[rrd-users] Sanity checks on Buffer Cache and I/O at peak aggregation times

Derek Haynes derek.haynes at gmail.com
Sun Sep 18 19:01:34 CEST 2011

Hi all,

I'm doing some tuning work on our rrdtool setup: we'd like to get a
bit more disk i/o overhead. I have two questions/assumptions I'd love
to get a sanity check on.

First, some background on our setup:

* Hardware
** 16 2.27 GHZ CPUs
** 16 GB of RAM
** Dedicated to RRDTool - no other services are installed on this server.
** SSD Drive

* RRDTool Config
** RRDtool 1.4.5
** 20k RRD Files, each 539K in size
** 20 data sources per-file
** 18 RRAs per file (Min/Max/Average of each: 5 minutes for 6 hours,
10 minutes for 12 hours, 1 hour for 3 days, 6 hours for one week, 12
hours for 2 weeks, 1 day for 2 years)

* Performance
** Typically, almost no reads (showing buffer cache is working)
** 315 writes/sec | 4 MB/sec

Second, what we're planning:

I've load tested several different rrdtool file configurations and
what I saw aligned with the behavior in this paper by David Plonka:
If I'm summarizing the paper correctly, the 2 RRDTool file
configurations that impact I/O activity are (1) # of data sources and
(2) # of RRAs. The number of rows in an RRA does not impact

* Reducing data sources per-file to 5: Most of our fields are reserved
for later use (since you can't add a new data source to an existing
file via an rrdtool command). Instead, we're going to start with 5 and
manually add new data sources via rrdtool dump / restore. It's rare to
add new data sources to a file.
* Reducing the number of RRAs to 7 (Adding LAST of one-minute data for
60 minutes, extending 5-minutes for 1 week, and dropping all other
RRAs but daily).

When needing to graph data over a several day period, I'm planning on
using the --step option with a 60 minute or greater value to limit the
number of data points returned. I haven't tested this yet, but I'm
assuming reading the many more rows of 5-minute storage may be the
primary performance penalty of the new RRD setup.

And finally, the RRD performance pieces I'm having trouble understanding:

Memory Sizing ---

The "Tuning RRD" wiki page states: "To keep the header (4k) and the
active block of at least one RRA (4k) in memory, you need 8k per RRD
file." For our current configuration, we have 20 data sources and 18

>From the illustration on the page, it looks like our DS Header storage
space would be:
4k per data source * 20 data sources = 80k

Is my maths legit?

For the RRA Header, is it (1) one per-RRA independent of the number of
data sources or (2) one per-RRA * the number of data sources?

If one-per RRA independent of data sources:
18 RRAs * 4k = 72k

If one-per RRA for each data source:
18 RRA * 20 data sources = 360k

Assuming my data source math checks out, this means either 152k or
440k must be stored in the buffer cache for the entire header. free -m
shows that we have 5,119 MB cached: with nearly 20k rrd files, it
looks like the header contains one RRA for each data source. Does this
check out?

High I/O Wait during peak aggregation periods ---

At 00:00 UTC, we see our peak I/O Wait. At this time, reads increase
to 11 reads/sec (typically we have almost no read activity) and writes
increase to 700 writes/sec (typically around 315 writes/sec). One
observation: even though our read throughput is far less than our
write throughput, this increase in reads seems to have a dramatic
impact on I/O Wait.

I can see why there is an increase in writes: currently, we have 18
RRAs across 20 data sources that need to append data to the file at
this time. I'm a bit confused why the read activity increases

* We have 16 GB of RAM
* It looks like < 5 GB of that is used by the buffer cache

My low-level Linux fu is poor: can anyone give some insight into the
read activity at these peak aggregation times?

RRDTool is am amazing piece of work: it's a testament to how well it
works that we haven't needed to investigate performance significantly
in the 3+ years we've been using it.



Derek Haynes
Scout Web Monitoring and Reporting ~ http://scoutapp.com
Blog ~ http://blog.scoutapp.com

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