I have a very basic question and I didn't get if I googled wrong or if the answer is so simple that I haven't seen it.
I'm implementing a web app using hls.js as Javascript library and I need a way to get the absolute elapsed time of a live streaming e.g. if a user join the live after 10 minutes, I need a way to detect that the user's 1st second is 601st second of the streaming.
Inspecting the streaming fragments I found some information like startPTS and endPTS, but all these information were always related to the retrieved chunks instead of the whole streaming chunks e.g. if a user join the live after 10 minutes and the chunks duration is 2 seconds, the first chunk I'll get will have startPTS = 0 and endPTS = 2, the second chunk I'll get will have startPTS = 2 and endPTS = 4 and so on (rounding the values to the nearest integer).
Is there a way to extract the absolute elapsed time as I need from an HLS live streaming ?
I'm having the exact same need on iOS (AVPlayer) and came with the following solution:
read the m3u8 manifest, for me it looks like this:
#EXTM3U
#EXT-X-VERSION:3
#EXT-X-MEDIA-SEQUENCE:410
#EXT-X-TARGETDURATION:8
#EXTINF:8.333,
410.ts
#EXTINF:8.333,
411.ts
#EXTINF:8.334,
412.ts
#EXTINF:8.333,
413.ts
#EXTINF:8.333,
414.ts
#EXTINF:8.334,
415.ts
Observe that the 409 first segments are not part of the manifest
Multiply EXT-X-MEDIA-SEQUENCE by EXT-X-TARGETDURATION and you have an approximation of the clock time for the first available segment.
Let's also notice that each segment is not exactly 8s long, so when I'm using the target duration, I'm actually accumulating an error of about 333ms per segment:
410 * 8 = 3280 seconds = 54.6666 minutes
In this case for me the segments are always 8.333 or 8.334, so by EXTINF instead, I get:
410 * 8.333 = 3416.53 seconds = 56.9421 minutes
These almost 56.9421 minutes is still an approximation (since we don't exactly know how many time we accumulated the new 0.001 error), but it's much much closer to the real clock time.
Related
I am using Databricks to create an algorithm for big data. I am wondering why the last 1% of my running process takes a lot of time?
I am writing the result in S3, the result for 111991 data (out of 116367) is done in 5 minutes and just for the last 5000 takes more than a hour!!!!!
can I fix this issue?
in the following picture it takes hour 119 become 120, but it came to 199 in a few minutes
Please check you are writing file in one shot or writing in one chunk.
If you are writing in one shot some time it switching log will take time. Also check if you are printing logs then it may take time.
I have a rrd file with average ping times to a server (GAUGE) every minute and when the server is offline (which is very frequent for reasons that doesn't matter now) it stores a NaN/unknown.
I'd like to create a graph with the percentage the server is offline each hour which I think can be achieved by counting every NaN within 60 samples and then dividing by 60.
For now I get to the point where I define a variable that is 1 when the server is offline and 0 otherwise, but I already read the docs and don't know how to aggregate this:
DEF:avg=server.rrd:rtt:AVERAGE CDEF:offline=avg,UN,1,0,IF
Is it possible to do this when creating a graph? Or I will have to store that info in another rrd?
I don't think you can do exactly what you want, but you have a couple of options.
You can define a sliding window average, that shows the percentage of the previous hour that was unknown, and graph that, using TRENDNAN.
DEF:avg=server.rrd:rtt:AVERAGE:step=60
CDEF:offline=avg,UN,100,0,IF
CDEF:pcavail=offline,3600,TREND
LINE:pcavail#ff0000:Availability
This defines avg as the 1-min time series of ping data. Note we use step=60 to ensure we get the best resolution of data even in a smaller graph. Then we define offline as 100 when the server is there, 0 when not. Then, pcavail is a 1-hour sliding window average of this, which will in effect be the percentage of time during the previous hour during which the server was available.
However, there's a problem in that RRDTool will silently summarise the source data before you get your hands on it, if there are many data points to a pixel in the graph (this won't happen if doing a fetch of course). To get around that, you'd need to have the offline CDEF done at store time -- IE, have a COMPUTE type DS that is 100 or 0 depending on if the avg DS is known. Then, any averaging will preserve data (normal averaging omits the unknowns, or the xff setting makes the whole cdp unknown).
rrdtool create ...
DS:rtt:GAUGE:120:0:9999
DS:offline:COMPUTE:rtt,UN,100,0,IF
rrdtool graph ...
DEF:offline=server.rrd:offline:AVERAGE:step=3600
LINE:offline#ff0000:Availability
If you are able to modify your RRD, and do not need historical data, then use of a COMPUTE in this way will allow you to display your data in a 1-hour stepped graph as you wanted.
I would like to process a real-time stream of data (from Kafka) using Spark Streaming. I need to compute various stats from the incoming stream and they need to be computed for windows of varying durations. For example, I might need to compute the avg value of a stat 'A' for the last 5 mins while at the same time compute the median for stat 'B' for the last 1 hour.
In this case, what's the recommended approach to using Spark Streaming? Below are a few options I could think of:
(i) Have a single DStream from Kafka and create multiple DStreams from it using the window() method. For each of these resulting DStreams, the windowDuration would be set to different values as required. eg:
// pseudo-code
val streamA = kafkaDStream.window(Minutes(5), Minutes(1))
val streamB = kafkaDStream.window(Hours(1), Minutes(10))
(ii) Run separate Spark Streaming apps - one for each stat
Questions
To me (i) seems like a more efficient approach. However, I have a couple of doubts regarding that:
How would streamA and streamB be represented in the underlying
datastructure.
Would they share data - since they originate from the
KafkaDStream? Or would there be duplication of data?
Also, are there more efficient methods to handle such a use case.
Thanks in advance
Your (i) streams look sensible, will share data, and you can look at WindowedDStream to get an idea of the underlying representation. Note your streams are of course lazy, so only the batches being computed upon are in the system at any given time.
Since the state you have to maintain for the computation of an average is small (2 numbers), you should be fine. I'm more worried about the median (which requires a pair of heaps).
One thing you haven't made clear, though, is if you really need the update component of your aggregation that is implied by the windowing operation. Your streamA maintains the last 5 minutes of data, updated every minute, and streamB maintains the last hour updated every 10 minutes.
If you don't need that freshness, not requiring it will of course should minimize the amount of data in the system. You can have a streamA with a batch interval of 5mins and a streamB which is deducted from it (with window(Hours(1)), since 60 is a multiple of 5) .
I'm using JMeter client to test the throughtput of a certain workload (PHP+MySQL, 1 page) on a certain server. Basically I'm doing a "capacity test" with an increasing number of threads over the time.
I installed the "Statistical Aggregate Report" JMeter plugin and this was the result (ignore the "Response time" line):
At the same time I used the "Simple Data Writer" listener to write a log file ("JMeter.csv"). Then I tried to "manually" calculate the throughput for every second of the test.
Each line of "JMeter.csv" has this format:
timestamp elaspedtime responsecode success bytes
1385731020607 42 200 true 325
... ... ... ... ...
The timestamp is referred to the time when the request is made by the client, and not when the request is served by the server. So I simply did: totaltime = timestamp + elapsedtime.
In the next step I converted the totaltime to a date format, like: 13:17:01.
I have more than 14K samples and with Excel I was able to do this quickly.
Then I counted how many samples there were for each second. Example:
totaltime samples (requestsServed/second)
13:17:01 204
13:17:02 297
... ...
When I tried to plot the results I obtained the following graphic:
As you can notice it is far different from the first graphic.
Given that the first graphic is correct, what is the mistake of my formula/procedure to calculate the throughput?
It turns out that this plugin is plotting something that I don't know... I tried many times and my considerations were actually correct. Be careful with this plugin (or check its source code).
Throughput can be view in Jmeter Summary Report and you can calculate by saving your Test Results file in xml file in Summary Report.
Throughput = Number of samples/(Max (ts+t) - Min ts)*1000
Throughput = (Number of samples/The difference between Maximum and minimum response time)*1000
By this formula you can calculate Throughput for each and every http requests in Summary Report.
Example:
Max Response Time = 1485538701633+569 = 1485538702202
Min Response Time = 1485538143112
Throughput = (2/1485538702202-1485538143112)*1000
Throughput = (2/1505) *1000
Throughput = 0.00132890*1000
Throughput = 1.3/sec
You can read more with examples Here(http://www.wikishown.com/how-to-calculate-throughput-in-jmeter/), i got a good idea about Throughput Calculation.
We have a metric that we increment every time a user performs a certain action on our website, but the graphs don't seem to be accurate.
So going off this hunch, we invested the updates.log of carbon and discovered that the action had happened over 4 thousand times today(using grep and wc), but according the Integral result of the graph it returned only 220ish.
What could be the cause of this? Data is being reported to statsd using the statsd php library, and calling statsd::increment('metric'); and as stated above, the log confirms that 4,000+ updates to this key happened today.
We are using:
graphite 0.9.6 with statsD (etsy)
After some research through the documentation, and some conversations with others, I've found the problem - and the solution.
The way the whisper file format is designed, it expect you (or your application) to publish updates no faster than the minimum interval in your storage-schemas.conf file. This file is used to configure how much data retention you have at different time interval resolutions.
My storage-schemas.conf file was set with a minimum retention time of 1 minute. The default StatsD daemon (from etsy) is designed to update to carbon (the graphite daemon) every 10 seconds. The reason this is a problem is: over a 60 second period StatsD reports 6 times, each write overwrites the last one (in that 60 second interval, because you're updating faster than once per minute). This produces really weird results on your graph because the last 10 seconds in a minute could be completely dead and report a 0 for the activity during that period, which results in completely nuking all of the data you had written for that minute.
To fix this, I had to re-configure my storage-schemas.conf file to store data at a maximum resolution of 10 seconds, so every update from StatsD would be saved in the whisper database without being overwritten.
Etsy published the storage-schemas.conf configuration that they were using for their installation of carbon, which looks like this:
[stats]
priority = 110
pattern = ^stats\..*
retentions = 10:2160,60:10080,600:262974
This has a 10 second minimum retention time, and stores 6 hours worth of them. However, due to my next problem, I extended the retention periods significantly.
As I let this data collect for a few days, I noticed that it still looked off (and was under reporting). This was due to 2 problems.
StatsD (older versions) only reported an average number of events per second for each 10 second reporting period. This means, if you incremented a key 100 times in 1 second and 0 times for the next 9 seconds, at the end of the 10th second statsD would report 10 to graphite, instead of 100. (100/10 = 10). This failed to report the total number of events for a 10 second period (obviously).Newer versions of statsD fix this problem, as they introduced the stats_counts bucket, which logs the total # of events per metric for each 10 second period (so instead of reporting 10 in the previous example, it reports 100).After I upgraded StatsD, I noticed that the last 6 hours of data looked great, but as I looked beyond the last 6 hours - things looked weird, and the next reason is why:
As graphite stores data, it moves data from high precision retention to lower precision retention. This means, using the etsy storage-schemas.conf example, after 6 hours of 10 second precision, data was moved to 60 second (1 minute) precision. In order to move 6 data points from 10s to 60s precision, graphite does an average of the 6 data points. So it'd take the total value of the oldest 6 data points, and divide it by 6. This gives an average # of events per 10 seconds for that 60 second period (and not the total # of events, which is what we care about specifically).This is just how graphite is designed, and for some cases it might be useful, but in our case, it's not what we wanted. To "fix" this problem, I increased our 10 second precision retention time to 60 days. Beyond 60 days, I store the minutely and 10-minutely precisions, but they're essentially there for no reason, as that data isn't as useful to us.
I hope this helps someone, I know it annoyed me for a few days - and I know there isn't a huge community of people that are using this stack of software for this purpose, so it took a bit of research to really figure out what was going on and how to get a result that I wanted.
After posting my comment above I found Graphite 0.9.9 has a (new?) configuration file, storage-aggregation.conf, in which one can control the aggregation method per pattern. The available options are average, sum, min, max, and last.
http://readthedocs.org/docs/graphite/en/latest/config-carbon.html#storage-aggregation-conf