Add SQL command timeout option to database loader.

src/Microsoft.ML.Data/DataLoadSave/Database/DatabaseLoaderCursor.cs

@@ -71,6 +71,7 @@ public DbCommand Command
                     {
                         _command = Connection.CreateCommand();
                         _command.CommandText = _source.CommandText;
+                        _command.CommandTimeout = _source.CommandTimeoutInSeconds;
                     }
                     return _command;
                 }

src/Microsoft.ML.Data/DataLoadSave/Database/DatabaseSource.cs

@@ -25,9 +25,29 @@ public DatabaseSource(DbProviderFactory providerFactory, string connectionString
             CommandText = commandText;
         }
 
+        /// <summary>Creates a new instance of the <see cref="DatabaseSource" /> class.</summary>
+        /// <param name="providerFactory">The factory used to create the <see cref="DbConnection"/>..</param>
+        /// <param name="connectionString">The string used to open the connection.</param>
+        /// <param name="commandText">The text command to run against the data source.</param>
+        /// <param name="commandTimeoutInSeconds">The time in seconds to wait for the command to execute.</param>
+        public DatabaseSource(DbProviderFactory providerFactory, string connectionString, string commandText, int commandTimeoutInSeconds)
+        {
+            Contracts.CheckValue(providerFactory, nameof(providerFactory));
+            Contracts.CheckValue(connectionString, nameof(connectionString));
+            Contracts.CheckValue(commandText, nameof(commandText));
+
+            ProviderFactory = providerFactory;
+            ConnectionString = connectionString;
+            CommandText = commandText;
+            CommandTimeoutInSeconds = commandTimeoutInSeconds;
+        }
+
         /// <summary>Gets the text command to run against the data source.</summary>
         public string CommandText { get; }
 
+        /// <summary>Gets the command timeout.</summary>
+        public int CommandTimeoutInSeconds { get; }
+
         /// <summary>Gets the string used to open the connection.</summary>
         public string ConnectionString { get; }
 

test/Microsoft.ML.Tests/DatabaseLoaderTests.cs

@@ -2,6 +2,7 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
+using System;
 using System.Data;
 using System.Data.SqlClient;
 using System.IO;
@@ -79,6 +80,59 @@ public void IrisLightGbm()
             }).PredictedLabel);
         }
 
+        [LightGBMFact]
+        public void IrisLightGbmCommandTimeout()
+        {
+            if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
+            {
+                // https://github.com/dotnet/machinelearning/issues/4156
+                return;
+            }
+
+            var mlContext = new MLContext(seed: 1);
+
+            var connectionString = GetConnectionString(TestDatasets.irisDb.name);
+            var commandText = $@"WAITFOR DELAY '00:00:02'; SELECT * FROM ""{TestDatasets.irisDb.trainFilename}""";
+            var commandTimeout = 1;
+
+            var loaderColumns = new DatabaseLoader.Column[]
+            {
+                new DatabaseLoader.Column() { Name = "Label", Type = DbType.Int32 },
+                new DatabaseLoader.Column() { Name = "SepalLength", Type = DbType.Single },
+                new DatabaseLoader.Column() { Name = "SepalWidth", Type = DbType.Single },
+                new DatabaseLoader.Column() { Name = "PetalLength", Type = DbType.Single },
+                new DatabaseLoader.Column() { Name = "PetalWidth", Type = DbType.Single }
+            };
+
+            var loader = mlContext.Data.CreateDatabaseLoader(loaderColumns);
+
+            var databaseSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, commandText, commandTimeout);
+
+            bool passed=false;
+
+            try
+            {
+                var trainingData = loader.Load(databaseSource);
+
+                IEstimator<ITransformer> pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
+                .Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
+                .AppendCacheCheckpoint(mlContext)
+                .Append(mlContext.MulticlassClassification.Trainers.LightGbm())
+                .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
+
+                var model = pipeline.Fit(trainingData);
+            }
+            catch(Exception e)
+            {
+                if (e.InnerException.Message.Contains("Timeout"))
+                    passed = true;
+                else
+                    throw;
+            }
+
+            Assert.True(passed);
+        }
+
         [LightGBMFact]
         public void IrisLightGbmWithLoadColumnName()
         {