spark.Udf().Register<string, bool>("GeneralReg", log => Regex.IsMatch(log, s_apacheRx));

DataFrame generalDf = spark.Sql("SELECT logs.value, GeneralReg(logs.value) FROM Logs");

generalDf = generalDf.Filter(generalDf["GeneralReg(value)"]);
generalDf.Show();

// Choose valid log entries that start with 10
spark.Udf().Register<string, bool>(
    "IPReg",
    log => Regex.IsMatch(log, "^(?=10)"));

generalDf.CreateOrReplaceTempView("IPLogs");

// Apply UDF to get valid log entries starting with 10
DataFrame ipDf = spark.Sql(
    "SELECT iplogs.value FROM IPLogs WHERE IPReg(iplogs.value)");
ipDf.Show();

// Choose valid log entries that start with 10 and deal with spam
spark.Udf().Register<string, bool>(
    "SpamRegEx",
    log => Regex.IsMatch(log, "\\b(?=spam)\\b"));

ipDf.CreateOrReplaceTempView("SpamLogs");

// Apply UDF to get valid, start with 10, spam entries
DataFrame spamDF = spark.Sql(
    "SELECT spamlogs.value FROM SpamLogs WHERE SpamRegEx(spamlogs.value)");

int numGetRequests = spamDF 
    .Collect() 
    .Where(r => ContainsGet(r.GetAs<string>("value"))) 
    .Count();

// Use regex matching to group data 
// Each group matches a column in our log schema 
// i.e. first group = first column = IP
public static bool ContainsGet(string logLine) 
{ 
    Match match = Regex.Match(logLine, s_apacheRx);

    // Determine if valid log entry is a GET request
    if (match.Success)
    {
        Console.WriteLine("Full log entry: ‘{0}‘", match.Groups[0].Value);
    
        // 5th column/group in schema is "method"
        if (match.Groups[5].Value == "GET")
        {
            return true;
        }
    }

    return false;

}

3 运行程序

spark-submit --class org.apache.spark.deploy.dotnet.DotnetRunner --master local /path/to/microsoft-spark-<version>.jar dotnet /path/to/netcoreapp<version>/LoggingApp.dll

var mySqlQuery = "SELECT * FROM table WHERE id = " + std_name;