openstack
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swift
Code Issues Proposed changes

Add Pros/Cons docs for global cluster consideration 

This comes from discussion in Bristol Hackathon (Feb 2016).
Currently Swift has a couple of choices (Global Cluster and Container
Sync) to sync the stored data into geographically distributed locations.

This patch adds the summary of the discussion comparing between
Global Cluster and Container Sync to enable operators to know which
functionality fits their own use case.

And, to be fairness with container-sync, this patch moves global
cluster docs into overview_global_cluster.rst from admin_guide.rst.

Co-Authored-By: Alistair Coles <alistair.coles@hpe.com>

Change-Id: I624eb519503ae71dbc82245c33dab6e8637d0f8b
This commit is contained in:
Kota Tsuyuzaki 2016-04-11 20:51:00 -07:00 committed by Alistair Coles
parent e9f5e7966a
commit dfa5523d8c
3 changed files with 185 additions and 119 deletions
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170
doc/source/admin_guide.rst
View File

@ -496,133 +496,65 @@ When you specify a policy the containers created also include the policy index,
thus even when running a container_only report, you will need to specify the
policy not using the default.
-----------------------------------
Geographically Distributed Clusters
-----------------------------------
-----------------------------------------------
Geographically Distributed Swift Considerations
-----------------------------------------------
Swift's default configuration is currently designed to work in a
single region, where a region is defined as a group of machines with
high-bandwidth, low-latency links between them. However, configuration
options exist that make running a performant multi-region Swift
cluster possible.
Swift provides two features that may be used to distribute replicas of objects
across multiple geographically distributed data-centers: with
:doc:`overview_global_cluster` object replicas may be dispersed across devices
from different data-centers by using `regions` in ring device descriptors; with
:doc:`overview_container_sync` objects may be copied between independent Swift
clusters in each data-center. The operation and configuration of each are
described in their respective documentation. The following points should be
considered when selecting the feature that is most appropriate for a particular
use case:
For the rest of this section, we will assume a two-region Swift
cluster: region 1 in San Francisco (SF), and region 2 in New York
(NY). Each region shall contain within it 3 zones, numbered 1, 2, and
3, for a total of 6 zones.
#. Global Clusters allows the distribution of object replicas across
data-centers to be controlled by the cluster operator on per-policy basis,
since the distribution is determined by the assignment of devices from
each data-center in each policy's ring file. With Container Sync the end
user controls the distribution of objects across clusters on a
per-container basis.
~~~~~~~~~~~~~
read_affinity
~~~~~~~~~~~~~
#. Global Clusters requires an operator to coordinate ring deployments across
multiple data-centers. Container Sync allows for independent management of
separate Swift clusters in each data-center, and for existing Swift
clusters to be used as peers in Container Sync relationships without
deploying new policies/rings.
This setting, combined with sorting_method setting, makes the proxy server prefer local backend servers for
GET and HEAD requests over non-local ones. For example, it is
preferable for an SF proxy server to service object GET requests
by talking to SF object servers, as the client will receive lower
latency and higher throughput.
#. Global Clusters seamlessly supports features that may rely on
cross-container operations such as large objects and versioned writes.
Container Sync requires the end user to ensure that all required
containers are sync'd for these features to work in all data-centers.
By default, Swift randomly chooses one of the three replicas to give
to the client, thereby spreading the load evenly. In the case of a
geographically-distributed cluster, the administrator is likely to
prioritize keeping traffic local over even distribution of results.
This is where the read_affinity setting comes in.
#. Global Clusters makes objects available for GET or HEAD requests in both
data-centers even if a replica of the object has not yet been
asynchronously migrated between data-centers, by forwarding requests
between data-centers. Container Sync is unable to serve requests for an
object in a particular data-center until the asynchronous sync process has
copied the object to that data-center.
Example::
#. Global Clusters may require less storage capacity than Container Sync to
achieve equivalent durability of objects in each data-center. Global
Clusters can restore replicas that are lost or corrupted in one
data-center using replicas from other data-centers. Container Sync
requires each data-center to independently manage the durability of
objects, which may result in each data-center storing more replicas than
with Global Clusters.
[app:proxy-server]
sorting_method = affinity
read_affinity = r1=100
This will make the proxy attempt to service GET and HEAD requests from
backends in region 1 before contacting any backends in region 2.
However, if no region 1 backends are available (due to replica
placement, failed hardware, or other reasons), then the proxy will
fall back to backend servers in other regions.
Example::
[app:proxy-server]
sorting_method = affinity
read_affinity = r1z1=100, r1=200
This will make the proxy attempt to service GET and HEAD requests from
backends in region 1 zone 1, then backends in region 1, then any other
backends. If a proxy is physically close to a particular zone or
zones, this can provide bandwidth savings. For example, if a zone
corresponds to servers in a particular rack, and the proxy server is
in that same rack, then setting read_affinity to prefer reads from
within the rack will result in less traffic between the top-of-rack
switches.
The read_affinity setting may contain any number of region/zone
specifiers; the priority number (after the equals sign) determines the
ordering in which backend servers will be contacted. A lower number
means higher priority.
Note that read_affinity only affects the ordering of primary nodes
(see ring docs for definition of primary node), not the ordering of
handoff nodes.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
write_affinity and write_affinity_node_count
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This setting makes the proxy server prefer local backend servers for
object PUT requests over non-local ones. For example, it may be
preferable for an SF proxy server to service object PUT requests
by talking to SF object servers, as the client will receive lower
latency and higher throughput. However, if this setting is used, note
that a NY proxy server handling a GET request for an object that was
PUT using write affinity may have to fetch it across the WAN link, as
the object won't immediately have any replicas in NY. However,
replication will move the object's replicas to their proper homes in
both SF and NY.
Note that only object PUT requests are affected by the write_affinity
setting; POST, GET, HEAD, DELETE, OPTIONS, and account/container PUT
requests are not affected.
This setting lets you trade data distribution for throughput. If
write_affinity is enabled, then object replicas will initially be
stored all within a particular region or zone, thereby decreasing the
quality of the data distribution, but the replicas will be distributed
over fast WAN links, giving higher throughput to clients. Note that
the replicators will eventually move objects to their proper,
well-distributed homes.
The write_affinity setting is useful only when you don't typically
read objects immediately after writing them. For example, consider a
workload of mainly backups: if you have a bunch of machines in NY that
periodically write backups to Swift, then odds are that you don't then
immediately read those backups in SF. If your workload doesn't look
like that, then you probably shouldn't use write_affinity.
The write_affinity_node_count setting is only useful in conjunction
with write_affinity; it governs how many local object servers will be
tried before falling back to non-local ones.
Example::
[app:proxy-server]
write_affinity = r1
write_affinity_node_count = 2 * replicas
Assuming 3 replicas, this configuration will make object PUTs try
storing the object's replicas on up to 6 disks ("2 * replicas") in
region 1 ("r1"). Proxy server tries to find 3 devices for storing the
object. While a device is unavailable, it queries the ring for the 4th
device and so on until 6th device. If the 6th disk is still unavailable,
the last replica will be sent to other region. It doesn't mean there'll
have 6 replicas in region 1.
You should be aware that, if you have data coming into SF faster than
your replicators are transferring it to NY, then your cluster's data distribution
will get worse and worse over time as objects pile up in SF. If this
happens, it is recommended to disable write_affinity and simply let
object PUTs traverse the WAN link, as that will naturally limit the
object growth rate to what your WAN link can handle.
#. Global Clusters execute all account/container metadata updates
synchronously to account/container replicas in all data-centers, which may
incur delays when making updates across WANs. Container Sync only copies
objects between data-centers and all Swift internal traffic is
confined to each data-center.
#. Global Clusters does not yet guarantee the availability of objects stored
in Erasure Coded policies when one data-center is offline. With Container
Sync the availability of objects in each data-center is independent of the
state of other data-centers once objects have been synced. Container Sync
also allows objects to be stored using different policy types in different
data-centers.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Checking handoff partition distribution

1
doc/source/index.rst
View File

@ -52,6 +52,7 @@ Overview and Concepts
ratelimit
overview_large_objects
overview_object_versioning
overview_global_cluster
overview_container_sync
overview_expiring_objects
cors

133
doc/source/overview_global_cluster.rst Normal file
View File

@ -0,0 +1,133 @@
===============
Global Clusters
===============
--------
Overview
--------
Swift's default configuration is currently designed to work in a
single region, where a region is defined as a group of machines with
high-bandwidth, low-latency links between them. However, configuration
options exist that make running a performant multi-region Swift
cluster possible.
For the rest of this section, we will assume a two-region Swift
cluster: region 1 in San Francisco (SF), and region 2 in New York
(NY). Each region shall contain within it 3 zones, numbered 1, 2, and
3, for a total of 6 zones.
---------------------------
Configuring Global Clusters
---------------------------
~~~~~~~~~~~~~
read_affinity
~~~~~~~~~~~~~
This setting, combined with sorting_method setting, makes the proxy
server prefer local backend servers for GET and HEAD requests over
non-local ones. For example, it is preferable for an SF proxy server
to service object GET requests by talking to SF object servers, as the
client will receive lower latency and higher throughput.
By default, Swift randomly chooses one of the three replicas to give
to the client, thereby spreading the load evenly. In the case of a
geographically-distributed cluster, the administrator is likely to
prioritize keeping traffic local over even distribution of results.
This is where the read_affinity setting comes in.
Example::
[app:proxy-server]
sorting_method = affinity
read_affinity = r1=100
This will make the proxy attempt to service GET and HEAD requests from
backends in region 1 before contacting any backends in region 2.
However, if no region 1 backends are available (due to replica
placement, failed hardware, or other reasons), then the proxy will
fall back to backend servers in other regions.
Example::
[app:proxy-server]
sorting_method = affinity
read_affinity = r1z1=100, r1=200
This will make the proxy attempt to service GET and HEAD requests from
backends in region 1 zone 1, then backends in region 1, then any other
backends. If a proxy is physically close to a particular zone or
zones, this can provide bandwidth savings. For example, if a zone
corresponds to servers in a particular rack, and the proxy server is
in that same rack, then setting read_affinity to prefer reads from
within the rack will result in less traffic between the top-of-rack
switches.
The read_affinity setting may contain any number of region/zone
specifiers; the priority number (after the equals sign) determines the
ordering in which backend servers will be contacted. A lower number
means higher priority.
Note that read_affinity only affects the ordering of primary nodes
(see ring docs for definition of primary node), not the ordering of
handoff nodes.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
write_affinity and write_affinity_node_count
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This setting makes the proxy server prefer local backend servers for
object PUT requests over non-local ones. For example, it may be
preferable for an SF proxy server to service object PUT requests
by talking to SF object servers, as the client will receive lower
latency and higher throughput. However, if this setting is used, note
that a NY proxy server handling a GET request for an object that was
PUT using write affinity may have to fetch it across the WAN link, as
the object won't immediately have any replicas in NY. However,
replication will move the object's replicas to their proper homes in
both SF and NY.
Note that only object PUT requests are affected by the write_affinity
setting; POST, GET, HEAD, DELETE, OPTIONS, and account/container PUT
requests are not affected.
This setting lets you trade data distribution for throughput. If
write_affinity is enabled, then object replicas will initially be
stored all within a particular region or zone, thereby decreasing the
quality of the data distribution, but the replicas will be distributed
over fast WAN links, giving higher throughput to clients. Note that
the replicators will eventually move objects to their proper,
well-distributed homes.
The write_affinity setting is useful only when you don't typically
read objects immediately after writing them. For example, consider a
workload of mainly backups: if you have a bunch of machines in NY that
periodically write backups to Swift, then odds are that you don't then
immediately read those backups in SF. If your workload doesn't look
like that, then you probably shouldn't use write_affinity.
The write_affinity_node_count setting is only useful in conjunction
with write_affinity; it governs how many local object servers will be
tried before falling back to non-local ones.
Example::
[app:proxy-server]
write_affinity = r1
write_affinity_node_count = 2 * replicas
Assuming 3 replicas, this configuration will make object PUTs try
storing the object's replicas on up to 6 disks ("2 * replicas") in
region 1 ("r1"). Proxy server tries to find 3 devices for storing the
object. While a device is unavailable, it queries the ring for the 4th
device and so on until 6th device. If the 6th disk is still unavailable,
the last replica will be sent to other region. It doesn't mean there'll
have 6 replicas in region 1.
You should be aware that, if you have data coming into SF faster than
your replicators are transferring it to NY, then your cluster's data
distribution will get worse and worse over time as objects pile up in SF.
If this happens, it is recommended to disable write_affinity and simply let
object PUTs traverse the WAN link, as that will naturally limit the
object growth rate to what your WAN link can handle.