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#  Copyright (C) 2004  Stanislav Sinyagin
#  Copyright (C) 2004  Christian Schnidrig
#
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# $Id: scalability.pod.in,v 1.1 2010-12-27 00:04:32 ivan Exp $
# Stanislav Sinyagin <ssinyagin@yahoo.com>
#
#

=head1 Torrus Scalability Guide

=head2 Introduction

Installing Torrus in big enterprise or carrier networks requires special
planning and design measures, in order to ensure its reliable and efficient
function.


=head2 Hardware Platform Recommendations

Hardware planning for large Torrus installations is of big importance.
It is vital to understand the potential bottlenecks and performance limits
before purchasing the hardware.

First of all, you need to estimate the number of devices that you are
going to monitor, with some room for future growth. It is a good practice
first to model the situation on a test server, and then project the
results to a bigger number of network devices. The utilities that
would help you in assessing the requirements are C<torrus configinfo> and
C<torrus schedulerinfo>.

The resources for planning are the server CPU, RAM, and disks.
While CPU and RAM are of great importance, it is the disk subsystem that
often becomes the bottleneck.

=head3 CPU

For large installations, CPU power is one of the critical resources.

One of CPU-intensive processes is XML configuration compiler. A configuration
for few hundred of nodes may take few dozens of minutes to compile. In some
complicated configuration, it may require few hours to recompile the whole
datasource tree. Here CPU power means literally your time while testing the
configuration changes or troubleshooting a problem.

The SNMP collector is quite moderate in CPU usage, still when the number of
SNMP variables reaches dozens of thousands, the CPU power becomes
an important resource to pay attention to. In addition, the collector
process initialization time can be quite CPU-intensive. This happens every
time the collector process starts, or when the configuration has been
recompiled.

The empiric estimation made by Christian Schnidrig is that one SNMP counter
collection every 5 minutes occupies approximately 1.0e-5 of the
Intel Xeon 2.8GHz time, including the OS overhead. For example,
the Torrus collectors running on 60'000 counters would make the server
busy at the average of 60%.


=head3 Memory

The collector would need RAM space to store all the counters information,
and of course it's undesirable to swap. In addition, the more RAM you have
available for disk cache, the faster your collector may update the data files.

Each update of an RRD file consists of a number of operations: open a file,
read the header, seek to the needed offset, and then write. With enough disk
cache, it is possible that the read operations are made solely from RAM,
and that significantly speeds up the collector running cycle.

According to Christian Schnidrig's empiric estimations, 30 KB RAM per counter
should be enough to hold all the neccessary data, including the disk cache.
For example, for 60'000 counters this gives 1'757 MB, thus 2 GB of server RAM
should be enough.

In addition, Apache with mod_perl occupies 20-30 MB RAM per process, so
few hundred extra megabytes of RAM would be good to have.


=head3 Disk storage

It is not recommended to use IDE disks. They are not designed for
continuous and intensive use. As experienced by Christian Schnidrig,
IDE disks don't live long under such load.

It is recommended to reduce the number of RRD files by grouping
the datasources. This reduces dramatically the number of read and write
operations during the update process.

As noted by Rodrigo Cunha, reducing the size of read-ahead in the filesystem
may lead to significant optimisation of disk cache usage. RRD update process
reads only a short header in the beginnin of RRD file, and the rest of
readahead data is never reused. On Linux, the following command would
set the readahead size to 4 KB, which equals to i386 page size:

 /sbin/hdparm -a 4 /dev/sda

For servers with dozens of thousands RRD files, it is recommended to use
hashed data directories. Then the data directories will form a structure of
256 directories, with hash function based on hostnames. See I<Torrus SNMP
Discovery User Guide> for more details.

Spreading the data files over several physical disks is also a good plus.



=head2 Operating System Tuning

Depending on the number of trees and processes that run on a single server,
you might require to increase the maximum number of filehandles that
may be opened at the same time, system-wide and per process.
See the manuals for your operating system  for more details.


=head2 Torrus Configuration Recommendatations

=head3 BerkeleyDB configuration tuniung

When using lots of collectors and/or lots of HTTP processes, it is
important to increase the size of BerkeleyDB lock region.
The command

  db_stat -h @dbhome@ -c

would show you the current number of locks and lockers, and their maximum
quantities during the database history.
The maximum numbers of lock objects and lockers can be tuned by creating the
file F<DB_CONFIG> in the database home directory, F<@dbhome@>.
The following settings would work fine with about 20 collector processes
and 5 HTTP daemon processes:

   set_lk_max_lockers   6000
   set_lk_max_locks     3000

It is also recommended to increase the cache size from default 256KB to some
bigger amount. Especially if the database has to hold large Torrus trees
(hundreds or thousands monitored devices). The following line in
F<DB_CONFIG> sets the cache size to 16MB:

   set_cachesize        0 16777216 1

After updating F<DB_CONFIG>, stop all Torrus processes,
including HTTP server, then run

  db_recover -h @dbhome@

Then start the processes again. Futher info is available at:

=over 4

=item * General access method configuration (BDB Reference)

http://tinyurl.com/ybymk7t

=item * DB_CONFIG configuration file (BDB Reference)

http://tinyurl.com/y9qjodv

=item * Configuring locking: sizing the system (BDB Reference)

http://tinyurl.com/ya6dtww

=item * C API reference

http://tinyurl.com/yczgnab

=back


=head3 XML compilation time

For large datasource trees, XML compilation may take dozens of minutes,
if not hours. Other processes are not suspended during the compilation, and
they use the previous configuration version.

For debugging and testing, it is recommended to create a new tree,
separate from large production trees. That would save you a lot of time and
would allow you to see the result of changes quickly.



=head3 Collector schedule tuning

The Torrus collector has a very flexible scheduling mechanism. Each data source
has its own pair of scheduler parameters. These parameters are I<period>
and I<timeoffset>. Period is usually set to default 300 seconds.
The time is divided into even intervals. For the default 5-minutes period,
each hour's intervals would start at 00, 05, 10, 15, etc. minutes.
The timeoffset determines the moment within each interval when the data source
should be collected. The default value for timeoffset is 10 seconds. This
means that the collector process would try to collect the values at
00:00:10, 00:05:10, ..., 23:55:10 every day.

Data sources with the same period and timeoffset values are grouped together. 
The SNMP collector works asynchronously, and it tries to send as many SNMP
packets at the same time as possible. Due to the asynchronous architecture,
the collector is able to perform thousands of queries at the same time
with very small delay. Within the same collector process, a large number of
datasources configured with the same schedule is usually not a problem.

If you configured  several datasource trees all with the same period and
timeoffset values, each collector process would start flooding the SNMP
packets to the network at the same time. This may lead to packet loss and
collector timeouts. In addition, all collector processes would try to update
the RRD files concurrently, and this would cause overall performance
degradation. Therefore, it is better to assign different timeoffset values
to different trees. This may be achieved by manually specifying the
C<collector-timeoffset> parameter in discovery configuration files.

In large installations, the collector schedules need thorough planning and
tuning to insure maximum performance and minimize load on the network devices'
CPUs. The C<torrus schedulerinfo> utility is designed to help you in
this planning.
It shows two types of reports: configuration report gives you the idea
of how many datasources are queried at which moments in time. The runtime
report gives you realtime statistics of collector schedules, including
average and maximum running cycle, and statistics on missed or delayed cycles.

There is a feature that eases the load in large installations. With
dispersed timeoffsets enabled, the timeoffset for each datasource is
evenly assigned to one of allowed values, based on the name of the host,
and name of the interface. By default, these values are: 0, 30, 60, ..., 270.
With thousands of datasources, this feature smoothens the CPU and disk load
on Torrus server, and avoids CPU usage peaks on network devices with big number
of SNMP variables per device. It is recommended to analyse the current
scheduler statistics before using this feature. If you run several large
datasource trees, don't forget to plan and analyse the schedules for the whole
system, not just for one tree.


=head2 Distributed setup

=head3 NFS-based setup

The following setup allows you to distribute the load among several
physical servers.

Several Torrus (backend) servers which run collectors
and store RRD files in the local storage, shared by NFS.
The frontend server runs the Web interface, and probably some monitor
processes, accessing the data files by NFS.

It is possible to organize the directory structure so that each data file
would be seen at the same path on every server. Then you can keep identical
Torrus configurations on all servers, and launch the collector process only on
one of them. XML configuration files may be shared via NFS too.

Be aware that BerkeleyDB database home directory cannot be NFS-mounted.
See the following link for more details:
http://www.sleepycat.com/docs/ref/env/remote.html

Backend servers may run near the limits of their system capacities.
70-80% CPU usage should not be a problem. For the frontend machine,
it is preferred that at least 50% of average CPU time is idle.


=head1 Authors

Copyright (c) 2004-2005 Stanislav Sinyagin E<lt>ssinyagin@yahoo.comE<gt>

Copyright (c) 2004 Christian Schnidrig E<lt>christian.schnidrig@bluewin.chE<gt>