osd自杀问题跟踪

1. 说明

故障现象描述：

Flapping OSD's when RGW buckets have millions of objects
● Possible causes
○ The first issue here is when RGW buckets have millions of objects their
bucket index shard RADOS objects become very large with high
number OMAP keys stored in leveldb. Then operations like deep-scrub,
bucket index listing etc takes a lot of time to complete and this triggers
OSD's to flap. If sharding is not used this issue become worse because
then only one RADOS index objects will be holding all the OMAP keys.

RGW的index数据以omap形式存储在OSD所在节点的leveldb中，当单个bucket存储的Object数量高达百万数量级的时候，deep-scrub和bucket list一类的操作将极大的消耗磁盘资源，导致对应OSD出现异常，如果不对bucket的index进行shard切片操作(shard切片实现了将单个bucket index的LevelDB实例水平切分到多个OSD上)，数据量大了以后很容易出事。

○ The second issue is when you have good amount of DELETEs it causes
loads of stale data in OMAP and this triggers leveldb compaction all the
time which is single threaded and non optimal with this kind of workload
and causes osd_op_threads to suicide because it is always compacting
hence OSD’s starts flapping.

RGW在处理大量DELETE请求的时候，会导致底层LevelDB频繁进行数据库compaction(数据压缩，对磁盘性能损耗很大)操作，而且刚好整个compaction在LevelDB中又是单线程处理，很容易到达osdopthreads超时上限而导致OSD自杀。

● Possible causes contd ...
○ OMAP backend is leveldb in jewel and older clusters. Any luminous
clusters which were upgraded from older releases have leveldb as
OMAP backend.

jewel以及之前的版本的OMAP都是以LevelDB作为存储引擎，如果是从旧版本升级到最新的luminous，那么底层OMAP仍然是LevelDB。

○ All new luminous clusters have default OMAP backend as rocksdb
which is great because rocksdb has multithreaded compaction and in
Ceph we use 8 compaction thread by default and many other enhanced
features as compare to leveldb.

最新版本的Luminous开始，OMAP底层的存储引擎换成了rocksDB，rocksDB采用多线程方式进行compaction(默认8个），所以rocksdb在compaction效率上要比LevelDB强很多。

2. 根因跟踪

当bucket index所在的OSD omap过大的时候，一旦出现异常导致OSD进程崩溃，这个时候就需要进行现场"救火"，用最快的速度恢复OSD服务。

先确定对应OSD的OMAP大小，这个过大会导致OSD启动的时候消耗大量时间和资源去加载levelDB数据，导致OSD无法启动（超时自杀）。

特别是这一类OSD启动需要占用非常大的内存消耗，一定要注意预留好内存。（物理内存40G左右，不行用swap顶上）

3. 修复方式

3.1 临时解决方案

关闭集群scrub, deep-scrub提升集群稳定性

$ ceph osd set noscrub
$ ceph osd set nodeep-scrub

调高timeout参数，减少OSD自杀的概率

osd_op_thread_timeout = 90 #default is 15
osd_op_thread_suicide_timeout = 2000 #default is 150
If filestore op threads are hitting timeout
filestore_op_thread_timeout = 180 #default is 60
filestore_op_thread_suicide_timeout = 2000 #default is 180
Same can be done for recovery thread also.
osd_recovery_thread_timeout = 120 #default is 30
osd_recovery_thread_suicide_timeout = 2000 #default is 300

手工压缩LevelDB OMAP 在可以停OSD的情况下，可以对OSD进行compact操作，推荐在ceph 0.94.6以上版本，低于这个版本有bug。 https://github.com/ceph/ceph/pull/7645/files

○ The third temporary step could be taken if OSD's have very large OMAP
directories you can verify it with command: du -sh /var/lib/ceph/osd/ceph-$id/current/omap, then do manual leveldb compaction for OSD's.
■ ceph tell osd.$id compact or
■ ceph daemon osd.$id compact or
■ Add leveldb_compact_on_mount = true in [osd.$id] or [osd] section
and restart the OSD.
■ This makes sure that it compacts the leveldb and then bring the
OSD back up/in which really helps.

#开启noout操作
$ ceph osd set noout
 
#停OSD服务
$ systemctl stop ceph-osd@<osd-id>
 
#在ceph.conf中对应的[osd.id]加上下面配置
leveldb_compact_on_mount = true
 
#启动osd服务
$ systemctl start ceph-osd@<osd-id>
 
 
#使用ceph -s命令观察结果，最好同时使用tailf命令去观察对应的OSD日志.等所有pg处于active+clean之后再继续下面的操作
$ ceph -s
#确认compact完成以后的omap大小:
du -sh /var/lib/ceph/osd/ceph-$id/current/omap
 
#删除osd中临时添加的leveldb_compact_on_mount配置
 
#取消noout操作(视情况而定，建议线上还是保留noout):
ceph osd unset noout

3.2 永久方案

3.2.1 对bucket做reshard操作

对bucket做reshard操作，可以实现调整bucket的shard数量，实现index数据的重新分布。仅支持ceph 0.94.10以上版本，需要停bucket读写，有数据丢失风险，慎重使用。

另外最新的Luminous可以实现动态的reshard(根据单个bucket当前的Object数量，实时动态调整shard数量），其实这里面也有很大的坑，动态reshard对用户来讲不够透明，而且reshard过程中会造成bucket的读写发生一定时间的阻塞，所以从我的个人经验来看，这个功能最好关闭，能够做到在一开始就设计好单个bucket的shard数量，一步到位是最好。至于如何做好一步到位的设计可以看公众号之前的文章。(《RGW Bucket Shard设计与优化》系列)

#注意下面的操作一定要确保对应的bucket相关的操作都已经全部停止，之后使用下面命令备份bucket的index
 
$ radosgw-admin bi list --bucket=<bucket_name> > <bucket_name>.list.backup
 
#通过下面的命令恢复数据
radosgw-admin bi put --bucket=<bucket_name> < <bucket_name>.list.backup
 
#查看bucket的index id
$ radosgw-admin bucket stats --bucket=bucket-maillist
{
    "bucket": "bucket-maillist",
    "pool": "default.rgw.buckets.data",
    "index_pool": "default.rgw.buckets.index",
    "id": "0a6967a5-2c76-427a-99c6-8a788ca25034.54133.1", #注意这个id
    "marker": "0a6967a5-2c76-427a-99c6-8a788ca25034.54133.1",
    "owner": "user",
    "ver": "0#1,1#1",
    "master_ver": "0#0,1#0",
    "mtime": "2017-08-23 13:42:59.007081",
    "max_marker": "0#,1#",
    "usage": {},
    "bucket_quota": {
        "enabled": false,
        "max_size_kb": -1,
        "max_objects": -1
    }
}
 
 
#Reshard对应bucket的index操作如下:
#使用命令将"bucket-maillist"的shard调整为4，注意命令会输出osd和new两个bucket的instance id
 
$ radosgw-admin bucket reshard --bucket="bucket-maillist" --num-shards=4
*** NOTICE: operation will not remove old bucket index objects ***
***         these will need to be removed manually             ***
old bucket instance id: 0a6967a5-2c76-427a-99c6-8a788ca25034.54133.1
new bucket instance id: 0a6967a5-2c76-427a-99c6-8a788ca25034.54147.1
total entries: 3
 
 
#之后使用下面的命令删除旧的instance id
 
$ radosgw-admin bi purge --bucket="bucket-maillist" --bucket-id=0a6967a5-2c76-427a-99c6-8a788ca25034.54133.1
 
#查看最终结果
$ radosgw-admin bucket stats --bucket=bucket-maillist
{
    "bucket": "bucket-maillist",
    "pool": "default.rgw.buckets.data",
    "index_pool": "default.rgw.buckets.index",
    "id": "0a6967a5-2c76-427a-99c6-8a788ca25034.54147.1", #id已经变更
    "marker": "0a6967a5-2c76-427a-99c6-8a788ca25034.54133.1",
    "owner": "user",
    "ver": "0#2,1#1,2#1,3#2",
    "master_ver": "0#0,1#0,2#0,3#0",
    "mtime": "2017-08-23 14:02:19.961205",
    "max_marker": "0#,1#,2#,3#",
    "usage": {
        "rgw.main": {
            "size_kb": 50,
            "size_kb_actual": 60,
            "num_objects": 3
        }
    },
    "bucket_quota": {
        "enabled": false,
        "max_size_kb": -1,
        "max_objects": -1
    }
}

4. 总结

另外可以做到的就是单独使用SSD或者NVME作为index pool的OSD，但是Leveldb从设计上对SSD的支持比较有限，最好能够切换到rocksdb上面去，同时在jewel之前的版本还不支持切换omap引擎到rocksdb，除非打上下面的补丁 https://github.com/ceph/ceph/pull/18010
bucket 的index shard数量提前做好规划，这个可以参考本公众号之前的几篇bucket index shard相关内容。
jewel之前的版本LevelDB如果硬件条件允许可以考虑切换到rocksdb同时考虑在业务高峰期关闭deep-scrub。如果是新上的集群用L版本的ceph，放弃Filestore，同时使用Bluestore作为默认的存储引擎。

总而言之bucket index的性能需要有SSD加持，大规模集群一定要做好初期设计，等到数据量大了再做调整，很难做到亡羊补牢！

posted @ 2020-11-18 16:27 Oops!# 阅读(636) 评论(0) 编辑收藏举报

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