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Getting Started with Monitoring using Graphite.

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Hexagonal graph paper, also called hex paper, is a network of tiled hexagons that form a grid. This type of graph paper can be used when studying tessellations, but is more often used by quilt makers and computer gamers. Quilt makers use the hexagonal graph paper to design intricate piece-work patterns; gamers use it in the creation of maps to aid game navigation. Our hexagonal graph paper is measured by a regular hexagon inscribing a circle of a user specified diameter.

Probability graph paper is used when graphing variables along a normal distribution. This type of graph paper uses a probability scale along one axis and a linear scale along the other. This paper is mostly used in Statistics. Smith chart is a type of graph paper used in electrical engineering to plot variances of complex transmission impedance along its length.

Smith charts also simplify the matching of the line to its load. Image size Auto x x x x x x x x x x x x x x Font color. Background color. Your graffiti font letter is generated! Close Download. I would stay it is much tougher than any obstacle, mud or trail run.

Spartan and Tough Mudder races can not compare in toughness. Mountain Mist has been known for thick mud, rain, snow, sleet and extreme cold temperatures. The version of Mountain Mist had perfect conditions with warm temperatures and minimal mud.

The conditions were great but the course was still the same… it was hard. The year was hard for a lot of people. However, as I review the past twelve months, I see a theme of mental toughness. Beyond my endurance sports, mental toughness has played a huge role in how I attacked and conquered As I reflect, was like an Ironman Triathlon to me. In the first two months of the year, it was an easy swim. I started a new job at a camp, achieved a few certifications and attended a conference.

Nothing too taxing at the time. I have been told I can swim all day long. I ran a few races at Disney World… Dopey Challenge, and then a local half marathon where a blood hound ran the whole course. By the time March arrived, I had discovered that the camp I worked at was in need of major repair. This started the long miles on the bike. A constant churning at work to accomplish renovations and repairs was similar to riding miles in the summer heat.

I worked most everyday from March to August. I compare that to not having a great swim to bike transition during a triathlon.

Once camp started the number one focus was the campers and their needs. That meant training stopped for me. I was chugging way with focused intensity on the work that needed to be done. While at camp in Guntersville, my mom called to report my nephew had been in a 4-wheeler accident.

I scrambled to put things in place to leave camp. I relied on a great support team at camp to make this happen. They would have been my sherpas to continue the Ironman analogy.

God is good all of the time, Rhett survived the 4-wheeler accident but he would have to recover from a brain injury. It took three months of hospital and rehabilitation before he could go home. He is good! And in many ways, I think my family is stronger because of the accident.

It became the priority. I would rely on my mental toughness to grit through IM Chatty. I trained in the time I had left but nothing I trained for could prepare me for the heat in Chattanooga during the event. I needed to stay hydrated and keep my resiliency to cross the finish line. I had a few more events during the last three months of the year, this would be the marathon of my year long Ironman.

Mentally, I am tired after all of that. The last day of brought one last event, a 50K. This would be a great finish line to my year long Ironman mental toughness extravaganza.

Oh, I had a sinus infection just to add a level of difficulty to the simple 3 mile-loop course. I have mental toughness. I think the only reason, I convinced myself to grit this race out, was because of a book I read. In this book, billionaire Jesse Itzler, hires a former Navy SEAL and endurance athlete to live and train with him so he could conquer goals of his own.

It is a book devoted to mental toughness and how to train your body not to quit. I relied on my grit to get me out of foolish race events. I knew in my head, I could do two Ironman races in 41 days even if my body told me different. Yes, I am mentally tough. But what if my body was just as tough as my brain. If I complete most everything I do on limited training and my mental strength, what if focused more on training than ever before.

I ask what is the goal… gutting things out with mental toughness or bettering myself? Only forty-one days separated the two Ironman events. I woke up just a few minutes before my alarm clock at AM. I quickly dressed and got ready for the long day ahead of me. Nothing was out of the ordinary for me other than I was doing my first full distance triathlon somewhere other than Chattanooga, TN. This meant a drive to the race location not a walk down the street.

No worries, it was just about two miles up the beach. Body marking was fantastic! There was a guy who proudly proclaimed good penmanship. He was correct, he marked me perfectly with fantastic handwriting. Good start to the day!

Only thing I struggled with before the race was just not knowing certain things. No one felt the need to line up at the start line, because it was a rolling start. Everyone just wandered around during the hours before the start. I like structure and this was a little unnerving for me. As the sun started rising, I saw people headed toward the beach. Once I was on the beach, I was much calmer.

The water temperature for the race was This was a wet suit legal race, but I opted out of wearing my wet suit. I feel like I swim better without my wet suit. Yes, there is a buoyancy advantage with a wet suit… but I was in salt water. For me, it was the best thing.

I felt good in the swim and at times got a little hot. The water was calm but the swimmers around me were violent. Pushing, shoving, kicking and elbowing.

At one point, I thought it was MMA swimming not triathlon swimming. However, I did enjoy the swim. I saw glowing plankton, a jellyfish, two horseshoe crabs and a whole school of fish. I wonder what the fish think of all the triathletes swimming around them. I am sure the fish are not as excited to see me as I am to see them. I finished the swim in which was a little slow for me.

NOPE… everyone was single file like little ants marching up be beach. There was just more walking than I wanted. I knew my swim would be longer than Chatty, DUH. Transition was really good. It was a larger transition area to run through than other triathlons, but the changing area was the hotel convention hall! This was very nice compared to the changing tent at Chatty. I put my bike gear on, nutrition set and headed out for magical miles. The first few miles were down the coast line where we road between large high rise hotels and other tourist attractions.

At this point we had a crosswind, but shortly that would turn into a significant head wind. Wind would be the main storyline for most all of the athletes. Countless cyclist would pass me and comment on the wind. My hope was, if I had a head wind, I had to have a tail wind at some point on the course.

I would keep an eye on all the flags along the course waiting for the wind to turn in my favor. So many great and kindhearted triathletes. The volunteers and police officers kept us safe! I felt like the way they controlled traffic and directed us through turns and lane changes was fantastic.

There was so many locals who came out to cheer the athletes throughout the day. This was top notch. The course was pretty much flat, but there were some small up and downs. It was an enjoyable course for the most part. When I was headed back toward T2 and finally had that tailwind, an athlete passed me.

He was such a nice guy. He told me I was a strong cyclist and shared how difficult it was for him to get out of T1 because of his extreme sea sickness after the 2. I was encouraged by his compliment.

I also felt good because if he could overcome sea sickness, I could over come the strong winds on the bike. I rolled into T2 with a bike time of My mind and gear needed to quickly convert to the Each time I dismount from an Ironman bike, I thank God for safely getting me through the discipline without mechanical failure or some type of collision.

The first few miles were difficult because my Achilles tendon was very tight and pushed pain all the way into my calf. But as soon as I loosened my lower leg up, the pain was gone. I had everything I needed.

My hydration and my nutrition seemed to be dialed in early in the run. I even had my socks just like I liked them. Everything was great, especially the crowd support on the run course.

I even enjoyed running through the state park at the far end of the run course loop. Running along the sand dunes reminded me of Big Sur, just not as epic! I took the top off of my water bottle to fill it up with ice water. Seemed like a great plan. However, when I discarded the empty cups, I discarded the top to my water bottle.

I quickly had to figure out what I would do from mile twelve to the finish line. I emptied the pouch and pockets on my water bottle, drank all of the contents and handed to CK the Sherpa. I discarded the bottle and the negative thoughts about how I could do something so stupid.

I told myself, I would just walk the water station and beyond to get the water I needed. I ran the majority of the run all alone.

It was tough, but I did make friends with a runner wearing an University of Alabama jersey. Our favorite aid station, had a football score board and a platoon of Young Marines. These were just a couple of examples of the chatter I heard as I ran through the station.

It was amazing. Little did they know at one point in time, I decamped over a dozen Young Marines at Aviation Challenge. The second loop was very dark. I wished I had run with a head lamp. Running through the state park was extra dark and the pavement was uneven. With each step, the finish line was closer. The crowd at the finish line was large and very loud. Crossing the finish line with a marathon time of was a little emotional, especially since this was my second Ironman in 6 weeks.

I was very surprised to see one of the Super Pack at the finish line volunteering as a catcher. Kelsey was was there and recognized me. Not too shocking, I did have the exact same gear on in Florida as I did in Chattanooga. I gave her a sweaty hug and then tried to find my people. I was proud of my time.

Ironman is emotional and bigger than just one person. She drove over from Destin to cheer for me and hang out with CK.

It was humbling to know that someone I only kept up with on Facebook would be there for me at the finish line. But then again, so many people were there with me at the finish line. There were people watching the live coverage, following on social media posts or checking my location on the worthless Beacon application.

No matter what, long distance triathlon is a team sport. I was not only physically exhausted but mentally exhausted. The whole year leading up to Ironman Chattanooga was an exercise of endurance, resiliency and grit. But just like running an Ironman triathlon, you have to take one challenge, one hurdle or one difficulty at a time. So lets talk triathlon now. Pre Race I woke up much earlier than I had for other line-up starts. Typically, I like to be at the back of a swim line to stay out of traffic.

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But I felt like I would need every minute to finish this triathlon. I also wanted to enjoy as much of the early morning temperatures on the bike. The forecasters had predicted a high of 95 degrees for the day. I found my spot in line at 5 AM and camped out for the next hours with Roxanne, a triathlete from California with Alabama roots. At that point, I hoped and prayed I could make it to the finish line later in the evening.

Swim Not much to say about the swim. The water was 83 degrees and I felt very sorry for swimmers who chose to swim in wetsuits. I know the benefits of wearing a wetsuit, but the heat would be a factor all day long. As I entered the water, wanted to get away from the bank and near the center of the river, but I felt like I over shot that goal.

For the first half of the swim, I felt like I had set myself up poorly. I refocused and started spotting on a large white boat near the shoreline. Once I saw the bridges, I felt like I was home free. Those bridges, whether running over or swimming under, they are a welcome site. I popped out of the swim with a time. I knew the easiest part of my day was over and I needed to shift my mental focus from an easy river swim to a long bike ride through Georgia.

Bike The plan was to not push too hard on the bike. I had a pace set in my mind that could get me safely into T2 before the cut off time of PM. For all practical purposes, I took two months off during the summer while working at Camp Cha La Kee. No matter what, I was on the bike and as I crossed into Georgia the reality was slow and steady finishes the race.

The first loop was somewhat pleasant and enjoyable. I was happy with the partial shade and cooler temperatures on most of the first 40 miles.

My plan of an early start was working as I kept moving forward. Around mile 25, an athlete passed me who I clearly seemed to not be paying attention. I slowed my cadence and watched him carefully as I distance my self from him.

Roughly 30 seconds later, there were four cyclists caught in his cone of chaos. Everyone was OK, but water bottles, bikes, nutrition and people were scattered along the road. I stopped and did all I could to help the riders and clear the path. The second loop was much hotter than the first. The temperature hit a high of 97 during the last half of the miles.

The pavement felt like a hot skillet radiating heat. Because of the extreme heat, I questioned, what did a DNF look like or feel like mentally and physically?

If you can identify the monster it is easier to stay away from the monster. I decided to create rules for myself. With these simple rules, I kept moving forward. To survive the heat, everyone had focused intensity that would rival hardened professional triathletes.

My focus had to be on the finish line and nothing else. As Admiral Wm. I started to drink more fluids worried that dehydration would get the best of me. I drank more and peddled as hard as I could. My bike and I returned to T2 just before PM. Plenty of cushion between me and the bike cut off time. Run I felt OK as I transitioned from cycling to running. Only trouble I had in T2 was my socks.

We looked for the left sock for a bit before finding it in her hand. No worries! I could hear the sweet sound of cowbells as I started the run.

 
 

 

Persecond graphite free –

 
simply sum series: sumSeries(*), the result metrics is many times greater than the actual value;; scale to 1 second. Use hosted Graphite from MetricFire and monitor your metrics easily. book a demo with our experts or sign up for a free trial today. Analyzing Graphite metrics usage This page explains: Finding which are the more These are counters, so use perSecond() to see the rate per second.

 
 

– 25 Graphite, Grafana and Statsd gotchas | Grafana Labs

 
 

All three are fantastic tools and solve very real problems. Hence peraecond continued use and recommendation. I hope this will prove useful to users while we, open source monitoring persecond graphite free, work on ironing out these kinks. For network sniffing, I almost always persecond graphite free these commands: ngrep -d any -W byline port carbon persecond graphite free ngrep -d any -W byline port statsd traffic.

Many dashboards, including Grafana already do this, so you can use the browser network inspector persecond graphite free analyze requests. For the whisper files, Graphite comes with various useful utilities such as whisper-info, whisper-dump, whisper-fetch, etc.

Did all our traffic just drop? Null handling in math functions. Null handling during runtime consolidation. No consolidation or aggregation for источник статьи data. Limited storage aggregation persecond graphite free. Runtime consolidation is detached from storage aggregation. Grafana consolidation is persecond graphite free from storage aggregation and runtime consolidation.

Aggregating percentiles. Deriving and integration in Graphite. Graphite quantization. Improperly time-attributed metrics. The persecond graphite free between timestamps and the intervals they describe. The statsd timing type is not only for timings. The choice of metric keys depends on how you посетить страницу источник statsd. What can I send as input? Hostnames and ip addresses in metric keys.

Example: if your data is stored at 10s resolution, then atit will show data up until Once the clock hitsit will also include that point in its response. It typically takes some time for perseconr services to send data for that timestamp, and for it читать больше be processed by Graphite, so Graphite will typically return persecond graphite free null here for that timestamp. See below for some other related issues. When you request something like sumSeries diskspace.

If all of the servers geaphite a null for a given timestamp, the result will be null as well for that time. However, if only some – but not all – of the terms are null, they are counted as 0. So when some of the series have the occasional null, it may look like a drop in the summed series. Building this on top of Graphite would make a lot of sense, and in all нажмите сюда everything works fine, but in production the decisions rfee by the algorithm seem nonsensical, because the storage capacity seems to fluctuate all over the place.

Lesson learned. No videos were hurt. But a low number of nulls in a large sum is usually not a big persecond graphite free and having a result with the nulls counted as 0 can be more useful than no result at all. Especially when averaging, since those points can typically be safely excluded without impact on the output. Graphite has the persecond graphite free option in storage-aggregation.

It would be nice if these on-demand math requests would take an xFilesFactor argument to do the same thing. Imagine a series that measures throughput with points 10, 12, 11, null, нажмите сюда, 10, 11, 12, This will visually look like a drop in throughput, even though there probably was none.

For some functions like avg, a missing value amongst several valid values is usually not a big deal, but the likeliness of gdaphite becoming a big deal increases with the amount of nulls, especially with sums.

For runtime consolidation, Graphite needs something similar to the xFilesFactor setting for rollups. Never send multiple values for the same interval. However, carbon-aggregator or carbon-relay-ng may be of help see above. In storage-aggregation. It can be limiting that you can only choose one. Often you may want to retain both the max persecond graphite free as well as the averages for example. This feature exists in RRDtool. Another issue with this is that often these functions will be misconfigured.

The function chosen in peesecond. If Graphite performs any runtime consolidation it will always use average unless told otherwise through consolidateBy.

It would be nice if hraphite configured roll-up function would also apply here and also the xFilesFactor, as aboveFor now, just be careful :. For now, it just computes these from the received data, which may already have been consolidated twice: in storage and in runtime using different functions, so these results persecond graphite free not be always representative. Pwrsecond persecond graphite free peeve of many, it has been written about a lot: If you have percentiles, such as those collected by statsd, e.

This issue appears when rolling up the data in the storage layer, as well when doing runtime consolidation, or when trying to combine the data for all servers multiple series graphige. This is especially true when the amount of requests, represented by each latency measurement, is in the same order of magnitude.

You can always set up a separate alerting rule for unbalanced servers or drops in throughput. We already saw in the consolidation paragraph that for multiple points per interval, last write wins. But you should also know that any data point submitted gets the timestamp rounded down. Example : You record points every 10 seconds but submit a point with timestamp atin Graphite this will be stored at To persecond graphite free more precise if you submit points atandand have 10s resolution, Graphite will pick the point graphitw but store it at Statsd lets you configure a flushInterval, i.

However, the exact timing is pretty arbitrary and depends grxphite when statsd is started. Example : If you start statsd at with a flushInterval of 10, then it will emit values with timestamps at, etc this is what you wantbut if you happened to start it at then persecond graphite free will submit values with timestamps, etc. In the latter example, by 9 seconds. This can make troubleshooting harder, especially when comparing statsd metrics to metrics persecond graphite free a different service.

Statsd applies its own timestamp when it flushes the data. So this persecond graphite free prone to various mostly network delays. This could result in a metric being generated in a certain interval only arriving in statsd after the next interval has prrsecond. But it can get worse. Note that the metric is only sent after the full query has completed. So during the full minute where queries were slow, there are no metrics or only some metrics that look good, they came through persecond graphite free they graphjte part of a group of queries that managed to execute timelyand only after a minute do you get the stats that reflect queries spawned a minute ago.

The higher a timing value, the higher the attribution error, the more into the past the values it represents and the longer the issue will go perwecond or invisible. Keep in mind that other things, such as garbage collection cycles or paused goroutines under cpu saturation may also delay your metrics reporting.

Also persecond graphite free out for the queries aborting all together, causing the metrics never to be sent and these faults to be invisble! Make sure you properly monitor throughput and the functioning timeouts, errors, etc of the service from the client perspective, to get a more accurate picture. When I look at a point at a graph that represents a spike in latency, a drop in throughput, or anything interesting really, I always wonder whether it describes the timeframe before, or after it.

Example : With points every minute, and a spike atdoes it mean the spike happened in the timeframe between andor between and ? As we already saw statsd postmarks, and many tools seem to do this, but some, including Graphite, premark.

We saw above that any persecond graphite free received by Graphite for a point in between an interval is adjusted to get the timestamp at the beginning of the interval. Furthermore, during aggregation say, aggregating sets of 10 minutely points into 10min pointseach 10 minutes taken together get assigned the timestamp that precedes those 10 intervals. So essentially, Graphite likes to show metric values before they actually happened, especially after persecond graphite free, whereas other tools rather use a timestamp in the future of the event than in the past.

As a monitoring persecond graphite free, we should probably standardize on an approach. I personally favor postmarking because measurements being late is fairly intuitive, predicting the future not so much. The naming is a bit confusing, but anything you want to compute summary statistics min, max, mean, percentiles, etc for for example message or packet sizes can be submitted as a timing metric.

Note that if you want to time an operation that happens at consistent intervals, you may just as persecohd simply use a statsd gauge persecond graphite free it. This is a детальнее на этой странице way to assure you have a lot of processing capacity. However, if multiple statsd servers receive the same metric, they will узнать больше здесь their own independent computations and emit the same output metric, overwriting each other.

This probably stems from this snippet in the original statsd announcement. UDP sends do have an overhead that can slow your application down. Freee specific, it depends on the following factors:. Your network stack. UDP sends are not asynchronous, which is a common assumption.

I found this out the hard way once when an application I was testing on my laptop ran much slower than persecond graphite free due to my consumer grade WiFi adapter, which was holding back my program by hanging on statsd calls. This is a common, and the original setting, for statsd instrumentation calls. But I have conducted cpu profiling of high-performance Golang applications, both at Vimeo and raintank, and when statsd was used, the apps were often spending most of their time in UDP writes caused by statsd invocations.

Typically they were using a simple statsd client, where each statsd call corresponds to a UDP write. There are some statsd clients that buffer messages and send batches of data to persecond graphite free, reducing the UDP writes, which is a lot more efficient. You can also use sampling to lower the volume. This does come with some problems however, see the gotcha below.

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