Improve memory model for map-side sorts
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Key: HADOOP-2054
URL: https://issues.apache.org/jira/browse/HADOOP-2054
Project: Hadoop
Issue Type: Improvement
Components: mapred
Reporter: Arun C Murthy
Assignee: Arun C Murthy
Fix For: 0.16.0
{{MapTask#MapOutputBuffer}} uses a plain-jane {{DataOutputBuffer}} which
defaults to a buffer of size 32-bytes, and the {{DataOutputBuffer#write}} call
doubles the underlying byte-array when it needs more space.
However for maps which output any decent amount of data (e.g. 128MB in
examples/Sort.java) this means the buffer grows painfully slowly from 2^6 to
2^28, and each time this results in a new array being created, followed by an
array-copy:
{noformat}
public void write(DataInput in, int len) throws IOException {
int newcount = count + len;
if (newcount > buf.length) {
byte newbuf[] = new byte[Math.max(buf.length << 1, newcount)];
System.arraycopy(buf, 0, newbuf, 0, count);
buf = newbuf;
}
in.readFully(buf, count, len);
count = newcount;
}
{noformat}
I reckon we could do much better in the {{MapTask}}, specifically...
For e.g. we start with a buffer of size 1/4KB and quadruple, rather than
double, upto, say 4/8/16MB. Then we resume doubling (or less).
This means that it quickly ramps up to minimize no. of {{System.arrayCopy}}
calls and small-sized buffers to GC; and later start doubling to ensure we
don't ramp-up too quickly to minimize memory wastage due to fragmentation.
Of course, this issue is about benchmarking and figuring if all this is worth
it, and, if so, what are the right set of trade-offs to make.
Thoughts?
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