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Ring doc cleanups

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Tim Burke 2017-05-16 14:33:31 -07:00
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doc/source/overview_ring.rst
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@ -5,38 +5,40 @@ The Rings
The rings determine where data should reside in the cluster. There is a
separate ring for account databases, container databases, and individual
object storage policies but each ring works in the same way. These rings are
externally managed, in that the server processes themselves do not modify the
rings, they are instead given new rings modified by other tools.
externally managed. The server processes themselves do not modify the
rings; they are instead given new rings modified by other tools.
The ring uses a configurable number of bits from a path's MD5 hash as a
partition index that designates a device. The number of bits kept from the hash
is known as the partition power, and 2 to the partition power indicates the
partition count. Partitioning the full MD5 hash ring allows other parts of the
cluster to work in batches of items at once which ends up either more efficient
or at least less complex than working with each item separately or the entire
cluster all at once.
The ring uses a configurable number of bits from the MD5 hash of an item's path
as a partition index that designates the device(s) on which that item should
be stored. The number of bits kept from the hash is known as the partition
power, and 2 to the partition power indicates the partition count. Partitioning
the full MD5 hash ring allows the cluster components to process resources in
batches. This ends up either more efficient or at least less complex than
working with each item separately or the entire cluster all at once.
Another configurable value is the replica count, which indicates how many of
the partition->device assignments comprise a single ring. For a given partition
number, each replica will be assigned to a different device in the ring.
Another configurable value is the replica count, which indicates how many
devices to assign for each partition in the ring. By having multiple devices
responsible for each partition, the cluster can recover from drive or network
failures.
Devices are added to the ring to describe the capacity available for
part-replica assignment. Devices are placed into failure domains consisting
of region, zone, and server. Regions can be used to describe geographical
systems characterized by lower-bandwidth or higher latency between machines in
different regions. Many rings will consist of only a single region. Zones
can be used to group devices based on physical locations, power separations,
network separations, or any other attribute that would lessen multiple
replicas being unavailable at the same time.
partition replica assignments. Devices are placed into failure domains
consisting of region, zone, and server. Regions can be used to describe
geographical systems characterized by lower bandwidth or higher latency between
machines in different regions. Many rings will consist of only a single
region. Zones can be used to group devices based on physical locations, power
separations, network separations, or any other attribute that would lessen
multiple replicas being unavailable at the same time.
Devices are given a weight which describes relative weight of the device in
comparison to other devices.
Devices are given a weight which describes the relative storage capacity
contributed by the device in comparison to other devices.
When building a ring all of each part's replicas will be assigned to devices
according to their weight. Additionally, each replica of a part will attempt
to be assigned to a device who's failure domain does not already have a
replica for the part. Only a single replica of a part may be assigned to each
device - you must have as many devices as replicas.
When building a ring, replicas for each partition will be assigned to devices
according to the devices' weights. Additionally, each replica of a partition
will preferentially be assigned to a device whose failure domain does not
already have a replica for that partition. Only a single replica of a
partition may be assigned to each device - you must have at least as many
devices as replicas.
.. _ring_builder:
@ -45,24 +47,24 @@ Ring Builder
------------
The rings are built and managed manually by a utility called the ring-builder.
The ring-builder assigns partitions to devices and writes an optimized Python
structure to a gzipped, serialized file on disk for shipping out to the servers.
The server processes just check the modification time of the file occasionally
and reload their in-memory copies of the ring structure as needed. Because of
how the ring-builder manages changes to the ring, using a slightly older ring
usually just means one of the three replicas for a subset of the partitions
will be incorrect, which can be easily worked around.
The ring-builder assigns partitions to devices and writes an optimized
structure to a gzipped, serialized file on disk for shipping out to the
servers. The server processes check the modification time of the file
occasionally and reload their in-memory copies of the ring structure as needed.
Because of how the ring-builder manages changes to the ring, using a slightly
older ring usually just means that for a subset of the partitions the device
for one of the replicas will be incorrect, which can be easily worked around.
The ring-builder also keeps its own builder file with the ring information and
additional data required to build future rings. It is very important to keep
multiple backup copies of these builder files. One option is to copy the
builder files out to every server while copying the ring files themselves.
Another is to upload the builder files into the cluster itself. Complete loss
of a builder file will mean creating a new ring from scratch, nearly all
partitions will end up assigned to different devices, and therefore nearly all
data stored will have to be replicated to new locations. So, recovery from a
builder file loss is possible, but data will definitely be unreachable for an
extended time.
The ring-builder also keeps a separate builder file which includes the ring
information as well as additional data required to build future rings. It is
very important to keep multiple backup copies of these builder files. One
option is to copy the builder files out to every server while copying the ring
files themselves. Another is to upload the builder files into the cluster
itself. Complete loss of a builder file will mean creating a new ring from
scratch, nearly all partitions will end up assigned to different devices, and
therefore nearly all data stored will have to be replicated to new locations.
So, recovery from a builder file loss is possible, but data will definitely be
unreachable for an extended time.
-------------------
Ring Data Structure
@ -77,13 +79,13 @@ to calculate the partition for the hash.
List of Devices
***************
The list of devices is known internally to the Ring class as devs. Each item in
the list of devices is a dictionary with the following keys:
The list of devices is known internally to the Ring class as ``devs``. Each
item in the list of devices is a dictionary with the following keys:
====== ======= ==============================================================
id integer The index into the list devices.
zone integer The zone the device resides in.
region integer The region the zone resides in.
id integer The index into the list of devices.
zone integer The zone in which the device resides.
region integer The region in which the zone resides.
weight float The relative weight of the device in comparison to other
devices. This usually corresponds directly to the amount of
disk space the device has compared to other devices. For
@ -94,10 +96,10 @@ weight float The relative weight of the device in comparison to other
data than desired over time. A good average weight of 100.0
allows flexibility in lowering the weight later if necessary.
ip string The IP address or hostname of the server containing the device.
port int The TCP port the listening server process uses that serves
port int The TCP port on which the server process listens to serve
requests for the device.
device string The on disk name of the device on the server.
For example: sdb1
device string The on-disk name of the device on the server.
For example: ``sdb1``
meta string A general-use field for storing additional information for the
device. This information isn't used directly by the server
processes, but can be useful in debugging. For example, the
@ -105,47 +107,54 @@ meta string A general-use field for storing additional information for the
be stored here.
====== ======= ==============================================================
Note: The list of devices may contain holes, or indexes set to None, for
devices that have been removed from the cluster. However, device ids are
reused. Device ids are reused to avoid potentially running out of device id
slots when there are available slots (from prior removal of devices). A
consequence of this device id reuse is that the device id (integer value) does
not necessarily correspond with the chronology of when the device was added to
the ring. Also, some devices may be temporarily disabled by setting their
weight to 0.0. To obtain a list of active devices (for uptime polling, for
example) the Python code would look like: ``devices = list(self._iter_devs())``
.. note::
The list of devices may contain holes, or indexes set to ``None``, for
devices that have been removed from the cluster. However, device ids are
reused. Device ids are reused to avoid potentially running out of device id
slots when there are available slots (from prior removal of devices). A
consequence of this device id reuse is that the device id (integer value)
does not necessarily correspond with the chronology of when the device was
added to the ring. Also, some devices may be temporarily disabled by
setting their weight to ``0.0``. To obtain a list of active devices (for
uptime polling, for example) the Python code would look like::
devices = list(self._iter_devs())
*************************
Partition Assignment List
*************************
This is a list of array('H') of devices ids. The outermost list contains an
array('H') for each replica. Each array('H') has a length equal to the
partition count for the ring. Each integer in the array('H') is an index into
the above list of devices. The partition list is known internally to the Ring
class as _replica2part2dev_id.
The partition assignment list is known internally to the Ring class as
``_replica2part2dev_id``. This is a list of ``array('H')``\s, one for each
replica. Each ``array('H')`` has a length equal to the partition count for the
ring. Each integer in the ``array('H')`` is an index into the above list of
devices.
So, to create a list of device dictionaries assigned to a partition, the Python
code would look like: ``devices = [self.devs[part2dev_id[partition]] for
part2dev_id in self._replica2part2dev_id]``
code would look like::
array('H') is used for memory conservation as there may be millions of
devices = [self.devs[part2dev_id[partition]]
for part2dev_id in self._replica2part2dev_id]
``array('H')`` is used for memory conservation as there may be millions of
partitions.
*********************
Partition Shift Value
*********************
The partition shift value is known internally to the Ring class as _part_shift.
This value used to shift an MD5 hash to calculate the partition on which the
data for that hash should reside. Only the top four bytes of the hash is used
in this process. For example, to compute the partition for the path
/account/container/object the Python code might look like: ``partition =
unpack_from('>I', md5('/account/container/object').digest())[0] >>
self._part_shift``
The partition shift value is known internally to the Ring class as
``_part_shift``. This value is used to shift an MD5 hash of an item's path to
calculate the partition on which the data for that item should reside. Only the
top four bytes of the hash are used in this process. For example, to compute
the partition for the path ``/account/container/object``, the Python code might
look like::
For a ring generated with part_power P, the partition shift value is
32 - P.
objhash = md5('/account/container/object').digest()
partition = struct.unpack_from('>I', objhash)[0] >> self._part_shift
For a ring generated with partition power ``P``, the partition shift value is
``32 - P``.
*******************
Fractional Replicas
@ -155,11 +164,12 @@ A ring is not restricted to having an integer number of replicas. In order to
support the gradual changing of replica counts, the ring is able to have a real
number of replicas.
When the number of replicas is not an integer, then the last element of
_replica2part2dev_id will have a length that is less than the partition count
for the ring. This means that some partitions will have more replicas than
others. For example, if a ring has 3.25 replicas, then 25% of its partitions
will have four replicas, while the remaining 75% will have just three.
When the number of replicas is not an integer, the last element of
``_replica2part2dev_id`` will have a length that is less than the partition
count for the ring. This means that some partitions will have more replicas
than others. For example, if a ring has ``3.25`` replicas, then 25% of its
partitions will have four replicas, while the remaining 75% will have just
three.
.. _ring_dispersion:
@ -186,24 +196,24 @@ Overload
The ring builder tries to keep replicas as far apart as possible while
still respecting device weights. When it can't do both, the overload
factor determines what happens. Each device will take some extra
factor determines what happens. Each device may take some extra
fraction of its desired partitions to allow for replica dispersion;
once that extra fraction is exhausted, replicas will be placed closer
together than optimal.
together than is optimal for durability.
Essentially, the overload factor lets the operator trade off replica
dispersion (durability) against data dispersion (uniform disk usage).
dispersion (durability) against device balance (uniform disk usage).
The default overload factor is 0, so device weights will be strictly
The default overload factor is ``0``, so device weights will be strictly
followed.
With an overload factor of 0.1, each device will accept 10% more
With an overload factor of ``0.1``, each device will accept 10% more
partitions than it otherwise would, but only if needed to maintain
partition dispersion.
dispersion.
Example: Consider a 3-node cluster of machines with equal-size disks;
let node A have 12 disks, node B have 12 disks, and node C have only
11 disks. Let the ring have an overload factor of 0.1 (10%).
11 disks. Let the ring have an overload factor of ``0.1`` (10%).
Without the overload, some partitions would end up with replicas only
on nodes A and B. However, with the overload, every device is willing
@ -228,65 +238,63 @@ number of "nodes" to which keys in the keyspace must be assigned. Swift calls
these ranges `partitions` - they are partitions of the total keyspace.
Each partition will have multiple replicas. Every replica of each partition
must be assigned to a device in the ring. When a describing a specific
replica of a partition (like when it's assigned a device) it is described as a
must be assigned to a device in the ring. When describing a specific replica
of a partition (like when it's assigned a device) it is described as a
`part-replica` in that it is a specific `replica` of the specific `partition`.
A single device may be assigned different replicas from many parts, but it may
not be assigned multiple replicas of a single part.
A single device will likely be assigned different replicas from many
partitions, but it may not be assigned multiple replicas of a single partition.
The total number of partitions in a ring is calculated as ``2 **
<part-power>``. The total number of part-replicas in a ring is calculated as
``<replica-count> * 2 ** <part-power>``.
When considering a device's `weight` it is useful to describe the number of
part-replicas it would like to be assigned. A single device regardless of
weight will never hold more than ``2 ** <part-power>`` part-replicas because
it can not have more than one replica of any part assigned. The number of
part-replicas a device can take by weights is calculated as it's
`parts_wanted`. The true number of part-replicas assigned to a device can be
compared to it's parts wanted similarly to a calculation of percentage error -
this deviation in the observed result from the idealized target is called a
devices `balance`.
part-replicas it would like to be assigned. A single device, regardless of
weight, will never hold more than ``2 ** <part-power>`` part-replicas because
it can not have more than one replica of any partition assigned. The number of
part-replicas a device can take by weights is calculated as its `parts-wanted`.
The true number of part-replicas assigned to a device can be compared to its
parts-wanted similarly to a calculation of percentage error - this deviation in
the observed result from the idealized target is called a device's `balance`.
When considering a device's `failure domain` it is useful to describe the
number of part-replicas it would like to be assigned. The number of
part-replicas wanted in a failure domain of a tier is the sum of the
part-replicas wanted in the failure domains of it's sub-tier. However,
collectively when the total number of part-replicas in a failure domain
exceeds or is equal to ``2 ** <part-power>`` it is most obvious that it's no
longer sufficient to consider only the number of total part-replicas, but
rather the fraction of each replica's partitions. Consider for example a ring
with ``3`` replicas and ``3`` servers, while it's necessary for dispersion
that each server hold only ``1/3`` of the total part-replicas it is
additionally constrained to require ``1.0`` replica of *each* partition. It
would not be sufficient to satisfy dispersion if two devices on one of the
servers each held a replica of a single partition, while another server held
none. By considering a decimal fraction of one replica's worth of parts in a
failure domain we can derive the total part-replicas wanted in a failure
domain (``1.0 * 2 ** <part-power>``). Additionally we infer more about
`which` part-replicas must go in the failure domain. Consider a ring with
three replicas, and two zones, each with two servers (four servers total).
The three replicas worth of partitions will be assigned into two failure
domains at the zone tier. Each zone must hold more than one replica of some
parts. We represent this improper faction of a replica's worth of partitions
in decimal form as ``1.5`` (``3.0 / 2``). This tells us not only the *number*
of total parts (``1.5 * 2 ** <part-power>``) but also that *each* partition
must have `at least` one replica in this failure domain (in fact ``0.5`` of
the partitions will have ``2`` replicas). Within each zone the two servers
will hold ``0.75`` of a replica's worth of partitions - this is equal both to
"the fraction of a replica's worth of partitions assigned to each zone
(``1.5``) divided evenly among the number of failure domain's in it's sub-tier
(``2`` servers in each zone, i.e. ``1.5 / 2``)" but *also* "the total number
of replicas (``3.0``) divided evenly among the total number of failure domains
in the server tier (``2`` servers x ``2`` zones = ``4``, i.e. ``3.0 / 4``)".
It is useful to consider that each server in this ring will hold only ``0.75``
of a replica's worth of partitions which tells that any server should have `at
most` one replica of a given part assigned. In the interests of brevity, some
variable names will often refer to the concept representing the fraction of a
replica's worth of partitions in decimal form as *replicanths* - this is meant
to invoke connotations similar to ordinal numbers as applied to fractions, but
generalized to a replica instead of four*th* or a fif*th*. The 'n' was
probably thrown in because of Blade Runner.
When considering a device's `failure domain` it is useful to describe the number
of part-replicas it would like to be assigned. The number of part-replicas
wanted in a failure domain of a tier is the sum of the part-replicas wanted in
the failure domains of its sub-tier. However, collectively when the total
number of part-replicas in a failure domain exceeds or is equal to ``2 **
<part-power>`` it is most obvious that it's no longer sufficient to consider
only the number of total part-replicas, but rather the fraction of each
replica's partitions. Consider for example a ring with 3 replicas and 3
servers: while dispersion requires that each server hold only ⅓ of the total
part-replicas, placement is additionally constrained to require ``1.0`` replica
of *each* partition per server. It would not be sufficient to satisfy
dispersion if two devices on one of the servers each held a replica of a single
partition, while another server held none. By considering a decimal fraction
of one replica's worth of partitions in a failure domain we can derive the
total part-replicas wanted in a failure domain (``1.0 * 2 ** <part-power>``).
Additionally we infer more about `which` part-replicas must go in the failure
domain. Consider a ring with three replicas and two zones, each with two
servers (four servers total). The three replicas worth of partitions will be
assigned into two failure domains at the zone tier. Each zone must hold more
than one replica of some partitions. We represent this improper fraction of a
replica's worth of partitions in decimal form as ``1.5`` (``3.0 / 2``). This
tells us not only the *number* of total partitions (``1.5 * 2 **
<part-power>``) but also that *each* partition must have `at least` one replica
in this failure domain (in fact ``0.5`` of the partitions will have 2
replicas). Within each zone the two servers will hold ``0.75`` of a replica's
worth of partitions - this is equal both to "the fraction of a replica's worth
of partitions assigned to each zone (``1.5``) divided evenly among the number
of failure domains in its sub-tier (2 servers in each zone, i.e. ``1.5 / 2``)"
but *also* "the total number of replicas (``3.0``) divided evenly among the
total number of failure domains in the server tier (2 servers × 2 zones = 4,
i.e. ``3.0 / 4``)". It is useful to consider that each server in this ring
will hold only ``0.75`` of a replica's worth of partitions which tells that any
server should have `at most` one replica of a given partition assigned. In the
interests of brevity, some variable names will often refer to the concept
representing the fraction of a replica's worth of partitions in decimal form as
*replicanths* - this is meant to invoke connotations similar to ordinal numbers
as applied to fractions, but generalized to a replica instead of a four\*th* or
a fif\*th*. The "n" was probably thrown in because of Blade Runner.
-----------------
Building the Ring
@ -298,8 +306,8 @@ ring's topology based on weight.
Then the ring builder calculates the replicanths wanted at each tier in the
ring's topology based on dispersion.
Then the ring calculates the maximum deviation on a single device between it's
weighted replicanths and wanted replicanths.
Then the ring builder calculates the maximum deviation on a single device
between its weighted replicanths and wanted replicanths.
Next we interpolate between the two replicanth values (weighted & wanted) at
each tier using the specified overload (up to the maximum required overload).
@ -309,19 +317,19 @@ calculate the intersection of the line with the desired overload. This
becomes the target.
From the target we calculate the minimum and maximum number of replicas any
part may have in a tier. This becomes the replica_plan.
partition may have in a tier. This becomes the `replica-plan`.
Finally, we calculate the number of partitions that should ideally be assigned
to each device based the replica_plan.
to each device based the replica-plan.
On initial balance, the first time partitions are placed to generate a ring,
we must assign each replica of each partition to the device that desires the
most partitions excluding any devices that already have their maximum number
of replicas of that part assigned to some parent tier of that device's failure
domain.
On initial balance (i.e., the first time partitions are placed to generate a
ring) we must assign each replica of each partition to the device that desires
the most partitions excluding any devices that already have their maximum
number of replicas of that partition assigned to some parent tier of that
device's failure domain.
When building a new ring based on an old ring, the desired number of
partitions each device wants is recalculated from the current replica_plan.
partitions each device wants is recalculated from the current replica-plan.
Next the partitions to be reassigned are gathered up. Any removed devices have
all their assigned partitions unassigned and added to the gathered list. Any
partition replicas that (due to the addition of new devices) can be spread out
@ -335,21 +343,16 @@ Whenever a partition has a replica reassigned, the time of the reassignment is
recorded. This is taken into account when gathering partitions to reassign so
that no partition is moved twice in a configurable amount of time. This
configurable amount of time is known internally to the RingBuilder class as
min_part_hours. This restriction is ignored for replicas of partitions on
devices that have been removed, as removing a device only happens on device
``min_part_hours``. This restriction is ignored for replicas of partitions on
devices that have been removed, as device removal should only happens on device
failure and there's no choice but to make a reassignment.
The above processes don't always perfectly rebalance a ring due to the random
nature of gathering partitions for reassignment. To help reach a more balanced
ring, the rebalance process is repeated a fixed number of times until the
replica_plan is fulfilled or unable to be fulfilled (indicating we probably
replica-plan is fulfilled or unable to be fulfilled (indicating we probably
can't get perfect balance due to too many partitions recently moved).
---------------------
Ring Builder Analyzer
---------------------
.. automodule:: swift.cli.ring_builder_analyzer
.. _composite_rings:
@ -358,6 +361,11 @@ Composite Rings
---------------
.. automodule:: swift.common.ring.composite_builder
---------------------
Ring Builder Analyzer
---------------------
.. automodule:: swift.cli.ring_builder_analyzer
-------
History
-------
@ -371,7 +379,7 @@ discarded.
A "live ring" option was considered where each server could maintain its own
copy of the ring and the servers would use a gossip protocol to communicate the
changes they made. This was discarded as too complex and error prone to code
correctly in the project time span available. One bug could easily gossip bad
correctly in the project timespan available. One bug could easily gossip bad
data out to the entire cluster and be difficult to recover from. Having an
externally managed ring simplifies the process, allows full validation of data
before it's shipped out to the servers, and guarantees each server is using a
@ -385,16 +393,16 @@ like the current process but where servers could submit change requests to the
ring server to have a new ring built and shipped back out to the servers. This
was discarded due to project time constraints and because ring changes are
currently infrequent enough that manual control was sufficient. However, lack
of quick automatic ring changes did mean that other parts of the system had to
be coded to handle devices being unavailable for a period of hours until
of quick automatic ring changes did mean that other components of the system
had to be coded to handle devices being unavailable for a period of hours until
someone could manually update the ring.
The current ring process has each replica of a partition independently assigned
to a device. A version of the ring that used a third of the memory was tried,
where the first replica of a partition was directly assigned and the other two
were determined by "walking" the ring until finding additional devices in other
zones. This was discarded as control was lost as to how many replicas for a
given partition moved at once. Keeping each replica independent allows for
zones. This was discarded due to the loss of control over how many replicas for
a given partition moved at once. Keeping each replica independent allows for
moving only one partition replica within a given time window (except due to
device failures). Using the additional memory was deemed a good trade-off for
moving data around the cluster much less often.
@ -409,16 +417,16 @@ add up. In the end, the memory savings wasn't that great and more processing
power was used, so the idea was discarded.
A completely non-partitioned ring was also tried but discarded as the
partitioning helps many other parts of the system, especially replication.
partitioning helps many other components of the system, especially replication.
Replication can be attempted and retried in a partition batch with the other
replicas rather than each data item independently attempted and retried. Hashes
of directory structures can be calculated and compared with other replicas to
reduce directory walking and network traffic.
Partitioning and independently assigning partition replicas also allowed for
the best balanced cluster. The best of the other strategies tended to give
+-10% variance on device balance with devices of equal weight and +-15% with
devices of varying weights. The current strategy allows us to get +-3% and +-8%
the best-balanced cluster. The best of the other strategies tended to give
±10% variance on device balance with devices of equal weight and ±15% with
devices of varying weights. The current strategy allows us to get ±3% and ±8%
respectively.
Various hashing algorithms were tried. SHA offers better security, but the ring
@ -441,4 +449,5 @@ didn't always get it. After that, overload was added to the ring builder so
that operators could choose a balance between dispersion and device weights.
In time the overload concept was improved and made more accurate.
For more background on consistent hashing rings, please see :doc:`ring_background`.
For more background on consistent hashing rings, please see
:doc:`ring_background`.

4
doc/source/ring_background.rst
View File

@ -6,7 +6,7 @@ Building a Consistent Hashing Ring
Authored by Greg Holt
---------------------
This is compilation of five posts I made earlier discussing how to build
This is a compilation of five posts I made earlier discussing how to build
a consistent hashing ring. The posts seemed to be accessed quite frequently,
so I've gathered them all here on one page for easier reading.
@ -227,7 +227,7 @@ be done by creating “virtual nodes” for each node. So 100 nodes might have
90423 ids moved, 0.90%
There we go, we added 1% capacity and only moved 0.9% of existing data.
The vnode_range_starts list seems a bit out of place though. It’s values
The vnode_range_starts list seems a bit out of place though. Its values
are calculated and never change for the lifetime of the cluster, so let’s
optimize that out.

38
swift/common/ring/composite_builder.py
View File

@ -19,15 +19,16 @@ domains, but does not provide any guarantees such as placing at least one
replica of every partition into each region. Composite rings are intended to
provide operators with greater control over the dispersion of object replicas
or fragments across a cluster, in particular when there is a desire to
guarantee that some replicas or fragments are placed in certain failure
domains.
have strict guarantees that some replicas or fragments are placed in certain
failure domains. This is particularly important for policies with duplicated
erasure-coded fragments.
A composite ring comprises two or more component rings that are combined to
form a single ring with a replica count equal to the sum of the component
rings. The component rings are built independently, using distinct devices in
distinct regions, which means that the dispersion of replicas between the
components can be guaranteed. The composite_builder utilities may
then be used to combine components into a composite ring.
form a single ring with a replica count equal to the sum of replica counts
from the component rings. The component rings are built independently, using
distinct devices in distinct regions, which means that the dispersion of
replicas between the components can be guaranteed. The ``composite_builder``
utilities may then be used to combine components into a composite ring.
For example, consider a normal ring ``ring0`` with replica count of 4 and
devices in two regions ``r1`` and ``r2``. Despite the best efforts of the
@ -56,15 +57,16 @@ composite ring.
For rings to be formed into a composite they must satisfy the following
requirements:
* All component rings must have the same number of partitions
* All component rings must have the same part power (and therefore number of
partitions)
* All component rings must have an integer replica count
* Each region may only be used in one component ring
* Each device may only be used in one component ring
Under the hood, the composite ring has a replica2part2dev_id table that is the
union of the tables from the component rings. Whenever the component rings are
rebalanced, the composite ring must be rebuilt. There is no dynamic rebuilding
of the composite ring.
Under the hood, the composite ring has a ``_replica2part2dev_id`` table that is
the union of the tables from the component rings. Whenever the component rings
are rebalanced, the composite ring must be rebuilt. There is no dynamic
rebuilding of the composite ring.
.. note::
The order in which component rings are combined into a composite ring is
@ -77,11 +79,11 @@ of the composite ring.
The ``id`` of each component RingBuilder is therefore stored in metadata of
the composite and used to check for the component ordering when the same
composite ring is re-composed. RingBuilder id's are only assigned when a
RingBuilder instance is first saved. Older RingBuilders instances loaded
from file may not have an ``id`` assigned and will need to be saved before
they can be used as components of a composite ring. This can be achieved
by, for example::
composite ring is re-composed. RingBuilder ``id``\s are normally assigned
when a RingBuilder instance is first saved. Older RingBuilder instances
loaded from file may not have an ``id`` assigned and will need to be saved
before they can be used as components of a composite ring. This can be
achieved by, for example::
swift-ring-builder <builder-file> rebalance --force
@ -147,7 +149,7 @@ def pre_validate_all_builders(builders):
regions_info = {}
for builder in builders:
regions_info[builder] = set(
[dev['region'] for dev in builder._iter_devs()])
dev['region'] for dev in builder._iter_devs())
for first_region_set, second_region_set in combinations(
regions_info.values(), 2):
inter = first_region_set & second_region_set