Другие доклады
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Olivier Courtin DataPink
Tutorial: Advanced spatial analysis with PostgreSQL, PostGIS and Python
- Spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available)
- How could we mix and tied efficiently PostgreSQL and Python data types (as NumPy ndarray, and Pandas DataFrames)
- Tools to improve our data manipulation environment (Jupyter tricks, easy dataviz...)
- How to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)
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Dmitriy Sarafannikov Яндекс
How to save statistics during major update, and what can be the consequences
It's not a secret for anyone that statistics can not be transferred with a major upgrade. For small and not heavily loaded databases this is not a problem, you can quickly collect new statistics. But we have databases with a volume of about 5TB and a load of about 100k rps, for which it became a big problem: taking off without statistics, the replicas could not even replay WAL. In my report I'll tell you what tricks we went to upgrade these databases with requirements of 100% read only availability, about what mistakes were made, and about how these errors were painfully corrected. The result of these errors was the extension called "pg_dirty_hands", in which we will collect various hacks, which can be last resort to repair data corruption.
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Dmitriy Pavlov Arenadata
How to train your Greenplum
In the pitch I will talk about the most important nuances of deployment and operations of the distributed analytical open-source database based on PostgreSQL - Greenplum. I will analyze the typical mistakes in its use, give the best practices and warn about bottlenecks.