Question

我正在开发一个可以读取和解析自定义日志文件的Spark / Scala应用程序。我在解析多行日志条目时遇到问题。这是我的代码片段：

case class MLog(dateTime: String, classification: String, serverType: String, identification:String, operation: String)
val PATTERN = """(?s)(\d{4}-\d{2}-\d{2}\s\d{2}:\d{2}:\d{2},\d{3})\s+(\w+)s+\[(.*)\]\s+\[(.*)\]\s+(.*)"""


def parseLogLine(log: String): MLog={
     val res = PATTERN.findFirstMatchIn(log)
     if (res.isEmpty) {
     throw new RuntimeException("Cannot parse log line: " + log)

     MLog(m.group(1),m.group(2),m.group(3),m.group(4),m.group(5))
}

sc.textFile("/mydirectory/logfile").map(parseLogLine).foreach(println)

logfile中的某些条目跨越多行。正则表达式适用于单行条目，但是当读取多行条目时，如下所示：

2015-08-31 00:10:17,682 WARN  [ScheduledTask-10] [name=custname;mid=9999;ds=anyvalue;] datasource - Scheduled DataSource import failed.                 
com.xxx.common.service.ServiceException: system failure: Unable to connect to ANY server: LdapDataSource{id=xxx, type=xxx, enabled=true, name=xxx, host=xxx port=999, connectionType=ssl, username=xxx, folderId=99999}

我收到此错误：

无法解析日志行：com.xxx.common.service.ServiceException：系统故障：无法连接到任何服务器：LdapDataSource {id = xxx，type = xxx，enabled = true，name = xxx，host = xxx port = 999，connectionType = ssl，username = xxx，folderId = 99999}

如何让Spark从日志文件中读取多行日志条目？

Answer 1

由于输入文件很小，您可以使用SparkContext.wholeTextFiles。

// Parse a single file and return all extracted entries
def parseLogFile(log: String): Iterator[MLog] = {
    val p: scala.util.matching.Regex = ???
    p.findAllMatchIn(log).map(
        m => MLog(m.group(1), m.group(2), m.group(3), m.group(4), m.group(5))
    )
}

val rdd: RDD[MLog] = sc
   .wholeTextFiles("/path/to/input/dir")
   .flatMap{case (_, txt) => parseLogFile(txt)}

如何使用可能跨越多行的Spark来解析日志行

1 个答案: