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From Wayne <wav...@gmail.com>
Subject Re: Read/Write Performance
Date Thu, 30 Dec 2010 20:39:31 GMT
We have restarted with lzop compression, and now I am seeing some really
long and frequent stop the world pauses of the entire cluster. The load
requests for all regions all go to zero except for the meta table region. No
data batches are getting in (no loads are occurring) and everything seems
frozen. It seems to last for 5+ seconds. Is this GC on the master or GC in
the meta region? What could cause everything to stop for several seconds? It
appears to happen on a recurring basis as well. I think we saw it before
switching to lzo but it seems much worse now (lasts longer and occurs more
frequently).

Thanks.


On Thu, Dec 30, 2010 at 12:20 PM, Wayne <wav100@gmail.com> wrote:

> HBase Version 0.89.20100924, r1001068 w/ 8GB heap
>
> I plan to run for 1 week straight maxed out. I am worried about GC pauses,
> especially concurrent mode failures (does hbase/hadoop suffer these under
> extended load?). What should I be looking for in the gc log in terms of
> problem signs? The ParNews are quick but the CMS concurrent marks are taking
> as much as 4 mins with an average of 20-30 secs.
>
> Thanks.
>
>
>
> On Thu, Dec 30, 2010 at 12:00 PM, Stack <stack@duboce.net> wrote:
>
>> Oh, what versions are you using?
>> St.Ack
>>
>> On Thu, Dec 30, 2010 at 9:00 AM, Stack <stack@duboce.net> wrote:
>> > Keep going. Let it run longer.  Get the servers as loaded as you think
>> > they'll be in production.  Make sure the perf numbers are not because
>> > cluster is 'fresh'.
>> > St.Ack
>> >
>> > On Thu, Dec 30, 2010 at 5:51 AM, Wayne <wav100@gmail.com> wrote:
>> >> We finally got our cluster up and running and write performance looks
>> very
>> >> good. We are getting sustained 8-10k writes/sec/node on a 10 node
>> cluster
>> >> from Python through thrift. These are values written to 3 CFs so actual
>> >> hbase performance is 25-30k writes/sec/node. The nodes are currently
>> disk
>> >> i/o bound (40-50% utilization) but hopefully once we get lzop working
>> this
>> >> will go down. We have been running for 12 hours without a problem. We
>> hope
>> >> to get lzop going today and then load all through the long weekend.
>> >>
>> >> We plan to then test reads next week after we get some data in there.
>> Looks
>> >> good so far! Below are our settings in case there are some
>> >> suggestions/concerns.
>> >>
>> >> Thanks for everyone's help. It is pretty exciting to get performance
>> like
>> >> this from the start.
>> >>
>> >>
>> >> *Global*
>> >>
>> >> client.write.buffer = 10485760 (10MB = 5x default)
>> >>
>> >> optionalLogFlushInterval = 10000 (10 secs = 10x default)
>> >>
>> >> memstore.flush.size = 268435456 (256MB = 4x default)
>> >>
>> >> hregion.max.filesize = 1073741824 (1GB = 4x default)
>> >>
>> >> *Table*
>> >>
>> >> alter 'xxx', METHOD => 'table_att', DEFERRED_LOG_FLUSH => true
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> On Wed, Dec 29, 2010 at 12:55 AM, Stack <stack@duboce.net> wrote:
>> >>
>> >>> On Mon, Dec 27, 2010 at 11:47 AM, Wayne <wav100@gmail.com> wrote:
>> >>> > All data is written to 3 CFs. Basically 2 of the CFs are secondary
>> >>> indexes
>> >>> > (manually managed as normal CFs). It sounds like we should try
hard
>> to
>> >>> get
>> >>> > as much out of thrift as we can before going to a lower level.
>> >>>
>> >>> Yes.
>> >>>
>> >>> Writes need
>> >>> > to be "fast enough", but reads are more important in the end (and
>> are the
>> >>> > reason we are switching from a different solution). The numbers
you
>> >>> quoted
>> >>> > below sound like they are in the ballpark of what we are looking
to
>> do.
>> >>> >
>> >>>
>> >>> Even the tens per second that I threw in there to CMA?
>> >>>
>> >>> > Much of our data is cold, and we expect reads to be disk i/o based.
>> >>>
>> >>> OK.  FYI, we're not the best at this -- cache-miss cold reads -- what
>> >>> w/ a network hop in the way and currently we'll open a socket per
>> >>> access.
>> >>>
>> >>> > Given
>> >>> > this is 8GB heap a good place to start on the data nodes (24GB
ram)?
>> Is
>> >>> the
>> >>> > block cache managed on its own (being it won't blow up causing
OOM),
>> >>>
>> >>> It won't.  Its constrained.  Does our home-brewed sizeof.  Default,
>> >>> its 0.2 of total heap.  If you think cache will help, you could go up
>> >>> from there.  0.4 or 0.5 of heap.
>> >>>
>> >>> > and if
>> >>> > we do not use it (block cache) should we go even lower for the
heap
>> (we
>> >>> want
>> >>> > to avoid CMF and long GC pauses)?
>> >>>
>> >>> If you are going to be doing cache-miss most of the time and cold
>> >>> reads, then yes, you can do away with cache.
>> >>>
>> >>> In testing of 0.90.x I've been running w/ 1MB heaps with 1k regions
>> >>> but this is my trying to break stuff.
>> >>>
>> >>> > Are there any timeouts we need to tweak to
>> >>> > make the cluster more "accepting" of long GC pauses while under
>> sustained
>> >>> > load (7+ days of 10k/inserts/sec/node)?
>> >>> >
>> >>>
>> >>> If zookeeper client timesout, the regionserver will shut itself down.
>> >>> In 0.90.0RC2, the client sessionout is set high -- 3 minutes.  If you
>> >>> timeout that, then thats pretty extreme... something badly wrong I'd
>> >>> say.  Heres' a few notes on the config and others that you might want
>> >>> to twiddle (see previous section on required configs... make sure
>> >>> you've got those too):
>> >>>
>> >>>
>> http://people.apache.org/~stack/hbase-0.90.0-candidate-2/docs/important_configurations.html#recommended_configurations<http://people.apache.org/%7Estack/hbase-0.90.0-candidate-2/docs/important_configurations.html#recommended_configurations>
>> <
>> http://people.apache.org/%7Estack/hbase-0.90.0-candidate-2/docs/important_configurations.html#recommended_configurations
>> >
>> >>>
>> >>>
>> >>> > Does LZO compression speed up reads/writes where there is excess
CPU
>> to
>> >>> do
>> >>> > the compression? I assume it would lower disk i/o but increase
CPU a
>> lot.
>> >>> Is
>> >>> > data compressed on the initial write or only after compaction?
>> >>> >
>> >>>
>> >>> LZO is pretty frictionless -- i.e. little CPU cost -- and yes, usually
>> >>> helps speed things up (grab more in the one go).  What size are your
>> >>> records?  You might want to mess w/ hfile block sizes though the 64k
>> >>> default is usually good enough for all but very small cell sizes.
>> >>>
>> >>>
>> >>> > With the replication in the HDFS layer how are reads managed in
>> terms of
>> >>> > load balancing across region servers? Does HDFS know to spread
>> multiple
>> >>> > requests across the 3 region servers that contain the same data?
>> >>>
>> >>> You only read from one of the replicas, always the 'closest'.  If the
>> >>> DFSClient has trouble getting the first of the replicas, it moves on
>> >>> to the second, etc.
>> >>>
>> >>>
>> >>> > For example
>> >>> > with 10 data nodes if we have 50 concurrent readers with very
>> "random"
>> >>> key
>> >>> > requests we would expect to have 5 reads occurring on each data
node
>> at
>> >>> the
>> >>> > same time. We plan to have a thrift server on each data node, so
5
>> >>> > concurrent readers will be connected to each thrift server at any
>> given
>> >>> time
>> >>> > (50 in aggregate across 10 nodes). We want to be sure everything
is
>> >>> designed
>> >>> > to evenly spread this load to avoid any possible hot-spots.
>> >>> >
>> >>>
>> >>> This is different.  This is key design.  A thrift server will be doing
>> >>> some subset of the key space.  If the requests are evenly distributed
>> >>> over all of the key space, then you should be fine; all thrift servers
>> >>> will be evenly loaded.  If not, then there could be hot spots.
>> >>>
>> >>> We have a balancer that currently only counts regions per server, not
>> >>> regions per server plus hits per region so it could be the case that
a
>> >>> server by chance ends up carrying all of the hot regions.  HBase
>> >>> itself is not too smart dealing with this.  In 0.90.0, there is
>> >>> facility for manually moving regions -- i.e. closing in current
>> >>> location and moving the region off to another server w/ some outage
>> >>> while the move is happening (usually seconds) -- or you could split
>> >>> the hot region manually and then the daughters could be moved off to
>> >>> other servers... Primitive for now but should be better in next HBase
>> >>> versions.
>> >>>
>> >>> Have you been able to test w/ your data and your query pattern?
>> >>> That'll tell you way more than I ever could.
>> >>>
>> >>> Good luck,
>> >>> St.Ack
>> >>>
>> >>>
>> >>> >
>> >>> >
>> >>> > On Mon, Dec 27, 2010 at 1:49 PM, Stack <stack@duboce.net>
wrote:
>> >>> >
>> >>> >> On Fri, Dec 24, 2010 at 5:09 AM, Wayne <wav100@gmail.com>
wrote:
>> >>> >> > We are in the process of evaluating hbase in an effort
to switch
>> from
>> >>> a
>> >>> >> > different nosql solution. Performance is of course an
important
>> part
>> >>> of
>> >>> >> our
>> >>> >> > evaluation. We are a python shop and we are very worried
that we
>> can
>> >>> not
>> >>> >> get
>> >>> >> > any real performance out of hbase using thrift (and must
drop
>> down to
>> >>> >> java).
>> >>> >> > We are aware of the various lower level options for bulk
insert
>> or
>> >>> java
>> >>> >> > based inserts with turning off WAL etc. but none of these
are
>> >>> available
>> >>> >> to
>> >>> >> > us in python so are not part of our evaluation.
>> >>> >>
>> >>> >> I can understand python for continuous updates from your frontend
>> or
>> >>> >> whatever but you might consider hacking up a bit of java to
make us
>> of
>> >>> >> the bulk updater; you'll get upload rates orders of magnitude
>> beyond
>> >>> >> what you'd achieve going via the API via python (or java for
that
>> >>> >> matter).  You can also do incremental updates using the bulk
>> loader.
>> >>> >>
>> >>> >>
>> >>> >> We have a 10 node cluster
>> >>> >> > (24gb, 6 x 1TB, 16 core) that we setting up as data/region
nodes,
>> and
>> >>> we
>> >>> >> are
>> >>> >> > looking for suggestions on configuration as well as benchmarks
in
>> >>> terms
>> >>> >> of
>> >>> >> > expectations of performance. Below are some specific questions.
I
>> >>> realize
>> >>> >> > there are a million factors that help determine specific
>> performance
>> >>> >> > numbers, so any examples of performance from running clusters
>> would be
>> >>> >> great
>> >>> >> > as examples of what can be done.
>> >>> >>
>> >>> >> Yeah, you have been around the block obviously. Its hard to
give
>> out
>> >>> >> 'numbers' since so many different factors involved.
>> >>> >>
>> >>> >>
>> >>> >> Again thrift seems to be our "problem" so
>> >>> >> > non java based solutions are preferred (do any non java
based
>> shops
>> >>> run
>> >>> >> > large scale hbase clusters?). Our total production cluster
size
>> is
>> >>> >> estimated
>> >>> >> > to be 50TB.
>> >>> >> >
>> >>> >>
>> >>> >> There are some substantial shops running non-java; e.g. the
yfrog
>> >>> >> folks go via REST, the mozilla fellas are python over thrift,
>> >>> >> Stumbleupon is php over thrift.
>> >>> >>
>> >>> >> > Our data model is 3 CFs, one primary and 2 secondary indexes.
All
>> >>> writes
>> >>> >> go
>> >>> >> > to all 3 CFs and are grouped as a batch of row mutations
which
>> should
>> >>> >> avoid
>> >>> >> > row locking issues.
>> >>> >> >
>> >>> >>
>> >>> >> A write updates 3CFs and secondary indices?  Thats an expensive
Put
>> >>> >> relatively.  You have to run w/ 3CFs?  It facilitates fast
>> querying?
>> >>> >>
>> >>> >>
>> >>> >> > What heap size is recommended for master, and for region
servers
>> (24gb
>> >>> >> ram)?
>> >>> >>
>> >>> >> Master doesn't take much heap, at least not in the coming 0.90.0
>> HBase
>> >>> >> (Is that what you intend to run)?
>> >>> >>
>> >>> >> The more RAM you give the regionservers, the more cache your
>> cluster
>> >>> will
>> >>> >> have.
>> >>> >>
>> >>> >> Whats important to you read or write times?
>> >>> >>
>> >>> >>
>> >>> >> > What other settings can/should be tweaked in hbase to
optimize
>> >>> >> performance
>> >>> >> > (we have looked at the wiki page)?
>> >>> >>
>> >>> >> Thats a good place to start.  Take a look through this mailing
list
>> >>> >> for others (Its time for a trawl of mailing list and then
>> distilling
>> >>> >> the findings into a reedit of our perf page).
>> >>> >>
>> >>> >> > What is a good batch size for writes? We will start with
10k
>> >>> >> values/batch.
>> >>> >>
>> >>> >> Start small with defaults.  Make sure its all running smooth
first.
>> >>> >> Then rachet it up.
>> >>> >>
>> >>> >>
>> >>> >> > How many concurrent writers/readers can a single data
node handle
>> with
>> >>> >> > evenly distributed load? Are there settings specific to
this?
>> >>> >>
>> >>> >> How many clients you going to have writing HBase?
>> >>> >>
>> >>> >>
>> >>> >> > What is "very good" read/write latency for a single put/get
in
>> hbase
>> >>> >> using
>> >>> >> > thrift?
>> >>> >>
>> >>> >> "Very Good" would be < a few milliseconds.
>> >>> >>
>> >>> >>
>> >>> >> > What is "very good" read/write throughput per node in
hbase using
>> >>> thrift?
>> >>> >> >
>> >>> >>
>> >>> >> Thousands of ops per second per regionserver (Sorry, can't
be more
>> >>> >> specific than that).  If the Puts are multi-family + updates
on
>> >>> >> secondary indices, hundreds -- maybe even tens... I'm not sure
--
>> >>> >> rather than thousands.
>> >>> >>
>> >>> >> > We are looking to get performance numbers in the range
of 10k
>> >>> aggregate
>> >>> >> > inserts/sec/node and read latency < 30ms/read with
3-4 concurrent
>> >>> >> > readers/node. Can our expectations be met with hbase through
>> thrift?
>> >>> Can
>> >>> >> > they be met with hbase through java?
>> >>> >> >
>> >>> >>
>> >>> >>
>> >>> >> I wouldn't fixate on the thrift hop.  At SU we can do thousands
of
>> ops
>> >>> >> a second per node np from PHP frontend over thrift.
>> >>> >>
>> >>> >> 10k inserts a second per node into single CF might be doable.
 If
>> into
>> >>> >> 3CFs, then you need to recalibrate your expectations (I'd say).
>> >>> >>
>> >>> >> > Thanks in advance for any help, examples, or recommendations
that
>> you
>> >>> can
>> >>> >> > provide!
>> >>> >> >
>> >>> >> Sorry, the above is light on recommendations (for reasons cited
by
>> >>> >> Ryan above -- smile).
>> >>> >> St.Ack
>> >>> >>
>> >>> >
>> >>>
>> >>
>> >
>>
>
>

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