I have a schedule which runs my flow twice a day - at 0910 and 1520 BST.
This is spawning a massive number of DataFlow jobs - so far today just the second schedule (1520) has spawned 80 jobs:
$ gcloud dataflow jobs list
JOB_ID NAME TYPE CREATION_TIME STATE REGION
2018-07-29_12_17_06-14876588186269022154 project-name-513008-by-username Batch 2018-07-29 19:17:07 Running us-central1
2018-07-29_12_14_54-6436458673562317581 project-name-512986-by-username Batch 2018-07-29 19:14:55 Cancelled us-central1
2018-07-29_12_13_55-6167618802124600084 project-name-512985-by-username Batch 2018-07-29 19:13:57 Cancelled us-central1
...
(see PasteBin for the full list)
In the days after the DataPrep update last week, I had trouble accessing the run settings url for the flow. I suspect that there's a process as part of the run settings which walks back through the flow (I have 12 flows chained by reference datasets) and sanity checks it - it seems that my flow was just on the cusp of being complex enough to cause the page load to time out, and I had to cut out a couple of steps just to get to the run settings.
I wonder if each time this timed out, it somehow duplicated the schedule or something else in the process - but then again, the number of duplicated jobs is inconsistent.
I recently rebuilt this project after seeing some issues with sampling errors (in that the sample was corrupt, so I couldn't load the transformation UI, but also couldn't build a new sample). After a hefty attempt at resolving the issue, I took the chance to rebuild as a dedicated GCP project with structure improvements, etc. I didn't see this scheduling error before the rebuild.
Related
Workflow failed. Causes: The Dataflow job appears to be stuck because no worker activity has been seen in the last 1h. Please check the worker logs in Stackdriver Logging. You can also get help with Cloud Dataflow at https://cloud.google.com/dataflow/support.
I am using service account with all required IAM roles
Generally The Dataflow job appears to be stuck because no worker activity has been seen in the last 1h can be caused by too long setup progress. In order to solve this issue you can try to increase worker resources (via --machine_type parameter) to overcome the issue.
For example, While installing several dependencies that required building wheels (pystan, fbprophet) which will take more than an hour on the minimal machine (n1-standard-1 with 1 vCPU and 3.75GB RAM). Using a more powerful instance (n1-standard-4 which has 4 times more resources) will solve the problem.
You can debug this by looking at the worker startup logs in cloud logging. You are likely to see pip issues with installing dependencies.
Do you have any error logs showing that Dataflow Workers are crashing when trying to start?
If not, maybe worker VMs are started but they can't reach the Dataflow service, which is often related to network connectivity.
Please note that by default, Dataflow creates jobs using the network and subnetwork default (please check if it exists on your project), and you can change to a specific one by specifying --subnetwork. Check https://cloud.google.com/dataflow/docs/guides/specifying-networks for more information.
We are currently deploying flows on runtime using Airflow and face constant issues in terms of DAG deployment . The DAGs doesnt get picked by Scheduler on time and delays the user response in our workflow application .
Is their any way we can deploy DAGs into database of airflow ON DEMAND
? If not how can we make this process well defined .
Each DAG file is parsed every 30 seconds by default from Airflow 2.0.1.
This is controlled by https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#min-file-process-interval
Change that to 2 seconds or whatever number of seconds you think is appropriate for your case.
I have a AWS EMR cluster job which runs every 2 hours. I have set up a schedule using cloudWatch job to run every two hours.
But sometimes the next job (which runs after 2 hour from previous one) starts when previous one is not finished as it sometimes take more than 2 hours to completed depending on data to be processed.
I need some configuration by which I could prevent next job to be started if previous job is running.
I tried but couldn't found any set up. Can anyone knows how to do that please?
Add them as EMR steps. EMR steps run sequentially by default(Unless you change the concurrency setting)
actually the following steps to my data:
new objects in GCS bucket trigger a Google Cloud function that create a BigQuery Job to load this data to BigQuery.
I need low cost solution to know when this Big Query Job is finished and trigger a Dataflow Pipeline only after the job is completed.
Obs:
I know about BigQuery alpha trigger for Google Cloud Function but i
dont know if is a good idea,from what I saw this trigger uses the job
id, which from what I saw can not be fixed and whenever running a job
apparently would have to deploy the function again. And of course
it's an alpha solution.
I read about a Stackdriver Logging->Pub/Sub -> Google cloud function -> Dataflow solution, but i didn't find any log that
indicates that the job finished.
My files are large so isn't a good idea to use a Google Cloud Function to wait until the job finish.
Despite your mention about Stackdriver logging, you can use it with this filter
resource.type="bigquery_resource"
protoPayload.serviceData.jobCompletedEvent.job.jobStatus.state="DONE"
severity="INFO"
You can add dataset filter in addition if needed.
Then create a sink into Function on this advanced filter and run your dataflow job.
If this doesn't match your expectation, can you detail why?
You can look at Cloud Composer which is managed Apache Airflow for orchestrating jobs in a sequential fashion. Composer creates a DAG and executes each node of the DAG and also checks for dependencies to ensure that things either run in parallel or sequentially based on the conditions that you have defined.
You can take a look at the example mentioned here - https://github.com/GoogleCloudPlatform/professional-services/tree/master/examples/cloud-composer-examples/composer_dataflow_examples
I have a linear three step Dataflow pipeline - for some reason the last step started, but the preceding two steps hung in Not started for a long time before I gave up and killed the job. I'm not sure what caused this, as this same pipeline had successfully run in the past, and I'm surprised it didn't show any errors in the logs as to what was preventing the first two steps from starting. What can cause such a situation and how can I prevent it from happening?
This was happening because of an error in the worker start up. Certain Dataflow steps do not seem to require workers (e.g. writing to GCS), which is why that step was able to start - i.e. that step starting does not imply that workers are being created correctly. Worker start up is not displayed in the job logs by default - you need to click the link to Stackdriver in the job logs and then add worker-startup in the logs drop down in order to see any of those errors.