One of my Batch-Jobs tonight failed with a Runtime-Exception. It writes Data to Datastore like 200 other jobs that were running tonight. This one failed with a very long list auf causes, the root of it should be this:
Caused by: com.google.datastore.v1.client.DatastoreException: I/O error, code=UNAVAILABLE
at com.google.datastore.v1.client.RemoteRpc.makeException(RemoteRpc.java:126)
at com.google.datastore.v1.client.RemoteRpc.call(RemoteRpc.java:95)
at com.google.datastore.v1.client.Datastore.commit(Datastore.java:84)
at com.google.cloud.dataflow.sdk.io.datastore.DatastoreV1$DatastoreWriterFn.flushBatch(DatastoreV1.java:925)
at com.google.cloud.dataflow.sdk.io.datastore.DatastoreV1$DatastoreWriterFn.processElement(DatastoreV1.java:892)
Caused by: java.io.IOException: insufficient data written
at sun.net.www.protocol.http.HttpURLConnection$StreamingOutputStream.close(HttpURLConnection.java:3501)
at com.google.api.client.http.javanet.NetHttpRequest.execute(NetHttpRequest.java:81)
at com.google.api.client.http.HttpRequest.execute(HttpRequest.java:981)
at com.google.datastore.v1.client.RemoteRpc.call(RemoteRpc.java:87)
at com.google.datastore.v1.client.Datastore.commit(Datastore.java:84)
at com.google.cloud.dataflow.sdk.io.datastore.DatastoreV1$DatastoreWriterFn.flushBatch(DatastoreV1.java:925)
at com.google.cloud.dataflow.sdk.io.datastore.DatastoreV1$DatastoreWriterFn.processElement(DatastoreV1.java:892)
at com.google.cloud.dataflow.sdk.util.SimpleDoFnRunner.invokeProcessElement(SimpleDoFnRunner.java:49)
at com.google.cloud.dataflow.sdk.util.DoFnRunnerBase.processElement(DoFnRunnerBase.java:139)
at com.google.cloud.dataflow.sdk.runners.worker.SimpleParDoFn.processElement(SimpleParDoFn.java:188)
at com.google.cloud.dataflow.sdk.runners.worker.ForwardingParDoFn.processElement(ForwardingParDoFn.java:42)
at com.google.cloud.dataflow.sdk.runners.
How can this happen? It's very similar to all the other jobs I run. I am using the Dataflow-Version 1.9.0 and the standard DatastoreIO.v1().write....
The jobIds with this error message:
2017-08-29_17_05_19-6961364220840664744
2017-08-29_16_40_46-15665765683196208095
Is it possible to retrieve the errors/logs of a job from an outside application (Not cloud console) to automatically being able to restart jobs, if they would usually succeed and fail because of quota-issues or other reasons that are temporary?
Thanks in advance
This is most likely because DatastoreIO is trying to write more mutations in one RPC call than the Datastore RPC size limit allows. This is data-dependent - suppose the data for this job differs somewhat from data for other jobs. In any case: this issue was fixed in 2.1.0 - updating your SDK should help.
Related
I'm running a DAG in Google Cloud Composer (hosted Airflow) which runs fine in Airflow locally. All it does is print "Hello World". However, when I run it through Cloud Composer I receive the error:
*** Log file does not exist: /home/airflow/gcs/logs/matts_custom_dag/main_test/2020-04-20T23:46:53.652833+00:00/2.log
*** Fetching from: http://airflow-worker-d775d7cdd-tmzj9:8793/log/matts_custom_dag/main_test/2020-04-20T23:46:53.652833+00:00/2.log
*** Failed to fetch log file from worker. HTTPConnectionPool(host='airflow-worker-d775d7cdd-tmzj9', port=8793): Max retries exceeded with url: /log/matts_custom_dag/main_test/2020-04-20T23:46:53.652833+00:00/2.log (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f8825920160>: Failed to establish a new connection: [Errno -2] Name or service not known',))
I've also tried making the DAG add data into a database and it actually succeeds 50% of the time. However, it always returns this error message (and no other print statements or logs). Any help much appreciated on why this might be happening.
We also faced the same issue then raised a support ticket to GCP and got the following reply.
The message is related to the latency of syncing logs from Airflow workers to WebServer, it takes at least some minutes (depending on the number of objects and their size)
The total log size seems not large but it’s enough to noticeably slow down synchronization, hence, we recommend cleanup/archive the logs
Basically we recommend relying on Stackdriver logs instead, because of latency due to the design of this sync
I hope this will help you solve the problem.
I have the same problem after upgrading from 1.10.3 to 1.10.6 of Google Composer.
I can see in my logs that airflow is trying to get the logs from a bucket with a name ended with -tenant while the bucket in my account ends with -bucket
In the configuration, I can see something weird too.
## airflow.cfg
[core]
remote_base_log_folder = gs://us-east1-dada-airflow-xxxxx-bucket/logs
## also in the running configuration says
core remote_base_log_folder gs://us-east1-dada-airflow-xxxxx-tenant/logs env var
I wrote to google support and they said the team is working on a fix.
EDIT:
I've been accessing my logs with gsutil and replacing the bucket name suffix to -bucket
gsutil cat gs://us-east1-dada-airflow-xxxxx-bucket/logs/...../5.logs
I faced the same situation in multiple occasions.
As soon as when the job finished when I take a look at the log on Airflow Web UI, it used to give me the same error. Although when I check back the same logs on UI after a min or 2, I could see the logs properly.
As per the above answers, its a sync issue between the webserver and the Worker node.
In general, the issue describe here should be more like a sporadic issue.
In certain situations, what could help is setting default-task-retries to a value that allows for retrying a task at least 1.
This issue is resolved at least since Airflow version: 1.10.10+composer.
I am using Celery with Flask after working for a good long while, my celery is showing a celery.backends.rpc.BacklogLimitExceeded error.
My config values are below:
CELERY_BROKER_URL = 'amqp://'
CELERY_TRACK_STARTED = True
CELERY_RESULT_BACKEND = 'rpc'
CELERY_RESULT_PERSISTENT = False
Can anyone explain why the error is appearing and how to resolve it?
I have checked the docs here which doesnt provide any resolution for the issue.
Possibly because your process consuming the results is not keeping up with the process that is producing the results? This can result in a large number of unprocessed results building up - this is the "backlog". When the size of the backlog exceeds an arbitrary limit, BacklogLimitExceeded is raised by celery.
You could try adding more consumers to process the results? Or set a shorter value for the result_expires setting?
The discussion on this closed celery issue may help:
Seems like the database backends would be a much better fit for this purpose.
The amqp/RPC result backends needs to send one message per state update, while for the database based backends (redis, sqla, django, mongodb, cache, etc) every new state update will overwrite the old one.
The "amqp" result backend is not recommended at all since it creates one queue per task, which is required to mimic the database based backends where multiple processes can retrieve the result.
The RPC result backend is preferred for RPC-style calls where only the process that initiated the task can retrieve the result.
But if you want persistent multi-consumer result you should store them in a database.
Using rabbitmq as a broker and redis for results is a great combination, but using an SQL database for results works well too.
While implement Fire-base cloud messaging in browser with help of Service Worker getting an error "DOMException: Registration failed - storage error" please help why i am getting this error
This means that your Service Worker has stored so much data that the storage available to it is full, and upon SW installation it tries to store even more which in turn leads to an error.
You have to either manually remove stuff from your storage or use some library that automatically handles these situations for you in a way or another (eg. removes stuff FIFO style).
In my case the problem was caused by create-react-app's own service worker unregistration logic at the very bottom of index.js:
serviceWorker.unregister();
It was conflicting with the fact of using a service worker in the middle of our own application's logic.
Solution was simple – just removing that line from index.js
We have a new beta version of our software with some changes, but not around our database layer.
We've just started getting Error 3128 reported in our server logs. It seems that once it happens, it happens for as long as the app is open. The part of the code where it is most apparent is where we log data every second via SQLite. We've generated 47k errors on our server this month alone.
3128 Disk I/O error occurred. Indicates that an operation could not be completed because of a disk I/O error. This can happen if the runtime is attempting to delete a temporary file and another program (such as a virus protection application) is holding a lock on the file. This can also happen if the runtime is attempting to write data to a file and the data can't be written.
I don't know what could be causing this error. Maybe an anti-virus program? Maybe our app is getting confused and writing data on top of each other? We're using async connections.
It's causing lots of issues and we're at a loss. It has happened in our older version, but maybe 100 times in a month rather than 47,000 times. Either way I'd like to make it happen "0" times.
Possible solution: Exception Message: Some kind of disk I/O error occurred
Summary: There is probably not a problem with the database but a problem creating (or deleting) the temporary file once the database is opened. AIR may have permissions to the database, but not to create or delete files in the directory.
One answer that has worked for me is to use the PRAGMA statement to set the journal_mode value to something other than DELETE. You do this by issuing a PRAGMA statement in the same way you would issue a query statement.
PRAGMA journal_mode = OFF
Unfortunately, if the application crashes in the middle of a transaction when the OFF journaling mode is set, then the database file will very likely go corrupt.1.
1 http://www.sqlite.org/pragma.html#pragma_journal_mode
The solution was to make sure database delete, update, insert only happened one at at time by wrapping a little wrapper. On top of that, we had to watch for error 3128 and retry. I think this is because we have a trigger running that could lock the database after we inserted data.
We have a new beta version of our software with some changes, but not around our database layer.
We've just started getting Error 3128 reported in our server logs. It seems that once it happens, it happens for as long as the app is open. The part of the code where it is most apparent is where we log data every second via SQLite. We've generated 47k errors on our server this month alone.
3128 Disk I/O error occurred. Indicates that an operation could not be completed because of a disk I/O error. This can happen if the runtime is attempting to delete a temporary file and another program (such as a virus protection application) is holding a lock on the file. This can also happen if the runtime is attempting to write data to a file and the data can't be written.
I don't know what could be causing this error. Maybe an anti-virus program? Maybe our app is getting confused and writing data on top of each other? We're using async connections.
It's causing lots of issues and we're at a loss. It has happened in our older version, but maybe 100 times in a month rather than 47,000 times. Either way I'd like to make it happen "0" times.
Possible solution: Exception Message: Some kind of disk I/O error occurred
Summary: There is probably not a problem with the database but a problem creating (or deleting) the temporary file once the database is opened. AIR may have permissions to the database, but not to create or delete files in the directory.
One answer that has worked for me is to use the PRAGMA statement to set the journal_mode value to something other than DELETE. You do this by issuing a PRAGMA statement in the same way you would issue a query statement.
PRAGMA journal_mode = OFF
Unfortunately, if the application crashes in the middle of a transaction when the OFF journaling mode is set, then the database file will very likely go corrupt.1.
1 http://www.sqlite.org/pragma.html#pragma_journal_mode
The solution was to make sure database delete, update, insert only happened one at at time by wrapping a little wrapper. On top of that, we had to watch for error 3128 and retry. I think this is because we have a trigger running that could lock the database after we inserted data.