I am using a sort of code_ping for the time it took to process the whole page, to all my pages in my webportal.
I figured if I do a $count_start in the header initialised with current timestamp and a $count_end in the footer, the same, the difference is a meter to roughly let me know how well optimised the page is (queries, loading time of all things in that particular page).
Say for one page i get 0.0075 seconds, for others I get 0.045 etc...i'm working on optimising the queries better this way.
My question is. If one page says by this meter "rough loading time" that has 0.007 seconds,
will 1000 users querying the same page at the same time get each the result in 0.007 * 1000 = 7 seconds ? meaning they will each get the page after 7 seconds ?
thanks
Luckily, it doesn't usually mean that.
The missing variable in your equation is how your database and your application server and anything else in your stack handles concurrency.
To illustrate this strictly from the MySQL perspective, I wrote a test client program that establishes a fixed number of connections to the MySQL server, each in its own thread (and so, able to issue a query to the server at approximately the same time).
Once all of the threads have signaled back that they are connected, a message is sent to all of them at the same time, to send their query.
When each thread gets the "go" signal, it looks at the current system time, then sends the query to the server. When it gets the response, it looks at the system time again, and then sends all of the information back to the main thread, which compares the timings and generates the output below.
The program is written in such a way that it does not count the time required to establish the connections to the server, since in a well-behaved application the connections would be reusable.
The query was SELECT SQL_NO_CACHE COUNT(1) FROM ... (an InnoDB table with about 500 rows in it).
threads 1 min 0.001089 max 0.001089 avg 0.001089 total runtime 0.001089
threads 2 min 0.001200 max 0.002951 avg 0.002076 total runtime 0.003106
threads 4 min 0.000987 max 0.001432 avg 0.001176 total runtime 0.001677
threads 8 min 0.001110 max 0.002789 avg 0.001894 total runtime 0.003796
threads 16 min 0.001222 max 0.005142 avg 0.002707 total runtime 0.005591
threads 32 min 0.001187 max 0.010924 avg 0.003786 total runtime 0.014812
threads 64 min 0.001209 max 0.014941 avg 0.005586 total runtime 0.019841
Times are in seconds. The min/max/avg are the best/worst/average times observed running the same query. At a concurrency of 64, you notice the best case wasn't all that different than the best case with only 1 query. But biggest take-away here is the total runtime column. That value is the difference in time from when the first thread sent its query (they all send their query at essentially the same time, but "precisely" the same time is impossible since I don't have a 64-core machine to run the test script on) to when the last thread received its response.
Observations: the good news is that the 64 queries taking an average of 0.005586 seconds definitely did not require 64 * 0.005586 seconds = 0.357504 seconds to execute... it didn't even require 64 * 0.001089 (the best case time) = 0.069696 All of those queries were started and finished within 0.019841 seconds... or only about 28.5% of the time it would have theoretically taken for them to run one-after-another.
The bad news, of course, is that the average execution time on this query at a concurrency of 64 is over 5 times as high as the time when it's only run once... and the worst case is almost 14 times as high. But that's still far better than a linear extrapolation from the single-query execution time would suggest.
Things don't scale indefinitely, though. As you can see, the performance does deteriorate with concurrency and at some point it would go downhill -- probably fairly rapidly -- as we reached whichever bottleneck occurred first. The number of tables, the nature of the queries, any locking that is encountered, all contribute to how the server performs under concurrent loads, as do the performance of your storage, the size, performance, and architecture, of the system's memory, and the internals of MySQL -- some of which can be tuned and some of which can't.
But of course, the database isn't the only factor. The way the application server handles concurrent requests can be another big part of your performance under load, sometimes to a larger extent than the database, and sometimes less.
One big unknown from your benchmarks is how much of that time is spent by the database answering the queries, how much of the time is spent by the application server executing the logic business, and how much of the time is spent by the code that is rendering the page results into HTML.
Related
I need to compare few scenarios, which can be fulfilled either by calling:
an additional timestamp column from mysql database or
Looping over the resultant array.
Elaborating further:
CASE 1: 144 byte columns + 4 byte timestamp column for 10K rows, then looping on array of size 50.(download size- 1480000 Bytes)
CASE 2: 144 byte columns for 10K rows, looping on array of size 10000. (download size- 1440000 bytes)
Data download roughly 40KB more for Case1 while 10000 more loop iteration for case2.
Which of the 2 scenarios could be faster, downloading 40KB more or 10000 loop iterations?
Your first scenario is by far the best. Here is why.
SQL is designed to extract subsets of rows from tables. It's designed to allow your tables to be many orders of magnitude bigger than your RAM. If you use a query like
SELECT *
FROM mytable
WHERE mytimestamp >= TODAY() - INTERVAL 1 DAY
AND mytimestamp < TODAY()
you will get all the rows with timestamps anytime yesterday, for example. If you put an index on the mytimestamp column, SQL will satisfy this query very quickly and only give the rows you need. And it will satisfy any queries looking for a range of timestamps similarly quickly.
There are no answers that are true in 100% of situations. For example, I would prefer to do the first query when I use a fast enough network (anything 1Gbps or higher) and data transfer is free. The difference in the example you show is 40,000 bytes, but it's only 2.7% of the total.
On the other hand, if you need to optimize to minimize bandwidth usage, that's a different scenario. Like if you are transmitting data to a satellite over a very slow link. Or you have a lot of competing network traffic (enough to use up all the bandwidth), and saving 2.7% is significant. Or if you are using a cloud vendor that charges for total bytes transferred on the network.
You aren't taking into account the overhead of executing 1000 separate queries. That means 1000x the bytes sent to the database server, as you send queries. That takes some network bandwidth too. Also the database server needs to parse and optimize each query (MySQL does not cache optimization plans as some other RDBMS products do). And then begin executing the query, starting with an index lookup without the context of the previous query result.
"Performance" is a slippery term. You seem to be concerned only with bandwidth use, but there are other ways of measuring performance.
Throughput (bytes per second)
Latency (seconds per response)
Wait time (seconds until the request begins)
All of these can be affected by the total load, i.e. number of concurrent requests. A busy system may have traffic congestion or request queueing.
You can see that this isn't a simple problem.
I have two queries whose timing parameters I want to analyze.
The first query is taking much longer than the second one while in my opinion it should be the other way round.Any Explanations?
First query:
select mrn
from EncounterInformation
limit 20;
Second query:
select enc.mrn, fl.fileallocationid
from EncounterInformation as enc inner join
FileAllocation as fl
on enc.encounterIndexId = fl.encounterid
limit 20;
The first query runs in 0.760 seconds on MYSQL while second one runs in 0.509 seconds surprisingly.
There are many reasons why measured performance between two queries might be different:
The execution plans for the queries (the dominant factor)
The size of the data being returned (perhaps mrn is a string that is really long for the result set in the first query but not the second)
Other database activity, that locks tables and indexes
Other server activity
Data and index caches that are pre-loaded -- either in the database itself or in the underlying OS components
Your observation is correct. The first should be faster than the second. More importantly though is the observation that this simply does not make sense for your simple query:
The first query runs in 0.760 seconds
select mrn
from EncounterInformation
limit 20;
The work done for this would typically be to load one data page (or maybe a handful). That would only consistently take 0.760 seconds if:
You had really slow data storage (think "carrier pigeons").
EncounterInformation is a view and not a table.
You don't understand timings.
If the difference is between 0.760 milliseconds and 0.509 milliseconds, then the difference is really small and likely due to other issues -- warm caches, other activity on the server, other database activity.
It is also possible that you are measuring elapsed time and not database time, so network congestion could potentially be an issue.
If you are querying views, all bets are off without knowing what the views are. In fact, if you care about performance you should be including the execution plan in the question.
I can't explain the difference. What I can say is that your observation is reasonable, but your question lacks lots of information that suggests you need to learn more about how to understand timings. I would suggest that you start with learning about explain.
Below I executed a same select query five times (one after the other) and for each request the database takes different time to return the response. How cold that be possible?
Request to Database -- took 10 ms.
Request to Database -- took 6ms.
Request to Database -- took 12 ms
Request to Database -- took6 ms
Request to Database -- took 9 ms
Thanks
Shashidhar
Let's see the data. The minimum time needed was 50% of the maximum time needed. Generally, if the minimum is 80% of the maximum, then the needed time is considered to be fairly stable. If it is 40% or more, but less than 80%, then there is a moderate variance in needed time. If it is below 40%, then it can be considered volatile. Your needed time has a variance, but it is not volatile yet.
The cause of this variance depends on the tasks the server has to do while running your query. If there are a lot of requests to the database server, then, due to the increased concurrency the needed time is increased. Also, if data is varied (a lot of insertions and deletions occur in any short period of time), then selection's speed might increase or decrease, depending on the number of rows the table has and the number of rows matching the where criteria. If the table structure is varied (for instance an index is added or removed often), that may vary the performance of your operation as well. Last, but not least, if you did not actually check that the query being executed was the same each time, then there are chances that there were differences, especially if the query was generated dynamically.
So, the things you have observed are not strange, but whole minutes needed for a query to be executed seems to be a lot of time and maybe you should think about optimizing things.
We have lot of MySQL select queries for some reporting need. Most are little complex and they generally include 1. five-six join statements 2. three-four inner queries in select clause.
All the indexes are properly in place in the production environment. We have checked with explain query syntax multiple time and they are OK.
Some of the query behave very strangely in in terms of response time. The same query returns in less than 500 milli secs at times (which shows all index working fine), and when we run it after 1 min or so - it gives result with a high response time (varying from five-six seconds to 30 seconds.) Some time (around once in 20 times..) it gives a time out error.
This might be due to server load - but the high variance is so frequent that we think there is something else to set to solve it.
Can some one please show me some direction on what else to do!
Thanks,
Sumit
This kind of behaviour is usually caused by a bottleneck in your stack.
It's like a rotating door in a building - the door can handle 1 person at a time, and each person takes 3 seconds; as long as people don't arrive at a rate over 1 person every 3 seconds, you don't know it's a bottleneck. If people arrive at a faster rate for a short period of time, the queue grows a little but disappears quickly. If people arrive at a rate of 1 person every 2.5 seconds for an hour, the queue becomes unmanageable, and can take far longer than that 1 hour to disappear.
Your database system is made up of a long corridor with rotating doors - most doors can operate in parallel, but they are all limited.
(Sorry for the rubbish analogy, but I find it helps to visualize these things with real-world images).
If the queries are showing a high degree of variance in their performance profile, I'd look at the system performance monitor (top in Linux, Perfmon in windows) and try to correlate slow performance with the behaviour of the system. If you see a sudden spike in CPU utilization when the queries slow down, that's likely to be your bottleneck; if you see a sudden spike in disk throughput, you might look there.
Once you have a hypothesis about the bottleneck, you can look at ways of resolving them - throwing hardware at the problem is usually cheapest.
Inspired by this xckd cartoon I wondered exactly what is the best mechanism to provide an estimate to the user of a file copy / movement?
The alt tag on xkcd reads as follows:
They could say "the connection is probably lost," but it's more fun to do naive time-averaging to give you hope that if you wait around for 1,163 hours, it will finally finish.
Ignoring the funny, is that really how it's done in Windows? How about other OS? Is there a better way?
Have a look at my answer to a similar question (and the other answers there) on how the remaining time is estimated in Windows Explorer.
In my opinion, there is only one way to get good estimates:
Calculate the exact number of bytes to be copied before you begin the copy process
Recalculate you estimate regularly (every 1, 5 or 10 seconds, YMMV) based on the current transfer speed
The current transfer speed can fluctuate heavily when you are copying on a network, so use an average, for example based on the amount of bytes transfered since your last estimate.
Note that the first point may require quite some work, if you are copying many files. That is probably why the guys from Microsoft decided to go without it. You need to decide yourself if the additional overhead created by that calculation is worth giving your user a better estimate.
I've done something similar to estimate when a queue will be empty, given that items are being dequeued faster than they are being enqueued. I used linear regression over the most recent N readings of (time,queue size).
This gives better results than a naive
(bytes_copied_so_far / elapsed_time) * bytes_left_to_copy
Start a global timer that fires say, every 1000 milliseconds and update a total elpased time counter. Let's call this variable "elapsedTime"
While the file is being copied, update some local variable with the amount already copied. Let's call this variable "totalCopied"
In the timer event that is periodically raised, divide totalCopied by totalElapsed to give the number of bytes copied per timer interval (in this case, 1000ms). Let's call this variable "bytesPerSec"
Divide the total file size by bytesPerSec and obtain the total number of seconds theoretically required to copy this file. Let's call this variable remainingTime
Subtract elapsedTime from remainingTime and you a somewhat accurate calculation for file copy time.
I think dialogs should just admit their limitations. It's not annoying because it's failing to give a useful time estimate, it's annoying because it's authoritatively offering an estimate that's obvious nonsense.
So, estimate however you like, based on current rate or average rate so far, rolling averages discarding outliers, or whatever. Depends on the operation and the typical durations of events which delay it, so you might have different algorithms when you know the file copy involves a network drive. But until your estimate has been fairly consistent for a period of time equal to the lesser of 30 seconds or 10% of the estimated time, display "oh dear, there seems to be some kind of holdup" when it's massively slowed, or just ignore it if it's massively sped up.
For example, dialog messages taken at 1-second intervals when a connection briefly stalls:
remaining: 60 seconds // estimate is 60 seconds
remaining: 59 seconds // estimate is 59 seconds
remaining: delayed [was 59 seconds] // estimate is 12 hours
remaining: delayed [was 59 seconds] // estimate is infinity
remaining: delayed [was 59 seconds] // got data: estimate is 59 seconds
// six seconds later
remaining: 53 seconds // estimate is 53 seconds
Most of all I would never display seconds (only hours and minutes). I think it's really frustrating when you sit there and wait for a minute while the timer jumps between 10 and 20 seconds. And always display real information like: xxx/yyyy MB copied.
I would also include something like this:
if timeLeft > 5h --> Inform user that this might not work properly
if timeLeft > 10h --> Inform user that there might be better ways to move the file
if timeLeft > 24h --> Abort and check for problems
I would also inform the user if the estimated time varies too much
And if it's not too complicated, there should be an auto-check function that checks if the process is still alive and working properly every 1-10 minutes (depending on the application).
speaking about network file copy, the best thing is to calculate file size to be transfered, network response and etc. An approach that i used once was:
Connection speed = Ping and calculate the round trip time for packages with 15 Kbytes.
Get my file size and see, theorically, how many time it would take if i would break it in
15 kb packages using my connection speed.
Recalculate my connection speed after transfer is started and ajust the time that will be spended.
I've been pondering on this one myself. I have a copy routine - via a Windows Explorer style interface - which allows the transfer of selected files from an Android Device, to a PC.
At the start, I know the total size of the file(s) that are to be copied, and as I am using C#.NET, I am using a Stopwatch, to get the elapsed time, and while the copy is in progress, I am keeping a total of what is copied so far, in terms of bytes.
I haven't actually tested it yet, but the best way seems to be this -
estimated = elapsed * ((totalSize - copiedSoFar) / copiedSoFar)
I never saw it the way you guys are explaining it-by trasfeed bytes & total bytes.
The "experience" always made a lot more sense (not good/accurate) if you instead use bytes of each file, and file count. This is how the estimate swings wildly.
If you are transferring large files first, the estimate goes long-even with the connection static. It is like it naively thinks that all files are the average size of those thus transferred, and then makes a guess assuming that the average file size will remain accurate for the entire time.
This, and the other ways, all get worse when the connection 'speed' varies...