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Resolved! How do I change the log level in Databricks?
How can I change the log level of the Spark Driver and executor process?
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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...
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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(...
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No, currently it's not configurable. Synching of notebooks to Git repositories is configurable and can be turned off at the workspace level.
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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.
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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.
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- 1907 Views
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Few things you should not do in Databricks!
Few things you should not do in Databricks!
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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....
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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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Resolved! Can I download files from DBFS to my local machine? I see only the Upload option in the Web UI.
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Yes, it's possible to download files from DBFS. To download the filesFiles stored in /FileStore are accessible in your web browser at https://<databricks-instance-name>.cloud.databricks.com/files/. For example, the file you stored in /FileStore/my-da...
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- 2378 Views
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What is the best way to convert a very large parquet table to delta ? possibly without downtime!
What is the best way to convert a very large parquet table to delta ? possibly without downtime!
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I vouch for Sajith's answer. The main advantage with "CONVERT TO DELTA" is that operations are metadata centric which means we are not reading the full data for the conversion. For any other file format conversion, it's necessary to read the data com...
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- 2497 Views
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Why should I move to Auto-loader?
I have a streaming workload using the S3-SQS Connector. The streaming job is running fine within the SLA. Should I migrate my job to use the auto-loader? If Yes, what are the benefits? who should migrate and who should not?
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That makes sense @Anand Ladda​ ! One major improvement that will have a direct impact on the performance is the architectural difference. S3-SQS uses an internal implementation of the Delta table to store the checkpoint details about the source files...
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Stats collected on a Delta column are either using for Partitioning Pruning, Data Skipping. See here - https://docs.databricks.com/delta/optimizations/file-mgmt.html#delta-data-skipping for detailsIn additional stats are also used for Metadata only q...
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What are the recommendations around collecting stats on long strings in a Delta Table
It is best to avoid collecting stats on long strings. You typically want to collect stats on column that are used in filter, where clauses, joins and on which you tend to performance aggregations - typically numerical valuesYou can avoid collecting s...
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Delta Optimistic Transactions Resolution and Exceptions
What is the best way to deal with concurrent exceptions in Delta when you have multiple writers on the same delta table ?
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While you can try-catch-retry , it would be expensive to retry as the underlying table snapshot would have changed. So the best approach is to avoid conflicts using partitioning and disjoint command conditions as much as possible.
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by default a delta table has stats collected on the first 32 columns. This setting can be configured using the following.set spark.databricks.delta.properties.defaults.dataSkippingNumIndexedCols = 3However there's a time trade-off to having a large n...
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Its typically a good idea to run optimize aligned with the frequency of updates to the Delta Table. However you also don't want to over do as there's a cost/performance trade-off. Unless there are very frequent updates to the table that can cause sma...
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Optimize merges small files into larger ones and can involve shuffling and creation of large in-memory partitions. Thus its recommended to use a memory optimized executor configuration to prevent spilling to disk. IN additional use of autoscaling wil...
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