Wie wir HAProxy optimiert haben, um 2.000.000 gleichzeitige SSL-Verbindungen zu erreichen

Wenn Sie sich den obigen Screenshot genau ansehen, finden Sie zwei wichtige Informationen:

  1. Auf diesem Computer wurden 2,38 Millionen TCP-Verbindungen hergestellt
  2. Der verwendete RAM-Speicher beträgt ca. 48 Gigabyte .

Ziemlich genial, oder? Was noch großartiger wäre, wäre, wenn jemand die Setup-Komponenten und die erforderlichen Abstimmungen bereitstellen würde, um diese Art von Skalierung auf einer einzelnen HAProxy-Maschine zu erreichen. Nun, genau das mache ich in diesem Beitrag;)

Dies ist der letzte Teil der mehrteiligen Serie zum Lasttest von HAProxy. Wenn Sie Zeit haben, empfehle ich Ihnen, zuerst die ersten beiden Teile der Serie zu lesen. Auf diese Weise erhalten Sie einen Überblick über die Kernel-Level-Einstellungen, die auf allen Computern in diesem Setup erforderlich sind.

Lasttest HAProxy (Teil-1)

Lasttest? HAProxy? Wenn Ihnen das alles griechisch erscheint, machen Sie sich keine Sorgen. Ich werde Inline-Links bereitstellen, um zu erfahren, was… medium.com Lasttests HAProxy (Teil 2)

Dies ist der zweite Teil der dreiteiligen Serie zum Testen der Leistung des berühmten TCP-Load-Balancers und Reverse-Proxys… medium.com

Es gibt viele kleine Komponenten, die uns geholfen haben, das gesamte Setup zusammenzuführen und diese Zahlen zu erreichen.

Bevor ich Ihnen die endgültige HAProxy-Konfiguration erkläre, die wir verwendet haben (wenn Sie sehr ungeduldig sind, können Sie nach unten scrollen), möchte ich darauf aufbauen, indem ich Sie durch unser Denken führe.

Was wir testen wollten

Die Komponente, die wir testen möchten, war HAProxy Version 1.6. Wir verwenden dies derzeit in der Produktion auf 4-Kern-30-Gig-Maschinen. Die gesamte Konnektivität basiert jedoch nicht auf SSL.

Wir wollten zwei Dinge aus dieser Übung heraus testen:

  1. Der CPU-Prozentsatz steigt, wenn wir die gesamte Last von Nicht-SSL-Verbindungen auf SSL-Verbindungen verlagern. Die CPU-Auslastung sollte aufgrund des längeren 5-Wege-Handshakes und der anschließenden Paketverschlüsselung definitiv zunehmen.
  2. Zweitens wollten wir die Grenzen unseres aktuellen Produktions-Setups in Bezug auf die Anzahl der Anforderungen und die maximale Anzahl gleichzeitiger Verbindungen testen, die unterstützt werden können, bevor sich die Leistung verschlechtert.

Wir benötigten den ersten Teil wegen eines wichtigen Feature-Rollouts, der in vollem Gange ist und die Kommunikation über SSL erfordert. Wir benötigten den zweiten Teil, um die Menge an Hardware zu reduzieren, die in der Produktion für HAProxy-Maschinen vorgesehen ist.

Die beteiligten Komponenten

  • Mehrere Client-Computer, um den HAProxy zu belasten.
  • Einzelne HAProxy-Maschine Version 1.6 auf verschiedenen Setups

    * 4 Kern, 30 Gig

    * 16 Kern, 30 Gig

    * 16 Kern, 64 Gig

  • Backend-Server, die alle diese gleichzeitigen Verbindungen unterstützen.

HTTP und MQTT

Wenn Sie den ersten Artikel dieser Reihe durchgesehen haben, sollten Sie wissen, dass unsere gesamte Infrastruktur über zwei Protokolle unterstützt wird:

  • HTTP und
  • MQTT.

In unserem Stack verwenden wir kein HTTP 2.0 und verfügen daher nicht über die Funktionalität dauerhafter Verbindungen unter HTTP. In der Produktion liegt die maximale Anzahl von TCP-Verbindungen auf einem einzelnen HAProxy-Computer (eingehend + ausgehend) bei etwa (2 * 150.000). Obwohl die Anzahl der gleichzeitigen Verbindungen eher gering ist, ist die Anzahl der Anforderungen pro Sekunde ziemlich hoch.

Auf der anderen Seite ist MQTT eine völlig andere Art der Kommunikation. Es bietet hervorragende Servicequalität und dauerhafte Konnektivität. So kann eine bidirektionale kontinuierliche Kommunikation über einen MQTT-Kanal erfolgen. Bei HAProxy, das MQTT-Verbindungen (zugrunde liegende TCP-Verbindungen) unterstützt, werden in der Spitzenzeit auf einem einzelnen Computer etwa 600 bis 700.000 TCP-Verbindungen angezeigt.

Wir wollten einen Auslastungstest durchführen, der genaue Ergebnisse für HTTP- und MQTT-basierte Verbindungen liefert.

Es gibt viele Tools, mit denen wir einen HTTP-Server problemlos laden können, und viele dieser Tools bieten erweiterte Funktionen wie zusammengefasste Ergebnisse, Konvertieren textbasierter Ergebnisse in Diagramme usw. Wir konnten jedoch kein Stresstest-Tool finden für MQTT. Wir haben ein Tool, das wir selbst entwickelt haben, aber es war nicht stabil genug, um diese Art von Last in dem Zeitrahmen zu unterstützen, den wir hatten.

Also haben wir uns für Lasttest-Clients für HTTP entschieden und das MQTT-Setup mit demselben simuliert;) Interessant, oder?

Gut weiterlesen.

Die Ersteinrichtung

Dies wird ein langer Beitrag sein, da ich viele Details bereitstellen werde, von denen ich denke, dass sie für jemanden, der ähnliche Lasttests oder Feinabstimmungen durchführt, wirklich hilfreich wären.

  • Wir haben zunächst eine 16-Kern-30-Gig-Maschine für die Einrichtung von HAProxy verwendet. Wir haben uns nicht für unser aktuelles Produktionssetup entschieden, weil wir dachten, dass der CPU-Hit aufgrund der SSL-Beendigung am HAProxy-Ende enorm sein würde.
  • Für das Serverende haben wir uns für einen einfachen NodeJs-Server entschieden, der beim pongEmpfang einer pingAnfrage mit antwortet .
  • Für den Kunden haben wir zunächst Apache Bench verwendet. Der Grund, warum wir uns dafür entschieden haben, abwar, dass es ein sehr bekanntes und stabiles Tool zum Testen von HTTP-Endpunkten war und dass es schöne zusammengefasste Ergebnisse liefert, die uns sehr helfen würden.

Das abTool bietet viele interessante Parameter, die wir für unseren Belastungstest verwendet haben, wie:

  • - c, concurrency Gibt die Anzahl der gleichzeitigen Anforderungen an, die den Server treffen würden.
  • -n, no. of requests Gibt, wie der Name schon sagt, die Gesamtzahl der Anforderungen des aktuellen Ladelaufs an.
  • -p POST file Enthält den Hauptteil der POST-Anforderung (wenn Sie dies testen möchten).

Wenn Sie sich diese Parameter genau ansehen, werden Sie feststellen, dass viele Permutationen möglich sind, wenn Sie alle drei optimieren. Eine Beispiel-Ab-Anfrage würde ungefähr so ​​aussehen

ab -S -p post_smaller.txt -T application/json -q -n 100000 -c 3000 //test.haproxy.in:80/ping

Ein Beispielergebnis einer solchen Anfrage sieht ungefähr so ​​aus

Die Zahlen, an denen wir interessiert waren, waren

  • 99% Latenz.
  • Zeit pro Anfrage.
  • Anzahl fehlgeschlagener Anfragen.
  • Anfragen pro Sekunde.

Das größte Problem abbesteht darin, dass kein Parameter zur Steuerung der Anzahl der Anforderungen pro Sekunde bereitgestellt wird. Wir mussten die Parallelitätsstufe anpassen, um unsere gewünschten Anforderungen pro Sekunde zu erhalten, und dies führte zu vielen Spuren und Fehlern.

Der allmächtige Graph

Wir konnten nicht zufällig mehrere Ladeläufe durchführen und weiterhin Ergebnisse erzielen, da dies keine aussagekräftigen Informationen liefern würde. Wir mussten diese Tests auf eine bestimmte Art und Weise durchführen, um aussagekräftige Ergebnisse zu erzielen. Also folgten wir dieser Grafik

In diesem Diagramm wird angegeben, dass die Latenz bis zu einem bestimmten Punkt nahezu gleich bleibt, wenn wir die Anzahl der Anforderungen weiter erhöhen. Jedoch über einen gewissen Wendepunkt wird die Latenz exponentiell zu erhöhen beginnen. Diesen Wendepunkt für eine Maschine oder ein Setup wollten wir messen.

Ganglien

Bevor ich einige Testergebnisse vorlege, möchte ich Ganglia erwähnen.

Ganglia ist ein skalierbares verteiltes Überwachungssystem für Hochleistungsrechnersysteme wie Cluster und Grids.

Look at the following screenshot of one of our machines to get an idea about what ganglia is and what sort of information it provides about the underlying machine.

Pretty interesting, eh?

Moving on, we constantly monitored ganglia for our HAProxy machine to monitor some important things.

  1. TCP established This tells us the total number of tcp connections established on the system. NOTE: this is the sum of inbound as well as outbound connections.
  2. packets sent and received We wanted to see the total number of tcp packets being sent and received by our HAProxy machine.
  3. bytes sent and received This shows us the total data that we sent and received by the machine.
  4. memory The amount of RAM being used over time.
  5. network The network bandwidth consumption because of the packets being sent over the wire.

Following are the known limits found via previous tests/numbers that we wanted to achieve via our load test.

700.000 TCP-Verbindungen hergestellt,

50.000 gesendete Pakete, 60.000 empfangene Pakete,

10–15 MB gesendete und empfangene Bytes,

14–15Gig Speicher in der Spitze,

7 MB Netzwerk.

ALL these values are on a per second basis

HAProxy Nbproc

Als wir anfingen, HAProxy zu testen, stellten wir fest, dass mit SSL die CPU ziemlich früh im Prozess getroffen wurde, aber die Anforderungen pro Sekunde waren sehr niedrig. Bei der Untersuchung des obersten Befehls stellten wir fest, dass HAProxy nur einen Kern verwendete. Wir hatten noch 15 Kerne übrig.

Das Googeln für ungefähr 10 Minuten führte dazu, dass wir diese interessante Einstellung in HAProxy fanden, mit der HAProxy mehrere Kerne verwenden kann.

Es heißt nbprocund um einen besseren Überblick darüber zu erhalten, was es ist und wie es eingestellt wird, lesen Sie diesen Artikel:

//blog.onefellow.com/post/82478335338/haproxy-mapping-process-to-cpu-core-for-maximum

Tuning this setting was the base of our load testing strategy moving forward. Because the ability to use multiple cores by HAProxy gave us the power to form multiple combinations for our load testing suite.

Load Testing with AB

When we had started out with our load testing journey, we were not clear on the things we should be measuring and what we need to achieve.

Initially we had only one goal in mind and that was to find the tipping point only by variation of all the below mentioned parameters.

I maintained a table of all the results for the various load tests that we gave. All in all I gave over 500 test runs to get to the ultimate result. As you can clearly see, there are a lot of moving parts to each and every test.

Single Client issues

We started seeing that the client was becoming bottleneck as we kept on increasing our requests per second. Apache bench uses a single core and from the documentation it is evident that it does not provide any feature for using multiple cores.

To run multiple clients efficiently we found an interesting linux utility called Parallel. As the name suggests, it helps you run multiple commands in parallel and utilises multiple cores. Exactly what we wanted.

Have a look at a sample command that runs multiple clients using parallel.

cat hosts.txt | parallel 'ab -S -p post_smaller.txt -T application/json -n 100000 -c 3000 {}'
[email protected]:~$ cat hosts.txt//test.haproxy.in:80/ping//test.haproxy.in:80/ping//test.haproxy.in:80/ping

The above command would run 3 ab clients hitting the same URL. This helped us remove the client side bottleneck.

The Sleep and Times parameter

We talked about some parameters in ganglia that we wanted to track. Lets discuss them once by one.

  1. packets sent and received This can be simulated by sending some data as a part of the post request. This would also help us generate some network as well as bytes sent and received portions in ganglia
  2. tcp_established This is something which took us a long, long time to actually simulate in our scenario. Imagine if a single ping request takes about a second, that would take us about 700k requests per second to reach our tcp_established milestone.

    Now this number might seem easier to achieve on production, but it was impossible to generate it in our scenario.

What did we do you might ask? We introduced a sleep parameter in our POST call that specifies the number of milliseconds the server needs to sleep before sending out a response. This would simulate a long running request on production. So now say we have a sleep of about 20 minutes (Yep), that would take us around 583 requests per second to reach the 700k mark.

Additionally, we also introduced another parameter in our POST calls to the HAProxy and that was the times parameter. That specified number of times the server should write a response on the tcp connection before terminating it. This helped us simulated even more data transferred over the wire.

Issues with apache bench

Although we found out a lot of results with apache bench, we also faced a lot of issues along the way. I won’t be mentioning all of them here as they are not important for this post as I’ll be introducing another client shortly.

We were pretty content with the numbers we were getting out of apache bench, but at one point of time, generating the required tcp connections just became impossible. Somehow the apache bench was not handling the sleep parameter we had introduced, properly and was not scaling for us.

Although running multiple ab clients on a single machine was sorted out by using the parallel utility. Running this setup across multiple client machines was still a pain for us. I had not heard of the pdsh utility by then and was practically stuck.

Also, we were not focussing on any timeouts as well. There are some default set of timeouts on the HAProxy, the ab client and the server and we had completely ignored these. We figured out a lot of things along the way and organized ourselves a lot on how to go about testing.

We used to talk about the tipping point graph but we deviated a lot from it as time went on. Meaningful results, however, could only be found by focusing on that.

With apache bench a point came where the number of TCP connections were not increasing. We had around 40–45 clients running on 5–6 different client boxes but were not able to achieve the scale we wanted. Theoretically, the number of TCP connections should have jumped as we went on increasing the sleep time, but it wasn’t working for us.

Enter Vegeta

I was searching for some other load testing tools that might be more scalable and better functionality wise as compared to apache bench when I came across Vegeta.

From my personal experience, I have seen Vegeta to be extremely scalable and provides much better functionality as compared to apache bench. A single Vegeta client was able to produce the level of throughput equivalent to 15 apache bench clients in our load test.

Moving forward, I will be providing load test results that have been tested using Vegeta itself.

Load Testing with Vegeta

First, have a look at the command that we used to run a single Vegeta client. Interestingly, the command to put load on the backend servers is called attack :p

echo "POST //test.haproxy.in:443/ping" | vegeta -cpus=32 attack -duration=10m -header="sleep:30000" -body=post_smaller.txt -rate=2000 -workers=500 | tee reports.bin | vegeta report

Just love the parameters provided by Vegeta. Let’s have a look at some of these below.

  1. -cpus=32 Specifies the number of cores to be used by this client. We had to expand our client machines to 32core, 64Gig because of the amount of load to be generated. If you look closely above, the rate isn’t much. But it becomes difficult to sustain such a load when a lot of connections are in sleep state from the server end.
  2. -duration=10m I guess this is self explanatory. If you don’t specify any duration, the test will run forever.
  3. -rate=2000 The number of requests per second.

So as you can see above, we reached a hefty 32k requests per second on a mere 4 core machine. If you remember the tipping point graph, you will be able to notice it clearly enough above. So the tipping point in this case is 31.5k Non SSL requests.

Have a look at some more results from the load test.

16k SSL connections is also not bad at all. Please note that at this point in our load testing journey, we had to start from scratch because we had adopted a new client and it was giving us way better results than ab. So we had to do a lot of stuff again.

An increase in the number of cores led to an increase in the number of requests per second that the machine can take before the CPU limit is hit.

We found that there wasn’t a substantial increase in the number of requests per second if we increased the number of cores from 8 to 16. Also, if we finally decided to go with a 8 core machine in production, we would never allocate all of the cores to HAProxy or be it a any other process for that matter. So we decided to perform some tests with 6 cores as well to see if we had acceptable numbers.

Not bad.

Introducing the sleep

We were pretty satisfied with our load test results till now. However, this did not simulate the real production scenario. That happened when we introduced a sleep time as well which was absent till now in our tests.

echo "POST //test.haproxy.in:443/ping" | vegeta -cpus=32 attack -duration=10m -header="sleep:1000" -body=post_smaller.txt-rate=2000 -workers=500 | tee reports.bin | vegeta report

So a sleep time of 1000 milliseconds would lead to server sleeping for x amount of time where 0< x <; 1000 and is selected randomly. So on an average the above load test will give a latency of ≥ 500ms

The numbers in the last cell represent

TCP established, Packets Rec, Packets Sent

respectively. As you can clearly see the max requests per second that the 6 core machine can support has decreased to 8k from 20k. Clearly, the sleep has its impact and that impact is the increase in the number of TCP connections established. This is however nowhere near to the 700k mark that we set out to achieve.

Milestone #1

How do we increase the number of TCP connections? Simple, we keep on increasing the sleep time and they should rise. We kept playing around with the sleep time and we stopped at the 60 seconds sleep time. That would mean an average latency of around 30 sec.

There is an interesting result parameter that Vegeta provides and that is % of requests successful. We saw that with the above sleep time, only 50% of the calls were succeeding. See the results below.

We achieved a whooping 400k TCP established connections with 8k requests per second and 60000 ms sleep time. The R in 60000R means Random.

The first real discovery we made was that there is a default call timeout in Vegeta which is of 30 seconds and that explained why 50% of our calls were failing. So we increased that to about 70s for our further tests and kept on varying it as and when the need arose.

We hit the 700k mark easily after tweaking the timeout value from the client end. The only problem with this was that these were not consistent. These were just peaks. So the system hit a peak of 600k or 700k but did not stay there for very long.

Wir wollten jedoch etwas Ähnliches

Dies zeigt einen stabilen Zustand, in dem 780k-Verbindungen aufrechterhalten werden. Wenn Sie sich die obigen Statistiken genau ansehen, ist die Anzahl der Anfragen pro Sekunde sehr hoch. In der Produktion haben wir jedoch viel weniger Anfragen (ungefähr 300) auf einer einzelnen HAProxy-Maschine.

Wir waren uns sicher, dass wir, wenn wir die Anzahl der HAProxies, die wir in der Produktion haben, drastisch reduzieren (etwa 30, was 30 * 300 ~ 9k Verbindungen pro Sekunde bedeutet), die Maschinengrenzen für die Anzahl der TCP-Verbindungen zuerst und nicht für die CPU erreichen werden.

Daher haben wir uns entschlossen, 900 Anfragen pro Sekunde zu erreichen und 30 MB / s Netzwerk- und 2,1 Millionen TCP-Verbindungen herzustellen. Wir haben uns auf diese Zahlen geeinigt, da dies das Dreifache unserer Produktionslast auf einem einzelnen HAProxy wäre.

Plus, till now we had settled on 6 cores being used by HAProxy. We wanted to test out 3 cores only because this is what would be easiest for us to roll out on our production machines (Our production machines, as mentioned before are 4 core 30 Gig. So for rolling out changes with nbproc = 3 would be easiest for us.

REMEMBER the machine we had at this point in time was 16 core 30 Gig machine with 3 cores being allocated to HAProxy.

Milestone #2

Now that we had max limits on requests per second that different variations in machine configuration could support, we only had one task left as mentioned above.

Achieve 3X the production load which is

  • 900 requests per second
  • 2.1 million TCP established and
  • 30 MB/s network.

We got stuck yet again as the TCP established were taking a hard hit at 220k. No matter what the number of client machines or what the sleep time was, number of TCP connections seemed to have stuck there.

Let’s look at some calculations. 220k TCP established connections and 900 requests per second = 110,000 / 900 ~= 120 seconds .I took 110k because 220k connections include both incoming and outgoing. So it’s two way.

Our doubt about 2 minutes being a limit somewhere in the system was verified when we introduced logs on the HAProxy side. We could see 120000 ms as total time for a lot of connections in the logs.

Mar 23 13:24:24 localhost haproxy[53750]: 172.168.0.232:48380 [23/Mar/2017:13:22:22.686] api~ api-backend/http31 39/0/2062/-1/122101 -1 0 - - SD-- 1714/1714/1678/35/0 0/0 {0,"",""} "POST /ping HTTP/1.1"
122101 is the timeout value. See HAProxy documentation on meanings of all these values. 

On investigating further we found out that NodeJs has a default request timeout of 2 minutes. Voila !

how to modify the nodejs request default timeout time?

I was using nodejs request, the default timeout of nodejs http is 120000 ms, but it is not enough for me, while my…stackoverflow.comHTTP | Node.js v7.8.0 Documentation

The HTTP interfaces in Node.js are designed to support many features of the protocol which have been traditionally…nodejs.org

But our happiness was apparently short lived. At 1.3 million, the HAProxy connections suddenly dropped to 0 and started increasing again. We soon checked the dmesg command that provided us some useful kernel level information for our HAProxy process.

Basically, the HAProxy process had gone out of memory. So we decided to increase the machine RAM and we shifted to 16 core 64 Gig machine with nbproc = 3 and because of this change we were able to reach 2.4 million connections.

Backend Code

Following is the backend server code that was being used. We had also used statsd in the server code to get consolidated data on requests per second that were being received by the client.

var http = require('http');var createStatsd = require('uber-statsd-client');qs = require('querystring');
var sdc = createStatsd({host: '172.168.0.134',port: 8125});
var argv = process.argv;var port = argv[2];
function randomIntInc (low, high){ return Math.floor(Math.random() * (high - low + 1) + low);}
function sendResponse(res,times, old_sleep){ res.write('pong'); if(times==0) { res.end(); } else { sleep = randomIntInc(0, old_sleep+1); setTimeout(sendResponse, sleep, res,times-1, old_sleep); }}
var server = http.createServer(function(req, res) headers = req.headers; old_sleep = parseInt(headers["sleep"]); times = headers["times"] );
server.timeout = 3600000;server.listen(port);

We also had a small script to run multiple backend servers. We had 8 machines with 10 backend servers EACH (yeah !). We literally took the idea of clients and backend servers being infinite for the load test, seriously.

counter=0while [ $counter -le 9 ]do port=$((8282+$counter)) nodejs /opt/local/share/test-tools/HikeCLI/nodeclient/httpserver.js $port & echo "Server created on port " $port
 ((counter++))done
echo "Created all servers"

Client Code

As for the client, there was a limitation of 63k TCP connections per IP. If you are not sure about this concept, please refer my previous article in this series.

So in order to achieve 2.4 million connections (two sided which is 1.2 million from the client machines), we needed somewhere around 20 machines. Its a pain really to run the Vegeta command on all 20 machines one by one and even of you found a way to do that using something like csshx, you still would need something to combine all the results from all the Vegeta clients.

Check out the script below.

result_file=$1
declare -a machines=("172.168.0.138" "172.168.0.141" "172.168.0.142" "172.168.0.18" "172.168.0.5" "172.168.0.122" "172.168.0.123" "172.168.0.124" "172.168.0.232" " 172.168.0.244" "172.168.0.170" "172.168.0.179" "172.168.0.59" "172.168.0.68" "172.168.0.137" "172.168.0.155" "172.168.0.154" "172.168.0.45" "172.168.0.136" "172.168.0.143")
bins=""commas=""
for i in "${machines[@]}"; do bins=$bins","$i".bin"; commas=$commas","$i; done;
bins=${bins:1}commas=${commas:1}
pdsh -b -w "$commas" 'echo "POST //test.haproxy.in:80/ping" | /home/sachinm/.linuxbrew/bin/vegeta -cpus=32 attack -connections=1000000 -header="sleep:20" -header="times:2" -body=post_smaller.txt -timeout=2h -rate=3000 -workers=500 > ' $result_file
for i in "${machines[@]}"; do scp [email protected]$i:/home/sachinm/$result_file $i.bin ; done;
vegeta report -inputs="$bins"

Apparently, Vegeta provides information on this utility called pdsh that lets you run a command concurrently on multiple machines remotely . Additionally, the Vegeta allows us to combine multiple results into one and that’s really all we wanted.

HAProxy Configuration

This is probably what you came here looking for, below is the HAProxy config that we used in our load test runs. The most important part being that of the nbproc setting and the maxconn setting. The maxconn setting allows us to provide the maximum number of TCP connections that the HAProxy can support overall (one way).

Changes to maxconn setting leads to increase in HAProxy process’ ulimit. Take a look below

The max open files has increased to 4 million because of the max connections for HAProxy being set at 2 million. Neat !

Check the article below for a whole lot of HAProxy optimisations that you can and should do to achieve the kind of stats we achieved.

Use HAProxy to load balance 300k concurrent tcp socket connections: Port Exhaustion, Keep-alive and…

I'm trying to build up a push system recently. To increase the scalability of the system, the best practice is to make…www.linangran.com

The http30 goes on to http83 :p

That’s all for now folks. If you’ve it so far, I’m truly amazed :)

A special shout out to Dheeraj Kumar Sidana who helped us all the way through this and without whose help we would not have been able to reach any meaningful results. :)

Do let me know how this blog post helped you. Also, please recommend (❤) and spread the love as much as possible for this post if you think this might be useful for someone.