I have noticed that the "service deployed time" of a proxy service and the "System Up time" of the wso2esb it's different.
If my proxy service is deployed when I start wso2esb, why the times are different?
In my case:
Server Start Time 2013-10-30 13:11:17
System Up Time 15 day(s) 19 hr(s) 45 min(s) 13 sec(s)
Service Deployed Time 2013-11-15 05:13:12
Service Up Time 3hr(s) 45min(s)
The difference between the start time and the deployed time is very big. No reboots has been done. Why has my proxy service been deployed without my knowledge?
Thank you.
Related
I have a Cloud Run service setup and I have a Cloud Scheduler task that calls an endpoint on that service. When the task completes (http handler returns), I'm seeing the following error:
The request failed because the HTTP connection to the instance had an error.
However, the actual handler returns HTTP 200 and successfully exists. Does anyone know what this error means and under what circumstances it shows up?
I'm also attaching a screenshot of the logs.
Does your job take longer than 120 seconds? I was having the same issue and figured out node versions prior to 13 has 120 seconds server.timeout limit. I installed node 13 on docker and problem is gone.
Error 503 is returned by the Google Frontend (GFE). The Cloud Run service either has a transient issue, or the GFE has determined that your service is not ready or not working correctly.
In your log entries, I see a POST request. 7 ms later is the error 503. This tells me your Cloud Run application is not yet ready (in a ready state determined by Cloud Run).
One minute, 8 seconds before, I see ReplaceService. This tells me that your service is not yet in a running state and that if you retry later, you will see success.
I've run an incremental sleep test on my FLASK endpoint which returns 200 within 1 min, 2 min and 10 min of waiting time. Having triggered the endpoint via the Cloud Scheduler, the job failed only in the 10 min test. I've found that it was one of the properties of my Cloud Scheduler job causing the failure. The following solved my issue.
gcloud scheduler jobs describe <my_test_scheduler>
There, you'll see a property called 'attemptDeadline' which was set to 180 seconds by default.
You can update that property using:
gcloud scheduler jobs update http <my_test_scheduler> --attempt-deadline 1000s
Ref: scheduler update
I am planning to use throttling in wso2-ei 6.4.0, From local system i tested the scenario i face some problems could please help me if any one know thanks in advance.
If we restart the wso2-ei node policy is not working. It taking again from starting ( suppose request limit is 10 for 1 hour,Before restarting the node it processing 5 request after restarting it should take remaining 5 request but it accepting 10 request
Throttling is working based on wso2-ei node level but suppose Linux server having 10 nodes how to distribute the throttling policy in Linux server level .
How to consider client ip in throttling. If request coming from F5 load balance i need to consider the requested system IP not F5 server IP.
If we restart the wso2-ei node policy is not working. It taking again from starting ( suppose request limit is 10 for 1 hour,Before restarting the node it processing 5 request after restarting it should take remaining 5 request but it accepting 10 request
The throttle mediator does not store the throttle count. Therefore if you perform a server restart it will reset the throttle count value and start from zero. In a production environment, it is not expected to have frequent server restarts.
Throttling is working based on wso2-ei node level but suppose Linux server having 10 nodes how to distribute the throttling policy in Linux server level .
If you want to maintain the throttle count across all the nodes you need to cluster the nodes. Throttle mediator uses hazelcast cluster messages to maintain a global count across the cluster.
I deployed a sample HelloWorld app on Google Cloud Run, which is basically k-native, and every call to the API takes 1.4 seconds at best, in an end-to-end manner. Is it supposed to be so?
The sample app is at https://cloud.google.com/run/docs/quickstarts/build-and-deploy
I deployed the very same app on my localhost as a docker container and it takes about 22ms, end-to-end.
The same app on my GKE cluster takes about 150 ms, end-to-end.
import os
from flask import Flask
app = Flask(__name__)
#app.route('/')
def hello_world():
target = os.environ.get('TARGET', 'World')
return 'Hello {}!\n'.format(target)
if __name__ == "__main__":
app.run(debug=True,host='0.0.0.0',port=int(os.environ.get('PORT', 8080)))
I am a little experience in FaaS and I expect API calls would get faster as I invoked them in a row. (as in cold start vs. warm start)
But no matter how many times I execute the command it doesn't go below 1.4 seconds.
I think the network distance isn't the dominant factor here. The round-trip time via ping to the API endpoint is only 50ms away, more or less
So my questions are as follows:
Is it potentially an unintended bug? Is it a technical difficulty which will be resolved eventually? Or maybe nothing's wrong, it's just the SLA of k-native?
If nothing's wrong with Google Cloud Run and/or k-native, what is the dominant time-consuming factor here for my API call? I'd love to learn the mechanism.
Additional Details:
Where I am located at: Seoul/Asia
The region for my Cloud Run app: us-central1
type of Internet connection I am testing under: Business, Wired
app's container image size: 343.3MB
the bucket location that Container Registry is using: gcr.io
WebPageTest from Seoul/Asia (warmup time):
Content Type: text/html
Request Start: 0.44 s
DNS Lookup: 249 ms
Initial Connection: 59 ms
SSL Negotiation: 106 ms
Time to First Byte: 961 ms
Content Download: 2 ms
WebPageTest from Chicago/US (warmup time):
Content Type: text/html
Request Start: 0.171 s
DNS Lookup: 41 ms
Initial Connection: 29 ms
SSL Negotiation: 57 ms
Time to First Byte: 61 ms
Content Download: 3 ms
ANSWER by Steren, the Cloud Run product manager
We have detected high latency when calling Cloud Run services from
some particular regions in the world. Sadly, Seoul seems to be one of
them.
[Update: This person has a networking problem in his area. I tested his endpoint from Seattle with no problems. Details in the comments below.]
I have worked with Cloud Run constantly for the past several months. I have deployed several production applications and dozens of test services. I am in Seattle, Cloud Run is in us-central1. I have never noticed a delay. Actually, I am impressed with how fast a container starts up.
For one of my services, I am seeing cold start time to first byte of 485ms. Next invocation 266ms, 360ms. My container is checking SSL certificates (2) on the Internet. The response time is very good.
For another service which is a PHP website, time to first byte on cold start is 312ms, then 94ms, 112ms.
What could be factors that are different for you?
How large is your container image? Check Container Registry for the size. My containers are under 100 MB. The larger the container the longer the cold start time.
Where is the bucket located that Container Registry is using? You want the bucket to be in us-central1 or at least US. This will change soon with when new Cloud Run regions are announced.
What type of Internet connection are you testing under? Home based or Business. Wireless or Ethernet connection? Where in the world are you testing from? Launch a temporary Compute Engine instance, repeat your tests to Cloud Run and compare. This will remove your ISP from the equation.
Increase the memory allocated to the container. Does this affect performance? Python/Flask does not require much memory, my containers are typically 128 MB and 256 MB. Container images are loaded into memory, so if you have a bloated container, you might now have enough memory left reducing performance.
What does Stackdriver logs show you? You can see container starts, requests, and container terminations.
(Cloud Run product manager here)
We have detected high latency when calling Cloud Run services from some particular regions in the world. Sadly, Seoul seems to be one of them.
We will explicitly capture this as a Known issue and we are working on fixing this before General Availability. Feel free to open a new issue in our public issue tracker
We are doing evaluation for metering purpose using WSO2 API Manager and DAS. (Latest versions)
Environment:
WSO2 API Manager runs as 2 node active-active deployment model using Hazlecast. (4 Core 8GB Ram) &
DAS runs as single node.
Both are connecting to backend RDBMS as mysql.
DAS and MYSQL shares the same server of 12 Core 24GB RAM. We dedicatedly allocated 12GB to MYSQL.
We started the test at the rate of 750reads/sec and everything went well for 27hrs until the metering reaches 72 Million and after which we have got the below error.
At API Manager: [PassThroughMessageProcessor-130] WARN DataPublisher Event queue is full, unable to process the event for endpoint Group.
At Das: (After 10 mins), we have got INFO {com.leansoft.bigqueue.page.MappedPageFactoryImpl} - Page file /$DAS_HOME/repository/data/index_staging_queues/4P/index/page-12.dat was just deleted. {com.leansoft.bigqueue.page.MappedPageFactoryImpl}.
Is this something that we have reached the limit w.r.t the infra setup or some performance issues w.r.t DAS. Can you please help us?
You need to tune the server performance of DAS and APIM
I am trying to call a long running web service from WebServiceTask in SSIS, but the TimeOut value for it seems to be a max of 300 seconds or 5 mins. But i am afraid that it might get timed out.
is there a way to increase the timeout vale
Unfortunately no. The Web Service Task, as I see it, is one of those "after thoughts." It satisfies a check box but doesn't go beyond the surface of functionality. I have found that I have a better web service experience if I just use a script component.