Book Image

Mastering MongoDB 4.x - Second Edition

By : Alex Giamas
Book Image

Mastering MongoDB 4.x - Second Edition

By: Alex Giamas

Overview of this book

MongoDB is the best platform for working with non-relational data and is considered to be the smartest tool for organizing data in line with business needs. The recently released MongoDB 4.x supports ACID transactions and makes the technology an asset for enterprises across the IT and fintech sectors. This book provides expertise in advanced and niche areas of managing databases (such as modeling and querying databases) along with various administration techniques in MongoDB, thereby helping you become a successful MongoDB expert. The book helps you understand how the newly added capabilities function with the help of some interesting examples and large datasets. You will dive deeper into niche areas such as high-performance configurations, optimizing SQL statements, configuring large-scale sharded clusters, and many more. You will also master best practices in overcoming database failover, and master recovery and backup procedures for database security. By the end of the book, you will have gained a practical understanding of administering database applications both on premises and on the cloud; you will also be able to scale database applications across all servers.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Basic MongoDB – Design Goals and Architecture
4
Section 2: Querying Effectively
10
Section 3: Administration and Data Management
15
Section 4: Scaling and High Availability

Operations

When connecting to our production MongoDB servers, we want to make sure that our operations are as lightweight as possible (and are certainly non-destructive) and do not alter the database state in any sense.

The two useful utilities that we can chain to our queries are as follows:

> db.collection.find(query).maxTimeMS(999)

Our query will only take up to 999 ms, and will then return an exceeded time limit error:

> db.collection.find(query).maxScan(1000)

Our query will examine 1000 documents at the most, in order to find results and then return (no error raised).

Whenever we can, we should bind our queries by time or document result size to avoid running unexpectedly long queries that may affect our production database. A common reason for accessing our production database is troubleshooting degraded cluster performance. This can be investigated via cloud monitoring...