[concurrency-interest] ConcurrentSkipListMap performance

David Holmes davidcholmes at aapt.net.au
Sun Oct 23 18:01:03 EDT 2011

As Ben said, not enough details to be able to interpret or diagnoze. Details
on the test system - how may processors vs. cores vs HT might be useful.

I also don't find 3888ms a negligible difference.

David Holmes
  -----Original Message-----
  From: concurrency-interest-bounces at cs.oswego.edu
[mailto:concurrency-interest-bounces at cs.oswego.edu]On Behalf Of Ted Yu
  Sent: Monday, 24 October 2011 5:02 AM
  To: concurrency-interest at cs.oswego.edu
  Subject: [concurrency-interest] ConcurrentSkipListMap performance

  HBase expects ConcurrentSkipListMap to have good performance.
  See the following discussion.

  Comments are welcome.

  ---------- Forwarded message ----------
  From: Akash Ashok <thehellmaker at gmail.com>
  Date: Sun, Oct 23, 2011 at 2:57 AM
  Subject: Re: SILT - nice keyvalue store paper
  To: dev at hbase.apache.org

  I was running some similar tests and came across a surprising finding. I
compared reads and write on ConcurrentSkipListMap ( which the memstore uses)
and synchronized TreeMap ( Which was literally treemap synchronized).
Executed concurrent reads, writes and deletes on both of them.
  Surprisingly synchronized treeMap performed better, though just slightly
better, than ConcurrentSkipListMap which KeyValueSkipListSet uses.

  Here are the output of a few runs

  Sometimes the difference was considerable
  Using HBaseMap it took 20438ms
  Using TreeMap it took 11613ms
  Time Difference:8825ms

  And sometimes the difference was negligible
  Using HBaseMap it took 13370ms
  Using TreeMap it took 9482ms
  Time Difference:3888ms

  I've attaching the test  java file which I wrote to test it.
  This might be a very minor differece but still surprising considering the
fact that ConcurrentSkipListMap uses fancy 2 level indexes which they say
improves the deletion performance.

  And here are the details about the test run.
  100 Threads each fetching 10,000 records
  100 threads each adding 10,000 records.
  100 threads each deletin 10,000 records
  ( Reads, Writes and deletes simultaneously )

  Akash A
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