Working with Data and Schemas in Marten 1
Marten is an open source library for .NET that enables the use of an underlying PostgreSQL database as a document database. Learn how to work with data and schemas in it!
What you'll learn
Marten can be used as a replacement or alternative to other document databases such as MongoDb or RavenDb and can also be an alternative to ORMs such as Entity Framework. While Marten is easy to get started with, it also provides an array of customization, diagnostic, data manipulation, and performance related features that can be additionally employed. In this course,Working with Data and Schemas in Marten 1, you'll learn how to improve query performance, diagnose and log SQL statements, implement offline optimistic concurrency, and customize PostgreSQL schema objects. First, you'll discover how to log and diagnose SQL queries and DDL commands generated by Marten. Next, you'll learn how to prevent data loss with offline optimistic concurrency and how to soft-delete documents. Then, you'll move on to learning how to reduce database round trips, improve PostgreSQL query performance, and how to reduce client CPU costs. Finally, you'll discover how to take control over the PostgreSQL schema objects that Marten defines and creates. By the end of this course, you'll have a better understanding of how to work with data and schemas in Marten.
Table of contents
- Version Check 0m
- Introduction 3m
- Overview of Marten Logging 4m
- Creating a Custom Session Logger 10m
- Creating a Custom Marten Logger 5m
- Refactoring to a Single Logger Class 3m
- Using the Pre-supplied Logger 1m
- Previewing LINQ Generated SQL 3m
- Diagnosing PostgreSQL Query Plan Performance 2m
- Tracking the Number of Issued Session Commands 3m
- Summary 1m
- Introduction 3m
- Understanding Compiled Queries 2m
- Implementing a Compiled Query 7m
- Additional Compiled Query Features 3m
- Reducing Network Round Trips with Batched Queries 4m
- Getting Total Documents During Paged Queries 2m
- Improving Query Performance with JSON Indexes 4m
- Duplicated Indexed Fields 2m
- Reducing Serialization Costs 2m
- Retrieving Raw JSON Data 3m
- Summary 2m