In my notebook, i am performing few join operations which are taking more than 30s in cluster 14.3 LTS where same operation is taking less than 4s in 13.3 LTS cluster. Can someone help me how can i optimize pyspark operations like joins and withColum...
Hi guys, i am running my Databricks jobs on a cluster job from azure datafactory using a databricks Python activity When I monitor my jobs in workflow-> job runs . I see that the run name is a concatenation of adf pipeline name , Databricks python ac...
I don't think that level of customisation is provided. However, I can suggest some workarounds:REST API: Create a job on the fly with desired name within ADF and trigger it using REST API in Web activity. This way you can track job completion status ...
User is running a job triggered from ADF in Databricks. In this job they need to use custom libraries that are in jars. Most of the times jobs are running fine, however sometimes it fails with:java.lang.NoClassDefFoundError: Could not initializeAny s...
Can you please check if there are more than one jar containing this class . If multiple jars of the same type are available on the cluster, then there is no guarantee of JVM picking the proper classes for processing, which results in the intermittent...
Hi everyone!I'm setting up a workflow using Databricks Assets Bundles (DABs). And I want to configure my workflow to be trigger on file arrival. However all the examples I've found in the documentation use schedule triggers. Does anyone know if it is...
I'm using Databricks asset bundles and I have pipelines that contain "if all done rules". When running on CI/CD, if a task fails, the pipeline returns a message like "the job xxxx SUCCESS_WITH_FAILURES" and it passes, potentially deploying a broken p...
Awesome answer, I will try the first approach. I think it is a less intrusive solution than changing the rules of my pipeline in development scenarios. This way, I can maintain a general pipeline for deployment across all environments. We plan to imp...
I've defined a streaming deltlive table in a notebook using python.running on "preview" channeldelta cache accelerated (Standard_D4ads_v5) computeIt fails withorg.apache.spark.sql.streaming.StreamingQueryException: [STREAM_FAILED] Query [id = xxx, ru...
Hi @smedegaard,
You’re encountering a StreamingQueryException with the message: “getPrimaryKeys not implemented for debezium SQLSTATE: XXKST.”
This error suggests that the getPrimaryKeys operation is not supported for the Debezium connector in your ...
Hi Team,Is there any impact when integrating Databricks with Boomi as opposed to Azure Event Hub? Could you offer some insights on the integration of Boomi with Databricks?https://boomi.com/blog/introducing-boomi-event-streams/Regards,Janga
Hi @Phani1, Let’s explore the integration of Databricks with Boomi and compare it to Azure Event Hub.
Databricks Integration with Boomi:
Databricks is a powerful data analytics platform that allows you to process large-scale data and build machin...
Hello All,My scenario required me to create a code that reads tables from the source catalog and writes them to the destination catalog using Spark. Doing one by one is not a good option when there are 300 tables in the catalog. So I am trying the pr...
Hi @ETLdeveloper You can use the multithreading that help you to run notebook in parallel.Attaching code for your reference - from concurrent.futures import ThreadPoolExecutor
class NotebookData:
def __init__(self, path, timeout, parameters = Non...
Hi All! Im in a project where i need to connect azure devops and databricks using managed identity to avoid the using of service account, PAT, etc.The thing is i cant move forward with the connection since i cannot take the ownership of the files wh...
Hi @TitaMn, Connecting Azure DevOps and Azure Databricks using managed identity is a great approach to enhance security and avoid using service accounts or personal access tokens (PATs).
Let’s explore some options:
Azure Managed Identity for Dat...
Hi,Would anyone happen to know whether it's possible to cache a dataframe in memory that the result of a query on a federated table?I have a notebook that queries a federated table, does some transformations on the dataframe and then writes this data...
@daniel_sahal , this is the code snippet:lsn_incr_batch = spark.sql(f"""select start_lsn,tran_begin_time,tran_end_time,tran_id,tran_begin_lsn,cast('{current_run_ts}' as timestamp) as appendedfrom externaldb.cdc.lsn_time_mappingwhere tran_end_time > '...
Hi Community,i was trying to load a ML Model from a Azure Storageaccount (abfss://....) with: model = PipelineModel.load(path) i set the spark config: spark.conf.set("fs.azure.account.auth.type", "OAuth")
spark.conf.set("fs.azure.account.oauth.provi...
I am reaching out to bring attention to a performance issue we are encountering while processing XML files using Spark-XML, particularly with the configuration spark.read().format("com.databricks.spark.xml").Currently, we are experiencing significant...
@amar1995 - Can you try this streaming approach and see if it works for your use case (using autoloader) - https://kb.databricks.com/streaming/stream-xml-auto-loader
I have an Azure web app running flask web server. From flask server, I want to run some queries on the data stored in ADLS Gen2 storage. I already created Databricks notebooks running these queries. The flask server will pass some parameters in ...
We have a data feed with files whose filenames stays the same but the contents change over time (brand_a.csv, brand_b.csv, brand_c.csv ....).Copy Into seems to ignore the files when they change.If we set the Force flag to true and run it, we end up w...
Thanks for the validation, Werners! That's the path we've been heading down (copy + merge). I still have some DLT experiments planned but - at least for this situation - copy + merge works just fine.