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From tim robertson <timrobertson...@gmail.com>
Subject Re: Hadoop / MySQL
Date Tue, 28 Apr 2009 13:27:01 GMT

[Ankur]: How can make sure this happens?
-- show processlist is how we spot it... literally it takes hours on
our set up so easy to find.

So we ended up with 2 DBs
- DB1 we insert to, prepare and do batch processing
- DB2 serving the read only web app

Periodically we dump the DB1, point the read-only web app at DB1, load
the dump into DB2, and then DB2 becomes the write DB... and an endless
cycle.  We can afford data latency in our system though.

Why did we end up with this?  Because of locking on writes that kill
reads as you say... basically you can't insert when a read is
happening on myisam as it locks the whole table.  InnoDB has row level
locking to get around this but in our experience (at the time we had
130million records) it just didn't work either.  We spent €10,000 for
the supposed "european expert" on mysql from their professional
services and were unfortunately very disappointed.  Seems such large
tables are just problematic with mysql.  We are now very much looking
into Lucene technologies for search and Hadoop for reporting and
datamining type operations. SOLR does a lot of what our DB does for

So with myisam... here is what we learnt:

Only very latest mysql versions (beta still I think) support more than
4G memory for indexes (you really really need the index in memory, and
where possible the FK for joins in the index too).  Mysql has
differing join strategies between innoDB and myisam, so be aware.
An undocumented feature of myisam is you can create memory buffers for
single indexes:
In the my.cnf:
     taxon_concept_cache.key_buffer_size=3990M    -- for some reason
you have to drop a little under 4G

then in the DB run:
    cache index taxon_concept in taxon_concept_cache;
    load index into cache taxon_concept;

This allows for making sure an index gets into memory for sure.

And here is our main cnf changes:

table_cache = 512
sort_buffer_size = 128M
read_buffer_size = 64M
read_rnd_buffer_size = 300M
thread_cache_size = 8
query_cache_type = 0
query_cache_size = 0
thread_concurrency = 8
# this is used for repair by sorting and is faster than keycache by far
myisam_max_sort_file_size = 200G
myisam_max_extra_sort_file_size = 200G
# this is used for repair by sorting and is limited to 4G
myisam_sort_buffer_size = 3990M
# maximum value for this is 4g for now but see section on specific
keys for large indexes

-- for repair by sorting, here are the required values
myisam_max_sort_file_size = 200G
myisam_max_extra_sort_file_size = 200G
myisam_sort_buffer_size = 3990M

I guess just archive this mail off for if you ever do try myisam for
something... these params are the result of a lot of trial and error
and days of waiting!

Our DB has about 44G of index which we can keep in memory but are
hitting limits - hence on the hadoop train.



On Tue, Apr 28, 2009 at 2:49 PM, Wang Zhong <wangzhong.neu@gmail.com> wrote:
> Hi,
> That's really cool. It seems that Hadoop could work with SQL DBs like
> Mysql with bearable time. I thought when inserting data to Mysql, the
> expense of communication was always a big problem. You got a method to
> reduce the expense.
> Using Distribution Databases like HBase is another good choice. It is
> based on Hadoop and HDFS, so there is no problem with communication
> expense.
> On Tue, Apr 28, 2009 at 6:46 PM, Ankur Goel <ankur.goel@corp.aol.com> wrote:
>> hello hadoop users,
>> Recently I had a chance to lead a team building a log-processing system that uses
Hadoop and MySQL. The system's goal was to process the incoming information as quickly as
possible (real time or near real time), and make it available for querying in MySQL. I thought
it would be good to share the experience and the challenges with the community. Couldn't think
of a better place than these mailing lists as I am not much of a blogger :-)
>> The information flow in the system looks something like
>> [Apache-Servers] -> [Hadoop] -> [MySQL-shards] -> [Query-Tools]
>> Transferring from Apache-Servers to Hadoop was quite easy as we just had to organize
the data in timely buckets (directories). Once that was running smooth we had to make sure
that map-reduce jobs are fired at regular intervals and they pick up the right data. The jobs
would then process/aggregate the date and dump the info into MySQL shards from the reducers
[we have our own DB partioning set up]. This is where we hit major bottlenecks [any surprises?
>> The table engine used was InnoDB as there was a need for fast replication and writes
but only moderate reads (should eventually support high read rates). The data would take up
quite a while to load completely far away from being near-real time. And so our optimization
journey begin.
>> 1. We tried to optimize/tune InnoDB parameters like increasing the buffer pool size
to 75 % of available RAM. This helped but only till the time DBs were lightly loaded i.e.
innoDB had sufficient buffer pool to host the data and indexes.
>> 2. We also realized that InnoDB has considerable locking overhead because of which
write concurrency is really bad when you have a large number of concurrent threads doing writes.
The default thread concurrency for us was set to no_of_cpu * 2 = 8 which is what the official
documentation advises as the optimal limit. So we limited the number of reduce tasks and consequently
the number of concurrent writes and boy the performance improved 4x. We were almost there
>> 3. Next thing we tried is the standard DB optimzation techniques like de-normalizing
the schema and dropping constraints. This gave only a minor performance improvement, nothing
earth shattering. Note that we were already caching connections in reducers to each MySQL
shard and partionining logic was embedded into reducers.
>> 4. Falling still short of our performance objectives, we finally we decided to get
rid of JDBC writes from reducers and work on an alternative that uses MySQLs LOAD utility.
>> - The processing would partition the data into MySQL shard specific files resident
in HDFS.
>> - A script would then spawn processes via ssh on different physical machines to download
this data.
>> - Each spawned process just downloads the data for the shard it should upload to.
>> - All the processes then start uploading data in parallel into their respective MySQL
shards using LOAD DATA infile.
>> This proved to be the fastest approach, even in the wake of increasing data loads.
The enitre processing/loading would complete in less than 6 min. The system has been holding
up quite well so far, even though we've had to limit the number of days for which we keep
the data or else the MySQLs get overwhelmed.
>> Hope this is helpful to people.
>> Regards
>> -Ankur
> --
> Wang Zhong

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