I have a series of Azure SQL Data Warehouse databases (for our development/evaluation purposes). Due to a recent unplanned extended outage (due to an issue with the Tenant Ring associated with some of these databases), I decided to resume the canary queries I had been running before but had quiesced for a couple of months due to frequent exceptions.
The canary queries are not running particularly frequently on any specific database, say every 15 minutes. On one database, I've received two indications of issues completing the canary query in 24 hours. The error is:
Msg 110802, Level 16, State 1, Server adwscdev1, Line 1110802;An internal DMS error occurred that caused this operation to fail. Details: A timeout occurred while waiting for memory resources to execute the query in resource pool 'SloDWPool' (2000000007). Rerun the query.
This database is under essentially no load, running at more than 100 DWU.
Other databases on the same logical server may be running under a load, but I have not seen the error on them.
What is the explanation for this error?
Please open a support ticket for this issue, support will have full access to the DMS logs and be able to see exactly what is going on. this behavior is not expected.
While I agree a support case would be reasonable I think you should also try scaling up to say DWU400 and retrying. I would also consider trying largerc or xlargerc on DWU100 and DWU400 as described here. Note it gets more memory and resources per query.
Run the following then retry your query:
EXEC sp_addrolemember 'largerc', 'yourLoginName'
Related
Been running an AWS m4.large.elasticsearch Elasticsearch (service) instance with 2 data nodes for more than a year now without any severe issues. Because of the increased demand we have set up 2 additional r6g.large Elasticsearch instances (which have the same amount of vCPU and memory as the m4.large, but should even offer better performance according to the docs).
Ever since using these we have been getting "429 Too Many Requests" errors inside our application. After some digging on https://aws.amazon.com/es/premiumsupport/knowledge-center/resolve-429-error-es/ the following things have been tried without success:
Increasing the circuit breaker limit to 90% => Does not solve the issue
Switching to c6g.xlarge (Compute optimized instances with double the capacity) => Does not solve the issue
Enabled slow search logs + error logs in the hope of getting more info => Nothing is being logged
If anyone has an idea on how we could go around solving this that would be much appreciated!
PS: "Old" version is running Elasticsearch 7.7 while the new one is running 7.10, but would be astonished that this would be the cause.
A 429 error message as a write rejection indicates a bulk queue error.A bulk queue on each node can hold between 50 and 200 requests, depending on which Elasticsearch version you are using. There have been multiple reports on this and older version of Elasticache is the probable root cause.
After running for 17 hours, my Dataflow job failed with the following message:
The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures.
The 4 failures consist of 3 workers losing contact with the service, and one worker reported dead:
****-q15f Root cause: The worker lost contact with the service.
****-pq33 Root cause: The worker lost contact with the service.
****-fzdp Root cause: The worker ****-fzdp has been reported dead. Aborting lease 4624388267005979538.
****-nd4r Root cause: The worker lost contact with the service.
I don't see any errors in the worker logs for the job in Stackdriver. Is this just bad luck? I don't know how frequently work items need to be retried, so I don't know what the probability is that a single work item will fail 4 times over the course of a 24 hour job. But this same type of job failure happens frequently for this long-running job, so it seems like we need some way to either decrease the failure rate of work items, or increase the allowed number of retries. Is either possible? This doesn't seem related to my pipeline code, but in case it's relevant, I'm using the Python SDK with apache-beam==2.15.0. I'd appreciate any advice on how to debug this.
Update: The "STACK TRACES" section in the console is totally empty.
I was having the same problem and it was solved by scaling up my workers resources. Specifically, I set --machine_type=n1-highcpu-96 in my pipeline configs. See this for a more extensive list on machine type options.
Edit: Set it to highcpu or highmem depending on the requirements of your pipeline process
I run Airflow in a managed Cloud-composer environment (version 1.9.0), whic runs on a Kubernetes 1.10.9-gke.5 cluster.
All my DAGs run daily at 3:00 AM or 4:00 AM. But sometime in the morning, I see a few Tasks failed without a reason during the night.
When checking the log using the UI - I see no log and I see no log either when I check the log folder in the GCS bucket
In the instance details, it reads "Dependencies Blocking Task From Getting Scheduled" but the dependency is the dagrun itself.
Although the DAG is set with 5 retries and an email message it does not look as if any retry took place and I haven't received an email about the failure.
I usually just clear the task instance and it run successfully on the first try.
Has anyone encountered a similar problem?
Empty logs often means the Airflow worker pod was evicted (i.e., it died before it could flush logs to GCS), which is usually due to an out of memory condition. If you go to your GKE cluster (the one under Composer's hood) you will probably see that there is indeed a evicted pod (GKE > Workloads > "airflow-worker").
You will probably see in "Tasks Instances" that said tasks have no Start Date nor Job Id or worker (Hostname) assigned, which, added to no logs, is a proof of the death of the pod.
Since this normally happens in highly parallelised DAGs, a way to avoid this is to reduce the worker concurrency or use a better machine.
EDIT: I filed this Feature Request on your behalf to get emails in case of failure, even if the pod was evicted.
What does this error mean?
I have 5 Lambda Functions deployed using Java that worked perfectly but since this afternoon all of them started displaying the same message when I execute each:
Service error.
No output, no logs, only that message in a red box.
In http://status.aws.amazon.com/ they say:
6:05 PM PDT We are investigating increased error rates and elevated
latencies for AWS Lambda requests in the US-EAST-1 Region. Newly
created functions and console editing are also affected.
Why does it happen and is there a way to prevent it?
From time to time, parts of Amazon's AWS service fail. Sometimes the failure is very small and short-lived, and in other cases there are larger distributed failures.
Your system design needs to take into account the possibility that the piece of AWS that you are counting on will not work at the moment, and try to route around the damage. For instance, you can run Lambda in multiple regions. (It already runs in multiple availability zones inside a single region, so you don't have to worry about that). This gives you some isolation against failures in any one region.
Getting distributed systems to work at small scale can be hard because the failures that you need to protect against don't happen very often. At large scale, you get systematic efforts like Netflix's "Chaos Monkey", which deliberately introduces failures so that automated processes can detect and correct those issues.
"A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable." -- Leslie Lamport
"When a Fail-Safe system fails, it fails by failing to fail safe." -- John Gall
We have been using AWS Elasticache for about 6 months now without any issues. Every night we have a Java app that runs which will flush DB 0 of our redis cache and then repopulate it with updated data. However we had 3 instances between July 31 and August 5 where our DB was successfully flushed and then we were not able to write the new data to the database.
We were getting the following exception in our application:
redis.clients.jedis.exceptions.JedisDataException:
redis.clients.jedis.exceptions.JedisDataException: READONLY You can't
write against a read only slave.
When we look at the cache events in Elasticache we can see
Failover from master node prod-redis-001 to replica node
prod-redis-002 completed
We have not been able to diagnose the issue and since the app was running fine for the past 6 months I am wondering if it is something related to a recent Elasticache release that was done on the 30th of June.
https://aws.amazon.com/releasenotes/Amazon-ElastiCache
We have always been writing to our master node and we only have 1 replica node.
If someone could offer any insight it would be much appreciated.
EDIT: This seems to be an intermittent problem. Some days it will fail other days it runs fine.
We have been in contact with AWS support for the past few weeks and this is what we have found.
Most Redis requests are synchronous including the flush so it will block all other requests. In our case we are actually flushing 19m keys and it takes more then 30 seconds.
Elasticache performs a health check periodically and since the flush is running the health check will be blocked, thus causing a failover.
We have been asking the support team how often the health check is performed so we can get an idea of why our flush is only causing a failover 3-4 times a week. The best answer we can get is "We think its every 30 seconds". However our flush consistently takes more then 30 seconds and doesn't consistently fail.
They said that they may implement the ability to configure the timing of the health check however they said this would not be done anytime soon.
The best advice they could give us is:
1) Create a completely new cluster for loading the new data on, and
instead of flushing the previous cluster, re-point your application(s)
to the new cluster, and remove the old one.
2) If the data that you are flushing is an update version of the data,
consider not flushing, but updating and overwriting new keys?
3) Instead of flushing the data, set the expiry of the items to be
when you would normally flush, and let the keys be reclaimed (possibly
with a random time to avoid thundering herd issues), and then reload
the data.
Hope this helps :)
Currently for Redis versions from 6.2 AWS ElastiCache has a new feature of thread monitoring. So the health check doesn't happen in the same thread as all other actions of Redis. Redis can continue to proceed a long command / lua script, but will still considered healthy. Because of this new feature failovers should happen less.