org.apache.spark.scheduler

InputFormatInfo

object InputFormatInfo

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  8. def computePreferredLocations(formats: Seq[InputFormatInfo]): Map[String, Set[SplitInfo]]

    Computes the preferred locations based on input(s) and returned a location to block map.

    Computes the preferred locations based on input(s) and returned a location to block map. Typical use of this method for allocation would follow some algo like this:

    a) For each host, count number of splits hosted on that host. b) Decrement the currently allocated containers on that host. c) Compute rack info for each host and update rack -> count map based on (b). d) Allocate nodes based on (c) e) On the allocation result, ensure that we dont allocate "too many" jobs on a single node (even if data locality on that is very high) : this is to prevent fragility of job if a single (or small set of) hosts go down.

    go to (a) until required nodes are allocated.

    If a node 'dies', follow same procedure.

    PS: I know the wording here is weird, hopefully it makes some sense !

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