We are using Testlink and everything is working fine. We also schedule automatic backup for testing and it generates backup everyday at define time. From few weeks i observed that the file size generated for backup is not getting increase more that 34MB. Even some time size automatically get reduced from previous backup without deleting anything in the project. Can anybody help to find the reason or anyone else have observed same issue?
Is the backup taking the attachments and screenshots?Maybe it can be the reason...
Can you please provide more information related to the scheduled automatic backup?
Regards, David.
give a look to audit tables (transaction & events) may be you are getting lot of warning (useless to back) warning messages
Related
I am trying to delete a dataset from one of our premium workspaces and am getting an error saying it's included in the published app. However, as you can see below, the dataset in question (Construction Daily Report) is not included in the app and no reports reference it. I also tried deleting it using PowerShell but that didn't work either. Has anyone run into this same issue?
I have sometimes experienced significant lag between removing content from an app and republishing the app, until being allowed to actually remove the dataset from the workspace environment.
If you unpublished this very recently, simply try to wait a bit until all systems are fully up to date with currently published app contents. If a significant amount of time has passed, perhaps contact Microsoft directly.
Really basic question, but I can't seem to find it in the documentation. When continuous scanning is turned on, at what frequency and at which time the registry will be scanned? I've turned it on a couple of hours ago but it hasn't scanned yet.
Continuous scanning will be executed when a new vulnerability is received by AWS. I couldn't pinpoint when the initial scan happened. It took approximately a half day.
I had a same issue and came across with this question. I will post the docs quote.
https://docs.aws.amazon.com/AmazonECR/latest/userguide//image-scanning.html?icmpid=docs_ecr_hp-registry-private
As new vulnerabilities appear, the scan results are updated and Amazon Inspector emits an event to EventBridge to notify you.
did you turn one the "Scan on push" option, go the Edit option end then enable that option, it will automatically scan your repo after each push.
if you go for manual scan it's generally trigger immediately.
please give a try
I have an AWS Glue job, with max concurrent runs set to 1. The job is currently not running. But when I try to run it, I keep getting the error: "Max concurrent runs exceeded".
Deleting and re-creating the job does not help. Also, other jobs in the same account run fine, so it cannot be a problem with account wide service quotas.
Why am I getting this error?
I raised this issue with AWS support, and they confirmed that it is a known bug:
I would like to inform you that this is a known bug, where an internal distributed counter that keeps track of job concurrency goes into a stale state due to an edge case, causing this error. Our internal Service team has to manually reset the counter to fix this issue. Service team has already added the bug fix in their product roadmap and will be working on it. Unfortunately I may not be able to comment on the ETA on the deployment, as we don’t have any visibility on product teams road map and fix release timeline.
The suggested workarounds are:
Increase the max concurrency to 2 or higher
Re-create the job with a different name
Glue container is start and its taking some time same when your job end container shutdown taking some time in between if you try to execute new Jon and default concurrency is 1 so you will get this error.
How to resolve:
Go to your Glue Job --> Under Job detail tab you can find "Maximum concurrency" default value is 1 change it to 3 or more as per your need.
I tried changing "Maximum concurrency" to 2 and then run it !
It worked but again running it cause the same issue, but I looked into my s3 ,it has dumped the data ,so it run for once!
I'm still looking for a stable solution but this may work!
I've been struggling to find out what is wrong with my spark job that indefinitely hangs where I try to write it out to either S3 or HDFS (~100G of data in parquet format).
The line that causes the hang:
spark_df.write.save(MY_PATH,format='parquet',mode='append')
I have tried this in overwrite as well as append mode, and tried saving to HDFS and S3, but the job will hang no matter what.
In the Hadoop Resource Manager GUI, it shows the state of the spark application as "RUNNING", but looking it seems nothing is actually being done by Spark and when I look at the Spark UI there are no jobs running.
The one thing that has gotten it to work is to increase the size of the cluster while it is in this hung state (I'm on AWS). This, however, doesn't matter if I start the cluster with 6 workers and increase to 7, or if I start with 7 and increase to 8 which seems somewhat odd to me. The cluster is using all of the memory available in both cases, but I am not getting memory errors.
Any ideas on what could be going wrong?
Thanks for the help all. I ended up figuring out the problem was actually a few separate issues. Here's how I understand them:
When I was saving directly to S3, it was related to the issue that Steve Loughran mentioned where the renames on S3 were just incredibly slow (so it looked like my cluster was doing nothing). On writes to S3, all the data is copied to temporary files and then "renamed" on S3 -- the problem is that renames don't happen like they do on a filesystem and actually take O(n) time. So all of my data was copied to S3 and then all of the time was spent renaming the files.
The other problem I faced was with saving my data to HDFS and then moving it to S3 via s3-dist-cp. All of my clusters resources were being used by Spark, and so when the Application Master tried giving resources to move the data to via s3-dist-cp it was unable to. The moving of data couldn't happen because of Spark, and Spark wouldn't shut down because my program was still trying to copy data to S3 (so they were locked).
Hope this can help someone else!
Am guessing that it might just retain the metadata ID (redeploy) as opposed to generating a new one (deploy), is that the only difference though?
It is only difference but it is very important. You should always redeploy jobs that any flow is dependent on. If you deploy job that was already added to a flow, the flow will be damaged.
I disagree with the previous comment by #barjey that the flow is damaged. It is not damaged , its just that a New metadata ID is created in the form of a new .sas file for the same job, like JOB_NAME_00000.sas and deployed
This adds to lot of confusion and a lot of versions of the same jobs float around, which is incorrect. That's the reason a job is always re-deployed so that the previous version of the code is over-written and new changes reflected in the flow.
Yo redeploy a job to incorporate the environmental changes by automatically identifying the environment to which you have deployed the job .These changes are then reflected at back end where the job is actually saved (job which is scheduled in a flow).