function endpoint, along with any data that you want to pass to the function.
When the Logic App calls the Azure Function, it can include any necessary input data in the request payload. The Azure Function can then process the data and perform any necessary actions. Once the Azure Function completes its processing, it can return a response to the Logic App, which can then continue with its own processing.
In both scenarios, the request and response data is handled using standard HTTP protocols. The data can be passed as JSON, XML, or any other format that is supported by the API being used. The Azure Function and Logic App can also perform any necessary data transformations to convert the data between different formats.
Is there any tool available in azure to convert durable function to logic app and vice versa ?
There is currently no tool available in Azure to convert Durable Functions to Logic Apps or vice versa.
Durable Functions and Logic Apps are designed for different use cases and have different architectures, so a direct conversion between the two may not always be feasible or desirable.
However, both services provide similar functionality for workflow orchestration, and it may be possible to achieve similar outcomes using either service. If you have an existing Durable Function and want to explore the possibility of using a Logic App instead, you can review the Logic Apps documentation and evaluate whether it can meet your requirements. Similarly, if you have an existing Logic App and want to explore the possibility of using a Durable Function instead, you can review the Durable Functions documentation and evaluate whether it can meet your requirements.
In either case, you may need to rewrite some of your workflow logic or data transformations to adapt to the new service. You can also explore using Azure Functions or Azure Logic Apps together in the same workflow, depending on the specific requirements of your scenario.
While calling logic app from function app. I may want to continue without waiting for logic app response. Also how to make function to wait for logic app response then execute next statement. Provide example for both scenarios ?
When calling a Logic App from a Function App, you have two options for handling the response from the Logic App: synchronous and asynchronous.
In the synchronous approach, the Function App will wait for the Logic App to complete and return a response before continuing with its own processing. This can be useful when you need to ensure that the Logic App has completed before proceeding with the next step in the Function App.
Here’s an example of calling a Logic App synchronously from a Function App using the?HttpClient?class:
public static async Task<IActionResult> Run(HttpRequest req, ILogger log)
{
// Create an instance of the HttpClient class
using (HttpClient client = new HttpClient())
{
// Call the Logic App's HTTP trigger URL and pass in any required input data
HttpResponseMessage response = await client.PostAsync("https://<logic-app-name>.azurewebsites.net/api/<http-trigger-name>?code=<function-key>", null);
// Check if the response indicates success
if (response.IsSuccessStatusCode)
{
// Read the response content
string responseBody = await response.Content.ReadAsStringAsync();
// Do something with the response data
log.LogInformation($"Logic App response: {responseBody}");
// Continue with Function App processing
return new OkResult();
}
else
{
// Handle the error condition
log.LogError($"Error calling Logic App: {response.ReasonPhrase}");
return new BadRequestResult();
}
}
}
In the asynchronous approach, the Function App will immediately continue with its own processing after calling the Logic App, without waiting for a response. This can be useful when you don’t need to wait for the Logic App to complete and can proceed with other work in the Function App.
Here’s an example of calling a Logic App asynchronously from a Function App using the?HttpClient?class:
public static async Task<IActionResult> Run(HttpRequest req, ILogger log)
{
// Create an instance of the HttpClient class
using (HttpClient client = new HttpClient())
{
// Call the Logic App's HTTP trigger URL and pass in any required input data
await client.PostAsync("https://<logic-app-name>.azurewebsites.net/api/<http-trigger-name>?code=<function-key>", null);
// Continue with Function App processing
return new OkResult();
}
}
In this case, the Function App doesn’t need to wait for a response from the Logic App and can immediately continue with its own processing.
Suppose we call Logic App asynchronously from function app. And after few statements we need to check and get the response if it completed. How is that possible from function app ?
If you call a Logic App asynchronously from a Function App, you won’t be able to retrieve the response immediately after the function call, since the Logic App may still be running in the background.
One option for checking the status of the Logic App and retrieving its response data is to use the Azure Logic Apps API. This API allows you to get the status of a Logic App run and retrieve its output data.
Here’s an example of how you could use the Azure Logic Apps API to check the status of a Logic App run from a Function App:
csharpCopy code
public static async Task<IActionResult> Run(HttpRequest req, ILogger log)
{
// Call the Logic App's HTTP trigger URL and pass in any required input data
using (HttpClient client = new HttpClient())
{
await client.PostAsync("https://<logic-app-name>.azurewebsites.net/api/<http-trigger-name>?code=<function-key>", null);
}
// Wait for some time to allow the Logic App to run
await Task.Delay(TimeSpan.FromSeconds(30));
// Get the status of the Logic App run using the Azure Logic Apps API
using (HttpClient client = new HttpClient())
{
string workflowRunId = "<workflow-run-id>"; // The ID of the Logic App run
string azureToken = "<azure-token>"; // An Azure authentication token with the necessary permissions
string apiUrl = $"https://management.azure.com/subscriptions/<subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.Logic/workflows/<workflow-name>/runs/{workflowRunId}?api-version=2016-06-01";
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", azureToken);
HttpResponseMessage response = await client.GetAsync(apiUrl);
// Check if the response indicates success
if (response.IsSuccessStatusCode)
{
// Read the response content
string responseBody = await response.Content.ReadAsStringAsync();
// Deserialize the response JSON into a C# object
JObject responseJson = JObject.Parse(responseBody);
// Get the status of the Logic App run
string status = responseJson["properties"]["status"].ToString();
// Get the output data of the Logic App run
JObject output = JObject.Parse(responseJson["properties"]["outputs"].ToString());
// Do something with the status and output data
log.LogInformation($"Logic App status: {status}");
log.LogInformation($"Logic App output: {output}");
// Continue with Function App processing
return new OkResult();
}
else
{
// Handle the error condition
log.LogError($"Error calling Azure Logic Apps API: {response.ReasonPhrase}");
return new BadRequestResult();
}
}
}
In this example, the Function App calls the Logic App asynchronously and then waits for some time (30 seconds in this case) to allow the Logic App to run. After that, the Function App uses the Azure Logic Apps API to get the status and output data of the Logic App run, and can then continue with its own processing based on that data. Note that this example requires an Azure authentication token with the necessary permissions to call the Azure Logic Apps API.
How long an activity function can run ? Is it similar to normal function ?
The maximum duration that an activity function can run in Azure Durable Functions is determined by the function app’s timeout setting. By default, the function app timeout is set to 5 minutes, but you can adjust this value by configuring the?functionTimeout?setting in the?host.json?file.
Note that the timeout setting applies to the entire function app, not just individual activity functions. If an activity function takes longer to run than the function app timeout, it will be terminated by the Azure Functions runtime.
This is similar to the behavior of normal functions in Azure Functions. If a normal function takes longer to execute than the function app timeout, it will be terminated by the runtime.
Can we set 2 function app time out to different value ? If no, then provide some workaround of this problem ?
The timeout setting in Azure Functions applies to the entire function app, so it is not possible to set different timeouts for individual functions within the same function app.
However, you can use a separate function app for functions that require a longer timeout. This allows you to configure the function app timeout setting to meet the needs of those functions without affecting the other functions in your solution.
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Another approach is to split long-running functions into smaller functions that can be executed within the timeout limit of the function app. You can then use Durable Functions to orchestrate the execution of these smaller functions to achieve the desired outcome.
Finally, you can use the Durable Functions extension’s built-in support for sub-orchestrations to split a long-running activity into smaller, more manageable activities that can be executed within the timeout limit of the function app.
During a function run how can we determine total execution/running time from start to till now ?
In Azure Functions, you can use the?ExecutionContext?object to retrieve information about the current function execution, including the start time of the function. To determine the total execution time of the function, you can subtract the start time from the current time.
Here is an example of how to calculate the total execution time of a function in C#:
public static async Task<IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Function, "get", "post", Route = null)] HttpRequest req,
ILogger log, ExecutionContext context)
{
DateTime startTime = context.InvocationId.GetCreationTimeUtc();
// Execute the function logic here
DateTime endTime = DateTime.UtcNow;
TimeSpan executionTime = endTime - startTime;
log.LogInformation($"Total execution time: {executionTime}");
return new OkResult();
}
In this example, the?GetCreationTimeUtc()?method is used to retrieve the start time of the function execution from the?InvocationId?property of the?ExecutionContext?object. The current time is then obtained using the?DateTime.UtcNow?property. The?TimeSpan?object is used to calculate the difference between the start time and the end time, which represents the total execution time of the function.
From a webapi how can we call function app and inject some dependency from api ?
In Azure Functions, you can use Dependency Injection (DI) to inject dependencies into your functions. To inject a dependency from a web API, you can use the?IServiceProvider?interface to create a service provider that can be used to resolve dependencies.
Here is an example of how to inject a dependency into an Azure Function and call it from a web API:
public interface IMyService
{
string GetMessage();
}
public class MyService : IMyService
{
public string GetMessage()
{
return "Hello from MyService!";
}
}
2.?Register the dependency with the function app by adding it to the app’s service collection in the?Startup?class:
[assembly: FunctionsStartup(typeof(MyFunctionApp.Startup))]
namespace MyFunctionApp
{
public class Startup : FunctionsStartup
{
public override void Configure(IFunctionsHostBuilder builder)
{
builder.Services.AddSingleton<IMyService, MyService>();
}
}
}
3. Inject the dependency into the Azure Function and call it:
public static async Task<IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Function, "get", "post", Route = null)] HttpRequest req,
ILogger log, IMyService myService)
{
string message = myService.GetMessage();
log.LogInformation($"Message from MyService: {message}");
return new OkResult();
}
4. Call the function from the web API and inject the dependency:
public async Task<IActionResult> CallFunction()
{
var serviceProvider = new ServiceCollection()
.AddSingleton<IMyService, MyService>()
.BuildServiceProvider();
var functionUri = new Uri("https://<function-app-name>.azurewebsites.net/api/<function-name>?code=<function-key>");
var functionClient = new HttpClient();
var functionRequest = new HttpRequestMessage(HttpMethod.Post, functionUri);
var functionContext = new ExecutionContext();
functionContext.FunctionAppDirectory = Environment.CurrentDirectory;
functionContext.InvocationId = Guid.NewGuid();
functionRequest.Properties.Add("MS_AzureFunctionsExecutionContext", functionContext);
var functionResult = await functionClient.SendAsync(functionRequest);
// Handle the function result here
return new OkResult();
}
In this example, the?ServiceCollection?class is used to create a service provider that includes the?IMyService?dependency. The function app is called using an?HttpClient?instance and a?HttpRequestMessage?object that includes the function's URL and key. The?ExecutionContext?class is used to create a context object that is passed to the function app using the?Properties?collection of the?HttpRequestMessage?object. Finally, the function result is obtained from the?HttpResponseMessage?object returned by the function call.
List different way activity function can be called from durable function ? Can activity function get called directly by api or trigger ?
Activity functions in Azure Durable Functions are typically called indirectly from an orchestrator function. However, there are several ways that activity functions can be invoked from an orchestrator function:
string result = await context.CallActivityAsync<string>("MyActivityFunction", input);
2. Using the?CallActivityWithRetryAsync?method:?This method is similar to?CallActivityAsync, but it also supports automatic retries in case of failure.
string result = await context.CallActivityWithRetryAsync<string>("MyActivityFunction", retryOptions, input);
3. Using the?IDurableActivityContext?interface:?This interface can be used to create an activity function that is compatible with Azure Durable Functions. The?IDurableActivityContext?interface provides access to the input data, output data, and other context information for the activity function.
[FunctionName("MyActivityFunction")]
public static async Task<string> RunActivityAsync([ActivityTrigger] IDurableActivityContext context)
{
string input = context.GetInput<string>();
string output = DoSomething(input);
return output;
}
Activity functions cannot be triggered directly by an API or other trigger in Azure Functions. They must be invoked indirectly from an orchestrator function. However, you can create an HTTP-triggered function that calls an orchestrator function, which in turn invokes one or more activity functions. This allows you to create an HTTP API that can perform complex workflows using Azure Durable Functions.
List different way function app or durable function can be invoked ? Also provide some practical scenarios where all this can be used ?
There are several ways to invoke a Function App or a Durable Function in Azure:
Practical scenarios where these different triggers can be used include:
List some of the limitations of durable functions ? How many times we can call function within itself ? Suggest tips for calling function recursively ? Using recursion can we make a function run more then 5 minute ?
Here are some of the limitations of durable functions:
Regarding the question about how many times a function can call itself, as mentioned above, a Durable Function can call itself recursively up to 100,000 times before reaching the maximum call stack size limit.
If you need to call a function recursively more than 100,000 times, you can use an iterative approach instead of a recursive one. This means that instead of calling the function itself, you can use a loop to call the function multiple times until the desired result is achieved.
Using recursion, it is not possible to make a function run more than 5 minutes if it exceeds the maximum execution time limit. In such cases, you can use an orchestration function to break down the long-running task into smaller, more manageable chunks that can be executed within the 5-minute time limit.
What are the important binding properties in a function app ?
Bindings are used in Azure Functions to connect input and output data to a function. Here are some important binding properties in a Function app:
These binding properties are used to define how the function interacts with the input and output data. For example, the Path property is used to specify the path to a blob in Blob storage, while the Connection property is used to specify the connection string or app setting name for the storage account. The DataType property can be used to validate the data that is passed to the function, ensuring that it is of the expected type.
Show example of DataType binding properties ?
Sure! Here is an example of how to use the DataType binding property in a C# Azure Function:
[FunctionName("ExampleFunction")]
public static void Run(
[BlobTrigger("example-container/{name}", Connection = "StorageConnection")] Stream myBlob,
string name,
ILogger log)
{
log.LogInformation($"C# Blob trigger function processed blob\n Name:{name} \n Data Type: {myBlob.GetType()}");
}
In this example, the BlobTrigger binding is used to trigger the function when a blob is added to the “example-container” container in Blob storage. The myBlob parameter is bound to the contents of the blob, and the name parameter is bound to the name of the blob.
The DataType binding property is used to specify the data type of the myBlob parameter. In this case, the data type is Stream, which represents a sequence of bytes. By specifying the data type, the Azure Functions runtime can automatically deserialize the contents of the blob into a Stream object.
In the function body, the contents of the blob are logged along with its name and data type. This can be useful for debugging and understanding the data that is being passed to the function.
What is Master Key in Azure Functions?
In Azure Functions, the Master Key is a special access key that grants full administrative access to all functions within a function app. It is a secret value that is used to authenticate administrative operations, such as creating, updating, or deleting functions.
The Master Key is one of two access keys that are automatically generated for each function app (the other being the Function Key). By default, the Master Key is only accessible to the owner of the function app and is not visible in the Azure portal or in the function code.
The Master Key can be used to perform administrative tasks programmatically, such as deploying functions, managing function triggers, or updating function settings. It can also be used to authenticate third-party tools or services that integrate with Azure Functions, such as source control systems or continuous integration/continuous deployment (CI/CD) pipelines.
However, since the Master Key provides full administrative access to all functions within a function app, it is important to keep it secret and only share it with trusted individuals or services. Leaking the Master Key could result in unauthorized access to sensitive data or functions, so it is recommended to rotate the key periodically and keep it secure.
What is Function Key in Azure Functions?
In Azure Functions, a Function Key is a special access key that provides authentication and authorization for invoking a specific function within a function app. It is a secret value that can be used to control access to specific functions, without granting full administrative access to the entire function app.
Each function within a function app can have multiple Function Keys, each with its own set of permissions and access restrictions. Function Keys can be generated, managed, and revoked through the Azure portal or using the Azure Functions API.
Function Keys are commonly used to secure access to functions that expose sensitive data or perform critical operations, such as accessing databases, processing payments, or triggering other workflows. By requiring a valid Function Key to invoke a function, developers can ensure that only authorized users or services are able to access the function’s resources.
Function Keys can also be used to limit the number of invocations or enforce rate limits on a specific function, which can help prevent abuse or overload of the function app.
Unlike the Master Key, which grants full administrative access to the entire function app, Function Keys provide fine-grained access control and can be safely shared with third-party services or users without granting them full administrative access.
How to avoid Cold Start in Azure Functions?
Cold start is a phenomenon where the first request to a function after a period of inactivity can result in a longer than usual response time due to the need to spin up a new instance of the function. This can impact the performance and user experience of serverless applications. Here are some tips to avoid or minimize cold starts in Azure Functions:
By implementing these strategies, you can reduce or eliminate the impact of cold starts on your Azure Functions application, improving performance and user experience.
What is the purpose of scale controller in Azure Functions?
The Scale Controller in Azure Functions is responsible for monitoring the load on the function app and automatically scaling out or in the app based on the configuration settings. It continuously monitors the function app’s activity and can scale the app out to add more instances or scale it in to remove instances, depending on the current demand. The purpose of the scale controller is to ensure that the function app is always available to handle incoming requests, while also optimizing resource usage and minimizing costs. By automating the scaling process, the scale controller helps to reduce the operational overhead and complexity of managing function apps at scale.
What is the max limit for scale-out in Azure Functions?
The maximum limit for scale-out in Azure Functions depends on the hosting plan that you are using.
For the Consumption plan, which is the most commonly used hosting plan for Azure Functions, the maximum scale-out limit is 200 instances per function app. This means that the function app can automatically scale out to up to 200 instances to handle spikes in traffic.
For the Premium plan, the maximum scale-out limit is higher and varies based on the selected plan and the number of virtual CPUs (vCPUs) assigned to the plan.
It is important to note that while it is possible to scale out to a large number of instances, the actual number of instances required to handle a given workload depends on a variety of factors such as the complexity of the functions, the memory usage, and the network latency. It is recommended to perform load testing to determine the optimal scale-out configuration for your function app.
Explain client and entity type durable function and its use cases ?
Durable Functions supports two types of entities, namely Client and Entity Type. Both entity types are long-running and can maintain state between function executions.
A Client Entity Type is a type of entity that can be activated by a client using its identifier. It is created and managed by the Durable Functions runtime and is unique for a specific client identifier. Client Entity Types are used to maintain state between function executions across multiple function invocations for a specific client. The state associated with a Client Entity Type can be accessed by any function instance that has the client identifier. Client Entity Types are commonly used for scenarios such as shopping cart, user session management, or any scenario that requires maintaining state across multiple function invocations for a specific client.
2. Entity Type:
An Entity Type is a type of entity that is activated by a unique identifier and can be accessed by multiple function instances. Entity Types are used to maintain shared state between multiple function executions. The state associated with an Entity Type can be accessed by any function instance that has the entity identifier. Entity Types are commonly used for scenarios such as leaderboards, global counters, or any scenario that requires maintaining state that is shared across multiple function invocations.
Both Client and Entity Type durable functions can be written in any of the supported languages, such as C#, Java, Python, and JavaScript. The choice between using a Client Entity Type or an Entity Type depends on the specific use case and the requirements of the application.
What do we mean by Azure Function Proxies?
Azure Function Proxies is a feature of Azure Functions that allows you to create a reverse proxy layer over your functions. It allows you to define simple rules that redirect requests to different backend services based on the incoming request URL or other conditions.
With Function Proxies, you can create a single endpoint that serves as the entry point to your application and have it route requests to multiple backend services. It is particularly useful for building microservices architectures where you have multiple independent services that need to be accessed through a single entry point.
Function Proxies can be used to:
Function Proxies are defined using a simple JSON configuration file, which you can deploy along with your function app. The configuration file allows you to define routes, rewrite rules, and other settings for each proxy endpoint.
Do Azure Functions support JWT Bearer Tokens by default?
Azure Functions do not support JWT Bearer Tokens by default, but it is possible to use JWT tokens with Azure Functions by implementing authentication and authorization using Azure Active Directory (Azure AD) or a third-party identity provider.
To use JWT tokens with Azure AD, you can configure your Azure Function app to require authentication using Azure AD. This involves creating an Azure AD application registration and configuring the function app to accept tokens issued by that application registration. You can then use the?Microsoft.Azure.WebJobs.Extensions.AzureActiveDirectory?NuGet package to validate JWT tokens in your Azure Functions.
Alternatively, you can use a third-party identity provider to issue JWT tokens and authenticate users in your Azure Functions. This can be done by configuring your function app to use an external authentication provider, such as Google or Facebook, using Azure App Service Authentication/Authorization feature or by implementing custom authentication and authorization logic in your function app code.