On Sun, Oct 30, 2011 at 6:53 PM, Chris Goffinet <c...@chrisgoffinet.com> wrote: > > > On Sun, Oct 30, 2011 at 3:34 PM, Sorin Julean <sorin.jul...@gmail.com> > wrote: >> >> Hey Chris, >> >> Thanks for sharing all the info. >> I have few questions: >> 1. What are you doing with so much memory :) ? How much of it do you >> allocate for heap ? > > max heap is 12GB. we use the rest for cache. we run memcache on each node > and allocate the remaining to that.
Is this using off heap cache of Cassandra? > >> >> 2. What your network speed ? Do you use trunks ? Do you have a dedicated >> VLAN for gossip/store traffic ? >> > No dedicated VLAN for gossip. We run at 2Gb/s. We have bonded NIC's. > >> >> Cheers, >> Sorin >> >> >> On Sun, Oct 30, 2011 at 5:00 AM, Chris Goffinet <c...@chrisgoffinet.com> >> wrote: >>> >>> RE: RAID0 Recommendation >>> Cassandra supports multiple data file directories. Because we do >>> compactions, it's just much easier to deal with (1) data file directory that >>> is stripped across all disks as 1 volume (RAID0). There are other ways to >>> accomplish this though. At Twitter we use software raid (RAID0 & RAID10). >>> We own the physical hardware and have found that even with hardware raid, >>> software raid in Linux actually faster. The reason being is: >>> http://en.wikipedia.org/wiki/Non-standard_RAID_levels#Linux_MD_RAID_10 >>> We have found that using far-copies is much faster over near-copies. We >>> set the i/o scheduler to noop at the moment. We might move back to CFQ with >>> more tuning in the future. >>> We use RAID10 for cases where we need better disk performance if we are >>> hitting the disk often, sacrificing storage. We initially thought RAID0 >>> should be faster over RAID10 until we found out about the near vs far >>> layouts. >>> RE: Hardware >>> This is going to depend on how well your automated infrastructure is, but >>> we chose the path of finding the cheapest servers we could get from >>> Dell/HP/etc. 8/12 cores, 72gb memory per node, 2TB/3TB, 2.5". >>> We are in the process of making changes to our servers, I'll report back >>> in when we have more details to share. >>> I wouldn't recommend 75 CFs. It could work but just seems too complex. >>> Another recommendation for clusters, always go big. You will be thankful >>> in the future for this. Even if you can do this on 3-6 nodes, go much larger >>> for future expansion. If you own your hardware and racks, I recommend making >>> sure to size out the rack diversity and # of nodes per rack. Also take into >>> account the replication factor when doing this. RF=3, should be min of 3 >>> racks, and # of nodes per rack should be divisible by the replication >>> factor. This has worked out pretty well for us. Our biggest problems today >>> are adding 100s of nodes to existing clusters at once. I'm not sure how many >>> other companies are having this problem, but it's certainly on our radar to >>> improve, if you get to that point :) >>> >>> On Tue, Oct 25, 2011 at 5:23 AM, Alexandru Sicoe <adsi...@gmail.com> >>> wrote: >>>> >>>> Hi everyone, >>>> >>>> I am currently in the process of writing a hardware proposal for a >>>> Cassandra cluster for storing a lot of monitoring time series data. My >>>> workload is write intensive and my data set is extremely varied in types of >>>> variables and insertion rate for these variables (I will have to handle an >>>> order of 2 million variables coming in, each at very different rates - the >>>> majority of them will come at very low rates but there are many that will >>>> come at higher rates constant rates and a few coming in with huge spikes in >>>> rates). These variables correspond to all basic C++ types and arrays of >>>> these types. The highest insertion rates are received for basic types, out >>>> of which U32 variables seem to be the most prevalent (e.g. I recorded 2 >>>> million U32 vars were inserted in 8 mins of operation while 600.000 doubles >>>> and 170.000 strings were inserted during the same time. Note this >>>> measurement was only for a subset of the total data currently taken in). >>>> >>>> At the moment I am partitioning the data in Cassandra in 75 CFs (each CF >>>> corresponds to a logical partitioning of the set of variables mentioned >>>> before - but this partitioning is not related with the amount of data or >>>> rates...it is somewhat random). These 75 CFs account for ~1 million of the >>>> variables I need to store. I have a 3 node Cassandra 0.8.5 cluster (each >>>> node is a 4 real core with 4 GB RAM and split commit log directory and data >>>> file directory between two RAID arrays with HDDs). I can handle the load in >>>> this configuration but the average CPU usage of the Cassandra nodes is >>>> slightly above 50%. As I will need to add 12 more CFs (corresponding to >>>> another ~ 1 million variables) plus potentially other data later, it is >>>> clear that I need better hardware (also for the retrieval part). >>>> >>>> I am looking at Dell servers (Power Edge etc) >>>> >>>> Questions: >>>> >>>> 1. Is anyone using Dell HW for their Cassandra clusters? How do they >>>> behave? Anybody care to share their configurations or tips for buying, what >>>> to avoid etc? >>>> >>>> 2. Obviously I am going to keep to the advice on the >>>> http://wiki.apache.org/cassandra/CassandraHardware and split the commmitlog >>>> and data on separate disks. I was going to use SSD for commitlog but then >>>> did some more research and found out that it doesn't make sense to use SSDs >>>> for sequential appends because it won't have a performance advantage with >>>> respect to rotational media. So I am going to use rotational disk for the >>>> commit log and an SSD for data. Does this make sense? >>>> >>>> 3. What's the best way to find out how big my commitlog disk and my data >>>> disk has to be? The Cassandra hardware page says the Commitlog disk >>>> shouldn't be big but still I need to choose a size! >>>> >>>> 4. I also noticed RAID 0 configuration is recommended for the data file >>>> directory. Can anyone explain why? >>>> >>>> Sorry for the huge email..... >>>> >>>> Cheers, >>>> Alex >>> >> > >
