Improving Query Performance in PostgreSQL
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
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Course Description
Losing time on slow queries? Hesitant to share your queries with more seasoned coworkers? In this course, you will learn how to structure your PostgreSQL to run in a fraction of the time. Exploring intertwined data relating Olympic participation, country climate, and gross domestic product, you will experience firsthand how changes in filtering method and using subqueries impact query performance. You will learn the properties of a row oriented database while also seeing how Hawaii's volcanos impact air quality. Restructuring your queries with the query planner and the SQL order of operations, you will soon be dazzling your coworkers with your effortless efficiency.
- 1
Bringing Together the Data
FreeBundle up as you dive into the Winter Olympics! You will learn how to join, subquery, and create temporary tables while finding which Olympic athletes brave sub-freezing temperatures to train. You will also learn about the query planner and how its functionality can guide your SQL structure to faster queries.
All about joins50 xpWhere are all the athletes from100 xpUsing different joins to explore athletes' regions100 xpWhat about the weather100 xpSubqueries and common table expressions (cte)50 xpFiltering to freezing with a subquery100 xpWhere winter is white100 xpCountries with subqueries or CTEs100 xpWorking with temporary tables50 xpCanadians temp table100 xpAnalyze that temp table100 xp - 2
Minimizing Results and Decreasing the Load
Dig up those past algebra memories while learning the SQL order of operations. Find which countries "should" have the most athletes by looking at population and gross domestic product (GDP) while learning the best way to filter. You will also learn when your query aggregates (sums, counts, etc.) and how you can structure your query to optimize this process.
What you write is not what SQL sees50 xpOrder of operations impact on query structure50 xpGroup by and aggregations100 xpCount and count distinct100 xpFiltering in the WHERE clause50 xpOR versus IN with athletes100 xpData type filters100 xpEXPLAIN the filter query plan step100 xpFiltering while joining50 xpWhere to place a region filter100 xpFiltering in the join, where, and select100 xpAggregating with different data granularities50 xpAggregate before joining tables100 xpSouth African trends100 xp - 3
Using Database Designed Properties
Zero in on the properties that improve database performance. Discover when your table is not a table but a view. Learn how your database's storage structure (row or column oriented) impacts your query structure. You will explore volcanic smog while using partitions and indexes to speed your queries.
Queries and tables and views, oh my50 xpData loading and storage50 xpFinding the table type100 xpRow-oriented storage and partitions50 xpRow-oriented storage properties50 xpPreviewing a row-oriented table100 xpPartitioning on location100 xpUsing and creating indexes50 xpFinding the database indexes100 xpCreating and using an index100 xpCompare runtimes100 xpUsing column-oriented storage50 xpColumn-oriented storage properties50 xpUsing the information schema100 xp - 4
Assessing Query Performance
Learn the lingo of the Query Lifecycle and dive into the query planner. Explore how the query planner creates and optimizes the query plan. Find your next vacation locale by looking for countries with recent population growth while also seeing how a join impacts the query steps. Fine tune your optimization techniques by seeing how different filters speed your query times.
Query lifecycle and the planner50 xpExploring pg_tables100 xpBasic EXPLAIN100 xpIndex scans100 xpEXPLAIN the WHERE100 xpA deeper dive into EXPLAIN50 xpEXPLAIN parameters100 xpAggregating and sorting populations100 xpJoining in the query plan100 xpQuery structure and query execution50 xpSubqueries vs. CTEs100 xpWhy the difference?50 xpFiltering impacts100 xpCongratulations50 xp
In the following tracks
SQL for Database Administrators
Becca Robins
Mona KhalilPrerequisites
Intermediate SQL
Amy McCarty
Data Analyst
Amy is a data analyst, policy wonk, and all around data nerd. She has a master's degree in policy analytics from Carnegie Mellon and has worked as an analyst in both the nonprofit and for-profit sectors. She loves to learn and shares that passion as an adjunct analytics professor. When not at the computer, Amy stays busy broadening her multicultural culinary prowess and learning to hear all of those silent consonants in the French language.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Louis Maiden
Harvard Business School
DataCamp is by far my favorite website to learn from.
Ronald Bowers
Decision Science Analytics, USAA
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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).