Optimizing Azure SQL Performance: Bulk Inserts and Commit Control in JDBC

 There are still some ways you can improve the performance of your inserts. Here are a few suggestions:

1. Use Parameterized Queries: You're already using parameterized queries, which is good. It helps with query performance and prevents SQL injection. Make sure the column types in your table match the data types you're setting in the Prepared Statement.

2. Increase Batch Size: Instead of executing the batch every 10,000 rows, you can try increasing the batch size to a larger number. This can help reduce the number of round trips to the database and improve performance. Experiment with different batch sizes to find the optimal value for your specific scenario.

3. Use Connection Pooling: You can use HikariCP for connection pooling, which is recommended for efficient connection management. Connection pooling helps reuse existing connections instead of creating new connections for each insert, which can significantly improve performance.

4. Disable Auto-Commit: By default, JDBC commits each individual statement execution, which can be costly for bulk inserts. You can try disabling auto-commit mode before executing the batch and commit explicitly after the batch completes. This can help reduce the overhead of committing after every single statement.

Here's an updated version of your code incorporating these suggestions:

public void saveAllJdbc(List<JdbcDetail> jdbcDetails) {
    String sql = String.format("INSERT INTO %s VALUES (?, ?, ?, ?)", "my-table");

    try (Connection connection = hikariDataSource.getConnection();
         PreparedStatement statement = connection.prepareStatement(sql)) {

        int counter = 0;

        connection.setAutoCommit(false);  // Disable auto-commit

        for (JdbcDetail row : jdbcDetails) {
            statement.clearParameters();
            statement.setInt(1, row.getQuantity());
            statement.setDate(2, Date.valueOf(row.getDate()));
            statement.setFloat(3, row.getId());
            statement.setInt(4, row.getNum());

            statement.addBatch();

            if ((counter + 1) % 10000 == 0) {
                statement.executeBatch();
                statement.clearBatch();
                connection.commit();  // Explicitly commit the batch
            }

            counter++;
        }

        statement.executeBatch();  // Execute any remaining statements in the batch
        connection.commit();  // Commit the final batch

        connection.setAutoCommit(true);  // Re-enable auto-commit

    } catch (Exception e) {
        e.printStackTrace();
    }
}

By increasing the batch size and disabling auto-commit, you should see improvements in the performance of your insert operations in Azure SQL. Remember to experiment with different batch sizes to find the optimal value for your specific scenario.

Streamlining File Archiving: How Azure Logic Apps, Office 365, and Blob Storage Solved Our Client's Document Management Challenge

In this blog post, we share a real-world problem faced by one of our clients regarding document management and how we successfully addressed it using Azure Logic Apps, Office 365, and Blob Storage. We will discuss the client's specific needs, how we conceptualized and implemented the solution, and the benefits it brought to their organization.

Client Challenge: Our client, a growing financial services company, was struggling with an inefficient and error-prone manual process for archiving important documents. They dealt with a high volume of emails containing attachments, and their team had to manually save each attachment to a local file system, leading to delays, misplaced files, and increased operational costs. They sought a streamlined and automated solution to improve their document management workflow.

Solution Design and Implementation: Understanding our client's pain points, we proposed an automated file archiving solution leveraging Azure Logic Apps, Office 365, and Blob Storage. Here is how we designed and implemented the solution:

1. Azure Logic Apps Setup: We created an Azure Logic App to orchestrate the workflow. The Logic App acted as the central hub for connecting the different components and driving the automation.

2. Office 365 Connector Integration: We integrated the Office 365 Outlook connector with the Logic App. This allowed us to leverage Office 365's powerful email capabilities, enabling seamless interaction with the client's mailbox.

3. Triggering the Workflow: To initiate the workflow, we configured a trigger that monitored the client's mailbox for new emails. We customized the trigger to filter emails based on specific criteria such as subject lines, senders, or keywords related to important documents.

4. Saving Attachments to Blob Storage: Using the Blob Storage connector within the Logic App, we seamlessly connected to the client's Azure Blob Storage account. When a new email arrived, the Logic App automatically extracted and saved the attachments directly to Blob Storage, eliminating the need for manual intervention.

5. Archiving and Organizing Files: To ensure efficient file organization, we implemented custom logic within the Logic App. This included renaming files, adding metadata, and organizing them into appropriate folders based on the email attributes or other relevant criteria defined by the client.

Benefits and Results: By implementing this integrated solution, our client experienced significant improvements in their document management process:

• Time and Cost Savings: The automated file archiving workflow drastically reduced manual efforts, saving the team countless hours each week. This allowed them to reallocate resources to more value-added tasks, leading to cost savings in the long run.

• Error Reduction: Manual errors and file misplacements were virtually eliminated, as the process became standardized and automated. The risk of losing critical documents was significantly mitigated.

• Enhanced Access and Searchability: Storing files in Blob Storage facilitated easy retrieval and improved searchability. With organized folder structures and metadata, the team could quickly locate specific documents when needed.

azure create Linux VM with ssh and storage options

 az vm create --name VMname --resource-group RGname --image UbuntuLTS --generate-ssh-keys

Create Azure VM using terraform ?

 To create an Azure virtual machine (VM) using Terraform, you need to follow these general steps:

1. Set up Azure credentials: Before you begin, you'll need to set up your Azure credentials to authenticate Terraform with your Azure account. You can create a service principal or use other authentication methods supported by Azure.

2. Create a Terraform configuration file: Create a file with a .tf extension (e.g., main.tf) to define your Terraform configuration. In this file, you'll specify the desired state of your Azure VM and other related resources.

Here's an example of a basic Terraform configuration file to create an Azure VM:

provider "azurerm" {
  features {}
}

resource "azurerm_resource_group" "example" {
  name     = "my-resource-group"
  location = "East US"
}

resource "azurerm_virtual_network" "example" {
  name                = "my-vnet"
  address_space       = ["10.0.0.0/16"]
  location            = azurerm_resource_group.example.location
  resource_group_name = azurerm_resource_group.example.name
}

resource "azurerm_subnet" "example" {
  name                 = "my-subnet"
  resource_group_name  = azurerm_resource_group.example.name
  virtual_network_name = azurerm_virtual_network.example.name
  address_prefixes     = ["10.0.1.0/24"]
}

resource "azurerm_network_interface" "example" {
  name                = "my-nic"
  location            = azurerm_resource_group.example.location
  resource_group_name = azurerm_resource_group.example.name

  ip_configuration {
    name                          = "my-ipconfig"
    subnet_id                     = azurerm_subnet.example.id
    private_ip_address_allocation = "Dynamic"
  }
}

resource "azurerm_virtual_machine" "example" {
  name                  = "my-vm"
  location              = azurerm_resource_group.example.location
  resource_group_name   = azurerm_resource_group.example.name
  network_interface_ids = [azurerm_network_interface.example.id]
  vm_size               = "Standard_DS1_v2"

  storage_image_reference {
    publisher = "Canonical"
    offer     = "UbuntuServer"
    sku       = "16.04-LTS"
    version   = "latest"
  }

  storage_os_disk {
    name              = "my-os-disk"
    caching           = "ReadWrite"
    create_option     = "FromImage"
    managed_disk_type = "Standard_LRS"
  }

  os_profile {
    computer_name  = "my-vm"
    admin_username = "adminuser"
    admin_password = "Password1234!"
  }

  os_profile_linux_config {
    disable_password_authentication = false
  }
}

3. Initialize and apply the Terraform configuration: Run the following commands in the directory where you have your Terraform configuration file:

terraform init
terraform apply

The terraform init command initializes the Terraform working directory and downloads the necessary provider plugins. The terraform apply command creates or updates the Azure resources defined in your configuration based on the desired state.

Note: Make sure you have Terraform and the Azure provider installed before running these commands.

This is a basic example, and you can customize it further based on your specific requirements for the VM, such as specifying the VM size, storage options, networking configuration, and more. Refer to the Azure provider documentation in the Terraform website for more details and additional configuration options.

Remember to review and understand the changes that Terraform will make to your Azure resources before confirming the execution

How to create virtual machines in azure ? or What are different methods available ?

 To create virtual machines (VMs) in Azure, there are several methods available. Here are the different approaches you can take:

1. Azure Portal: The Azure Portal provides a web-based graphical user interface (GUI) for managing Azure resources, including VMs. You can navigate to the Azure Portal, select the desired subscription and resource group, and use the "Create a resource" button to create a VM. The portal offers a step-by-step wizard where you can specify VM configurations, such as image, size, networking, and storage options.

2. Azure CLI: The Azure Command-Line Interface (CLI) is a cross-platform command-line tool that allows you to manage Azure resources from the command line. You can use the Azure CLI to create VMs by running commands that specify the desired VM properties, such as the image, size, resource group, and networking configurations.

3. Azure PowerShell: Azure PowerShell is a scripting environment that enables you to automate Azure management tasks using PowerShell scripts. With Azure PowerShell, you can create VMs by writing PowerShell scripts that define the VM properties, resource group, networking, and other configurations.

4. Azure Resource Manager (ARM) Templates: ARM templates are JSON files that describe the desired state of your Azure infrastructure. You can define the VM properties, networking, storage, and other configurations in an ARM template and deploy it to create VMs in a consistent and repeatable manner. ARM templates can be deployed using the Azure Portal, Azure CLI, or Azure PowerShell.

5. Azure DevOps: Azure DevOps provides a set of services for CI/CD (Continuous Integration/Continuous Deployment) pipelines and automating infrastructure deployment. Using Azure DevOps pipelines, you can define YAML or visual pipeline configurations that include steps to create VMs as part of your infrastructure deployment process.

These methods provide different levels of automation, flexibility, and programmability to create VMs in Azure. You can choose the approach that best suits your requirements and preferences. It's worth noting that Azure SDKs for various programming languages are also available if you prefer to programmatically create VMs using your preferred programming language.

When to Use Terraform Provisioners: Best Practices and Considerations

Terraform provisioners should generally be used as a last resort or as a final option because they introduce some limitations and potential complexities to your infrastructure provisioning process. Here are a few reasons why it's advisable to use Terraform provisioners sparingly:

1. Separation of Concerns: Terraform focuses primarily on infrastructure provisioning and management. It is designed to handle resource creation, updates, and deletions. By keeping the provisioning logic separate from configuration management or other tasks, you can maintain a clear separation of concerns and leverage specialized tools for each task.

2. Idempotency: Terraform's core strength lies in its ability to ensure the desired state of your infrastructure. It achieves this by comparing the desired state declared in your configuration files with the current state of the infrastructure and making the necessary changes to align them. Provisioners, on the other hand, introduce imperative actions that may not be idempotent. This means that running the same provisioner multiple times may lead to inconsistent results or unwanted side effects.

3. Dependencies and Ordering: Terraform handles resource dependencies and ordering automatically based on the defined relationships between resources. Provisioners, however, can introduce additional dependencies and ordering challenges since they rely on the availability and state of other resources. This can make it more difficult to manage complex provisioning sequences or handle failures gracefully.

4. Portability: Provisioners often rely on specific tools or scripts that may be tied to a particular operating system, environment, or external dependencies. This can limit the portability of your Terraform configurations across different environments or cloud providers, potentially causing compatibility issues or extra maintenance efforts.

5. Maintenance and Updates: Provisioners typically require more maintenance compared to other Terraform resources. If the provisioner logic or the external tooling it relies on needs to be updated or changed, it may require modifications to your Terraform configuration files, increasing complexity and potential errors.

While Terraform provisioners have their use cases, it's generally recommended to explore other options first, such as using native cloud provider APIs, infrastructure-as-code best practices, or specialized configuration management tools (like Ansible, Chef, or Puppet) for more complex configuration tasks. This approach helps maintain the separation of concerns, improves idempotency, and ensures a more streamlined and manageable infrastructure provisioning process.