mirror of https://github.com/ceph/ceph
211 lines
6.4 KiB
ReStructuredText
211 lines
6.4 KiB
ReStructuredText
============================
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PG (Placement Group) notes
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============================
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Miscellaneous copy-pastes from emails, when this gets cleaned up it
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should move out of /dev.
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Overview
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========
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PG = "placement group". When placing data in the cluster, objects are
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mapped into PGs, and those PGs are mapped onto OSDs. We use the
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indirection so that we can group objects, which reduces the amount of
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per-object metadata we need to keep track of and processes we need to
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run (it would be prohibitively expensive to track eg the placement
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history on a per-object basis). Increasing the number of PGs can
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reduce the variance in per-OSD load across your cluster, but each PG
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requires a bit more CPU and memory on the OSDs that are storing it. We
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try and ballpark it at 100 PGs/OSD, although it can vary widely
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without ill effects depending on your cluster. You hit a bug in how we
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calculate the initial PG number from a cluster description.
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There are a couple of different categories of PGs; the 6 that exist
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(in the original emailer's ``ceph -s`` output) are "local" PGs which
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are tied to a specific OSD. However, those aren't actually used in a
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standard Ceph configuration.
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Mapping algorithm (simplified)
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==============================
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| > How does the Object->PG mapping look like, do you map more than one object on
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| > one PG, or do you sometimes map an object to more than one PG? How about the
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| > mapping of PGs to OSDs, does one PG belong to exactly one OSD?
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| >
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| > Does one PG represent a fixed amount of storage space?
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Many objects map to one PG.
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Each object maps to exactly one PG.
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One PG maps to a single list of OSDs, where the first one in the list
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is the primary and the rest are replicas.
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Many PGs can map to one OSD.
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A PG represents nothing but a grouping of objects; you configure the
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number of PGs you want, number of OSDs * 100 is a good starting point
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, and all of your stored objects are pseudo-randomly evenly distributed
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to the PGs. So a PG explicitly does NOT represent a fixed amount of
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storage; it represents 1/pg_num'th of the storage you happen to have
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on your OSDs.
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Ignoring the finer points of CRUSH and custom placement, it goes
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something like this in pseudocode::
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locator = object_name
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obj_hash = hash(locator)
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pg = obj_hash % num_pg
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OSDs_for_pg = crush(pg) # returns a list of OSDs
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primary = osds_for_pg[0]
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replicas = osds_for_pg[1:]
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If you want to understand the crush() part in the above, imagine a
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perfectly spherical datacenter in a vacuum ;) that is, if all OSDs
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have weight 1.0, and there is no topology to the data center (all OSDs
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are on the top level), and you use defaults, etc, it simplifies to
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consistent hashing; you can think of it as::
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def crush(pg):
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all_osds = ['osd.0', 'osd.1', 'osd.2', ...]
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result = []
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# size is the number of copies; primary+replicas
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while len(result) < size:
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r = hash(pg)
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chosen = all_osds[ r % len(all_osds) ]
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if chosen in result:
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# OSD can be picked only once
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continue
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result.append(chosen)
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return result
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User-visible PG States
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======================
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.. todo:: diagram of states and how they can overlap
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*creating*
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the PG is still being created
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*active*
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requests to the PG will be processed
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*clean*
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all objects in the PG are replicated the correct number of times
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*down*
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a replica with necessary data is down, so the pg is offline
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*recovery_unfound*
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recovery could not finish because object(s) are unfound.
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*backfill_unfound*
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backfill could not finish because object(s) are unfound.
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*premerge*
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the PG is in a quiesced-IO state due to an impending PG merge. That
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happens when pg_num_pending < pg_num, and applies to the PGs with
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pg_num_pending <= ps < pg_num as well as the corresponding peer PG
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that it is merging with.
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*scrubbing*
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the PG is being checked for inconsistencies
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*degraded*
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some objects in the PG are not replicated enough times yet
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*inconsistent*
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replicas of the PG are not consistent (e.g. objects are
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the wrong size, objects are missing from one replica *after* recovery
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finished, etc.)
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*peering*
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the PG is undergoing the :doc:`/dev/peering` process
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*repair*
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the PG is being checked and any inconsistencies found will be repaired (if possible)
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*recovering*
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objects are being migrated/synchronized with replicas
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*backfill_wait*
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the PG is waiting in line to start backfill
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*incomplete*
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a pg is missing a necessary period of history from its
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log. If you see this state, report a bug, and try to start any
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failed OSDs that may contain the needed information.
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*stale*
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the PG is in an unknown state - the monitors have not received
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an update for it since the PG mapping changed.
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*remapped*
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the PG is temporarily mapped to a different set of OSDs from what
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CRUSH specified
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*deep*
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In conjunction with *scrubbing* the scrub is a deep scrub
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*backfilling*
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a special case of recovery, in which the entire contents of
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the PG are scanned and synchronized, instead of inferring what
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needs to be transferred from the PG logs of recent operations
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*backfill_toofull*
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backfill reservation rejected, OSD too full
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*recovery_wait*
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the PG is waiting for the local/remote recovery reservations
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*undersized*
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the PG can't select enough OSDs given its size
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*activating*
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the PG is peered but not yet active
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*peered*
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the PG peered but can't go active
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*snaptrim*
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the PG is trimming snaps
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*snaptrim_wait*
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the PG is queued to trim snaps
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*recovery_toofull*
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recovery reservation rejected, OSD too full
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*snaptrim_error*
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the PG could not complete snap trimming due to errors
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*forced_recovery*
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the PG has been marked for highest priority recovery
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*forced_backfill*
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the PG has been marked for highest priority backfill
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*failed_repair*
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an attempt to repair the PG has failed. Manual intervention is required.
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OMAP STATISTICS
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===============
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Omap statistics are gathered during deep scrub and displayed in the output of
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the following commands::
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ceph pg dump
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ceph pg dump all
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ceph pg dump summary
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ceph pg dump pgs
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ceph pg dump pools
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ceph pg ls
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As these statistics are not updated continuously they may be quite inaccurate in
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an environment where deep scrubs are run infrequently and/or there is a lot of
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omap activity. As such they should not be relied on for exact accuracy but
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rather used as a guide. Running a deep scrub and checking these statistics
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immediately afterwards should give a good indication of current omap usage.
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