abfs | Automatic Building Footprint Segmentation : U-Net Production | Machine Learning library

If you were to try version 1), you would first see warnings saying that sending large delayed objects is a bad pattern in Dask, and makes for large graphs and high memory use on the scheduler. You can send the data directly to workers using client.scatter, but it would still be essentially a serial process, bottlenecking on receiving and sending all of your data through the client process's network connection.

The best practice and canonical way to load data in Dask is for the workers to do it. All the built in loading functions work this way, and is even true when running locally (because any download or open logic should be easily parallelisable).

This is also true for the outputs of your processing. You haven't said what you plan to do next, but to grab all of those images to the client (e.g., .compute()) would be the other side of exactly the same bottleneck. You want to reduce and/or write your images directly on the workers and only handle small transfers from the client.

Note that there are examples out there of image processing with dask (e.g., https://examples.dask.org/applications/image-processing.html ) and of course a lot about arrays. Passing around whole image arrays might be fine for you, but this should be worth a read.

According to my test, we can use the package com.microsoft.azure:azure-storage:8.6.3 to upload files to Azure blob in spark.

For example

I am using

Java 8 (1.8.0_265) Spark 3.0.0 Hadoop 3.2.0 Python 3.6.9 Ubuntu 18.04

My code

The StreamingFileSink does not yet support Azure Data Lake.

As per my research, you will receive this error message when you have incompatible jar with the hadoop version.

I would request you to kindly go through the below issues:

http://mail-archives.apache.org/mod_mbox/spark-issues/201907.mbox/%3CJIRA.13243325.1562321895000.591499.1562323440292@Atlassian.JIRA%3E

https://issues.apache.org/jira/browse/HADOOP-16410

As per the comment, U-SQL does not work with Azure Data Lake Gen 2 and it's unlikely it ever will. There is a feedback item which you should read:

https://feedback.azure.com/forums/327234-data-lake/suggestions/36445702-add-support-for-adls-gen2-to-adla

In the year 2020, consider starting new Azure analytics projects with Azure Databricks.

The text of the error suggests that the service was temporarily down. If it persists, you may want to lodge an issue at adlfs; perhaps it could be as simple as more thorough retry logic on their end.

Indeed, the problem was due to the firewall settings. Thank you Axel R!

I was misled by the fact that I also have a ADLS Gen 1 with the same firewall settings and had no problem.

BUT, the devil is in the details. The Gen 1 firewall exceptions allow all Azure services to access the resource. The Gen 2, meanwhile, only allows trusted Azure services.

I hope this can help someone.

afraid that HADOOP_OPTIONAL_TOOLS env var isn't enough; you'll need to get hadoop-azure JAR and some others into common/lib

from share/hadoop/tools/lib copy hadoop-azure jar, azure-* and, if it's there, wildfly-openssl.jar into share/hadoop/common/lib

The cloudstore JAR is with diagnostics as it tells you which JAR is missing, e.g.

You got ClassCastException Table in which you inserting has a string, c double, b string,d double types And you inserting a string, b string, c double, d double Try to cast or change table DDL.

Or if you want to bind columns by names, this does not work in Hive. The order of columns should be the same in the select and in the table you inserting. Binding is positional.

Like this: