placement-group.rst

============================
 PG (Placement Group) notes
============================

Miscellaneous copy-pastes from emails, when this gets cleaned up it
should move out of /dev.

Overview
========

PG = "placement group". When placing data in the cluster, objects are
mapped into PGs, and those PGs are mapped onto OSDs. We use the
indirection so that we can group objects, which reduces the amount of
per-object metadata we need to keep track of and processes we need to
run (it would be prohibitively expensive to track eg the placement
history on a per-object basis). Increasing the number of PGs can
reduce the variance in per-OSD load across your cluster, but each PG
requires a bit more CPU and memory on the OSDs that are storing it. We
try and ballpark it at 100 PGs/OSD, although it can vary widely
without ill effects depending on your cluster. You hit a bug in how we
calculate the initial PG number from a cluster description.

There are a couple of different categories of PGs; the 6 that exist
(in the original emailer's ``ceph -s`` output) are "local" PGs which
are tied to a specific OSD. However, those aren't actually used in a
standard Ceph configuration.


Mapping algorithm (simplified)
==============================

| > How does the Object->PG mapping look like, do you map more than one object on
| > one PG, or do you sometimes map an object to more than one PG? How about the
| > mapping of PGs to OSDs, does one PG belong to exactly one OSD?
| >
| > Does one PG represent a fixed amount of storage space?

Many objects map to one PG.

Each object maps to exactly one PG.

One PG maps to a single list of OSDs, where the first one in the list
is the primary and the rest are replicas.

Many PGs can map to one OSD.

A PG represents nothing but a grouping of objects; you configure the
number of PGs you want, number of OSDs * 100 is a good starting point
, and all of your stored objects are pseudo-randomly evenly distributed
to the PGs. So a PG explicitly does NOT represent a fixed amount of
storage; it represents 1/pg_num'th of the storage you happen to have
on your OSDs.

Ignoring the finer points of CRUSH and custom placement, it goes
something like this in pseudocode::

	locator = object_name
	obj_hash = hash(locator)
	pg = obj_hash % num_pg
	OSDs_for_pg = crush(pg)  # returns a list of OSDs
	primary = osds_for_pg[0]
	replicas = osds_for_pg[1:]

If you want to understand the crush() part in the above, imagine a
perfectly spherical datacenter in a vacuum ;) that is, if all OSDs
have weight 1.0, and there is no topology to the data center (all OSDs
are on the top level), and you use defaults, etc, it simplifies to
consistent hashing; you can think of it as::

	def crush(pg):
	   all_osds = ['osd.0', 'osd.1', 'osd.2', ...]
	   result = []
	   # size is the number of copies; primary+replicas
	   while len(result) < size:
	       r = hash(pg)
	       chosen = all_osds[ r % len(all_osds) ]
	       if chosen in result:
	           # OSD can be picked only once
	           continue
	       result.append(chosen)
	   return result

User-visible PG States
======================

.. todo:: diagram of states and how they can overlap

*creating*
  the PG is still being created

*active*
  requests to the PG will be processed

*clean*
  all objects in the PG are replicated the correct number of times

*down*
  a replica with necessary data is down, so the pg is offline

*recovery_unfound*
  recovery could not finish because object(s) are unfound.

*backfill_unfound*
  backfill could not finish because object(s) are unfound.

*premerge*
  the PG is in a quiesced-IO state due to an impending PG merge.  That
  happens when pg_num_pending < pg_num, and applies to the PGs with
  pg_num_pending <= ps < pg_num as well as the corresponding peer PG
  that it is merging with.

*scrubbing*
  the PG is being checked for inconsistencies

*degraded*
  some objects in the PG are not replicated enough times yet

*inconsistent*
  replicas of the PG are not consistent (e.g. objects are
  the wrong size, objects are missing from one replica *after* recovery
  finished, etc.)

*peering*
  the PG is undergoing the :doc:`/dev/peering` process

*repair*
  the PG is being checked and any inconsistencies found will be repaired (if possible)

*recovering*
  objects are being migrated/synchronized with replicas

*backfill_wait*
  the PG is waiting in line to start backfill

*incomplete*
  a pg is missing a necessary period of history from its
  log.  If you see this state, report a bug, and try to start any
  failed OSDs that may contain the needed information.

*stale*
  the PG is in an unknown state - the monitors have not received
  an update for it since the PG mapping changed.

*remapped*
  the PG is temporarily mapped to a different set of OSDs from what
  CRUSH specified

*deep*
  In conjunction with *scrubbing* the scrub is a deep scrub

*backfilling*
  a special case of recovery, in which the entire contents of
  the PG are scanned and synchronized, instead of inferring what
  needs to be transferred from the PG logs of recent operations

*backfill_toofull*
  backfill reservation rejected, OSD too full

*recovery_wait*
  the PG is waiting for the local/remote recovery reservations

*undersized*
  the PG can't select enough OSDs given its size

*activating*
  the PG is peered but not yet active

*peered*
  the PG peered but can't go active

*snaptrim*
  the PG is trimming snaps

*snaptrim_wait*
  the PG is queued to trim snaps

*recovery_toofull*
  recovery reservation rejected, OSD too full

*snaptrim_error*
  the PG could not complete snap trimming due to errors

*forced_recovery*
  the PG has been marked for highest priority recovery

*forced_backfill*
  the PG has been marked for highest priority backfill

*failed_repair*
  an attempt to repair the PG has failed.  Manual intervention is required.


OMAP STATISTICS
===============

Omap statistics are gathered during deep scrub and displayed in the output of
the following commands::

  ceph pg dump
  ceph pg dump all
  ceph pg dump summary
  ceph pg dump pgs
  ceph pg dump pools
  ceph pg ls

As these statistics are not updated continuously they may be quite inaccurate in
an environment where deep scrubs are run infrequently and/or there is a lot of
omap activity. As such they should not be relied on for exact accuracy but
rather used as a guide. Running a deep scrub and checking these statistics
immediately afterwards should give a good indication of current omap usage.