fping -c $count -q -b $backoff -r $retry -4 -b $packetsize -t $timeout -i $mininterval -p $hostinterval $host [ $host ...]
... where those parameters are, by default:
$count
is 20 (packets)$backoff
is 1 (avoid exponential backoff)$timeout
is 1.5s$mininterval
is 0.01s (minimum wait interval between any target)$hostinterval
is 1.5s (minimum wait between probes on a single target)apt install prometheus-blackbox-exporter
dpkg-reconfigure prometheus-blackbox-exporter
prometheus.yml
(the default blackbox
exporter configuration is fine):
scrape_configs:
- job_name: blackbox
metrics_path: /probe
params:
module: [icmp]
scrape_interval: 5s
static_configs:
- targets:
- octavia.anarc.at
# hardcoded in DNS
- nexthop.anarc.at
- koumbit.net
- dns.google
relabel_configs:
- source_labels: [__address__]
target_label: __param_target
- source_labels: [__param_target]
target_label: instance
- target_label: __address__
replacement: 127.0.0.1:9115 # The blackbox exporter's real hostname:port.
Notice how we lower the scrape_interval
to 5 seconds to get more
samples. nexthop.anarc.at
was added into DNS to avoid hardcoding
my upstream ISP's IP in my configuration.sum(probe_icmp_duration_seconds phase="rtt" ) by (instance)
Legend
field to instance RTT
Draw modes
to lines
and Mode options
to staircase
Left Y
axis Unit
to duration(s)
Legend
As table
, with Min
, Avg
, Max
and
Current
enabled1-avg_over_time(probe_success[$__interval])!=0 or null
Legend
field to instance packet loss
Add series override
to Lines: false
, Null point mode:
null
, Points: true
, Points Radius: 1
, Color: deep red
,
and, most importantly, Y-axis: 2
Right Y
axis Unit
to percent (0.0-1.0)
and set
Y-max
to 1Repeat
, on target
,
vertically
. And you need to add a target
variable like
label_values(probe_success, instance)
.stddev(probe_icmp_duration_seconds phase="rtt",instance=~"$instance" )
stddev_over_time(probe_icmp_duration_seconds phase="rtt",instance=~"$instance" [$__interval])
stddev_over_time(probe_icmp_duration_seconds phase="rtt",instance=~"$instance" [1m])
The first two give zero for all samples. The latter works, but doesn't
look as good as Smokeping. So there might be something I'm missing.
SuperQ wrote a special exporter for this called
smokeping_prober that came out of this discussion in the blackbox
exporter. Instead of delegating scheduling and target definition
to Prometheus, the targets are set in the exporter.
They also take a different approach than Smokeping: instead of
recording the individual variations, they delegate that to Prometheus,
through the use of "buckets". Then they use a query like this:
histogram_quantile(0.9 rate(smokeping_response_duration_seconds_bucket[$__interval]))
This is the rationale to SuperQ's implementation:
Yes, I know about smokeping's bursts of pings. IMO, smokeping's data model is flawed that way. This is where I intentionally deviated from the smokeping exact way of doing things. This prober sends a smooth, regular series of packets in order to be measuring at regular controlled intervals. Instead of 20 packets, over 10 seconds, every minute. You send one packet per second and scrape every 15. This has the same overall effect, but the measurement is, IMO, more accurate, as it's a continuous stream. There's no 50 second gap of no metrics about the ICMP stream. Also, you don't get back one metric for those 20 packets, you get several. Min, Max, Avg, StdDev. With the histogram data, you can calculate much more than just that using the raw data. For example, IMO, avg and max are not all that useful for continuous stream monitoring. What I really want to know is the 90th percentile or 99th percentile. This smokeping prober is not intended to be a one-to-one replacement for exactly smokeping's real implementation. But simply provide similar functionality, using the power of Prometheus and PromQL to make it better. [...] one of the reason I prefer the histogram datatype, is you can use the heatmap panel type in Grafana, which is superior to the individual min/max/avg/stddev metrics that come from smokeping. Say you had two routes, one slow and one fast. And some pings are sent over one and not the other. Rather than see a wide min/max equaling a wide stddev, the heatmap would show a "line" for both routes.That's an interesting point. I have also ended up adding a heatmap graph to my dashboard, independently. And it is true it shows those "lines" much better... So maybe that, if we ignore legacy, we're actually happy with what we get, even with the plain blackbox exporter. So yes, we're missing pretty "fuzz" lines around the main lines, but maybe that's alright. It would be possible to do the equivalent to the InfluxDB hack, with queries like:
min_over_time(probe_icmp_duration_seconds phase="rtt",instance=~"$instance" [30s])
avg_over_time(probe_icmp_duration_seconds phase="rtt",instance=~"$instance" [5m])
max_over_time(probe_icmp_duration_seconds phase="rtt",instance=~"$instance" [30s])
The output looks something like this:
But there's a problem there: see how the middle graph "dips" sometimes
below 20ms? That's the min_over_time
function (incorrectly, IMHO)
returning zero. I haven't quite figured out how to fix that, and I'm
not sure it is better. But it does look more like Smokeping than the
previous graph.
Update: I forgot to mention one big thing that this setup is
missing. Smokeping has this nice feature that you can order and group
probe targets in a "folder"-like hierarchy. It is often used to group
probes by location, which makes it easier to scan a lot of
targets. This is harder to do in this setup. It might be possible to
setup location-specific "jobs" and select based on that, but it's not
exactly the same.
avg_over_time
query idea.
Eligibility International Travellers whose sole objective of visiting India is recreation , sight-seeing , casual visit to meet friends or relatives, short duration medical treatment or casual business visit.https://indianvisaonline.gov.in/visa/tvoa.html That this facility is being given to 130 odd countries is better still
Albania, Andorra, Anguilla, Antigua & Barbuda, Argentina, Armenia, Aruba, Australia, Austria, Bahamas, Barbados, Belgium, Belize, Bolivia, Bosnia & Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Cambodia, Canada, Cape Verde, Cayman Island, Chile, China, China- SAR Hong-Kong, China- SAR Macau, Colombia, Comoros, Cook Islands, Costa Rica, Cote d lvoire, Croatia, Cuba, Czech Republic, Denmark, Djibouti, Dominica, Dominican Republic, East Timor, Ecuador, El Salvador, Eritrea, Estonia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Iceland, Indonesia, Ireland, Israel, Jamaica, Japan, Jordan, Kenya, Kiribati, Laos, Latvia, Lesotho, Liberia, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Malta, Marshall Islands, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Montserrat, Mozambique, Myanmar, Namibia, Nauru, Netherlands, New Zealand, Nicaragua, Niue Island, Norway, Oman, Palau, Palestine, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Republic of Korea, Republic of Macedonia, Romania, Russia, Saint Christopher and Nevis, Saint Lucia, Saint Vincent & the Grenadines, Samoa, San Marino, Senegal, Serbia, Seychelles, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Suriname, Swaziland, Sweden, Switzerland, Taiwan, Tajikistan, Tanzania, Thailand, Tonga, Trinidad & Tobago, Turks & Caicos Island, Tuvalu, UAE, Ukraine, United Kingdom, Uruguay, USA, Vanuatu, Vatican City-Holy See, Venezuela, Vietnam, Zambia and Zimbabwe.This should make it somewhat easier for any Indian organizer as well as any participants from any of the member countries shared. There is possibility that this list would even get longer, provided we are able to scale our airports and all and any necessary infrastructure that would be needed for International Visitors to have a good experience. What has been particularly interesting is to know which ports of call are being used by International Visitors as well as overall growth rate
The Percentage share of Foreign Tourist Arrivals (FTAs) in India during November, 2016 among the top 15 source countries was highest from USA (15.53%) followed by UK (11.21%), Bangladesh (10.72%), Canada (4.66%), Russian Fed (4.53%), Australia (4.04%), Malaysia (3.65%), Germany (3.53%), China (3.14%), France (2.88%), Sri Lanka (2.49%), Japan (2.49%), Singapore (2.16%), Nepal (1.46%) and Thailand (1.37%).And port of call
The Percentage share of Foreign Tourist Arrivals (FTAs) in India during November 2016 among the top 15 ports was highest at Delhi Airport (32.71%) followed by Mumbai Airport (18.51%), Chennai Airport (6.83%), Bengaluru Airport (5.89%), Haridaspur Land check post (5.87%), Goa Airport (5.63%), Kolkata Airport (3.90%), Cochin Airport (3.29%), Hyderabad Airport (3.14%), Ahmadabad Airport (2.76%), Trivandrum Airport (1.54%), Trichy Airport (1.53%), Gede Rail (1.16%), Amritsar Airport (1.15%), and Ghojadanga land check post (0.82%) .The Ghojadanga land check post seems to be between West Bengal, India and Bangladesh. Gede Railway Station is also in West Bengal as well. So all and any overlanders could take any of those ways.Even Hardispur Land Check post comes in the Bengal-Bangladesh border only. In the airports, Delhi Airport seems to be attracting lot more business than the Mumbai Airport. Part of the reason I *think* is the direct link of Delhi Airport to NDLS via the Delhi Airport Express Line . The same when it will happen in Mumbai should be a game-changer for city too. Now if you are wondering why I have been suddenly talking about visas and airports in India, it came because Hong Kong is going to Withdraw Visa Free Entry Facility For Indians. Although, as rightly pointed out in the article doesn t make sense from economic POV and seems to be somewhat politically motivated. Not that I or anybody else can do anything about that. Seeing that, I thought it was a good opportunity to see how good/Bad our Government is and it seems to be on the right path. Although the hawks (Intelligence and Counter-Terrorist Agencies) will probably become a bit more paranoid , their work becomes tougher.
Written in honor of current revisions for the DSM, expected to be published in 2013
The Diagnostic and Statistical Manual of the American Psychiatric Association currently defines Massive Email Anxiety Disorder (MEAD) disorder in the following way. Please note that while this definition of MEAD is the most definitive and clearly produced to date, there are several potential problems with this definition that will hopefully be addressed by the task forces, editors, and research coordinators of the association as time progresses. The Current DSM-IV Definition (Abridged): A. A persistent fear of one or more emails situations in which an author of an email worries about the status of a sent email. The individual fears that the tone or content of a message was misinterpreted or that an email never arrived to its correct destination. Alternatively, they worry excessively about why they have not received a response. B. Exposure to the feared situation almost invariably provokes anxiety, which may take the form of a situationally bound or situationally pre-disposed Panic Attack. C. The person recognizes that this fear is unreasonable or excessive. D. The feared situations are avoided or else are endured with intense anxiety and distress. Alternatively the person suffering from MEAD shuffles over to their partner or office-mate to talk (obsessively) about the nature and possible effect of the email, sometimes for hours, sometimes even for days. E. The avoidance, anxious anticipation, or distress in writing email, which interferes significantly with the person s normal routine, occupational (academic) functioning, or social activities or relationships, or there is marked distress about having the phobia. F. In individuals under age 18 years, the duration is at least 6 months. G. The fear or avoidance is not due to direct physiological effects of a substance (e.g., drugs, medications) or a general medical condition not better accounted for by another mental disorder. Problems with the DSM Definition of Massive Email Anxiety Disorder While this definition is clearly the most definitive and precise official definition produced so far, Massive Email Anxiety Disorder has only been officially recognized since 2020, and the problem did not become adequately explained until the 2015 version of the DSM. Thus, the definition of MEAD disorder is becoming clearer and more precise with each edition.
Written in honor of current revisions for the DSM, expected to be published in 2013
The Diagnostic and Statistical Manual of the American Psychiatric Association currently defines Massive Email Anxiety Disorder (MEAD) disorder in the following way. Please note that while this definition of MEAD is the most definitive and clearly produced to date, there are several potential problems with this definition that will hopefully be addressed by the task forces, editors, and research coordinators of the association as time progresses. The Current DSM-IV Definition (Abridged): A. A persistent fear of one or more emails situations in which an author of an email worries about the status of a sent email. The individual fears that the tone or content of a message was misinterpreted or that an email never arrived to its correct destination. Alternatively, they worry excessively about why they have not received a response. B. Exposure to the feared situation almost invariably provokes anxiety, which may take the form of a situationally bound or situationally pre-disposed Panic Attack. C. The person recognizes that this fear is unreasonable or excessive. D. The feared situations are avoided or else are endured with intense anxiety and distress. Alternatively the person suffering from MEAD shuffles over to their partner or office-mate to talk (obsessively) about the nature and possible effect of the email, sometimes for hours, sometimes even for days. E. The avoidance, anxious anticipation, or distress in writing email, which interferes significantly with the person s normal routine, occupational (academic) functioning, or social activities or relationships, or there is marked distress about having the phobia. F. In individuals under age 18 years, the duration is at least 6 months. G. The fear or avoidance is not due to direct physiological effects of a substance (e.g., drugs, medications) or a general medical condition not better accounted for by another mental disorder. Problems with the DSM Definition of Massive Email Anxiety Disorder While this definition is clearly the most definitive and precise official definition produced so far, Massive Email Anxiety Disorder has only been officially recognized since 2020, and the problem did not become adequately explained until the 2015 version of the DSM. Thus, the definition of MEAD disorder is becoming clearer and more precise with each edition.
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Nisamox Ethernet statistics for the last week | 23.38 KB |
Nisamox disk space statistics for the last week | 24.29 KB |
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