- 1334 Views
- 0 replies
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Where is the root Azure Storage instance?
I am trying to access logs from my Databricks notebook which is run as a job. I would like to see these logs in an azure storage account.
- 1334 Views
- 0 replies
- 0 kudos
Forum Posts
- 2169 Views
- 0 replies
- 1 kudos
What is the most efficient way to read in a partitioned parquet file with pyspark?
I work with parquet files stored in AWS S3 buckets. They are multiple TB in size and partitioned by a numeric column containing integer values between 1 and 200, call it my_partition. I read in and perform compute actions on this data in Databricks w...
- 2169 Views
- 0 replies
- 1 kudos
- 3209 Views
- 1 replies
- 0 kudos
Resolved! Is it mandatory to checkpoint my streaming query.
I have ad-hoc one-time streaming queries where I believe checkpoint won't give any value add. Should I still use checkpointing
- 3209 Views
- 1 replies
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Latest Reply
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It's not mandatory. But the strong recommendation is to use Checkpointing for Streaming irrespective of your use case. This is because the default checkpoint location can get a lot of files over time as there is no graceful guaranteed cleaning in pla...
- 0 kudos
- 3813 Views
- 2 replies
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Should/Can I use spark streaming for Batch workloads?
Its preferable to use spark streaming (with Delta) for batch workloads rather then regular batch. With the trigger.once trigger whenever the streaming job is started it will process whatever is available in the source (kafka/kinesis/File System) and ...
- 3813 Views
- 2 replies
- 0 kudos
Latest Reply
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The streaming checkpoint mechanism is independent of the Trigger type. The way checkpoint works are it creates an offset file when processing the batch and once the batch is completed it creates a commit file for that batch in the checkpoint director...
- 0 kudos
- 1233 Views
- 1 replies
- 0 kudos
How to migrate to Auto-loader without downtime?
I have an S3-SQS workload. Is it possible to migrate the workload to autoloader without downtime? What are the migration guidelines.
- 1233 Views
- 1 replies
- 0 kudos
Latest Reply
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The SQS queue used by the existing application can be utilized by the auto-loader thereby ensuring minimal downtime
- 0 kudos
- 2031 Views
- 1 replies
- 0 kudos
Resolved! Why do I always get an error on querying the Parquet table - Parquet does not support timestamp
- 2031 Views
- 1 replies
- 0 kudos
Latest Reply
- 0 kudos
The issue can happen if the Hive syntax for table creation is used instead of the Spark syntax. Read more here: https://docs.databricks.com/spark/latest/spark-sql/language-manual/sql-ref-syntax-ddl-create-table-hiveformat.htmlThe issue mentioned in t...
- 0 kudos
- 6494 Views
- 1 replies
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Resolved! How to track the history of schema changes for a Delta table
I have a Delta table that had schema changes in multiple commits. I wanted to track all these schema changes that happened on the Delta table. The "DESCRIBE HISTORY" is not useful as it logs the schema change made by ALTER TABLE operations.
- 6494 Views
- 1 replies
- 0 kudos
Latest Reply
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When a write operation is performed with columns added. we are not explicitly showing that in DESCRIBE HISTORY output. Only an entry is made for write. and in the operation Parameters, it's not showing anything about schema evolution. whereas if we d...
- 0 kudos
- 3621 Views
- 1 replies
- 0 kudos
- 3621 Views
- 1 replies
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Latest Reply
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Yes, it's possible to use Kafka API to connect to the eventhub. Eventhub supports the usage of Kafka API to stream the data from the EventhubReference: https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-for-kafka-ecosystem-overviewSample pr...
- 0 kudos
- 18521 Views
- 1 replies
- 2 kudos
Resolved! How do I change the log level in Databricks?
How can I change the log level of the Spark Driver and executor process?
- 18521 Views
- 1 replies
- 2 kudos
Latest Reply
- 2 kudos
Change the log level of Driver:%scala spark.sparkContext.setLogLevel("DEBUG") spark.sparkContext.setLogLevel("INFO")Change the log level of a particular package in Driver logs:%scala org.apache.log4j.Logger.getLogger("shaded.databricks.v201809...
- 2 kudos
- 2322 Views
- 1 replies
- 0 kudos
Resolved! I do not have any Spark jobs running, but my cluster is not getting auto-terminated.
The cluster is Idle and there are no Spark jobs running on the Spark UI. Still I see my cluster is active and not getting terminated.
- 2322 Views
- 1 replies
- 0 kudos
Latest Reply
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Databricks cluster is treated as active if there are any spark or non-Spark operations running on the cluster. Even though there are no Spark jobs running on the cluster, it's possible to have some driver-specific application code running marking th...
- 0 kudos
- 5456 Views
- 1 replies
- 1 kudos
- 5456 Views
- 1 replies
- 1 kudos
Latest Reply
- 1 kudos
Disclaimer: This code snippet uses an internal API. It's not recommended to use internal API's in your application as they are subject to change or discontinuity. %python import requests API_URL = dbutils.notebook.entry_point.getDbutils().notebook(...
- 1 kudos
- 1416 Views
- 1 replies
- 1 kudos
- 1416 Views
- 1 replies
- 1 kudos
Latest Reply
- 1 kudos
No, currently it's not configurable. Synching of notebooks to Git repositories is configurable and can be turned off at the workspace level.
- 1 kudos
- 3257 Views
- 1 replies
- 0 kudos
Resolved! Why do I see my job marked as failed on the Databricks Jobs UI, even though it completed the operations in the application
I have a jar job running migrated from EMR to Databricks. The job runs as expected and completes all the operations in the application. However the job run is marked as failed on the Databricks Jobs UI.
- 3257 Views
- 1 replies
- 0 kudos
Latest Reply
- 0 kudos
Usage of spark.stop(), sc.stop() , System.exit() in your application can cause this behavior. Databricks manages the context shutdown on its own. Forcefully closing it can cause this abrupt behavior.
- 0 kudos
- 1550 Views
- 1 replies
- 2 kudos
Few things you should not do in Databricks!
Few things you should not do in Databricks!
- 1550 Views
- 1 replies
- 2 kudos
Latest Reply
- 2 kudos
Compared to OSS Spark, these are few things the users don't have to worry about when running the same job on Databricks. Memory management: Databricks use an internal formula to allocate the Driver and executor heap based on the size of the instance....
- 2 kudos
- 3338 Views
- 1 replies
- 0 kudos
- 3338 Views
- 1 replies
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Latest Reply
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Although not a hard limit, it's recommended to keep the number of cells in the notebook less than 100 for better UI experience as well as code readability. Having a really large block of code in a cell defeats the purpose of notebook execution and al...
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