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You're reading from  Apache Superset Quick Start Guide

Product typeBook
Published inDec 2018
Reading LevelIntermediate
Publisher
ISBN-139781788992244
Edition1st Edition
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Shashank Shekhar
Shashank Shekhar
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Shashank Shekhar

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.
Read more about Shashank Shekhar

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Flask-AppBuilder permissions

Superset uses the Flask-AppBuilder framework to store metadata required for permissions in Superset. Every time a Flask-AppBuilder app is initialized, permissions and views are automatically created for the Admin role. When multiple concurrent workers are started by Gunicorn, they might lead to contention and race conditions between the workers trying to write to one metadata database table.

The automatic updating of permissions in the metadata database can be disabled by setting the value of the SUPERSET_UPDATE_PERMS environment variable to zero. It is one or enabled by default:

export SUPERSET_UPDATE_PERMS=1 superset init
# Make sure superset init is called before Superset starts with a new metadata database
export SUPERSET_UPDATE_PERMS=0 gunicorn -w 10 … superset:app
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Apache Superset Quick Start Guide
Published in: Dec 2018Publisher: ISBN-13: 9781788992244

Author (1)

author image
Shashank Shekhar

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.
Read more about Shashank Shekhar