My website is hosted on GCP compute engine. The problem is that it takes too much time to load for the first time. almost 30-40 second in sometime.
I am using docker compose to run the container for both frontend and backend.
VM type n1, US zone, 20GB memory.I have analysed the CPU use history. Its seem okay, never got high in any point. Also the site is not getting too much traffic now. My location is currently Asia. I dont find any issue in the VM end, firewall is also open for 80 and 443 port. So theres nothing blocking the connection too. I'm not getting why the website is taking this much time to load.
URL: www.mindschoolbd.com
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Is there any advantage if I use Cloud Run instead of deploying a normal service/container in GKE?
I will try to add my perspective.
This answer does not cover running containers in Google Cloud Run Kubernetes. The reason is that we wanted an almost zero cost solution for a legacy PHP website. Cloud Run fit perfectly and we had an easy time both porting the code and learning Cloud Run.
We needed to do something with a legacy PHP website. This website was running on Windows Server 2012, IIS and PHP 7.0x. The cost was over $100.00 per month - mostly for Windows licensing fees for a VM in the cloud. The site was not accessed very much but was needed for various business reasons.
A decision was made Thursday (4/18/2019) was that we needed to learn Google Cloud Run, so we decided to port this site to a container and try to run the container in Google Cloud. Nothing like a real world example to learn the details.
Friday, we ported the PHP code to Apache. Very easy process. We did not worry about SSL as we intend to use Cloud Run SSL.
Saturday we started to learn Cloud Run. Within an hour we had the Hello World PHP example running. Link.
Within two hours we had the containerized website running in Cloud Run. Again, very simple.
Then we learned how to configure Cloud Run SSL with our DNS server.
End result:
Almost zero cost for a PHP website running in Cloud Run.
Approximately 1.5 days of effort to port the legacy code and learn Cloud Run.
Savings of about $100.00 per month (no Windows IIS server).
We do not have to worry about SSL certificates from now on for this site.
For small websites that are static, Cloud Run is a killer product. The learning curve is very small even if you do not know Google Cloud. You just need to configure gcloud for container builds and deployment. This means developers can be independant of needing to master GCP.
There are many distinctions in using Cloud Run to expose a service as compared to running it natively in GKE. The primary of these is that Cloud Run provides more of a serverless infrastructure. Basically you declare that you want to expose a service and then let GCP do the rest. Contrast this with creating a Kubernetes cluster and then defining your service in pods. With a manually created GKE cluster, the nodes and environment are always on which means that you are billed for them regardless of utilization. With Cloud Run, your service is merely available and you are only billed for actual consumption. If your service not being called, your costs are zero. Another advantage is that you don't have to predict your utilization needs and allocate sufficient nodes. Scaling happens automatically for you.
See also these presentations from Google Next 19:
Migrating from a Monolith to Microservices (Cloud Next '19)
What's New in Serverless Compute? (Cloud Next '19)
Run Containers on GCP's Serverless Infrastructure (Cloud Next '19)
Run Cloud Functions Everywhere (Cloud Next '19)
Container Once, Serverless Anywhere (Cloud Next '19)
I have set up Ganglia(Ganglia Core 3.6.0 and Ganglia Web 3.5.10) to monitor my cluster.
When gmond is restarted in a machine, metrics from all other gmond machines also gets stopped ie I am not able to see metrics getting published from other machines in Ganglia Web. And I can also see Hosts up going to 0 and Hosts down as 13(total number of machines). As time goes, the Hosts up comes back to 13.
Am I missing something ?? Can some one help me...
If it's always the same machine, it should be a gmond 'end-point'. The gmetad daemon is querying only one gmond (no redundancy), if he goes down everybody seems to be going down.
If there are a redundancy (eg. more than one host in a data source), you can expect some lag if the first one goes down because of the number of TCP queries before it timesout.
I am hosting a web application on Amazon's AWS Servers. I am currently in the process of load testing the application with JMeter. My main problem seems to be that when I go through an Elastic Load Balancer (ELB) to hit the Amazon server's rather than hitting the servers directly - I seem to hit a cap in my throughput.
If I hit my web application directly - for each server I am able to achieve a throughput of 50 RPS per server.
If I hit my web application via Amazon's ELB - I am only able to achieve a max throughput of 50 RPS (total)
I was wondering if anyone else has experienced similar behavior when load testing using Jmeter via Amazon's ELB.
For more context my web application is a REST application which allows users to download content (~150 kb) via HTTP requests.
I am running Jmeter with the following flag "-Dsun.net.inetaddr.ttl=0" and running it with 10 threads. I have tried running these tests with multiple clients on different machines.
Thanks for any help in advance.
Load balancers may be tricky to test as they may have different mechanisms of orchestrating traffic depending on origin. The most commonly used approach to distinguish origin of the request and redirect it to the same host, which served previous request is a cookie. You can look into HTTP Cookie Manager to correctly manipulate your cookies and make sure than you have different ones for each testing thread or thread group (depending on your use case). Another flaky area is origin host IP. You may require to bind each testing thread to different IP address in order to hit different servers behind the load balancer. There can be also some issues with DNS in regards to Amazon LBs. useful guide on how to test Amazon ELBs
Most probable cause would be DNS caching by jmeter. ELB returns IPs of additional servers depending on how autoscaling is set but JMeter does not use these additional servers. This problem can be solved by ensuring that Jmeter does not cache DNS results...
The ELB is a name, not IP, and can suffer from DNS caching. Make sure you use "-Dsun.net.inetaddr.ttl=0" when starting JMeter
http://wiki.apache.org/jmeter/JMeterAndAmazon
A really late response, and slightly different than the original question, but I hope this can help others as it took me a while to get it all straight. My original problem was not reduced throughput as a result of the ELB, but the introduction of HTTP 503 errors. Actually, the ELB increased my throughput as compared to querying the web application directly, though even with 1 hour tests, the results were sporadic to say the least.
First, the ELB has 2-staged load balancing going on. The first load balance is across the ELB's themselves. That's done by associating multiple IP addresses to the hostname provided by AWS for the ELB you provision. The second is then, of course, across your application instances behind the ELB.
Without trying to offend the SO gods, this is a really helpful article.
https://blazemeter.com/blog/dns-cache-manager-right-way-test-load-balanced-apps
The most helpful information in there was to use the DNS Cache Manager module in JMeter. This will query multiple DNS servers, and wipe out your DNS cache.
I implemented that module and then setup Wireshark, filtering on the two IP addresses belonging to the ELB hostname and sure enough, it was querying both IP addresses, though clearly favored one over the other.
That didn't make a big difference, at least not over short tests.
The real difference (2-3 times more throughput) came when I tweaked the ELB health settings. I initially had a high error rate, however after reducing the unhealthy threshold and the interval between health checks, my error rates dropped dramatically.
Additionally, whereas all my other tests had been 60 - 90 minutes in duration, this one was 8 hours. I started out with decent throughput and it then quickly dropped (by about 2/3). After about 20 minutes or more, the throughput then started ticking back up and by the end of the test, it had sustained throughput of about 5 times what I was getting without the ELB (which was similar to what the throughput was when it dropped shortly after beginning this test).
When we have a cluster of machines behind a load balancer (lb), generally hardware load balancer have persistent connections,
Now when we need to deploy some update on all machines (rolling update), the way to do is by bringing one machine Out of rotation, looks for no request sent to that server via lb. When the app reached no request state then update manually.
With 70-80 servers in picture this becomes very painful.
Can someone have a better way of doing it.
70-80 servers is a very horizontally scaled implementation... good job! Better is a very relative term, hopefully one of these suggestions count as "better".
Implement an intelligent health check for the application with the ability to adjust the health check while the application is running. What we do is have the health check start failing while the application is running just fine. This allows the load balancer to automatically take the system out of rotation. Our stop scripts query the load balancer to make sure that it is out of rotation and then shuts down normally which allows the existing connections to drain.
Batch multiple groups of systems together. I am assuming that you have 70 servers to handle peak load. This means that you should be able to restart several at a time. A standard way to do this is to implement a simple token granting service with a maximum of 10 tokens. Have your shutdown scripts checkout a token before continuing.
Another way to do this is with blue/green deploys. That means that you have an entire second server farm and then once the second server farm is updated switch load balancing to point to the new server farm.
This is an alternate to option 3. Install both versions of the app on the same servers and then have an internal proxy service (like haproxy) switch the connections between the version of the app that is deployed. For example:
haproxy listening on 8080
app version 0.1 listening on 9001
app version 0.2 listening on 9002
Once you are happy with the deploy of app version 0.2 switch haproxy to send traffic to 9002. When you release version 0.3 then switch load balancing back to 9001 etc.
I'm trying to understand why it can take from 20-60min to deploy a small application to Azure (using the configuration/package upload method, not from within VS).
I've read through this situation and this one but I'm still a little unclear - is there a weird non-technology ritual that occurs while the instances are distributing, like somebody over at Microsoft lighting a candle or doing a dance?
As a fellow Azure user, I share your pain - deploying isn't "quick"/"painless" - and this hurts especially when you're in a development cycle and want to test dev iterations on Azure. However, in general deployments should take much less than 60 minutes - and less than 20 minutes too.
Steve Marx provided a brief overview of the steps involved in deployment:
http://blog.smarx.com/posts/what-happens-when-you-deploy-on-windows-azure
And he references a deeper level explanation at: http://channel9.msdn.com/blogs/pdc2008/es19
There's a lot that goes on behind the scenes when you deploy an application to the Azure cloud. I don't have any special insight into what's going on behind the curtain, but having worked on the VS tools to upload projects to the Azure cloud, these are my impressions as an outsider looking in:
Among other things:
Hardware must be allocated from the available pool of servers
The VHD of the core OS must be uploaded to the machine
A VM instance must be initialized and booted off that VHD image
Your application package must be copied to the VM and installed
The VM monitor must wait for your service to start up, or fail
The data center load balancer and firewall must be made aware of your application's service endpoints
Once all of that has synchronized, your app is accessible from the web.
The VHD image is probably gigabytes in size, much larger than your app upload. Even on a superfast datacenter network, it takes time to move that much stuff into the VM, unpack it, and boot from it. Also, the load balancer and firewall are probably optimized to make routing requests the highest priority. Reconfiguring the firewall and load balancer is lower priority, and has to be done without interrupting traffic flow.
Also note that all this work only has to be done for a new deployment. Updating an existing deployment rolls out much faster - 2 to 3 minutes instead of 20 to 30 minutes.
Check out this PDC10 video by Mark Russinovich. He goes into great detail on what's going on inside Azure with some insights into the (admittedly slow) deployment process.
Original link is no longer working. Here's another link to a version of the same presentation: https://channel9.msdn.com/events/Build/BUILD2011/SAC-853T