log-extract program with Spark
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import org.apache.spark.{SparkConf, SparkContext} | |
object LogExtract { | |
val keys = Array[String]("dn", "stm", "ev_ac", "v_title", "v_uri", "pg_url") | |
val notBlankKeys = Array[String]("dn", "stm", "ev_ac", "pg_url") | |
val filterRegex = ".*(ac_pl`|ac_dl`).*" | |
val useCompress = false | |
def process(line: String): String = { | |
val fields = line.split("`") | |
val outputArr = Array.fill(keys.length)("") | |
try { | |
for (field <- fields) { | |
val pairs = field.split("=", 2) | |
val key = pairs(0) | |
val value = pairs(1) | |
if (keys.contains(key)) { | |
outputArr(keys.indexOf(key)) = value | |
} | |
} | |
// 如果必需的字段没有包含,整条日志应该被过滤 | |
for (notBlankKey <- notBlankKeys) { | |
if (outputArr(keys.indexOf(notBlankKey)).length == 0) { | |
return null | |
} | |
} | |
} catch { | |
case ex: Exception => return null | |
} | |
outputArr.mkString("\t") | |
} | |
def main(args: Array[String]) { | |
if (useCompress) { | |
System.setProperty("spark.hadoop.mapreduce.output.fileoutputformat.compress", "true") | |
System.setProperty("spark.hadoop.mapreduce.output.fileoutputformat.compress.codec", "org.apache.hadoop.io.compress.GzipCodec") | |
System.setProperty("spark.mapreduce.output.fileoutputformat.compress.type", "BLOCK") | |
} | |
val conf = new SparkConf().setAppName("SparkLogProcess") | |
val sc = new SparkContext(conf) | |
sc.textFile(args(0)).filter(line => line.matches(filterRegex)).map( | |
line => process(line) | |
).filter(line => line != null).saveAsTextFile(args(1)) | |
} | |
} |
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