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:i...@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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