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Download new version of Acoustica CD/DVD Label Maker for Android now!Q:
Django, Pyspark, Dask, Jupyter: How to get the latest Dataset from a Python session
I am working with Dask Dataframe in Jupyter Notebook in Jupyter. I am trying to start my Spark job using spark-submit. The dataset used by the Spark job is stored in S3 and fetched to the Jupyter.
I want to set this Spark job, so that each time I start Jupyter, the latest dataset is fetched from S3, so that I do not have to export the datapreview everytime to see if the job is up to date or not.
I want to follow the approach mentioned here:
How to load the most recent dataset into a Jupyter/pyspark notebook?
But that is not working.
The code:
import dask.dataframe as dd
from dask.distributed import Client
client = Client(‘xxx-xxxx-xxx.c2.xxxx.com:7020’, ‘username’, ‘password’)
ds = client.kernels.hot.hot_dataset(KEY_LOCATION)
dd.compute().run(ds)
Error:
ValueError: You need to provide a valid metadata directory in spark.sql.shuffle.io.metadata.dir when using SQLContext.SparkContext.setConf.
How to fix this?
A:
PySpark is based on the ability of a Jupyter Notebook to switch between different kernels (Python interpreters).
While it should be easy to store a source of data when working on a notebook, if you want it to be the latest you don’t want to copy it over every time, you want to re-read it from the source.
So if your notebook is prepared to use multiple kernels, you need to make the following adjustment in your code.
The call:
client = Client(‘xxx-xxxx-xxx.c2.xxxx.com:7020’, ‘username’, ‘password’)
The adjustment:
ds = client.kernels.jupyter.hot.hot
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