This Bicep module calculates the optimal deployment order for a list of resources, considering their interdependencies. It constructs a directed graph representing these dependencies and then performs a topological sort...

The algorithm first builds a dependency graph where nodes represent resources and directed edges indicate that one resource depends on another. It then employs a Depth First Search (DFS) based topological sort to determine a linear ordering of vertices such that for every directed edge from vertex u to vertex v, u comes before v in the ordering. The time complexity is O(V + E), where V is the number of resources and E is the number of dependencies, as both graph construction and DFS are linear in the size of the graph. Space complexity is also O(V + E) to store the graph and auxiliary structures for DFS. Edge cases handled include resources with no dependencies, dependencies not present in the input list, and critically, circular dependencies, which are detected during DFS and result in an empty deployment order being returned, preventing infinite loops or deployment failures. The correctness relies on the property that a topological sort is only possible for Directed Acyclic Graphs (DAGs).

Function deployResources(resources): graph = buildDependencyGraph(resources) deploymentOrder = topologicalSort(graph) return deploymentOrder Function buildDependencyGraph(resourceList): Initialize graph with empty adjace...

This Bicep code defines a function to determine if a given string is a palindrome, ignoring case and non-alphanumeric characters. It preprocesses the input string by converting it to lowercase and removing irrelevant cha...

The `isPalindrome` function first handles trivial edge cases: empty strings and single-character strings are considered palindromes. It then cleans the input string by converting all characters to lowercase and filtering out any character that is not an alphabet or a digit. This preprocessing ensures that comparisons are case-insensitive and unaffected by punctuation or spaces. A two-pointer approach is used: `left` starts at the beginning of the cleaned string, and `right` starts at the end. The loop continues as long as `left` is less than `right`. In each iteration, it compares the characters at the `left` and `right` pointers. If they differ, the string is not a palindrome, and `false` is returned. If they match, the pointers are moved inwards (`left` increments, `right` decrements). If the loop completes without finding any mismatches, it means the string is a palindrome, and `true` is returned. The time complexity is O(N), where N is the length of the input string, due to the single pass for cleaning and the single pass for comparison. The space complexity is also O(N) in the worst case for storing the `cleanedString`.

Function isPalindrome(str): If str is null or length of str is 0 or 1: Return true. cleanedString = empty string. For each character in str: If character is alphanumeric: Append lowercase version of character to cleanedS...

This Bicep module demonstrates the use of `for` loops to deploy multiple identical resources, specifically virtual machines. It takes a `vmNamePrefix` and `numberOfVms` as input, dynamically generating a unique name for...

The module utilizes Bicep's `for` loop construct combined with the `range` function to iterate a specified number of times (`validatedNumberOfVms`). In each iteration, a `Microsoft.Compute/virtualMachines` resource is declared. The `generateVmName` helper function is used to create a unique `name` and `properties.osProfile.computerName` for each VM by concatenating the `vmNamePrefix` with the current loop index `i`. The `@minValue(1)` decorator on `validatedNumberOfVms` ensures that at least one VM is always deployed, handling the edge case of zero VMs requested. The time complexity is linear with respect to `numberOfVms`, as each VM resource is provisioned independently. Space complexity is also linear, proportional to the number of VMs and their configurations. The primary edge case handled is ensuring a positive number of VMs, and the dynamic naming prevents conflicts when deploying multiple instances.

Define input parameters: VM name prefix, location, number of VMs. Validate that the number of VMs is at least 1. Create a helper function to generate a VM name by combining the prefix and the loop index. Use a `for` loop...

This Bicep module demonstrates advanced module composition by conditionally deploying nested modules based on input parameters. It showcases how to manage dependencies between modules and conditionally expose outputs. A...

The module uses Bicep's `module` declaration to include other Bicep files, effectively composing larger deployments from smaller, reusable units. Conditional deployments are achieved using the `if` expression on module declarations, allowing for dynamic infrastructure provisioning. Output chaining is handled by referencing the `outputs` property of deployed modules. The `checkForCircularDependencies` function is a conceptual representation of how one might integrate custom validation logic; in Bicep, the deployment engine inherently manages dependency graphs and prevents circular references. Time complexity is largely determined by the Bicep deployment engine's graph traversal and resource provisioning, which is typically efficient. Space complexity is minimal, primarily related to the Bicep template's structure and parameter passing. Edge cases include invalid parameter combinations leading to unmet dependencies or incorrect conditional logic, which are mitigated by Bicep's validation and the explicit conditional checks.

Define input parameters: base name, location, and a boolean for advanced features. Define a main module for a virtual machine. Define a module for networking. Conditionally define a module for storage if the advanced fea...

This Bicep module implements environment-specific configurations and conditional deployments. It takes an `environmentName` parameter and uses it to select appropriate resource settings (like VM size and storage SKU) and...

The module leverages Bicep's parameterization and conditional logic to create flexible and environment-aware deployments. The `validatedEnvironment` variable ensures that the `environmentName` parameter is one of the allowed values ('dev', 'staging', 'prod') by using `contains` and `error`. Helper functions `getVmSize` and `getStorageSku` use `switch` statements to return configuration values based on the validated environment. Resources like `virtualMachine` and `storageAccount` are deployed using these environment-specific configurations. A `Key Vault` is conditionally deployed only when `validatedEnvironment` is 'prod' using an `if` expression. Outputs are chained from nested modules, with the `keyVaultId` being conditionally exposed. The time complexity is primarily linear with respect to the number of resources and the complexity of the `switch` statements. Space complexity is minimal, related to the template structure and parameter storage. Edge cases handled include invalid environment names, which are caught by the validation logic, and ensuring that conditional outputs correctly reflect whether a resource was deployed.

Define input parameters: environment name, location. Define a list of allowed environment names. Validate the input environment name against the allowed list, throwing an error if invalid. Create a helper function to get...

This Bicep module demonstrates secure secret injection by deploying an Azure Key Vault and a secret within it. It utilizes a secure parameter (`@secure()`) to accept sensitive information, such as API keys or passwords,...

The module defines a Key Vault resource and a secret resource nested within it. The `secretValue` parameter is aliased to `secureSecretValue` and decorated with `@secure()`. This decorator instructs Bicep and the Azure Resource Manager (ARM) to treat the parameter's value as sensitive, encrypting it in transit and preventing it from being displayed in plain text in deployment outputs or logs. The `secureSecretValue` is then used as the `value` for the `Microsoft.KeyVault/vaults/secrets` resource. The `retrieveSecret` function is conceptual, illustrating that Bicep itself doesn't retrieve secrets but rather provisions them; actual retrieval happens via other Azure tools. Time complexity is minimal, dominated by the deployment of the two Azure resources. Space complexity is also minimal, related to the template's structure. The primary focus is on security, ensuring sensitive data is handled appropriately.

Define input parameters: Key Vault name, location, secret name, secret value. Mark the secret value parameter as secure using the `@secure()` decorator. Deploy a Key Vault resource. Deploy a secret resource within the Ke...

This Bicep module focuses on robust parameter validation for Network Security Group (NSG) rules. It defines strict constraints for rule priorities and port ranges, using helper functions to parse and validate these value...

The module employs Bicep's parameter validation decorators (`@allowed`, `@minValue`, `@maxValue`, `@minLength`, `@maxLength`) and custom validation logic via helper functions. The `parseAndValidatePortRange` function is critical; it handles wildcard ('*'), single port numbers, and port ranges (e.g., '80-100'), returning the validated string or throwing an error for invalid formats. The `validatePriority` function enforces a specific numerical range for NSG rule priorities. The `validatedNsgRules` variable uses a `for` loop to iterate through the input `nsgRules`, applying these validation functions to each rule's properties. The `checkForDuplicatePriorities` function is an additional check to ensure no two rules share the same priority, which is a common requirement for NSGs. Time complexity is linear with respect to the number of NSG rules and the complexity of parsing each port range. Space complexity is proportional to the number of rules and the size of the validation data structures. Edge cases handled include invalid port range formats, out-of-range priorities, and duplicate priorities.

Define input parameters: NSG name, location, and an array of NSG rule objects. Define constants for minimum/maximum priority and the wildcard port string. Create a helper function to parse and validate a port range strin...

This Bicep solution demonstrates module composition for deploying a typical web application stack. It breaks down the deployment into three distinct modules: one for the App Service Plan, one for the SQL Database, and on...

The solution is structured using multiple Bicep modules, promoting reusability and organization. The `main.bicep` file orchestrates the deployment by declaring instances of these modules. Bicep's deployment engine automatically resolves dependencies based on output references. For example, `webAppModule` depends on `appServicePlanModule` and `sqlDatabaseModule` because it uses their outputs (`appServicePlanId`, `sqlServerFqdn`, `sqlDatabaseName`). This creates an implicit dependency graph, ensuring that the App Service Plan and SQL Database are provisioned and their outputs are available before the Web App is deployed. The `generateResourceName` helper function illustrates a common pattern for creating unique resource names, enhancing manageability. Time complexity is largely determined by the Azure Resource Manager's provisioning process for the individual resources, which is generally efficient. Space complexity is minimal, related to the template structure and parameter passing. Edge cases are implicitly handled by Bicep's dependency resolution, preventing deployment errors due to resource ordering.

Define parameters for location, app service plan name, web app name, SQL server name, SQL database name, SQL admin login, and password. Deploy an App Service Plan module, passing its name and location. Deploy a SQL Datab...