The exception looks like this.
I am not able to understand the cause for this exception. I tried restarting the server but still it keeps occurring again and again.
This is an ElasticSearch error and not something controlled by Moqui. I have seen this and based on my limited research it appears to happen when there are multiple ElasticSearch nodes running on the same network (in your case probably multiple Moqui nodes on the same network) and they seem to find the other nodes but not successfully sync up with them because they are not configured for it.
I haven't seen this cause any problems with anything else, so it seems annoying but safely ignored. There may be some ElasticSearch configuration that resolves this.
Related
I'm trying to process a document and store many documents into ravendb which I have running locally.
I'm getting the error
Tried to send *ravendb.BatchCommand request via POST http://127.0.0.1:8080/databases/mydb/bulk_docs to all configured nodes in the topology, all of them seem to be down or not responding. I've tried to access the following nodes: http://127.0.0.1:8080
I was able to fetch mydb topology from http://127.0.0.1:8080.
Fetched topology: ( url: http://127.0.0.1:8080, clusterTag: A, serverRole: Member)
exit status 1
To me, it sounds like maybe my local cluster is running out of compute to process the large amount of data I'm trying to store.
RavenDB says I'm using 3 of 12 available cores, and I'd also like to make sure it's using a reasonable amount of the ram I have available on the machine (I'd even be happy with giving it a swap)
But reading around online, I'm not finding much helpful information for making sure RavenDB is able to use what it needs. I found the settings.json so I can add in configurations which theoretically should get included into the server but I'm not making much progress.
I also found some settings and changed "reassign cores" to 12 but it says that still 3/12 are being used and 6/31.1 GB of memory are being used.
If an alternative solution is recommended I'm all ears. I just need to run things locally and storing everything as json's doesn't enable fast enough retrieval for my usecase.
Update
I was able to install mongodb and setup a local database. It hasn't given me any problems yet. RavenDB looks appealing if I understood it better but I guess I'll stick with the tried and true for this project.
It is highly unlikely that you managed to run out of resources on the server with 3 cores / 6 GB unless you are pushing hundreds of millions of documents and doing very heavy work.
Do you get any error on the server? There should be more details on the error or in the server log.
I installed elasticsearch logstash and kibana in the ubuntu server. Before I starting these services the CPU utilization is less than 5% and after starting these services in the next minute the CPU utilization crossing 85%. I don't know why it is happening. Can anyone help me with this issue?
Thanks in advance.
There is not enough information in your question to give you a specific answer, but i will point out few possible scenarios and how to deal with them.
Did you wait long enough? sometimes there is a warmpup which is consuming higher CPU until all services are registered and finish to boot. if you have a fairly small machine it might consume higher CPU and take longer to finish.
folder write permissions. if any of the components of the ELK fails due to restricted access on needed directories either for logging, creating sub folders for sinceDB files or more it can cause it to go into an infinity loop and try again and again while it is consuming high CPU.
connection issues. ES should be the first component to start, if it fails, Kibana and Logstash will go and try to connect to the ES again and again until successful connection- which can cause high CPU.
bad logstash configuration. if logstash fails to read the file from the configurations or if you have a bad parsing, excessive parsing for example- your first "match" in the filter part will include the least common option it might consume high CPU.
For further investigation:
I suggest you to not start all of them together. start ES first. if everything goes well start Kibana and lastly start Logstash.
check the logs of all the ELK components to find error messages, failures, etc.
for a better answer I will need the yaml of all 3 components (ES, Kibana, Logstash)
I will need the logstash configuration file.
Would recommend you to analyse the CPU cycles consumed by each of the elasticsearch, logstash and kibana process.
Check specifically which process among the above is consuming the most memory/cpu via top command for example.
Start only ES first and allow it to settle and the node to be started completely before starting kibana and may be logstash after that.
Send me the logs for each and I can assist if there are any errors.
I'm trying to handle Couchbase bootstrap failure gracefully and not fail the application startup. The idea is to use "Couchbase as a service", so that if I can't connect to it, I should still be able to return a degraded response. I've been able to somewhat achieve this by using the Couchbase async API; RxJava FTW.
Problem is, when the server is down, the Couchbase Java client goes crazy and keeps trying to connect to the server; from what I see, the class that does this is ConfigEndpoint and there's no limit to how many times it tries before giving up. This is flooding the logs with java.net.ConnectException: Connection refused errors. What I'd like, is for it to try a few times, and then stop.
Got any ideas that can help?
Edit:
Here's a sample app.
Steps to reproduce the problem:
svn export https://github.com/asarkar/spring/trunk/beer-demo.
From the beer-demo directory, run ./gradlew bootRun. Wait for the application to start up.
From another console, run curl -H "Accept: application/json" "http://localhost:8080/beers". The client request is going to timeout due to the failure to connect to Couchbase, but Couchbase client is going to flood the console continuously.
The reason we choose to have the client continue connecting is that Couchbase is typically deployed in high-availability clustered situations. Most people who run our SDK want it to keep trying to work. We do it pretty intelligently, I think, in that we do an exponential backoff and have tuneables so it's reasonable out of the box and can be adjusted to your environment.
As to what you're trying to do, one of the tuneables is related to retry. With adjustment of the timeout value and the retry, you can have the client referenceable by the application and simply fast fail if it can't service the request.
The other option is that we do have a way to let your application know what node would handle the request (or null if the bootstrap hasn't been done) and you can use this to implement circuit breaker like functionality. For a future release, we're looking to add circuit breakers directly to the SDK.
All of that said, these are not the normal path as the intent is that your Couchbase Cluster is up, running and accessible most of the time. Failures trigger failovers through auto-failover, which brings things back to availability. By design, Couchbase trades off some availability for consistency of data being accessed, with replica reads from exception handlers and other intentionally stale reads for you to buy into if you need them.
Hope that helps and glad to get any feedback on what you think we should do differently.
Solved this issue myself. The client I designed handles the following use cases:
The client startup must be resilient of CB failure/availability.
The client must not fail the request, but return a degraded response instead, if CB is not available.
The client must reconnect should a CB failover happens.
I've created a blog post here. I understand it's preferable to copy-paste rather than linking to an external URL, but the content is too big for an SO answer.
Start a separate thread and keep calling ping on it every 10 or 20 seconds, one CB is down ping will start failing, have a check like "if ping fails 5-6 times continuous then close all the CB connections/resources"
I have been experiencing an issue where occasionally my Kibana stops working stating a time-out trying to connect to elasticsearch as the cause. (I have marvel installed. Something like: "plugin:elasticsearch Request Timeout"
Usually these go away by the next day, and occasionally I have been able to re-gain access to my data by increasing the timeout on kibana. However I can't figure out how to troubleshoot this issue. I suspect it may be that ES is storing some extremely large individual documents but I cannot find them, there's just too many logs to dig through by hand.
My elasticsearch cluster is perfectly healthy (green on health check), even when kibana cannot access it.
Where can I possibly start to try and troubleshoot why we are getting timeouts here? when I expand the timeout window, kibana comes back, and everything works FINE.
Any tips on where to start searching would be enormously appreciated!!
I'm having issues with a large number of conncurrent connections to an Amazon RDS database using propel as the ORM with PHP. The application runs fine during load testing with 20 to 50 connections open at a time, then seems to hit a wall, mushrooms up to maximum connections almost immediately, and everything dies.
I believe Propel is using mysql_pconnect, but I can't find where it designates that, or a simple way to turn it off. I may be chasing a red herring here, but I'm stumped, and there are enough comments on the net regarding pconnect causing problems with too many connections that I thought it would be worth a shot to remove it.
Anyone know how to do this? I have been searching using various phrases, can't seem to find anything.
As it turns out, the error was being caused by the RDS redo log. There is only one size for all RDS instance sizes. On the larger instances sizes, it's possible to fill the redo log and come back to the beginning before the data is written out to the database. At this point it does the 'furiously flushing' thing to get caught up, does not process any new requests, and they pile up like crazy. This eventually caused our app to crash. More, smaller RDS servers fixed the issue, though not very happy with Amazon over this. They need to be able to change the size of the redo logs.