I have a pipeline that has 10 Dataflow activities and each uses AutoResolveIntegrationRuntime default integration cluster.
When I trigger the pipeline, cluster startup takes around 4 mins for each Dataflow totalling 40 mins to complete pipeline execution. Can I avoid this? If so, how?
Thanks,
Karthik
You will want to either put those data flows on your pipeline canvas without dependency lines so that they all run in parallel, or set a TTL in your Azure IR and use that same Azure IR for each activity. This way, each subsequent activity can use a warm pool and start-up in 1-2 mins instead of 4 mins.
Here is an explanation of these different methods.
And here is how to configure TTL to set a warm pool for your factory.
Related
If I have for example a (multitask) Databricks job with 3 tasks in series and the second one fails - is there a way to start from the second task instead of running the whole pipeline again?
Right now this is not possible, but if you refer to the Databrick's Q3 (2021) public roadmap, there were some items around improving multi-task jobs.
Update: September 2022. This functionality was released back in May 2022nd with name repair & rerun
If you are running Databricks on Azure it is possible via Azure Data Factory.
Context
We're creating a pipeline of Spark Jobs in Azure Synapse (much like in Azure Data Factory) that read in data from various databases and merge it into a larger dataset. Originally, we had a single pipeline that worked, with many Spark Jobs leading into others. As part of a redesign, we were thinking that we would create a pipeline for each individual Spark job, so that we can create various orchestration pipelines. If the definition of a Spark job changes, we only have to change the definition file in one place.
Problem
When we run our "pipeline of pipelines", we get an error that we don't get with the single pipeline. The error:
Is a series of timeout errors like: Timeout waiting for idle object
Occurs in different areas of the pipeline on different runs
This results in the failure of the pipeline as a whole.
Questions
What is the issue happening here?
Is there a setting that could help solve it? (Azure Synapse does not provide many levers)
Is our pipeline-of-pipelines construction an anti-pattern? (Doesn't seem like it should be since Azure allows it)
Hypotheses
From the post here: are we running out of available connections in Azure? (how would you solve that?)
I need to know , how to stop a azure databricks cluster by doing configuration when it is running infinitely for executing a job.(without manual stopping)and as well as create an email alert for it, as the job running time exceeds its usual running time.
You can do this in the Jobs UI, Select your job, under Advanced, edit the Alerts and Timeout values.
This Databricks docs page may help you: https://docs.databricks.com/jobs.html
I want to automatically start a job on an Azure Batch AI cluster once a week. The jobs are all identical except for the starting time. I thought of writing a PowerShell Azure Function that does this, but Azure Functions v2 doesn't support PowerShell and I don't want to use v1 in case it will be phased out. I would prefer not to do this in C# or Java. How can I do this?
Currently, there's no option available to trigger a job on Azure Batch AI cluster. Maybe you want to run a shell script which in turn can create a regular schedule using system's task scheduler. Please see if this doc by Said Bleik helps:
https://github.com/saidbleik/batchai_mm_ad#scheduling-jobs
I assume this way you can add multiple schedules for the job!
Azure Batch portal has "Job schedules" tab. You can go there, add a Job, and set a schedule for the Job. You can specify the recurrence in the Schedule
Scheduled jobs
Job schedules enable you to create recurring jobs within the Batch service. A job schedule specifies when to run jobs and includes the specifications for the jobs to be run. You can specify the duration of the schedule--how long and when the schedule is in effect--and how frequently jobs are created during the scheduled period.
I have enable always on property in configuration, still long running jobs are aborting.
I have running 10 long running jobs concurrently in one Web APP. For Web App plan is standard. As per standard plan we can schedule 50 jobs in one web app. still I am facing issue of abort. That it wont abort all the jobs it will abort 3 to 4 jobs which are taking more CPU throughput. It will be great if any body come with answer. Thanks in advance.