The getstream.io documentation says that one should expect retrieving a feed in approximately 60ms. When I retrieve my feeds they contain a field named 'duration' which I take is the calculated server side processing time. This value is steadily around 10-40ms, with an average around 15ms.
The problem is, I seldomly get my feeds in less than 150ms and the average time is rather around 200-250ms and sometimes up to 300-400ms. This is the time for the getting the feed alone, no enrichment etc., and I have verified with tcpdump that the network roundtrip is low (around 25ms), and that the time is actually spent waiting for the server to respond.
I've tried to move around my application (eu-west and eu-central) but that doesn't seem to affect things much (again, network roundtrip is steadily around 25ms).
My question is - should I really expect 60ms and continue investigating, or is 200-400ms normal? On the getstream.io site it is explained that developer accounts receive "Low Priority Processing" - what does this mean in practise? How much difference could I expect with another plan?
I'm using the node js low level API.
Stream APIs use SSL to encrypt traffic. Unfortunately SSL introduces additional network I/O. Usually you need to pay for the increased latency only once because Stream HTTP APIs supports HTTP persistent connection (aka keep-alive).
Here's a Wireshark screenshot of the TCP traffic of 2 sequential API requests with keep alive disabled client side:
The 4 lines in red highlight that the TCP connection is getting closed each time. Another interesting thing is that the handshaking takes almost 100ms and it's done twice (the first bunch of lines).
After some investigation, it turns out that the library used to make API requests to Stream's APIs (request) does not have keep-alive enabled by default. Such change will be part of the library soon and is available on a development branch.
Here's a screenshot of the same two requests with keep-alive enabled (using the code from that branch):
This time there is not connection reset anymore and the second HTTP request does not do SSL handshaking.
Related
I need to make POST http request at exact timestamp in future, as accurate as possible, down to milliseconds. But there is network latency as well. How can I achieve such a goal?
setTimeout is not enough here, because it always takes some time resulting in latecomer request due vary network latency. And firing this request before target timestamp may result in early coming request.
My goal is to make request guaranteed came to server after target timestamp, but as soon as possible after it. Could you suggest any solutions with Nodejs?
The best you can do in nodejs (which is not a real-time system) is to do the following:
Premeasure the expected latency so you know about how much to presend the request.
Use setTimeout() to schedule the send at precisely the one-way latency time before your target time. There is no other mechanism in nodejs that would be more precise.
If your request involves a DNS lookup, you can prefetch the TCP address for your hostname and take the DNS lookup time out of your request cycle or at least prime the local DNS cache.
Create a dedicated nodejs program that does nothing else - so its event loop will not be doing anything else at the time the setTimeout() needs to run. You could run this as a child_process from your larger program if desired.
Run a number of tests to see how the timing works and, if you are consistently off by some margin, then adjust your latency offset.
You can develop a regular latency test to determine if the latency changes with time.
As others have said, there is no way to predict what the natural response time will be of the target server (how long it takes to start processing your request from the moment your network packets arrive there). If lots of incoming requests are all racing for the same time slot, then your request will get interleaved in among all the others and served in some order that you do not control.
Other things you can consider. If the target server supports the latest http specifications, then you can have a pre-established http connection with the host (perhaps targeting some other endpoint) that will be kept alive for you to send your precise timing request on. This would take some experimentation to figure out what the target host supports and if this would work.
I am hosting a RESTful API and my problem is that every first inbound request after a certain time will take about three seconds, compared to the normal ~100ms.
What I find most interesting is that it is always takes exactly 3100 to around 3250 milliseconds, not more and not less. So it seems pretty intentional to me.
I've already debugged the API and everything runs pretty much instantly except for one thing and that is this three second delay before my API even starts to receive the request.
My best guess is that something went wrong either in Apache or the DNS resolution but I don't know what exactly causes it (that's why I'm asking this question).
I am using the Apache ProxyPass like this:
ProxyRequests off
Timeout 54
ProxyTimeout 5400
ProxyPass /jokeapi http://localhost:8079
ProxyPassReverse /jokeapi http://localhost:8079
I'm using the Cloudflare/APNIC DNS gateway servers 1.1.1.1 and 0.0.0.0
Additionally, all my requests get routed through a Cloudflare SSL proxy before even reaching my network.
I've even partially rewritten the API so it responds with ReadStreams instead of loading the files into RAM and serving it at once but that didn't fix the problem.
My question is how I can fully debug the route a request takes and see precisely where this 3 second delay comes from.
Thanks!
PS: the server runs on NodeJS
I think the key is not related to network activity, but in the note that after a period of idle activity the first response to the API in a while requires slightly over 3 seconds. I am assuming that follow up actions are back to the 100ms window.
As you are using localhost, this is not a routing issue. If you want, you can just as easily use loopback, 127.0.0.1, to avoid a name resolution hit, but such a hit on a reserved hostname would be microseconds.
I suspect that the compiled version of your RESTful function has aged out of the cache for your system. The first hit after a period of non-use time then requires a recompile, and so long as the compiled instructions are exercised for a period of time they will remain in cache and contoninue to respond in the 100ms range. We observe this condition quite often in multiuser performance testing after cold boots of systems (setting initial conditions). Ramp-ups of the test users take the hit for the recompiles of common code before hitting the time under full load.
Another item to strike back at the network side of the house, DNS timeouts and bind cache entries tend to be quite long, usually significant portions of a day or even longer. Even so, the odds that a DNS lookup for an item which has aged out of the bind cache would not add three seconds to your initial connection time.
When we migrated our apps to azure from rackspace, we saw almost 50% of http requests getting read timeouts.
We tried placing the client both inside and outside azure with the same results. The client in this case is also a server btw, so no geographic/browser issues either.
We even tried increasing the size of the box to ensure azure wasn't throttling. But even using D boxes for a single request, the result was the same.
Once we moved out apps out of azure they started functioning properly again.
Each query was done directly on an instance using a public ip, so no load balancer issues either.
Almost 50% of queries ran into this issue. The timeout was set to 15 minutes.
Region was US East 2
Having 50% of HTTP requests timing out is not normal behavior. This is why you need to analyze what is causing those timeouts by validating the requests are hitting your VM. For this, I would recommend you running a packet capture on your server and analyze response times, as well as look for high number of retransmissions; it is even better if you can take a simultaneous network trace on your clients machines so you can do TCP sequence number analysis and compare packets sent vs received.
If you are seeing high latencies in the packet capture or high number of retransmissions, it requires detailed analysis. I strongly suggest you to open a support incident so Microsoft support can help you investigate your issue further.
In my company we experienced a serious problem today: our production server went down. Most people accessing our software via a browser were unable to get a connection, however people who had already been using the software were able to continue using it. Even our hot standby server was unable to communicate with the production server, which it does using HTTP, not even going out to the broader internet. The whole time the server was accessible via ping and ssh, and in fact was quite underloaded - it's normally running at 5% CPU load and it was even lower at this time. We do almost no disk i/o.
A few days after the problem started we have a new variation: port 443 (HTTPS) is responding but port 80 stopped responding. The server load is very low. Immediately after restarting tomcat, port 80 started responding again.
We're using tomcat7, with maxThreads="200", and using maxConnections=10000. We serve all data out of main memory, so each HTTP request completes very quickly, but we have a large number of users doing very simple interactions (this is high school subject selection). But it seems very unlikely we would have 10,000 users all with their browser open on our page at the same time.
My question has several parts:
Is it likely that the "maxConnections" parameter is the cause of our woes?
Is there any reason not to set "maxConnections" to a ridiculously high value e.g. 100,000? (i.e. what's the cost of doing so?)
Does tomcat output a warning message anywhere once it hits the "maxConnections" message? (We didn't notice anything).
Is it possible there's an OS limit we're hitting? We're using CentOS 6.4 (Linux) and "ulimit -f" says "unlimited". (Do firewalls understand the concept of Tcp/Ip connections? Could there be a limit elsewhere?)
What happens when tomcat hits the "maxConnections" limit? Does it try to close down some inactive connections? If not, why not? I don't like the idea that our server can be held to ransom by people having their browsers on it, sending the keep-alive's to keep the connection open.
But the main question is, "How do we fix our server?"
More info as requested by Stefan and Sharpy:
Our clients communicate directly with this server
TCP connections were in some cases immediately refused and in other cases timed out
The problem is evident even connecting my browser to the server within the network, or with the hot standby server - also in the same network - unable to do database replication messages which normally happens over HTTP
IPTables - yes, IPTables6 - I don't think so. Anyway, there's nothing between my browser and the server when I test after noticing the problem.
More info:
It really looked like we had solved the problem when we realised we were using the default Tomcat7 setting of BIO, which has one thread per connection, and we had maxThreads=200. In fact 'netstat -an' showed about 297 connections, which matches 200 + queue of 100. So we changed this to NIO and restarted tomcat. Unfortunately the same problem occurred the following day. It's possible we misconfigured the server.xml.
The server.xml and extract from catalina.out is available here:
https://www.dropbox.com/sh/sxgd0fbzyvuldy7/AACZWoBKXNKfXjsSmkgkVgW_a?dl=0
More info:
I did a load test. I'm able to create 500 connections from my development laptop, and do an HTTP GET 3 times on each, without any problem. Unless my load test is invalid (the Java class is also in the above link).
It's hard to tell for sure without hands-on debugging but one of the first things I would check would be the file descriptor limit (that's ulimit -n). TCP connections consume file descriptors, and depending on which implementation is in use, nio connections that do polling using SelectableChannel may eat several file descriptors per open socket.
To check if this is the cause:
Find Tomcat PIDs using ps
Check the ulimit the process runs with: cat /proc/<PID>/limits | fgrep 'open files'
Check how many descriptors are actually in use: ls /proc/<PID>/fd | wc -l
If the number of used descriptors is significantly lower than the limit, something else is the cause of your problem. But if it is equal or very close to the limit, it's this limit which is causing issues. In this case you should increase the limit in /etc/security/limits.conf for the user with whose account Tomcat is running and restart the process from a newly opened shell, check using /proc/<PID>/limits if the new limit is actually used, and see if Tomcat's behavior is improved.
While I don't have a direct answer to solve your problem, I'd like to offer my methods to find what's wrong.
Intuitively there are 3 assumptions:
If your clients hold their connections and never release, it is quite possible your server hits the max connection limit even there is no communications.
The non-responding state can also be reached via various ways such as bugs in the server-side code.
The hardware conditions should not be ignored.
To locate the cause of this problem, you'd better try to replay the scenario in a testing environment. Perform more comprehensive tests and record more detailed logs, including but not limited:
Unit tests, esp. logic blocks using transactions, threading and synchronizations.
Stress-oriented tests. Try to simulate all the user behaviors you can come up with and their combinations and test them in a massive batch mode. (ref)
More specified Logging. Trace client behaviors and analysis what happened exactly before the server stopped responding.
Replace a server machine and see if it will still happen.
The short answer:
Use the NIO connector instead of the default BIO connector
Set "maxConnections" to something suitable e.g. 10,000
Encourage users to use HTTPS so that intermediate proxy servers can't turn 100 page requests into 100 tcp connections.
Check for threads hanging due to deadlock problems, e.g. with a stack dump (kill -3)
(If applicable and if you're not already doing this, write your client app to use the one connection for multiple page requests).
The long answer:
We were using the BIO connector instead of NIO connector. The difference between the two is that BIO is "one thread per connection" and NIO is "one thread can service many connections". So increasing "maxConnections" was irrelevant if we didn't also increase "maxThreads", which we didn't, because we didn't understand the BIO/NIO difference.
To change it to NIO, put this in the element in server.xml:
protocol="org.apache.coyote.http11.Http11NioProtocol"
From what I've read, there's no benefit to using BIO so I don't know why it's the default. We were only using it because it was the default and we assumed the default settings were reasonable and we didn't want to become experts in tomcat tuning to the extent that we now have.
HOWEVER: Even after making this change, we had a similar occurrence: on the same day, HTTPS became unresponsive even while HTTP was working, and then a little later the opposite occurred. Which was a bit depressing. We checked in 'catalina.out' that in fact the NIO connector was being used, and it was. So we began a long period of analysing 'netstat' and wireshark. We noticed some periods of high spikes in the number of connections - in one case up to 900 connections when the baseline was around 70. These spikes occurred when we synchronised our databases between the main production server and the "appliances" we install at each customer site (schools). The more we did the synchronisation, the more we caused outages, which caused us to do even more synchronisations in a downward spiral.
What seems to be happening is that the NSW Education Department proxy server splits our database synchronisation traffic into multiple connections so that 1000 page requests become 1000 connections, and furthermore they are not closed properly until the TCP 4 minute timeout. The proxy server was only able to do this because we were using HTTP. The reason they do this is presumably load balancing - they thought by splitting the page requests across their 4 servers, they'd get better load balancing. When we switched to HTTPS, they are unable to do this and are forced to use just one connection. So that particular problem is eliminated - we no longer see a burst in the number of connections.
People have suggested increasing "maxThreads". In fact this would have improved things but this is not the 'proper' solution - we had the default of 200, but at any given time, hardly any of these were doing anything, in fact hardly any of these were even allocated to page requests.
I think you need to debug the application using Apache JMeter for number of connection and use Jconsole or Zabbix to look for heap space or thread dump for tomcat server.
Nio Connector of Apache tomcat can have maximum connections of 10000 but I don't think thats a good idea to provide that much connection to one instance of tomcat better way to do this is to run multiple instance of tomcat.
In my view best way for Production server: To Run Apache http server in front and point your tomcat instance to that http server using AJP connector.
Hope this helps.
Are you absolutely sure you're not hitting the maxThreads limit? Have you tried changing it?
These days browsers limit simultaneous connections to a max of 4 per hostname/ip, so if you have 50 simultaneous browsers, you could easily hit that limit. Although hopefully your webapp responds quickly enough to handle this. Long polling has become popular these days (until websockets are more prevalent), so you may have 200 long polls.
Another cause could be if you use HTTP[S] for app-to-app communication (that is, no browser involved). Sometimes app writers are sloppy and create new connections for performing multiple tasks in parallel, causing TCP and HTTP overhead. Double check that you are not getting an inflood of requests. Log files can usually help you on this, or you can use wireshark to count the number of HTTP requests or HTTP[S] connections. If possible, modify your API to handle multiple API calls in one HTTP request.
Related to the last one, if you have many HTTP/1.1 requests going across one connection, and intermediate proxy may be splitting them into multiple connections for load balancing purposes. Sounds crazy I know, but I've seen it happen.
Lastly, some crawl bots ignore the crawl delay set in robots.txt. Again, log files and/or wireshark can help you determine this.
Overall, run more experiments with more changes. maxThreads, https, etc. before jumping to conclusions with maxConnections.
I'm implementing a websocket-secure (wss://) service for an online game where all users will be connected to the service as long they are playing the game, this will use a high number of simultaneous connections, although the traffic won't be a big problem, as the service is used for chat, storage and notifications... not for real-time data synchronization.
I wanted to use Alchemy-Websockets, but it doesn't support TLS (wss://), so I have to look for another service like Fleck (or other).
Alchemy has been tested with high number of simultaneous connections, but I didn't find similar tests for Fleck, so I need to get some real info from users of fleck.
I know that Fleck is non-blocking and uses Async calls, but I need some real info, cuz it might be abusing threads, garbage collector, or any other aspect that won't be visible to lower number of connections.
I will use c# for the client as well, so I don't need neither hybiXX compatibility, nor fallback, I just need scalability and TLS support.
I finally added Mono support to WebSocketListener.
Check here how to run WebSocketListener in Mono.
10K connections is not little thing. WebSocketListener is asynchronous and it scales well. I have done tests with 10K connections and it should be fine.
My tests shows that WebSocketListener is almost as fast and scalable as the Microsoft one, and performs better than Fleck, Alchemy and others.
I made a test on a Windows machine with Core2Duo e8400 processor and 4 GB of ram.
The results were not encouraging as it started delaying handshakes after it reached ~1000 connections, i.e. it would take about one minute to accept a new connection.
These results were improved when i used XSockets as it reached 8000 simultaneous connections before the same thing happened.
I tried to test on a Linux VPS with Mono, but i don't have enough experience with Linux administration, and a few system settings related to TCP, etc. needed to change in order to allow high number of concurrent connections, so i could only reach ~1000 on the default settings, after that he app crashed (both Fleck test and XSocket test).
On the other hand, I tested node.js, and it seemed simpler to manage very high number of connections, as node didn't crash when reached the limits of tcp.
All the tests where echo test, the servers send the same message back to the client who sent the message and one random other connected client, and each connected client sends a random ~30 chars text message to the server on a random interval between 0 and 30 seconds.
I know my tests are not generic enough and i encourage anyone to have their own tests instead, but i just wanted to share my experience.
When we decided to try Fleck, we have implemented a wrapper for Fleck server and implemented a JavaScript client API so that we can send back acknowledgment messages back to the server. We wanted to test the performance of the server - message delivery time, percentage of lost messages etc. The results were pretty impressive for us and currently we are using Fleck in our production environment.
We have 4000 - 5000 concurrent connections during peak hours. On average 40 messages are sent per second. Acknowledged message ratio (acknowledged messages / total sent messages) never drops below 0.994. Average round-trip for messages is around 150 miliseconds (duration between server sending the message and receiving its ack). Finally, we did not have any memory related problems due to Fleck server after its heavy usage.