Chargement et stockage des données

La clause FROM d’une requête fait référence à des données provenant de plusieurs sources.

Lesquelles parmi les sources suivantes à la fois stockent et actualisent des données ?

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<cours>Améliorer les performances des requêtes dans PostgreSQL</cours>

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Cet exercice fait partie du cours

<cours>Améliorer les performances des requêtes dans PostgreSQL</cours>

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Bundle 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.

Exercise 1: All about joins Exercise 2: Where are all the athletes from Exercise 3: Using different joins to explore athletes' regions Exercise 4: What about the weather Exercise 5: Subqueries and common table expressions (cte)Exercise 6: Filtering to freezing with a subquery Exercise 7: Where winter is white Exercise 8: Countries with subqueries or CTEs Exercise 9: Working with temporary tables Exercise 10: Canadians temp table Exercise 11: Analyze that temp table

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.

Exercise 1: What you write is not what SQL sees Exercise 2: Order of operations impact on query structure Exercise 3: Group by and aggregations Exercise 4: Count and count distinct Exercise 5: Filtering in the WHERE clause Exercise 6: OR versus IN with athletes Exercise 7: Data type filters Exercise 8: EXPLAIN the filter query plan step Exercise 9: Filtering while joining Exercise 10: Where to place a region filter Exercise 11: Filtering in the join, where, and select Exercise 12: Aggregating with different data granularities Exercise 13: Aggregate before joining tables Exercise 14: South African trends

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.

Exercise 1: Des requêtes, des tables et des vues : tout un programme Exercise 2: Chargement et stockage des données

Exercice actuel

Exercise 3: Identifier le type de table Exercise 4: Stockage orienté lignes et partitions Exercise 5: Propriétés du stockage orienté lignes Exercise 6: Prévisualiser une table orientée lignes Exercise 7: Partitionner par localisation Exercise 8: Utiliser et créer des index Exercise 9: Trouver les index de la base de données Exercise 10: Créer et utiliser un index Exercise 11: Comparer les temps d’exécution Exercise 12: Utiliser un stockage orienté colonnes Exercise 13: Propriétés du stockage orienté colonnes Exercise 14: Utiliser le schéma d’information

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.

Exercise 1: Query lifecycle and the planner Exercise 2: Exploring pg_tables Exercise 3: Basic EXPLAIN Exercise 4: Index scans Exercise 5: EXPLAIN the WHERE Exercise 6: A deeper dive into EXPLAIN Exercise 7: EXPLAIN parameters Exercise 8: Aggregating and sorting populations Exercise 9: Joining in the query plan Exercise 10: Query structure and query execution Exercise 11: Subqueries vs. CTEs Exercise 12: Why the difference?Exercise 13: Filtering impacts Exercise 14: Congratulations