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From kisalay <kisa...@gmail.com>
Subject Re: Add client complexity or use a coprocessor?
Date Wed, 11 Apr 2012 05:59:01 GMT
Tom,

I was way too curious to resist a reply here.
If you want to store a bytearray estimating the unique count for a
particular OLAP cell, will you not see a lot of updates to the same
cell and create a hotspot ?

I think another option comes to my mind. I assume that you get all the
user activities. Now consider for a moment that you store each of
there activities in you Table in HBase with the row-key being
udid-timestamp and value being some blob representing the activity
detail. Now if I have to do a unique count of user for a OLAP cell, I
would create endpoint coprocessors, that would execute per region,
scan it, and prepare a bitset representing the unique counts for the
OLAP cellof interest for that region.

Now if you are implementing Probabilistic Counting, you can OR the
bitsets returned from each region to get the final bitset which will
give you the overall unique counts for all the regions together.

You would not only save on network transfers, as you are doing the
counts per region in coprocessor and returning only one bitset per
region, you would also be able to resolve the query in the time taken
by one coprocessor to scan one region.

I have a rudimentary implementation of Probabilistic Count which I
once used as a Bolt in Storm (Storm was developed at Backtype and
opensourced by Twitter) to count the unique users for a similar use
case. Let me know if you would to look at the implementation of the
algorithm.



~Kisalay

On Wed, Apr 11, 2012 at 5:23 AM, Andrew Purtell <apurtell@apache.org> wrote:
>> Even my implementation of an atomic increment
>> (using a coprocessor) is two orders of magnitude slower than the
>> provided implementation.  Are there properties inherent to
>> coprocessors or Incrementors that would force this kind of performance
>> difference?
>
>
> No.
>
>
> You may be seeing a performance difference if you are packing multiple Increments into
one round trip but not doing a similar kind of batching if calling a custom endpoint. Each
Endpoint invocation is a round trip unless you do something like:
>
>     List<Row> actions = new ArrayList<Row>();    actions.add(new Exec(conf,
row, protocol, method, ...));
>
>     actions.add(new Exec(conf, row, protocol, method, ...));
>
>     actions.add(new Exec(conf, row, protocol, method, ...));
>
>     Object[] results = table.batch(actions);
>     ...
>
>
> I've not personally tried that particular API combination but don't see why it would
not be possible.
>
>
> Beyond that, I'd suggest running a regionserver with your coprocessor installed under
a profiler to see if you have monitor contention or a hotspot or similar. It could be something
unexpected.
>
>
>> Can you think of an efficient way to implement an atomic bitfield
>> (other than adding it as a separate feature like atomic increments)?
>
> I think the idea of an atomic bitfield operation as part of the core API is intriguing.
It has applicability to your estimator use case and I can think of a couple of things I could
use it for. If there is more support for this idea, this may be something to consider.
>
>
> Best regards,
>
>
>     - Andy
>
> Problems worthy of attack prove their worth by hitting back. - Piet Hein (via Tom White)
>
>
>
> ----- Original Message -----
>> From: Tom Brown <tombrown52@gmail.com>
>> To: user@hbase.apache.org; Andrew Purtell <apurtell@apache.org>
>> Cc:
>> Sent: Tuesday, April 10, 2012 3:53 PM
>> Subject: Re: Add client complexity or use a coprocessor?
>>
>> Andy,
>>
>> I have attempted to use coprocessors to achieve a passable performance
>> but have failed so far. Even my implementation of an atomic increment
>> (using a coprocessor) is two orders of magnitude slower than the
>> provided implementation.  Are there properties inherent to
>> coprocessors or Incrementors that would force this kind of performance
>> difference?
>>
>> Can you think of an efficient way to implement an atomic bitfield
>> (other than adding it as a separate feature like atomic increments)?
>>
>> Thanks!
>>
>> --Tom
>>
>> On Tue, Apr 10, 2012 at 12:01 PM, Andrew Purtell <apurtell@apache.org>
>> wrote:
>>>  Tom,
>>>>  I am a big fan of the Increment class. Unfortunately, I'm not doing
>>>>  simple increments for the viewer count. I will be receiving duplicate
>>>>  messages from a particular client for a specific cube cell, and
>> don't
>>>>  want them to be counted twice
>>>
>>>  Gotcha.
>>>
>>>>  I created an RPC endpoint coprocessor to perform this function but
>>>>  performance suffered heavily under load (it appears that the endpoint
>>>>  performs all functions in serial).
>>>
>>>  Did you serialize access to your data structure(s)?
>>>
>>>>  When I tried implementing it as a region observer, I was unsure of how
>>>>  to correctly replace the provided "put" with my own. When I
>> issued a
>>>>  put from within "prePut", the server blocked the new put
>> (waiting for
>>>>  the "prePut" to finish). Should I be attempting to modify the
>> WALEdit
>>>>  object?
>>>
>>>  You can add KVs to the WALEdit. Or, you can get a reference to the
>> Put's familyMap:
>>>
>>>      Map<byte[], List<KeyValue>> familyMap = put.getFamilyMap();
>>>
>>>  and if you modify the map, you'll change what gets committed.
>>>
>>>>  Is there a way to extend the functionality of "Increment" to
>> provide
>>>>  arbitrary bitwise operations on a the contents of a field?
>>>
>>>  As a matter of design, this should be a new operation. It does sound
>> interesting and useful, some sort of atomic bitfield.
>>>
>>>
>>>  Best regards,
>>>
>>>      - Andy
>>>
>>>  Problems worthy of attack prove their worth by hitting back. - Piet Hein
>> (via Tom White)
>>>
>>>
>>>
>>>  ----- Original Message -----
>>>>  From: Tom Brown <tombrown52@gmail.com>
>>>>  To: user@hbase.apache.org
>>>>  Cc:
>>>>  Sent: Monday, April 9, 2012 10:14 PM
>>>>  Subject: Re: Add client complexity or use a coprocessor?
>>>>
>>>>  Andy,
>>>>
>>>>  I am a big fan of the Increment class. Unfortunately, I'm not doing
>>>>  simple increments for the viewer count. I will be receiving duplicate
>>>>  messages from a particular client for a specific cube cell, and
>> don't
>>>>  want them to be counted twice (my stats don't have to be 100%
>>>>  accurate, but the expected rate of duplicates will be higher than the
>>>>  allowable error rate).
>>>>
>>>>  I created an RPC endpoint coprocessor to perform this function but
>>>>  performance suffered heavily under load (it appears that the endpoint
>>>>  performs all functions in serial).
>>>>
>>>>  When I tried implementing it as a region observer, I was unsure of how
>>>>  to correctly replace the provided "put" with my own. When I
>> issued a
>>>>  put from within "prePut", the server blocked the new put
>> (waiting for
>>>>  the "prePut" to finish). Should I be attempting to modify the
>> WALEdit
>>>>  object?
>>>>
>>>>  Is there a way to extend the functionality of "Increment" to
>> provide
>>>>  arbitrary bitwise operations on a the contents of a field?
>>>>
>>>>  Thanks again!
>>>>
>>>>  --Tom
>>>>
>>>>>  If it helps, yes this is possible:
>>>>>
>>>>>>   Can I observe updates to a
>>>>>>   particular table and replace the provided data with my own?
>> (The
>>>>>>   client calls "put" with the actual user ID, my
>> co-processor
>>>>  replaces
>>>>>>   it with a computed value, so the actual user ID never gets
>> stored in
>>>>>>   HBase).
>>>>>
>>>>>  Since your option #2 requires atomic updates to the data structure,
>> have you
>>>>  considered native
>>>>>  atomic increments? See
>>>>>
>>>>>
>> http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/HTable.html#incrementColumnValue%28byte[],%20byte[],%20byte[],%20long,%20boolean%29
>>>>>
>>>>>
>>>>>  or
>>>>>
>>>>>
>> http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Increment.html
>>>>>
>>>>>  The former is a round trip for each value update. The latter allows
>> you to
>>>>  pack multiple updates
>>>>>  into a single round trip. This would give you accurate counts even
>> with
>>>>  concurrent writers.
>>>>>
>>>>>  It should be possible for you to do partial aggregation on the
>> client side
>>>>  too whenever parallel
>>>>>  requests colocate multiple updates to the same cube within some
>> small window
>>>>  of time.
>>>>>
>>>>>  Best regards,
>>>>>
>>>>>
>>>>>      - Andy
>>>>>
>>>>>  Problems worthy of attack prove their worth by hitting back. - Piet
>> Hein
>>>>  (via Tom White)
>>>>>
>>>>>  ----- Original Message -----
>>>>>>   From: Tom Brown <tombrown52@gmail.com>
>>>>>>   To: user@hbase.apache.org
>>>>>>   Cc:
>>>>>>   Sent: Monday, April 9, 2012 9:48 AM
>>>>>>   Subject: Add client complexity or use a coprocessor?
>>>>>>
>>>>>>   To whom it may concern,
>>>>>>
>>>>>>   Ignoring the complexities of gathering the data, assume that
I
>> will be
>>>>>>   tracking millions of unique viewers. Updates from each of our
>> millions
>>>>>>   of clients are gathered in a centralized platform and spread
>> among a
>>>>>>   group of machines for processing and inserting into HBase
>> (assume that
>>>>>>   this group can be scaled horizontally). The data is stored in
>> an OLAP
>>>>>>   cube format and one of the metrics I'm tracking across
>> various
>>>>>>   attributes is viewership (how many people from Y are watching
>> X).
>>>>>>
>>>>>>   I'm writing this to ask for your thoughts as to the most
>>>>  appropriate
>>>>>>   way to structure my data so I can count unique TV viewers
>> (assume a
>>>>>>   service like netflix or hulu).
>>>>>>
>>>>>>   Here are the solutions I'm considering:
>>>>>>
>>>>>>   1. Store each unique user ID as the cell name within the
>> cube(s) it
>>>>>>   occurs. This has the advantage of having 100% accuracy, but
>> the
>>>>>>   downside is the enormous space required to store each unique
>> cell.
>>>>>>   Consuming this data is also problematic as the only way to
>> provide a
>>>>>>   viewership count is by counting each cell. To save the
>> overhead of
>>>>>>   sending each cell over the network, counting them could be
>> done by a
>>>>>>   coprocessor on the region server, but that still doesn't
>> avoid the
>>>>>>   overhead of reading each cell from the disk. I'm also not
>> sure what
>>>>>>   happens if a single row is larger than an entire region (48
>> bytes per
>>>>>>   user ID * 10,000,000 users = 480GB).
>>>>>>
>>>>>>   2. Store a byte array that allows estimating unique viewers
>> (with a
>>>>>>   small margin of error*). Add a co-processor for updating this
>> column
>>>>>>   so I can guarantee the updates to a specific OLAP cell will be
>> atomic.
>>>>>>   The main benefit from this path is that there the nodes that
>> update
>>>>>>   HBase can be less complex. Another benefit I see is that the
I
>> can
>>>>>>   just add more HBase regions as scale requires. However,
>> I'm not
>>>>  sure
>>>>>>   if I can use a coprocessor the way I want; Can I observe
>> updates to a
>>>>>>   particular table and replace the provided data with my own?
>> (The
>>>>>>   client calls "put" with the actual user ID, my
>> co-processor
>>>>  replaces
>>>>>>   it with a computed value, so the actual user ID never gets
>> stored in
>>>>>>   HBase).
>>>>>>
>>>>>>   3. Store a byte array that allows estimating unique viewers
>> (with a
>>>>>>   small margin of error*). Re-arrange my architecture so that
>> each OLAP
>>>>>>   cell is only updated by a single node. The main benefit from
>> this
>>>>>>   would be that I don't need to worry about atomic
>> operations in
>>>>  HBase
>>>>>>   since all updates for a single cell will be atomic and in
>> serial. The
>>>>>>   biggest downside is that I believe it will add significant
>> complexity
>>>>>>   to my overall architecture.
>>>>>>
>>>>>>
>>>>>>   Thanks for your time, and I look forward to hearing your
>> thoughts.
>>>>>>
>>>>>>   Sincerely,
>>>>>>   Tom Brown
>>>>>>
>>>>>>   *(For information about the byte array mentioned in #2 and #3,
>> see:
>>>>>>
>>>>
>> http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html)
>>>>>>
>>>>
>>

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