Solving MiFID II Clock Synchronisation with minimum spend (part 6)

In this series we are attempting to solve a clock synchronisation problem to a degree of accuracy in order to satisfy MiFID II regulations, and we're trying to do it without spending a lot of money.

So far we have:

1. Talked about the regulations and how we might solve this with Linux software
2. Built a "PTP Bridge" with Puppet
3. Started recording metrics with collectd and InfluxDB, and
4. Finished recording metrics
5. Drawn lots of graphs with Grafana and found contention on our firewall

In this post we will look at introducing a second ASIC based firewall to route PTP through so that PTP accuracy is not affected by our management firewall getting busy.

The New Design

Here is the adjusted design diagram, it's not that much more complicated:

Routing To Another Firewall

In order for this design to work every PTP Client must send it's PTP traffic back through the PTP firewall, rather than the management firewall. Each client needs a routing rule to reflect this. There are similar changes needed on the PTP Bridge to send through the PTP firewall.

With the new firewall in place and the routing changes applied, everything should "just work", but it doesn't. We see multicast leaving the PTP Bridge correctly:

[root@ptp-bridge ~]# tcpdump -i p4p3 multicast -nn
tcpdump: verbose output suppressed, use -v or -vv for full protocol decode
listening on p4p3, link-type EN10MB (Ethernet), capture size 65535 bytes
16:10:07.519357 IP 192.168.0.4.319 > 224.0.1.129.319: UDP, length 44
16:10:07.519405 IP 192.168.0.4.320 > 224.0.1.129.320: UDP, length 44

Restart sfptpd on a client and we see it cannot subscribe to the multicast group:

2015-12-15 16:01:18.306138: info: using SO_TIMESTAMPNS software timestamps
2015-12-15 16:01:18.406259: notice: Now in state: PTP_LISTENING
2015-12-15 16:01:18.406353: info: creating NTP sync-module
2015-12-15 16:01:30.306177: warning: failed to receive Announce within 12.000 seconds

A packet trace shows that we see PIM announcements from the right firewall, and the IGMPv3 packets from sfptpd are leaving the client:

16:07:15.444343 IP 192.168.0.2 > 224.0.0.13: PIMv2, Hello, length 34
16:07:18.307942 IP 192.168.0.5 > 224.0.0.22: igmp v3 report, 2 group record(s)
16:07:18.369939 IP 192.168.0.5 > 224.0.0.22: igmp v3 report, 2 group record(s)
16:07:18.686936 IP 192.168.0.5 > 224.0.0.22: igmp v3 report, 2 group record(s)

Looking at the multicast groups on the new firewall, we don't have the PTP multicast addresses appearing as registered groups that the firewall can forward.

To cut a very, very long story short, this was a TTL problem compounded by two firewall brands behaving differently. The ptp4l process sets the TTL of it's multicast packets to 1 to restrict them to the same subnet. Our management firewall accepted this and did the multicast forwarding anyway, the PTP firewall did not. Setting udp_ttl=2 for the ptp4l process that's doing the multicast fixed this. The lmaxexchange-linuxptp module has been changed to take this option now too.

The performance improvement moving from the software firewall to an ASIC firewall is quite apparent. Whereas before our "normal" range of variance was ±20μs (top image), we're now getting ±5μs (bottom image):

Our mean accuracy has improved substantially - at a glance the hardware change has saved 15% of the tolerance the regulations allow.

There are interesting patterns in the most recent data; regular and somewhat uniform spikes in client offset are seen on both clients. The graph below draws both client offsets together:

The spikes are recorded at almost exactly the same second, they last for a few seconds, they appear to come in groups of two and are generally over 9000ns in size. Identifying how regular these spikes are may indicate what the problem is. If they are perfectly regular on both clients, I would start to think it might be something on the firewall, like an administrative or housekeeping function.

We can use an InfluxDB query to get the raw data:

SELECT "time","value" FROM "tail_ns" WHERE "instance" = 'sfptpd_stats' AND "type_instance" = 'offset-system' AND ("host" = 'client1') AND ( "value" > '0' OR "value" < '0' ) AND time >= '2016-02-05 09:43:19' AND value > 9000

Now with a little bit of Python we can work out the difference in time between spikes:

Unfortunately there's not as much uniformity in the spikes as I was hoping to see:

$ python diff.py < foo  | awk '{print $4}' | sort | uniq -c  | sort -n | tail
      6 0:01:08
      6 0:01:10
      6 0:01:14
      6 0:01:16
      8 0:01:00
      8 0:01:03
      8 0:01:17
      9 0:01:04
      9 0:01:05
     10 0:01:06

The most common time difference is around 60-70 seconds, which is between the individual spikes in a pair of two. The difference between the pairs of spikes themselves though is quite varied, anywhere from 2 minutes to 8 minutes apart. This doesn't mean it's not the firewall, but this data doesn't reliably pin the problem to some X minute recurring job in the internals of the firewall's operating system. The network engineers in my team are also quick to point out that this is an ASIC based firewall that's doing nothing but shifting a couple PTP packets per second, so firewall spikes of 20μs would be a very strange thing to be encountering. Before getting lost in a rabbit hole chasing performance spikes (something our engineers here know all too much about), there may be another way to mitigate this.

Attracting Attention

January was an interesting month for this project. We had several Solarflare representatives in to talk specifically about sfptpd, how it may be better utilised here and what feedback we could give to the Solarflare developers. A few days later I was tracked down on LinkedIn by Wojciech Owczarek, developer of the PTPd project. There are some very interesting features coming into PTPd, most of which we'll get to in a later blog post, but Wojciech did recommend tuning the PTP Sync and DelayReq interval higher to give the filters more samples to work with. This is a very simple thing to adjust and measure. So lets see if adjusting these values smooths out our spikes to any degree.

Faster Message Rates

Changing the sync rate of the ptp4l process on the PTP Bridge requires a minor code addition to the lmaxexchange-linuxptp Puppet module, and at the same time I'll rename repositories on GitHub to be more indicative that they are actually Puppet modules. Setting logSyncInterval to -2 means ptp4l will send out 4 Sync messages a second. The sfptpd daemons recognise this immediately:

2016-02-05 14:47:24.187549: info: received new Sync interval -2 from master (was 0)

After half an hour it appears as if the size of the spikes has lessened from upwards of 20μs to 15μs. Observing over a longer period appears to show the intensity of a spike has lowered slightly, see the before (top) and after (bottom) graphs below:

Changing the Delay Request rate of the sfptpd client is also simple. Setting ptp_delayreq_interval to -2 in the sfptpd configuration file means the client will send a DelayReq message 4 times a second. We also have two clients, so by changing only one we can compare the difference between the two.

An interesting pattern has emerged a day after changing one of our clients to send 4 Delay Requests per second; the spikes are no longer uniform across both clients as seen in the graph below:

This debunks the theory that it's the firewalls causing the jitter, each client is seeing it's own variance at different intervals. One of our key performance guys, Mark Price, tells me 10-20μs is well within the range of Operating System jitter he's seen, so on an un-tuned system, even with no workload, there's a good chance this is what we are seeing.

A Different Architecture

Lets step back a bit and look at what we've found so far. Routing PTP through a firewall is possible but we are heavily affected by contention. We can reduce this by using a dedicated firewall, but are still seeing 20μs spikes of variance, and there's a good chance it's from the Operating System. We could continue down the OS tuning route now - it's definitely something we'll have to do eventually - but it would be a big time investment and right we're still experimenting with architecture and design. Finally, our Networking team want their spare ASIC firewall back, so I'm losing my dedicated PTP firewall. The PTP Bridge generally keeps a baseline of 100ns accuracy (excluding jitter), perhaps it's time to look at switching-only designs.

In the very first post I mused about a design that didn't require a firewall, something that involved connecting the PTP Bridge to multiple separate management networks and having it send multiple independent PTP multicast streams. The idea had security implications - the PTP Bridge would join networks that don't usually join, networks that are usually protected from each other by a firewall. With careful attention to detail these implications can be mitigated.

Along side this, I've got new options in my choice of software. Solar Flare are interested in us replacing the PTP Bridge component with a purely Solar Flare approach - I'm told there is an undocumented feature that allows sfptpd to work in UDP Unicast mode, so it would be able to talk directly to our S300 Grand Master. There's also been some advances with PTPd that make it viable to replace linuxptp on the PTP Bridge, as well as replace sfptpd as the PTP clients.

In the next post we'll remove the PTP firewall from the mix and look at purely switched designs.

Solving MiFID II Clock Synchronisation with minimum spend (part 5)

In this series we are attempting to solve a clock synchronisation problem to a degree of accuracy in order to satisfy MiFID II regulations. So far we have:

1. Talked about the regulations and how we might solve this with Linux software
2. Built a "PTP Bridge" with Puppet
3. Started recording metrics with collectd and InfluxDB, and
4. Finished recording metrics

In this post we will look at the data we've gathered to see how accurate my first design is, and also see if we can explain why NTP and PTP think the time is so different.

Here's a quick refresh on what the design looks like:

Minor Improvements

I've made some slight improvements to Collectd and InfluxDB since I last posted code examples (if the queries look different to previous posts, this is why). I figured out why the Tail plugin always came up as "tail_" in Influx - it's because I used a custom Collectd Type that InfluxDB didn't know about. To fix this I pointed InfluxDB at my custom Collectd types database file, and now the measurement names contain the unit. I also fixed the type used for remote NTP offset to be in microseconds. The measurements now look like this, which is a bit more descriptive:

Graphing PTP Client Metrics

I've got data from the PTP Bridge as well as two test servers I'm using as PTP Clients. Lets look at the offset of the clients, arguably the most important metric:

The queries for the above graphs are:

SELECT mean("value") AS "value" FROM "tail_ns" WHERE "instance" = 'sfptpd_stats' AND "type_instance" = 'offset-system' AND "host" = 'FQDN' AND ( "value" > '0' OR "value" < '0' ) AND $timeFilter GROUP BY time($interval)

Generally we are ±20μs offset from the PTP Bridge. Most of the time the PTP Bridge itself is ±200ns offset from the Grand Master Clock, however there are some spikes. Here is the PTP Bridge offset for the same time period:

A better way to visualise the offset variance is to plot the min() and max() on the same graph using a larger GROUP BY time period. The two queries for an sfptpd client would be:

SELECT min("value") AS "value" FROM "tail_ns" WHERE "instance" = 'sfptpd_stats' AND "type_instance" = 'offset-system' AND "host" = 'FQDN' AND ( "value" > '0' OR "value" < '0' ) AND $timeFilter GROUP BY time($interval)

SELECT max("value") AS "value" FROM "tail_ns" WHERE "instance" = 'sfptpd_stats' AND "type_instance" = 'offset-system' AND "host" = 'FQDN' AND ( "value" > '0' OR "value" < '0' ) AND $timeFilter GROUP BY time($interval)

The PTP Bridge queries are:

SELECT min("value") AS "value" FROM "tail_ns" WHERE "instance" = 'ptp-master' AND "type_instance" = 'offset' AND ( "value" > '0' OR "value" < '0' ) AND $timeFilter GROUP BY time($interval)

SELECT max("value") AS "value" FROM "tail_ns" WHERE "instance" = 'ptp-master' AND "type_instance" = 'offset' AND ( "value" > '0' OR "value" < '0' ) AND $timeFilter GROUP BY time($interval)

I find this easier to read the offset variance - it's the space between the lines. The image below contains the PTP Bridge (top) and two clients:

It would be nice to be able to represent "offset variance" as a single number. I know the formula - absolute(Max) plus absolute(Min) - but I don't know how to do that in InfluxDB or Grafana.

If we ignore that one spike on the Master for now, our PTP Clients have about ±20μs variance. This is "OK", but not great. It's within the tolerance we need to hit to be compliant, however this is not the only step in the chain. Our financial software is what timestamps events, and this is subject to some amount of operating system jitter. A finger-in-the-air number from some of our developers about what jitter to expect from our software at the moment is in the area of ±40μs. Adding ±20μs PTP variance is ±60μs, which is getting uncomfortably near to our compliance limit of ±100μs.

The difference in offset variance is not just the firewall, although it's probably a large part of it, there are two other factors. First, there is no hardware timestamping on the clients receiving PTP multicast - the sfptpd software only supports hardware timestamping on Solar Flare interfaces and we are using an existing management network that's not Solar Flare NICs. Second, we haven't tuned or isolated the PTP software so it's subject to operating system jitter. While the OS jitter is probably very small right now (the test clients are essentially idle machines) it will become more of an issue if we put PTP on our servers doing a normal workload.

The time period in the graphs above are a small 3 minute window I chose during a "good" period to show the data. When you look at the data over a larger period a much more serious problem emerges, one that is going to force our hand in terms of architecture changes.

The problem with re-using infrastructure

Here is a client's offset tolerance from 21:30 to 23:30:

In case you missed the scale of the graph, that's spikes of ±700μs in this 2 hour period. I won't hold you in suspense for too long - in short, it's network resource contention on the switch / firewall introducing variance to PTP. 22:00 UTC is a special time in the Finance world, it is 17:00 EST (5pm New York time) which is when the financial markets close in NY. Our platform does some house keeping work at this time, including several backup jobs. These backup jobs ship large amounts of data across our management networks, which is the same switch and firewall that PTP is running through.

To look at this problem in detail here are tiles of all metrics for both the PTP Bridge and PTP Clients for that time period (the top image is the PTP Bridge):

Eyeballing all graphs we see that the backup period affects both the PTP Bridge and the PTP Clients, but on different scales. The PTP Bridge's Offset Tolerance is interesting because it shows the switch has ±7μs of overhead when under load, but the PTP Client offsets spike up to hundreds of microseconds. This indicates that while the switch is also noticeably busy, the firewall is contributing the majority of this variance - makes sense, it's a software (CPU-based) firewall and it's also trying to ship gigabytes of backups around at the same time.

If we zoom in on one of these spikes we can see what is most likely a queuing backlog on the firewall:

You can see Path Delay spikes up and then drains out slowly. The offset smooths out reasonably quickly because the PTP formula is able to figure out the correct offset as long as the delay is reasonably stable. It's the initial spike which gives us all the trouble.

When zoomed in like this the spike may not seem such a big deal, after all the offset appears to settle back to normal in about 10-15 seconds. Our platform can process hundreds of thousands of client messages in that time period, so it's not a trivial amount of time in the Financial world, nor is it anywhere near compliant.

Rethinking Architecture

We know that it's contention on the firewall that's causing the majority of these issues. The switch is also affected, but it's not that big a contributor. When discussing this problem in the office the next simplest idea we came up with was to just route the PTP traffic through a different firewall. It wouldn't even need to be a very powerful device, as long as it wasn't congested during backup periods (we joked about using someone's home Netgear at one point).

We use ASIC based firewalls in other areas where we care about speed, and the brand in question has a pretty inexpensive low-end model. Even though it's a break from our "don't spend any money" stance the cost to buy these small firewalls is low enough that's it's not that big a deal. We happen to have a similar unit available for testing, so in the next post we'll look at adding this ASIC firewall into the mix, configuring PTP to route through this firewall, and the results we get from it.

Solving MiFID II Clock Synchronisation with minimum spend (part 4)

This post is a continuation from Part 3, where we started on collecting metrics from our PTP Bridge and clients. We started graphing NTP offset as a proof of concept and noticed data that could indicate a problem with the accuracy of PTP, but we need more information. We're now diving right in where we left off - collecting PTP metrics.

Collecting PTP Metrics

We've got NTP Offset data into InfluxDB, but what we really want is data from PTP. I'll start with the PTP Bridge. In Part 2 of this series we set up various linuxptp processes being run with supervisord with Puppet, and we specified that STDOUT was written to specific log files under /var/log/linuxptp. We're interested in the stats from the ptp-master instance of ptp4l; this is the Slave Clock that receives Layer 2 PTP from the S300 Grandmaster Clock. A single line of stats output looks like this:

ptp4l[2089949.676]: master offset         38 s2 freq  +34476 path delay     35290

The three interesting stats here are offset (38), frequency adjustment (+34476) and path delay (35290). If you look at the output from a phc2sys process, the format is almost the same:

phc2sys[2090397.362]: phc offset         2 s2 freq  -12406 delay   2416

As mentioned before we chose Collectd as a stats gatherer because it had the ability to tail log files. Since the files are almost identical, we should be able to create a Defined Type in Puppet that will write a collectd Tail plugin configuration file and that we can re-use for any ptp4l or phc2sys log file we want to collect stats from:

There are a lot of lessons learned in the above code that need explaining. First, the type in each one of the matches is one of our custom PTP collectd types. The dstype parameter has to do with how collectd accumulates values. A Tail plugin matcher is designed to read a chunk of a file and accumulate it's contents into one metric. For example, read /var/log/maillog every minute and count how many emails were sent. It's not designed to generate a metric for every line of a file, which is what I want to do.

It can be made to do this by changing the collectd interval to be as fast as the PTP interval - in my case every 1 second - and setting Dstype to "GaugeLast". This will have collectd wake up every second, tail the log file which should find just one extra line from the last time it woke up, and then use that value found as a Gauge value.

Finally, the collectd Tail Plugin regular expressions are compatible with the regex library on the machine as described in the man page regex(7), which is not the same as Perl. I'm mentioning this because I spent almost a day debugging Perl-like regular expressions in collectd, only to find out that I misread the collectd documentation about what Regex engine was used under the hood (I was trying to use PCRE character classes; \w, \s, etc).

The way Collectd is designed to always aggregate data highlights that perhaps it is not the best tool for this job. A better tool might be to write a log file tailer script in Python and send metrics to InfluxDB using it's own wire format (not the collectd wire format). Collectd will do for now though, and I'll slowly build up a list of reasons to move away from it.

Now if we go back to the Puppet Profile for the PTP Bridge, we can use the above collectd tailer Defined Type for every linuxptp instance log file:

You may remember that we have four linuxptp processes running under supervisord, but we have only three collectd tailers here. The fourth process is the ptp4l multicast process (the one that multicasts to our PTP Slaves through a firewall), it is configured as a Master Clock and so doesn't log statistics, so there's no point tailing it's log file.

Lets look at our data in InfluxDB:

If you want to copy it, the query is:

SELECT value from "tail_" WHERE "instance" = 'ptp-master' and "type_instance" = 'offset' AND time > now() - 10s

InfluxDB 0.9 introduces "tags" to namespace sets of data points. In the query above we are filtering on tag "instance" having the value of 'ptp-master' and tag "type_instance" being 'offset'. These tags and their values are translated by InfluxDB from the collectd wire format. If you compare what tags are being created to the Puppet code, you can see the relationship.

The name of the measurement, tail_, is not the most descriptive. InfluxDB's translation of the collectd tail plugin data leaves a lot to be desired. A better way to structure this data in InfluxDB would be to have a measurement called "offset" (because that's what we're actually measuring) and tags of "instance" and "host". This is another +1 to dropping collectd and writing our own tailer script so we can write to InfluxDB in a better way. Anyway, it's working so let's soldier on.

Graphing PTP Metrics

Lets draw the ptp-master instance's offset for a small window of time:

The query for this graph is:

SELECT mean("value") AS "value" FROM "tail_" WHERE "instance" = 'ptp-master' AND "type_instance" = 'offset' AND ( "value" > '0' OR "value" &lt; '0' ) AND $timeFilter GROUP BY time($interval)

You may wonder why I've put the value>0 and value&lt;0. It is a workaround for this bug, which is actually a Go bug. To summarise, the collectd Tail plugin is writing "NaN" (Not a Number) into InfluxDB. It appears to do this on startup, when there are no metrics to read from the files. When you create a simple InfluxDB query in Grafana that returns these NaNs, you trigger that JSON error and the graphs don't draw. Since NaN is not less than zero nor is it greater than zero (because it's not a number), you can effectively "filter out" the NaNs by adding to the WHERE clause. This is the reason why I will always write the full InfluxDB query in Grafana, rather than use the query builder form - you can't specify brackets or OR statements using Grafana's query builder.

The data in this period says we're getting ±100-250ns maximum offset. I'm pretty happy with that result - it means that to receive PTP from our S300 Grandmaster Clock we're losing at most 1/4 of a microsecond in accuracy. That's a good start, but there's plenty more hops to go.

If we draw the ptp-master path delay we can see the time it takes for our PTP packets to cross the switch:

The query is very similar, only the type_instance value needs to change:

SELECT mean("value") AS "value" FROM "tail_" WHERE "instance" = 'ptp-master' AND "type_instance" = 'path-delay' AND ( "value" > '0' OR "value" &lt; '0' ) AND $timeFilter GROUP BY time($interval)

Between 35.25μs and 35.3μs. This may seem alarmingly high at first but remember that the value of path delay is not important to PTP, as long as it is consistent. The variance here, ±50ns, is not too bad.

(Not) Explaining the Negative NTP Offset

Hopefully you remember I stopped the previous post after graphing the NTP Offset of the PTP Bridge (which is the value of the "ntpdate -q" command) and that the data we got threw into question whether PTP or NTP was working correctly.

Here's the data for the same time range as the graphs above:

I said that if we look closely at the metrics and the network architecture that I'd be able to explain the difference. When I wrote that I had convinced myself that the NTP Offset discrepancy was caused by the time it takes to cross the switch, which we measure as the ptp-master Path Delay.

If we assume the clocks are perfectly synchronised by PTP and we know that it takes 35μs to cross the switch, the timestamp in the NTP packet would be 35μs behind. Add a little bit of operating system jitter... That explains our -25μs NTP offset, right?

... Not really. If the NTP packets were delayed by 35μs then our PTP Bridge would be 35μs ahead, not behind. Also, the assumptions about network architecture are incorrect, the NTP packets do not just cross a switch.

Here is a revised diagram of the network architecture from Part 2 showing the path that NTP takes:

The NTP packets flow from PTP Bridge through the switch to the firewall, then back onto the same switch to the S300. I also said that ntpdate was a "onetime" query and has no smarts built in to calculate network delays - this is also incorrect (lesson learned: don't blindly trust what you read on Stack Exchange). The ntpdate man page says it makes several requests to a single NTP server and attempts to calculate the offset, so it's not a "dumb" time query, it is trying to figure out it's own delay and compensate for it - explaining the NTP offset on the PTP Bridge just got a lot harder.

The ntpdate command reports a delay of 25.79 milliseconds. This seems way too high. The time to ping the NTP interface on the S300 is only 0.2ms. The delay to other NTP servers in the same network is around 25ms as well, so this could be a property of how ntpdate works, or the NTP server software. I seem to have more questions than answers at the moment, so lets look at gathering the stats from two PTP Clients and see if that enlightens anything.

Collecting sfptpd Metrics

The Puppet work we've done earlier for the PTP Bridge can be mostly re-used for our PTP Clients. The NTP Offset statistics is exactly the same, and the Collectd Tail plugin for a linuxptp stats file is similar in format to sfptpd, so we've pretty much got the regular expressions we need. There are two things that make it more complicated though; how sfptpd writes to disk, and how sfptpd synchronises all Solar Flare adapter clocks.

The first problem has to do with sfptpd's output. The linuxptp processes are line buffered, so that works nicely with our collectd tailer that reads a new line from the file every second, and we never miss a data point. sfptpd is block buffered, so you get a disk write every 5-10 seconds and it's almost never at the end of a line. Collectd's Tail plugin will read the latest data from the file but it this will be several lines of output. Due to the way the Tail plugin aggregates, it will only ever send one data point even if it read 10 lines.

The issue really lies with collectd's Tail plugin, which we talked about before. We can make sfptpd behave in a way that fits with collectd though; we can run the stdbuf(1) command beforehand to force sfptpd's STDOUT to be line buffered. I can do this by modifying the sfptpd init script, or by running sfptpd with supervisord.

I've gone with the second option because I already use supervisord for the linuxptp processes, and I think modifying the init script is "more dirty". Here is the modified Puppet code for sfptpd:


Nothing too hard there, the complicated bit is generating the collectd Tail Plugin config for the sfptpd stats file.

sfptpd will synchronise the adapter clocks of all Solar Flare adapters in the server, as well as the system clock. In the machines I'm using as PTP Clients a single second of stats logs looks like this:

2015-12-22 17:14:10.759688 [ptp-gm->system(em1)], offset: 348.000, freq-adj: 1444.090, in-sync: 1, one-way-delay: 57616.000, grandmaster-id: 0000:0000:0000:0000
2015-12-22 17:14:10.759688 [system->phc4(p5p1/p5p2)], offset: -227.562, freq-adj: -334.226, in-sync: 0
2015-12-22 17:14:10.759688 [system->phc5(p4p1/p4p2)], offset: -227.938, freq-adj: -261.973, in-sync: 0
2015-12-22 17:14:10.759688 [system->phc6(p3p1/p3p2)], offset: -223.562, freq-adj: 88.446, in-sync: 0

The first line is the "Local Reference Clock". The output "ptp-gm->system(em1)" shows that we are receiving PTP from a Grand Master using software timestamping on interface em1. The subsequent lines are Solar Flare adapters; there are three dual port cards, each with a single PTP Hardware Clock. The output "system->phc4(p5p1/p5p2)" shows that the adapter clock phc4 is being synchronised, which is the clock for interfaces p5p1 and p5p2.

The system line is the most interesting to me right now, but we should capture metrics for all cards as well. Specifying each Solar Flare adapter clock for every node would be very tedious. We also can't use Collectd's Tail plugin multiple times on the same file, so we have to build a big list of regular expressions of everything we want to match on one Puppet Array. Here's how I've done it automatically.

First, we need a Facter Fact to "know" which interfaces are our Solar Flare adapters. Here is a Custom Fact that will create Facts for every network interface in a machine and what driver they are using. An example of these Facts on a PTP Client:

nic_driver_bonding => bond0,bond1
nic_driver_igb => em1,em2,em3,em4
nic_driver_sfc => p3p1,p3p2,p4p1,p4p2,p5p1,p5p2
nic_driver_virtual => dummy0,lo

... and here is the Fact itself. It comes from an internal "standard lib" Puppet module which is not available to the public:


Next we need a way of knowing what PHC devices belong to our Solar Flare adapters. Many other brand of network card will create /dev/phcX devices on recent kernels, we just want the Solar Flare ones. Here is a Fact from the lmaxexchange-linuxptp Puppet module:


The last thing we want to do is take a template of Collectd Tail Plugin Matches and substitute in the PHC devices we are looking for. We need a chunk of imperative programming logic to do this efficiently, something which Puppet 3.x is very poor at. I've written a Ruby function that substitutes and replicates a template hash against an array. The Gist below has docs that demonstrate an example that explains all:


Now I put all these things together in a Puppet class to create the Collectd Tail Plugin config file for our sfptpd stats file:


The resulting file is large and repetitive, you can see an example from one of my PTP Clients here. With the above Puppet class applied we are now gathering PTP stats on the test clients.

All those code examples have made this a long post. The next post will be drawing graphs and analysing data.