openstack/gnocchi
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doc: move storage plan to running

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makes more sense

Change-Id: Id5eb6d15cf521b6ade14d37ebcb648f4489cb63c
This commit is contained in:
Julien Danjou 2017-05-15 18:14:14 +02:00
parent 25cc9d7a03
commit bca589a557
2 changed files with 30 additions and 30 deletions
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30
doc/source/architecture.rst
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@ -80,33 +80,3 @@ duration computing).
.. _PostgreSQL: http://postgresql.org
.. _MySQL: http://mysql.org
How to plan for Gnocchi’s storage
---------------------------------
Gnocchi uses a custom file format based on its library *Carbonara*. In Gnocchi,
a time series is a collection of points, where a point is a given measure, or
sample, in the lifespan of a time series. The storage format is compressed
using various techniques, therefore the computing of a time series' size can be
estimated based on its **worst** case scenario with the following formula::
number of points × 8 bytes = size in bytes
The number of points you want to keep is usually determined by the following
formula::
number of points = timespan ÷ granularity
For example, if you want to keep a year of data with a one minute resolution::
number of points = (365 days × 24 hours × 60 minutes) ÷ 1 minute
number of points = 525 600
Then::
size in bytes = 525 600 bytes × 6 = 3 159 600 bytes = 3 085 KiB
This is just for a single aggregated time series. If your archive policy uses
the 6 default aggregation methods (mean, min, max, sum, std, count) with the
same "one year, one minute aggregations" resolution, the space used will go up
to a maximum of 6 × 4.1 MiB = 24.6 MiB.

30
doc/source/running.rst
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@ -119,6 +119,36 @@ values are sent, the maximum pessimistic storage size is taken into account.
* maximum optimistic size per metric: 1 539 KiB
* maximum pessimistic size per metric: 277 172 KiB
How to plan for Gnocchi’s storage
=================================
Gnocchi uses a custom file format based on its library *Carbonara*. In Gnocchi,
a time series is a collection of points, where a point is a given measure, or
sample, in the lifespan of a time series. The storage format is compressed
using various techniques, therefore the computing of a time series' size can be
estimated based on its **worst** case scenario with the following formula::
number of points × 8 bytes = size in bytes
The number of points you want to keep is usually determined by the following
formula::
number of points = timespan ÷ granularity
For example, if you want to keep a year of data with a one minute resolution::
number of points = (365 days × 24 hours × 60 minutes) ÷ 1 minute
number of points = 525 600
Then::
size in bytes = 525 600 bytes × 6 = 3 159 600 bytes = 3 085 KiB
This is just for a single aggregated time series. If your archive policy uses
the 6 default aggregation methods (mean, min, max, sum, std, count) with the
same "one year, one minute aggregations" resolution, the space used will go up
to a maximum of 6 × 4.1 MiB = 24.6 MiB.
How many metricd workers do we need to run
==========================================