.NET Core web API + Dapper – QueryMultiple

Returning multiple recordsets

My current “day job” is building applications for managing portfolios of foreign currency instruments. A portfolio can contain many different types of instrument (Forwards, Options, Swaps etc.). One of the “optimisations” we use is retrieving all the different types of instruments in a portfolio with one stored procedure call.

SQL Server Management Studio Dependency viewer

The closest scenario I could come up with using the World Wide Importers database was retrieving a summary of all the information associated with an Invoice for display on a single screen.

CREATE PROCEDURE [Sales].[InvoiceSummaryGetV1](@InvoiceID as int)
AS
BEGIN

SELECT [InvoiceID]
--        ,[CustomerID]
--        ,[BillToCustomerID]
		,[OrderID]
		,[Invoices].[DeliveryMethodID]
		,[DeliveryMethodName]
--        ,[ContactPersonID]
--        ,[AccountsPersonID]
		,[SalespersonPersonID] as SalesPersonID
		,[SalesPerson].[PreferredName] as SalesPersonName
--        ,[PackedByPersonID]
		,[InvoiceDate]
		,[CustomerPurchaseOrderNumber]
		,[IsCreditNote]
		,[CreditNoteReason]
		,[Comments]
		,[DeliveryInstructions]
--        ,[InternalComments]
--        ,[TotalDryItems]
--        ,[TotalChillerItems]
		,[DeliveryRun]
		,[RunPosition] as DeliveryRunPosition
		,[ReturnedDeliveryData] as DeliveryData
		,[ConfirmedDeliveryTime] as DeliveredAt
		,[ConfirmedReceivedBy] as DeliveredTo
--        ,[LastEditedBy]
--        ,[LastEditedWhen]
	FROM [Sales].[Invoices]
	INNER JOIN [Application].[People] as SalesPerson ON (Invoices.[SalespersonPersonID] = [SalesPerson].[PersonID])
	INNER JOIN [Application].[DeliveryMethods] as DeliveryMethod ON (Invoices.[DeliveryMethodID] = DeliveryMethod.[DeliveryMethodID])
WHERE ([Invoices].[InvoiceID] = @InvoiceID)

SELECT [InvoiceLineID]
      ,[InvoiceID]
      ,[StockItemID]
      ,[Description] as StockItemDescription
      ,[InvoiceLines].[PackageTypeID]
	  ,[PackageType].[PackageTypeName]
      ,[Quantity]
      ,[UnitPrice]
      ,[TaxRate]
      ,[TaxAmount]
--      ,[LineProfit]
      ,[ExtendedPrice]
--      ,[LastEditedBy]
--      ,[LastEditedWhen]
	FROM [Sales].[InvoiceLines]
		INNER JOIN [Warehouse].[PackageTypes] as PackageType ON ([PackageType].[PackageTypeID] = [InvoiceLines].[PackageTypeID])
WHERE ([InvoiceLines].[InvoiceID] = @InvoiceID)

SELECT [StockItemTransactionID]
      ,[StockItemTransactions].[StockItemID]
      ,StockItem.[StockItemName] as StockItemName
      ,[StockItemTransactions].[TransactionTypeID]
      ,[TransactionType].[TransactionTypeName]
--      ,[CustomerID]
--      ,[InvoiceID]
--      ,[SupplierID]
--      ,[PurchaseOrderID]
      ,[TransactionOccurredWhen] as TransactionAt
      ,[Quantity]
--      ,[LastEditedBy]
--      ,[LastEditedWhen]
	FROM [Warehouse].[StockItemTransactions]
	INNER JOIN [Warehouse].[StockItems] as StockItem ON ([StockItemTransactions].StockItemID = [StockItem].StockItemID)
	INNER JOIN [Application].[TransactionTypes] as TransactionType ON ([StockItemTransactions].[TransactionTypeID] = TransactionType.[TransactionTypeID])
	WHERE ([StockItemTransactions].[InvoiceID] = @InvoiceID)

END

The stored procedure returns 3 recordsets, a “summary” of the Order, a summary of the associated OrderLines and a summary of the associated StockItemTransactions.

public async Task<ActionResult<Model.InvoiceSummaryGetDtoV1>>Get([Range(1, int.MaxValue, ErrorMessage = "Invoice id must greater than 0")] int id)
{
	Model.InvoiceSummaryGetDtoV1 response = null;

	try
	{
		using (SqlConnection db = new SqlConnection(this.connectionString))
		{
			var invoiceSummary = await db.QueryMultipleWithRetryAsync("[Sales].[InvoiceSummaryGetV1]", param: new { InvoiceId = id }, commandType: CommandType.StoredProcedure);

			response = await invoiceSummary.ReadSingleOrDefaultWithRetryAsync<Model.InvoiceSummaryGetDtoV1>();
			if (response == default)
			{
				logger.LogInformation("Invoice:{0} not found", id);

				return this.NotFound($"Invoice:{id} not found");
			}

			response.InvoiceLines = (await invoiceSummary.ReadWithRetryAsync<Model.InvoiceLineSummaryListDtoV1>()).ToArray();

			response.StockItemTransactions = (await invoiceSummary.ReadWithRetryAsync<Model.StockItemTransactionSummaryListDtoV1>()).ToArray();
		}
	}
	catch (SqlException ex)
	{
		logger.LogError(ex, "Retrieving Invoice, Invoice Lines or Stock Item Transactions");

		return this.StatusCode(StatusCodes.Status500InternalServerError);
	}

	return this.Ok(response);
}

I use Google Chrome, Mozilla Firefox, Microsoft Edgeium, and Opera but the screen capture was done with FireFox mainly because it formats the Java Script Object Notation(JSON) response payloads nicely.

FireFox displaying Invoice Summary response

I had to extend the DapperTransient module to add SqlMapper extension (plus all the different overloads) retry methods.

Smartish Edge Camera – Azure Storage basics

This project is another reworked version of on my ML.Net YoloV5 + Camera + GPIO on ARM64 Raspberry PI which supports only the uploading of camera and marked up images to Azure Storage.

My backyard test-rig consists of a Unv IPC675LFW Pan Tilt Zoom(PTZ) Security Camera, Power over Ethernet(PoE) module, and a Raspberry Pi 4B 8G.

Raspberry PI 4 B backyard test rig

The application can be compiled with Raspberry PI V2 Camera or Unv Security Camera (The security camera configuration may work for other cameras/vendors).

The appsetings.json file has configuration options for the Azure Storage Account, DeviceID (Used for the Azure Blob storage container name), the list of object classes of interest (based on the YoloV5 image classes) , and the image blob storage file names (used to “bucket” images).

{
  "Logging": {
    "LogLevel": {
      "Default": "Information",
      "Microsoft": "Warning",
      "Microsoft.Hosting.Lifetime": "Information"
    }
  },

  "Application": {
    "DeviceId": "edgecamera",

    "ImageTimerDue": "0.00:00:15",
    "ImageTimerPeriod": "0.00:00:30",

    "ImageCameraFilepath": "ImageCamera.jpg",
    "ImageMarkedUpFilepath": "ImageMarkedup.jpg",

    "ImageCameraUpload": true,
    "ImageMarkedupUpload": true,

    "YoloV5ModelPath": "YoloV5/yolov5s.onnx",

    "PredicitionScoreThreshold": 0.7,
    "PredictionLabelsOfInterest": [
      "bicycle",
      "person",
      "car"
    ],
    "OutputImageMarkup": true
  },

  "SecurityCamera": {
    "CameraUrl": "",
    "CameraUserName": "",
    "CameraUserPassword": ""
  },

  "RaspberryPICamera": {
    "ProcessWaitForExit": 1000,
    "Rotation": 180
  },

  "AzureStorage": {
    "ConnectionString": "FhisIsNotTheConnectionStringYouAreLookingFor",
    "ImageCameraFilenameFormat": "{0:yyyyMMdd}/camera/{0:HHmmss}.jpg",
    "ImageMarkedUpFilenameFormat": "{0:yyyyMMdd}/markedup/{0:HHmmss}.jpg"
  }
}

Part of this refactor was injecting(DI) the logging and configuration dependencies.

public class Program
{
	public static void Main(string[] args)
	{
		CreateHostBuilder(args).Build().Run();
	}

	public static IHostBuilder CreateHostBuilder(string[] args) =>
		 Host.CreateDefaultBuilder(args)
			.ConfigureServices((hostContext, services) =>
			{
				services.Configure<ApplicationSettings>(hostContext.Configuration.GetSection("Application"));
				services.Configure<SecurityCameraSettings>(hostContext.Configuration.GetSection("SecurityCamera"));
				services.Configure<RaspberryPICameraSettings>(hostContext.Configuration.GetSection("RaspberryPICamera"));
				services.Configure<AzureStorageSettings>(hostContext.Configuration.GetSection("AzureStorage"));
			})
			.ConfigureLogging(logging =>
			{
				logging.ClearProviders();
				logging.AddSimpleConsole(c => c.TimestampFormat = "[HH:mm:ss.ff]");
			})
			.UseSystemd()
			.ConfigureServices((hostContext, services) =>
			{
			  services.AddHostedService<Worker>();
			});
		}
	}
}

After the You Only Look Once(YOLOV5)+ML.Net+Open Neural Network Exchange(ONNX) plumbing has loaded a timer with a configurable due time and period is started.

private async void ImageUpdateTimerCallback(object state)
{
	DateTime requestAtUtc = DateTime.UtcNow;

	// Just incase - stop code being called while photo already in progress
	if (_cameraBusy)
	{
		return;
	}
	_cameraBusy = true;

	_logger.LogInformation("Image processing start");

	try
	{
#if CAMERA_RASPBERRY_PI
		RaspberryPIImageCapture();
#endif
#if CAMERA_SECURITY
		SecurityCameraImageCapture();
#endif
		if (_applicationSettings.ImageCameraUpload)
		{
					await AzureStorageImageUpload(requestAtUtc, _applicationSettings.ImageCameraFilepath, 
 azureStorageSettings.ImageCameraFilenameFormat);
		}

		List<YoloPrediction> predictions;

		using (Image image = Image.FromFile(_applicationSettings.ImageCameraFilepath))
		{
			_logger.LogTrace("Prediction start");
			predictions = _scorer.Predict(image);
			_logger.LogTrace("Prediction done");

			OutputImageMarkup(image, predictions, _applicationSettings.ImageMarkedUpFilepath);
		}

		if (_logger.IsEnabled(LogLevel.Trace))
		{
			_logger.LogTrace("Predictions {0}", predictions.Select(p => new { p.Label.Name, p.Score }));
		}

		var predictionsOfInterest = predictions.Where(p => p.Score > _applicationSettings.PredicitionScoreThreshold).Select(c => c.Label.Name).Intersect(_applicationSettings.PredictionLabelsOfInterest, StringComparer.OrdinalIgnoreCase);
		if (_logger.IsEnabled(LogLevel.Trace))
		{
			_logger.LogTrace("Predictions of interest {0}", predictionsOfInterest.ToList());
		}

		if (_applicationSettings.ImageMarkedupUpload && predictionsOfInterest.Any())
		{
			await AzureStorageImageUpload(requestAtUtc, _applicationSettings.ImageMarkedUpFilepath, _azureStorageSettings.ImageMarkedUpFilenameFormat);
		}

		var predictionsTally = predictions.Where(p => p.Score >= _applicationSettings.PredicitionScoreThreshold)
									.GroupBy(p => p.Label.Name)
									.Select(p => new
									{
										Label = p.Key,
										Count = p.Count()
									});

		if (_logger.IsEnabled(LogLevel.Information))
		{
			_logger.LogInformation("Predictions tally {0}", predictionsTally.ToList());
		}
	}
	catch (Exception ex)
	{
		_logger.LogError(ex, "Camera image download, post procesing, image upload, or telemetry failed");
	}
	finally
	{
		_cameraBusy = false;
	}

	TimeSpan duration = DateTime.UtcNow - requestAtUtc;

	_logger.LogInformation("Image processing done {0:f2} sec", duration.TotalSeconds);
}

In the ImageUpdateTimerCallback method a camera image is captured (by my Raspberry Pi Camera Module 2 or IPC675LFW Security Camera) and written to the local file system.

Raspberry PI4B console displaying image processing and uploading

The MentalStack YoloV5 model ML.Net support library processes the camera image on the local filesystem. The prediction output (can be inspected with Netron) is parsed generating list of objects that have been detected, their Minimum Bounding Rectangle(MBR) and class.

Image from security camera
Azure IoT Storage Explorer displaying list of camera images

The list of predictions is post processed with a Language Integrated Query(LINQ) which filters out predictions with a score below a configurable threshold(PredicitionScoreThreshold) and returns a count of each class. If this list intersects with the configurable PredictionLabelsOfInterest a marked up image is uploaded to Azure Storage.

Image from security camera marked up with Minimum Bounding Boxes(MBRs)
Azure IoT Storage Explorer displaying list of marked up camera images

The current implementation is quite limited, the camera image upload, object detection and image upload if there are objects of interest is implemented in a single timer callback. I’m considering implementing two timers one for the uploading of camera images (time lapse camera) and the other for running the object detection process and uploading marked up images.

Marked up images are uploaded if any of the objects detected (with a score greater than PredicitionScoreThreshold) is in the PredictionLabelsOfInterest. I’m considering adding a PredicitionScoreThreshold and minimum count for individual prediction classes, and optionally marked up image upload only when the list of objects detected has changed.

Azure Smartish Edge Camera – Background Service

This is a “note to self” post about deploying a .NET Core Worker Service to a Raspberry PI 4B 8G running Raspberry PI OS (Bullseye). After reading many posts, then a lot of trial and error this approach appeared to work reliably for my system configuration.(Though YMMV with other distros etc.)

The first step was to create a new Worker Service project in Visual Studio 2019

VS 2019 Add new Worker Service project wizard
VS 2019 Add new Worker Service project name
Visual Studio 2019 NuGet management

I intentionally did not update the Microsoft.Extensions.Hosting and Microsoft.Extensions.Hosting.Systemd (for UseSystemd) NuGet packages for my initial development.

using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;

namespace devMobile.IoT.MachineLearning.AzureIoTSmartEdgeCameraService
{
	public class Program
	{
		public static void Main(string[] args)
		{
			CreateHostBuilder(args).Build().Run();
		}

		public static IHostBuilder CreateHostBuilder(string[] args) =>
			 Host.CreateDefaultBuilder(args)
					.UseSystemd()
				  .ConfigureServices((hostContext, services) =>
				  {
					  services.AddHostedService<Worker>();
				  });
	}
}

program.cs

using System;
using System.Threading;
using System.Threading.Tasks;

using Microsoft.Extensions.Hosting;
using Microsoft.Extensions.Logging;

namespace devMobile.IoT.MachineLearning.AzureIoTSmartEdgeCameraService
{
	public class Worker : BackgroundService
	{
		private readonly ILogger<Worker> _logger;

		public Worker(ILogger<Worker> logger)
		{
			_logger = logger;
		}

		protected override async Task ExecuteAsync(CancellationToken stoppingToken)
		{
			while (!stoppingToken.IsCancellationRequested)
			{
				_logger.LogInformation("Worker running at: {time}", DateTimeOffset.Now);

				await Task.Delay(1000, stoppingToken);
			}
		}
	}
}

Worker.cs

The first step was to create a new directory (AzureIoTSmartEdgeCameraService) on the device. In Visual Studio 2019 I “published” my application and copied the contents of the “publish” folder to the Raspberry PI with Winscp. (This intermediary folder was to avoid issues with the permissions of the /usr/sbin/ & etc/systemd/system folders)

Using Winscp to copy files to AzureIoTSmartEdgeCameraService folder on my device
Install service

Test in application directory
	/home/pi/.dotnet/dotnet AzureIoTSmartEdgeCameraService.dll

Make service directory
	sudo mkdir /usr/sbin/AzureIoTSmartEdgeCameraService

Copy files to service directory
	sudo cp *.* /usr/sbin/AzureIoTSmartEdgeCameraService

Copy .service file to systemd folderclear
	sudo cp AzureIoTSmartEdgeCameraService.service /etc/systemd/system/AzureIoTSmartEdgeCameraService.service
 
Force reload of systemd configuration
	sudo systemctl daemon-reload

Start the Azure IoT SmartEdge Camera service
	sudo systemctl start AzureIoTSmartEdgeCameraService
Installing and starting the AzureIoTSmartEdgeCameraService
Uninstall service
	sudo systemctl stop AzureIoTSmartEdgeCameraService

	sudo rm /etc/systemd/system/AzureIoTSmartEdgeCameraService.service

	sudo systemctl daemon-reload

	sudo rm /usr/sbin/AzureIoTSmartEdgeCameraService/*.*

	sudo rmdir /usr/sbin/AzureIoTSmartEdgeCameraService

	See what is happening
	journalctl -xe

Stopping and removing the AzureIoTSmartEdgeCameraService

It took a lot of attempts to get a clean install then uninstall for the screen captures.

Azure Smartish Edge Camera – The basics

This project builds on my ML.Net YoloV5 + Camera + GPIO on ARM64 Raspberry PI with the addition of basic support for Azure IoT Hubs, the Azure IoT Hub Device Provisioning Service(DPS), and Azure IoT Central.

My backyard test-rig has consists of a Unv ADZK-10 Security Camera, Power over Ethernet(PoE) module, and an ASUS PE100A.

Backyard test-rig

The application can be compiled with support for Azure IoT Connection strings or the Device Provisioning Service(DPS). The appsetings.json file has configuration options for Azure IoT Hub connection string or DPS Global Device Endpoint+ScopeID+Group Enrollment key.

{
  "ApplicationSettings": {
    "DeviceId": "NotTheEdgeCamera",

    "ImageTimerDue": "0.00:00:15",
    "ImageTimerPeriod": "0.00:00:30",

    "CameraUrl": "http://10.0.0.55:85/images/snapshot.jpg",
    "CameraUserName": ",,,",
    "CameraUserPassword": "...",

    "ButtonPinNumer": 6,
    "LedPinNumer": 5,

    "InputImageFilenameLocal": "InputLatest.jpg",
    "OutputImageFilenameLocal": "OutputLatest.jpg",

    "ProcessWaitForExit": 10000,

    "YoloV5ModelPath": "Assets/YoloV5/yolov5s.onnx",

    "PredicitionScoreThreshold": 0.5,

    "AzureIoTHubConnectionString": "...",

    "GlobalDeviceEndpoint": "global.azure-devices-provisioning.net",
    "AzureIoTHubDpsIDScope": "...",
    "AzureIoTHubDpsGroupEnrollmentKey": "..."
  }
}

After the You Only Look Once(YOLOV5)+ML.Net+Open Neural Network Exchange(ONNX) plumbing has loaded a timer with a configurable due time and period is started.

private static async void ImageUpdateTimerCallback(object state)
{
	DateTime requestAtUtc = DateTime.UtcNow;

	// Just incase - stop code being called while photo already in progress
	if (_cameraBusy)
	{
		return;
	}
	_cameraBusy = true;

	Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss} Image processing start");

	try
	{
#if SECURITY_CAMERA
		Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} Security Camera Image download start");
		SecurityCameraImageCapture();
		Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} Security Camera Image download done");
#endif

#if RASPBERRY_PI_CAMERA
		Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} Raspberry PI Image capture start");
		RaspberryPICameraImageCapture();
		Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} Raspberry PI Image capture done");
#endif

		List<YoloPrediction> predictions;

		// Process the image on local file system
		using (Image image = Image.FromFile(_applicationSettings.InputImageFilenameLocal))
		{
			Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV5 inferencing start");
			predictions = _scorer.Predict(image);
			Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV5 inferencing done");

#if OUTPUT_IMAGE_MARKUP
			using (Graphics graphics = Graphics.FromImage(image))
			{
				Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} Image markup start");

				foreach (var prediction in predictions) // iterate predictions to draw results
				{
					double score = Math.Round(prediction.Score, 2);

					graphics.DrawRectangles(new Pen(prediction.Label.Color, 1), new[] { prediction.Rectangle });

					var (x, y) = (prediction.Rectangle.X - 3, prediction.Rectangle.Y - 23);

					graphics.DrawString($"{prediction.Label.Name} ({score})", new Font("Consolas", 16, GraphicsUnit.Pixel), new SolidBrush(prediction.Label.Color), new PointF(x, y));
				}

				image.Save(_applicationSettings.OutputImageFilenameLocal);

				Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} Image markup done");
			}
#endif
		}

#if AZURE_IOT_HUB_CONNECTION || AZURE_IOT_HUB_DPS_CONNECTION
		await AzureIoTHubTelemetry(requestAtUtc, predictions);
#endif
	}
	catch (Exception ex)
	{
		Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss} Camera image download, post procesing, image upload, or telemetry failed {ex.Message}");
	}
	finally
	{
		_cameraBusy = false;
	}

	TimeSpan duration = DateTime.UtcNow - requestAtUtc;

	Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss} Image processing done {duration.TotalSeconds:f2} sec");
	Console.WriteLine();
}

In the ImageUpdateTimerCallback method a camera image is captured (Raspberry Pi Camera Module 2 or Unv ADZK-10 Security Camera) and written to the local file system.

SSH Connection to Azure PE100 running Smartish Camera application

The YoloV5 model ML.Net support library then loads the image and processes the prediction output (can be inspected with Netron) generating list of objects that have been detected, their Minimum Bounding Rectangle(MBR) and class.

public static async Task AzureIoTHubTelemetry(DateTime requestAtUtc, List<YoloPrediction> predictions)
{
	JObject telemetryDataPoint = new JObject();

	foreach (var predictionTally in predictions.Where(p => p.Score >= _applicationSettings.PredicitionScoreThreshold).GroupBy(p => p.Label.Name)
					.Select(p => new
					{
						Label = p.Key,
						Count = p.Count()
					}))
	{
		Console.WriteLine("  {0} {1}", predictionTally.Label, predictionTally.Count);

		telemetryDataPoint.Add(predictionTally.Label, predictionTally.Count);
	}

	try
	{
		using (Message message = new Message(Encoding.ASCII.GetBytes(JsonConvert.SerializeObject(telemetryDataPoint))))
		{
			message.Properties.Add("iothub-creation-time-utc", requestAtUtc.ToString("s", CultureInfo.InvariantCulture));

			Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss} AzureIoTHubClient SendEventAsync prediction information start");
			await _deviceClient.SendEventAsync(message);
			Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss} AzureIoTHubClient SendEventAsync prediction information finish");
		}
	}
	catch (Exception ex)
	{
		Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss} AzureIoTHubClient SendEventAsync cow counting failed {ex.Message}");
	}
}

The list of predictions is post processed with a Language Integrated Query(LINQ) which filters out predictions with a score below a configurable threshold and returns a count of each class.

My backyard from the deck

The aggregated YoloV5 prediction results are then uploaded to an Azure IoT Hub or Azure IoT Central

Azure IoT Explorer Displaying message payloads from the Smartish Edge Camera
Azure IoT Central displaying message payloads from the Smartish Edge Camera

.NET Core web API + Dapper – Caching

Response Cache

In the beginning this was long long post about In-memory caching, distributed caching, Response caching, Response caching with middleware and Object reuse with ObjectPool. As I was re-reading the post before publishing it I came to the realisation that these different caching approaches didn’t require Dapper.

I started again, but kept the first section as it covers one of the simplest possible approaches to caching using the [ResponseCache] attribute and VaryByQueryKeys.

[HttpGet("Response")]
[ResponseCache(Duration = StockItemsListResponseCacheDuration)]
public async Task<ActionResult<IAsyncEnumerable<Model.StockItemListDtoV1>>> GetResponse()
{
	IEnumerable<Model.StockItemListDtoV1> response = null;

	logger.LogInformation("Response cache load");

	try
	{
		response = await dapper.QueryAsync<Model.StockItemListDtoV1>(sql: @"SELECT [StockItemID] as ""ID"", [StockItemName] as ""Name"", [RecommendedRetailPrice], [TaxRate] FROM [Warehouse].[StockItems]", commandType: CommandType.Text);
	}
	catch (SqlException ex)
	{
		logger.LogError(ex, "Retrieving list of StockItems");

		return this.StatusCode(StatusCodes.Status500InternalServerError);
	}

	return this.Ok(response);
}

[HttpGet("ResponseVarying")]
[ResponseCache(Duration = StockItemsListResponseCacheDuration, VaryByQueryKeys = new string[] { "id" })]
public async Task<ActionResult<Model.StockItemGetDtoV1>> Get([FromQuery(Name = "id"), Range(1, int.MaxValue, ErrorMessage = "Stock item id must greater than 0")] int id)
{
	Model.StockItemGetDtoV1 response = null;

	logger.LogInformation("Response cache varying load id:{0}", id);

	try
	{
		response = await dapper.QuerySingleOrDefaultAsync<Model.StockItemGetDtoV1>(sql: "[Warehouse].[StockItemsStockItemLookupV1]", param: new { stockItemId = id }, commandType: CommandType.StoredProcedure);
		if (response == default)
		{
			logger.LogInformation("StockItem:{0} not found", id);

			return this.NotFound($"StockItem:{id} not found");
		}
	}
	catch (SqlException ex)
	{
		logger.LogError(ex, "Looking up StockItem with Id:{0}", id);

		return this.StatusCode(StatusCodes.Status500InternalServerError);
	}

	return this.Ok(response);
}

I use Google Chrome, Mozilla Firefox, Microsoft Edgeium, and Opera but the screen captures have been done with FireFox mainly because it formats the Java Script Object Notation(JSON) response payloads nicely.

All the browsers appeared to respect the cache control headers but Firefox was the only one which did not initiate a new request when I pressed return in the Uniform Resource Locator(URL) field.

Firefox displaying list of stock items

I used Telerik Fiddler and FiddlerFox to capture the HTTP GET method request and response payloads.

Fiddler Fox extension details
Response payload for a list of StockItems with cache control headers highlighted
Firefox displaying single stock item
Response payload for a single StockItem with cache control headers highlighted

Dapper Cache

The Dapper Extensions Library has built in support for In-memory and Redis caching. The Dapper.Extensions Library extends Dapper’s functionality. It requires minimal configuration but I was tripped up by the default connection string requirement because I was using Dependency Injection

Dapper.Extensions NuGet package configuration

The configuration code in the application startup.cs supports in-memory and Redis caches.

// This method gets called by the runtime. Use this method to add services to the container.
public void ConfigureServices(IServiceCollection services)
{
	services.AddControllers();

	services.AddResponseCaching();

	services.AddDapperForMSSQL();

#if DAPPER_EXTENSIONS_CACHE_MEMORY
	services.AddDapperCachingInMemory(new MemoryConfiguration
	{
		AllMethodsEnableCache = false
	});
#endif
#if DAPPER_EXTENSIONS_CACHE_REDIS
	services.AddDapperCachingInRedis(new RedisConfiguration
	{
		AllMethodsEnableCache = false,
		KeyPrefix = Configuration.GetConnectionString("RedisKeyPrefix"),
		ConnectionString = Configuration.GetConnectionString("RedisConnection")
	}); 
#endif
	services.AddApplicationInsightsTelemetry();
}

The StockItemsCachingController was rewritten with the Dapper.Extensions QueryAsync and QuerySingleOrDefaultAsync methods.

[HttpGet("DapperMemory")]
public async Task<ActionResult<IAsyncEnumerable<Model.StockItemListDtoV1>>> GetDapper()
{
	List<Model.StockItemListDtoV1> response;

	logger.LogInformation("Dapper cache load");

	try
	{
		response = await dapper.QueryAsync<Model.StockItemListDtoV1>(
							sql: @"SELECT [StockItemID] as ""ID"", [StockItemName] as ""Name"", [RecommendedRetailPrice], [TaxRate] FROM [Warehouse].[StockItems]",
							commandType: CommandType.Text,
							enableCache: true,
							cacheExpire: TimeSpan.Parse(this.Configuration.GetValue<string>("DapperCachingDuration"))
					);

	}
	catch (SqlException ex)
	{
		logger.LogError(ex, "Retrieving list of StockItems");

		return this.StatusCode(StatusCodes.Status500InternalServerError);
	}

	return this.Ok(response);
}

[HttpGet("DapperMemoryVarying")]
public async Task<ActionResult<Model.StockItemGetDtoV1>> GetDapperVarying([FromQuery(Name = "id"), Range(1, int.MaxValue, ErrorMessage = "Stock item id must greater than 0")] int id)
{
	Model.StockItemGetDtoV1 response = null;

	logger.LogInformation("Dapper cache varying load id:{0}", id);

	try
	{
		response = await dapper.QuerySingleOrDefaultAsync<Model.StockItemGetDtoV1>(
					sql: "[Warehouse].[StockItemsStockItemLookupV1]",
					param: new { stockItemId = id },
					commandType: CommandType.StoredProcedure,
					cacheKey: $"StockItem:{id}",
					enableCache: true,
					cacheExpire: TimeSpan.Parse(this.Configuration.GetValue<string>("DapperCachingDuration"))
							);
		if (response == default)
		{
			logger.LogInformation("StockItem:{0} not found", id);

			return this.NotFound($"StockItem:{id} not found");
		}
	}
	catch (SqlException ex)
	{
		logger.LogError(ex, "Looking up StockItem with Id:{0}", id);

		return this.StatusCode(StatusCodes.Status500InternalServerError);
	}

	return this.Ok(response);
}

Both the Dapper.Extensions In-Memory and Redis cache reduced the number of database requests to the bare minimum. In a larger application the formatting of the cacheKey (cacheKey: “StockItems” & cacheKey: $”StockItem:{id}”) would be important to stop database query result collisions.

SQL Server Profiler displaying the list and single record requests.

I used Memurai which is a Microsoft Windows version of Redis for testing on my development machine before deploying to Microsoft Azure and using Azure Cache for Redis. Memurai runs as a Windows Service and supports master, replica, cluster node or sentinel roles.

Memurai running as a Windows Service on my development machine

When the Web API project was restarted the contents in-memory cache were lost. The Redis cache contents survive a restart and can be access from multiple clients.

The Dapper.Extensions Query, QueryAsync, QueryFirstOrDefaultAsync, QuerySingleOrDefault, QuerySingleOrDefaultAsync, QueryMultiple, QueryMultipleAsync, ExecuteReader, ExecuteReaderAsync, QueryPageAsync, QueryPageAsync, QueryPlainPage, QueryPlainPageAsync, Execute, ExecuteAsync, ExecuteScalar, ExecuteScalarAsync, BeginTransaction, CommitTransactionm and RollbackTransaction do not appear to a versions which “Retry” actions when there is a “Transient” failure. If there is no solution available I will build one using the approach in my DapperTransient module.