Oh, what versions are you using? St.Ack
On Thu, Dec 30, 2010 at 9:00 AM, Stack <[email protected]> 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 <[email protected]> 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 <[email protected]> wrote: >> >>> On Mon, Dec 27, 2010 at 11:47 AM, Wayne <[email protected]> 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> >>> >>> >>> > 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 <[email protected]> wrote: >>> > >>> >> On Fri, Dec 24, 2010 at 5:09 AM, Wayne <[email protected]> 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 >>> >> >>> > >>> >> >
