Question

我的代码类似于：

sc = SparkContext()
ssc = StreamingContext(sc, 30)

initRDD = sc.parallelize('path_to_data')
lines = ssc.socketTextStream('localhost', 9999)
res = lines.transform(lambda x: x.join(initRDD))

res.pprint()

我的问题是initRDD需要每天午夜更新。

我试着这样：

sc = SparkContext()
ssc = StreamingContext(sc, 30)

lines = ssc.socketTextStream('localhost', 9999)


def func(rdd):
    initRDD = rdd.context.parallelize('path_to_data')
    return rdd.join(initRDD)


res = lines.transform(func)

res.pprint()

但似乎initRDD每30秒更新一次，与batchDuration相同

有没有好的理想

Answer 1

一种选择是检查transform之前的截止日期。检查是一个简单的比较，因此在每个批次间隔都很便宜：

def nextDeadline() : Long = {
  // assumes midnight on UTC timezone.
  LocalDate.now.atStartOfDay().plusDays(1).toInstant(ZoneOffset.UTC).toEpochMilli()
}
// Note this is a mutable variable!
var initRDD = sparkSession.read.parquet("/tmp/learningsparkstreaming/sensor-records.parquet")
// Note this is a mutable variable!
var _nextDeadline = nextDeadline()

val lines = ssc.socketTextStream("localhost", 9999)
// we use the foreachRDD as a scheduling trigger. 
// We don't use the data, only the execution hook
lines.foreachRDD{ _ => 
    if (System.currentTimeMillis > _nextDeadline) {
      initRDD = sparkSession.read.parquet("/tmp/learningsparkstreaming/sensor-records.parquet")
      _nextDeadline = nextDeadline()
    }
}
// if the rdd was updated, it will be picked up in this stage.
val res = lines.transform(rdd => rdd.join(initRDD))

如何在spark流中定期更新rdd

1 个答案: