Hi,
How is it possible to change config on an ansible deployed cluster due to the missing ceph-deploy.
Thank you
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Hi everyone,
I try to configure a simple rados gateway with three nodes and I add a virtual IP address for the load balancer. I make calls to the gateway using S3 and SWIFT.
In the case of S3 everything works, I can make requests directly on the node where there is a gateway or use the virtual address and it responds well.
In the case of SWIFT, I can query the nodes and it's fine, but when I use the virtual IP address it doesn't respond. The call sequence is the same for the nodes and the virtual IP address.
Do you have any leads why it doesn't work?
Version Info
ceph version : 15.2.4 (7447c15c6ff58d7fce91843b705a268a1917325c) octopus (stable)
OS : CentOS 8
Host file (ceph-ansible)
...
[rgws]
node-01
node-02
node-03
[rgwloadbalancers]
node-01
node-02
node-03
...
rgwloadbalancers.yml (ceph-ansible)
###########
# GENERAL #
###########
haproxy_frontend_port: 80
haproxy_frontend_ssl_port: 443
haproxy_frontend_ssl_certificate:
haproxy_ssl_dh_param: 4096
haproxy_ssl_ciphers:
- EECDH+AESGCM
- EDH+AESGCM
haproxy_ssl_options:
- no-sslv3
- no-tlsv10
- no-tlsv11
- no-tls-tickets
#
virtual_ips:
- XXX.XXX.XXX.189
#
virtual_ip_netmask: 24
virtual_ip_interface: eno5
/etc/ceph/ceph.conf (node1 example)
[client.node1.rgw0]
host = node1
keyring = /var/lib/ceph/radosgw/ceph-rgw. node1.rgw0/keyring
log file = /var/log/ceph/ceph-rgw- node1.rgw0.log
rgw frontends = beast endpoint=xxx.xxx.xxx.190:8080
rgw thread pool size = 512
[client.rgw.node2.rgw0]
host = node2
keyring = /var/lib/ceph/radosgw/ceph-rgw.node2.rgw0/keyring
log file = /var/log/ceph/ceph-rgw- node2.rgw0.log
rgw frontends = beast endpoint=xxx.xxx.xxx.191:8080
rgw thread pool size = 512
[client.rgw.node3.rgw0]
host = node3
keyring = /var/lib/ceph/radosgw/ceph-rgw.node3.rgw0/keyring
log file = /var/log/ceph/ceph-rgw-node3.rgw0.log
rgw frontends = beast endpoint=xxx.xxx.xxx.192:8080
rgw thread pool size = 512
Thank you !
Sylvain
Hi,
Earlier I've synced the repos the following way:
rsync -avSH rsync://hk.ceph.com/rpm-luminous/el7 .
Today I've tried to sync from us-west and hk, but I got an error:
rsync -avSH rsync://us-west.ceph.com/rpm-nautilus/el7/noarch .
@ERROR: Unknown module 'rpm-nautilus'
rsync error: error starting client-server protocol (code 5) at main.c(1503) [receiver=3.0.6]
rsync -avSH rsync://us-west.ceph.com/rpm-nautilus/el7/noarch .
@ERROR: Unknown module 'rpm-nautilus'
rsync error: error starting client-server protocol (code 5) at main.c(1503) [receiver=3.0.6]
rsync -avSH rsync://download.ceph.com/rpm-nautilus/el7/noarch .
@ERROR: Unknown module 'rpm-nautilus'
rsync error: error starting client-server protocol (code 5) at main.c(1503) [receiver=3.0.6]
Also the HK repos doesn't have the newest nautilus, just 14.2.9.
Any issue?
Thank you
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Hi,
We are using 10 active MDS's with v12.2.12 -- so it is "stable" but we
have several measures and lots of experience to make it like that.
If I were starting a new cluster now, I would use the latest nautilus
or octopus and test the hell out of it before going into prod. Don't
start with mimic now, it's end-of-line.
First, are you really sure you need multi-active MDS? We only use it
where the metadata workload clearly exceeds the abilities of a single
active MDS. Evidence of this would be a high, flat-lined CPU usage on
the active mds, or better would be to track the "hcr" or
"handle_client_request" metric with your monitoring or locally on an
MDS with "ceph daemonperf mds.`hostname -s`". A single MDS can
normally achieve a few thousand hcr/second at best.
Otherwise, here are some relatively advanced things to try to validate
the setup... understanding and succeeding in these things should help
with your nerves:
- Start the cluster, run some workloads, try increasing and decreasing
max_mds on the fly and make sure this is working well
- is the metadata balancing working well with your common workloads?
run your test workloads for hours or days and check that the RSS of
each MDS is not growing unexpectedly
- does mds balancing make sense for your workload, or are there
some places where pinning to subdirs to a rank is worthwhile?
- with fully active mds, fully loaded metadata caches, test the
failover to standby several times. Try "nice" failovers (e.g.
systemctl stop ceph-mds.target on an active) as well as "not-so-nice"
failovers (e.g. killall -9 ceph-mds)
- Try the cephfs scrub features. Maybe even intentionally corrupt a
file or direntry object then check if cephfs scrub behaves as expected
Hope that helps!
Dan
On Thu, Jul 16, 2020 at 3:01 PM huxiaoyu(a)horebdata.cn
<huxiaoyu(a)horebdata.cn> wrote:
>
> Dear Cepher,
>
> I am planning a cephfs cluster with ca. 100 OSD nodes, each of which has 12 disks, and 2 NVMe (for db wal and cephfs metadata pool). Fpr performance and scalability reasons, i would like to try multi MDS working ative-active. From what i learned in the past, i am not sure about the following questions.
>
> 1 Which Ceph version should i run? I had a good experience with Luminous 12.2.13, and not familiar yet with Mimic and Nautilus. Is Lumious 12.2.13 stable enouth to run multiple active-active MDS servers for CephFS?
>
> 2 If i had to go Mimic or Nautilus for CephFS, which one is perferable?
>
> 3 I did has some experience with Ceph RBD, but not CephFS, So my question is, what should i pay attention to whening running CephFS? I am somehow nervous......
>
> best regards,
>
> Samuel
>
>
>
>
>
> huxiaoyu(a)horebdata.cn
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Hi All,
We’re using CEPH cluster (Nautulis 14.2.10) as a S3 object storage layer for Spark 3 with Yarn running in distributed environment. The issue we see however is slow performance when running even simple spark query on data stored on large number of objects, for example 50.000 objects. We’re aware of slow object listing in S3, but should that really kill the performance while using spark for reading\analyzing\writing the data on S3? Running the same query on the same dataset content but stored in 100 files is multiple times faster.
Bucket and bucket index we use for spark are stored on OSDs with SSDs (we’re having 150 of them) , we’re using 12 RGW instances, each limited to 32 concurrent connection by custom app to prevent RGW queue blowing up). When running spark queries, RGW queues rises – depending on number of spark executors – to around 30 per instance providing the number of executors per RGW instance is also in similar. We don’t see any other bottlenecks on infra side than RGW queues. We applied various tuning options for CEPH regarding RGW\OSD\Bluestore performance (ie: objecter_inflight_op_bytes, objecter_inflight_ops, rgw_bucket_index_max_aio, rgw_cache_lru_size ) but the spark works still really slow in above mentioned scenario.
The other problem we observed is that when using 4 RGW instead of 12 we see performance degradation only in about 40-50%. We would expect that RGW scaling will behave more efficiently.
Does anyone using CEPH in similar way as a storage layer for Spark? Do you observer similar behavior and maybe have some workarounds\solutions for slowness when working with high number of objects?