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1
Introduction
2
How big is big data
3
Data normalization
4
Adding new fields
5
Should we use ORM
6
Should we use stored procedures
7
Slow queries
8
Analyze buffets
9
materialized views and tables
10
Dead looks
11
Query button
12
Table Bloat
13
Serverside cases
14
Caching
15
Deletes
16
Questions
Description:
Explore strategies for optimizing PostgreSQL queries for datasets in the lower range of big data in this EuroPython 2017 conference talk. Learn about database design considerations, including entity design, normalization balance, and early sharding planning. Discover the pros and cons of using ORMs and stored procedures in web applications. Investigate techniques to bring data closer to the application, such as materialized views, deferred aggregations, and application-level caching. Gain insights into handling operational issues using EXPLAIN ANALYZE, managing index bloat, and reducing deadlocks. Understand how to minimize the impact of background maintenance jobs and plan data retention policies. Apply these lessons to improve query performance and maintain efficient database operations for datasets ranging from 400 million to 4.5 billion records.

Optimizing Queries for Not So Big Data in PostgreSQL

EuroPython Conference
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