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from pyspark.sql.types import *
schema = StructType([
StructField("c1", IntegerType(), True),
StructField("c2", StringType(), True),
StructField("c3", StringType(), True)])
df = spark.createDataFrame([(1, "2", None), (3, "4", None)], schema)
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df = spark.createDataFrame(sc.emptyRDD(), schema)Can you try this?
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Some of the Jobs are failing in prod with below error message:Can you please check and let us know the reason for this? These are running under pool cluster.Run result unavailable: job failed with error messageUnexpected failure while waiting for the...
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@Kunal Gaurav​ , This status code only occurs in one of two conditions:We’re able to request the instances for the cluster but can’t bootstrap them in time We setup the containers on each instance, but can’t start the containers in timethis is an edg...
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I would like to prevent file download via /files/ URL. For example: https://customer.databricks.com/files/some-file-in-the-filestore.txtIs there a way to do this?
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Unfortunately this is not possible from the platform.You can however use an external Web Application Firewall (e.g. Akmai) to filter all web traffic to your workspaces. This can block both Web access to download root bucket data.
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I would like to know if there are any broadcast joins limitations.
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Yes, there are a couple limitation. Please find below the details:> It will not perform broadcast join if the table has 512 million or more rows > It will not perform broadcast join if the table is larger than 8GB
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I would like to know how do disable broadcast join in my job to avoid this error message. Is there a Spark configuration?
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You can disable broadcast join by adding the following Spark configuration to you notebook:spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)In addition, you can also add this configuration to your cluster:spark.sql.autoBroadcastJoinThreshold...
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Im getting some weird messages when trying to run my Dbconnect. I would like to know if there is a troubleshooting guide to solve Python version mismatch errors.
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We have a troubleshooting section in our docs that could help you to solve this issue. Please check the docs here https://docs.databricks.com/dev-tools/databricks-connect.html#python-version-mismatch
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I would like to know if I can use Dbconnect to run all my structured streaming jobs.
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Unfortunately, no. You cannot use Dbconnect for your streaming jobs. This is one of Dbconnect's limitations. For more details please check the docs: https://docs.databricks.com/dev-tools/databricks-connect.html#limitations
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I know it's important to periodically run Optimize on my Delta tables, but how often should I be doing this? Am I supposed to do this after every time I load data?
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It would depend on how frequently you update the table and how often you read it. If you have a daily ETL job updating a delta table, it might make sense to run OPTIMIZE at the end of it so that subsequent reads would benefit from the performance imp...
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I am new to using Databricks and want to create a cluster, but there are many different worker types to choose from. How do I know which worker type is the right type for my use case?
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For delta workloads, where you could benefit from caching it is recommended to use storage optimized instances that come with NVMe SSDs. For other workloads, it would be a good idea to check Ganglia metrics to see whether your workload is Cpu/Memory ...
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Is spark the only type of code that can run on a Databricks cluster?
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Databricks has a Runtime for Machine Learning that comes with a lot of libraries/frameworks pre-installed. This allows you to run for example PyTorch / TensorFlow code without worrying about infrastructure setup, configuration and dependency manage...
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For security and privacy reasons I need to limit what datasets are available for access by end users. How can I accomplish this in a Databricks workspace?
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Unity Catalog is the recommended approach as it lets you manage fine-grained data permissions using standard ANSI SQL / UI . More details could be found here
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How do I know which mode I should be using when creating a cluster?
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High Concurrency clusters are ideal for groups of users who need to share resources or run ad-hoc jobs - for example data scientists sharing a cluster. They come with Query Watchdog, a process which keeps disruptive queries in check by automatically ...
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