Basic shuffle file consolidation

The Spark shuffle phase can produce a large number of files, as one file is created
per mapper per reducer. For large or repeated jobs, this often produces millions of
shuffle files, which sees extremely degredaded performance from the OS file system.
This patch seeks to reduce that burden by combining multipe shuffle files into one.

This PR draws upon the work of Jason Dai in https://github.com/mesos/spark/pull/669.
However, it simplifies the design in order to get the majority of the gain with less
overall intellectual and code burden. The vast majority of code in this pull request
is a refactor to allow the insertion of a clean layer of indirection between logical
block ids and physical files. This, I feel, provides some design clarity in addition
to enabling shuffle file consolidation.

The main goal is to produce one shuffle file per reducer per active mapper thread.
This allows us to isolate the mappers (simplifying the failure modes), while still
allowing us to reduce the number of mappers tremendously for large tasks. In order
to accomplish this, we simply create a new set of shuffle files for every parallel
task, and return the files to a pool which will be given out to the next run task.

1 files changed, 1 insertions, 1 deletions

diff --git a/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java b/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java
index ab790b7850..172c6e4b1c 100644
--- a/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java
+++ b/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java
@@ -51,7 +51,7 @@ class FileServerHandler extends ChannelInboundMessageHandlerAdapter<String> {
         ctx.flush();
         return;
       }
-      long length = file.length();
+      long length = fileSegment.length();
       if (length > Integer.MAX_VALUE || length <= 0) {
         ctx.write(new FileHeader(0, blockId).buffer());
         ctx.flush();