temporary-directory | A simple class to work with a temporary directory | File Utils library
kandi X-RAY | temporary-directory Summary
kandi X-RAY | temporary-directory Summary
This package allows you to quickly create, use and delete a temporary directory in the system's temporary directory.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Creates an exception for a directory name .
temporary-directory Key Features
temporary-directory Examples and Code Snippets
def remove_temp_dirpath(dirpath, strategy):
"""Removes the temp path after writing is finished.
Args:
dirpath: Original dirpath that would be used without distribution.
strategy: The tf.distribute strategy object currently used.
"""
public static String downloadFile(String urlString) throws IOException {
LOGGER.info("Downloading contents from url: {}", urlString);
var url = new URL(urlString);
var file = File.createTempFile("promise_pattern", null);
try (var buff
def report(self):
"""Generates a html graph file showing allocations over snapshots.
It create a temporary directory and put all the output files there.
If this is running under Google internal testing infra, it will use the
director
Community Discussions
Trending Discussions on temporary-directory
QUESTION
Using Python on an Azure HDInsight cluster, we are saving Spark dataframes as Parquet files to an Azure Data Lake Storage Gen2, using the following code:
...ANSWER
Answered 2021-Dec-17 at 16:58ABFS is a "real" file system, so the S3A zero rename committers are not needed. Indeed, they won't work. And the client is entirely open source - look into the hadoop-azure module.
the ADLS gen2 store does have scale problems, but unless you are trying to commit 10,000 files, or clean up massively deep directory trees -you won't hit these. If you do get error messages about Elliott to rename individual files and you are doing Jobs of that scale (a) talk to Microsoft about increasing your allocated capacity and (b) pick this up https://github.com/apache/hadoop/pull/2971
This isn't it. I would guess that actually you have multiple jobs writing to the same output path, and one is cleaning up while the other is setting up. In particular -they both seem to have a job ID of "0". Because of the same job ID is being used, what only as task set up and task cleanup getting mixed up, it is possible that when an job one commits it includes the output from job 2 from all task attempts which have successfully been committed.
I believe that this has been a known problem with spark standalone deployments, though I can't find a relevant JIRA. SPARK-24552 is close, but should have been fixed in your version. SPARK-33402 Jobs launched in same second have duplicate MapReduce JobIDs. That is about job IDs just coming from the system current time, not 0. But: you can try upgrading your spark version to see if it goes away.
My suggestions
- make sure your jobs are not writing to the same table simultaneously. Things will get in a mess.
- grab the most recent version spark you are happy with
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install temporary-directory
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page