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From Shi Yu <sh...@uchicago.edu>
Subject Re: large memory tasks
Date Wed, 15 Jun 2011 23:36:22 GMT
Suppose you are looking up a value V of a key K.   And V is required for 
an upcoming process. Suppose the data in the upcoming process  has the form

R1  K1 K2 K3,

where R1 is the record number, K1 to K3 are the keys occurring in the 
record, which means in the look up case you would query for V1, V2, V3

Using inner join you could attach all the V values for a single record 
and prepare the data like

R1 K1 K2 K3 V1 V2 V3

then each record has the complete information for the next process. So 
you pay the storage for the efficiency. Even taking into account the 
time required for preparing the data, it is still faster than the 
look-up approach.

I have also tried TokyoCabinet, you need to compile and install some 
extensions to get it working. Sometimes getting things and APIs to work 
can be painful. If you don't need to update the lookup table, install 
TC, MemCache, MongoDB locally on each node would be the most efficient 
solution because all the look-ups are local.

On 6/15/2011 5:56 PM, Ian Upright wrote:
> If the data set doesn't fit in working memory, but is still of a reasonable
> size  (lets say a few hundred gigabytes), then I'd probably use something
> like this:
> http://fallabs.com/tokyocabinet/
>  From reading the Hadoop docs (which I'm very new to), then I might use
> DistributedCache to replicate that database around.  My impression would be
> that this might be among the most efficient things one could do.
> However, for my particular application, even using tokycabinet introduces
> too much inefficiency, and a pure plain old memory-based lookups is by far
> the most efficient.  (not to mention that some of the lookups I'm doing are
> specialized trees that can't be done with tokyocabinet or any typical db,
> but thats beside the point)
> I'm having trouble understanding your more efficient method by using more
> data and HDFS, and having trouble understanding how it could possibly be any
> more efficient than say the above approach.
> How is increasing the size minimizing the lookups?
> Ian
>> I had the same problem before, a big lookup table too large to load in
>> memory.
>> I tried and compared the following approaches:  in-memory MySQL DB, a
>> dedicated central memcache server, a dedicated central MongoDB server,
>> local DB (each node has its own MongoDB server) model.
>> The local DB model is the most efficient one.  I believe dedicated
>> server approach could get improved if the number of server is increased
>> and distributed. I just tried single server.
>> But later I dropped out the lookup table approach. Instead, I attached
>> the table information in the HDFS (which could be considered as an inner
>> join DB process), which significantly increases the size of data sets
>> but avoids the bottle neck of table look up. There is a trade-off, when
>> no table looks up, the data to process is intensive (TB size). In
>> contrast, a look-up table could save 90% of the data storage.
>> According to our experiments on a 30-node cluster, attaching information
>> in HDFS is even 20%  faster than the local DB model. When attaching
>> information in HDFS, it is also easier to ping-pong Map/Reduce
>> configuration to further improve the efficiency.
>> Shi
>> On 6/15/2011 5:05 PM, GOEKE, MATTHEW (AG/1000) wrote:
>>> Is the lookup table constant across each of the tasks? You could try putting
it into memcached:
>>> http://hcil.cs.umd.edu/trs/2009-01/2009-01.pdf
>>> Matt
>>> -----Original Message-----
>>> From: Ian Upright [mailto:ian@upright.net]
>>> Sent: Wednesday, June 15, 2011 3:42 PM
>>> To: common-user@hadoop.apache.org
>>> Subject: large memory tasks
>>> Hello, I'm quite new to Hadoop, so I'd like to get an understanding of
>>> something.
>>> Lets say I have a task that requires 16gb of memory, in order to execute.
>>> Lets say hypothetically it's some sort of big lookuptable of sorts that
>>> needs that kind of memory.
>>> I could have 8 cores run the task in parallel (multithreaded), and all 8
>>> cores can share that 16gb lookup table.
>>> On another machine, I could have 4 cores run the same task, and they still
>>> share that same 16gb lookup table.
>>> Now, with my understanding of Hadoop, each task has it's own memory.
>>> So if I have 4 tasks that run on one machine, and 8 tasks on another, then
>>> the 4 tasks need a 64 GB machine, and the 8 tasks need a 128 GB machine, but
>>> really, lets say I only have two machines, one with 4 cores and one with 8,
>>> each machine only having 24 GB.
>>> How can the work be evenly distributed among these machines?  Am I missing
>>> something?  What other ways can this be configured such that this works
>>> properly?
>>> Thanks, Ian
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