Book Image

PostgreSQL 10 High Performance - Third Edition

By : Enrico Pirozzi
Book Image

PostgreSQL 10 High Performance - Third Edition

By: Enrico Pirozzi

Overview of this book

PostgreSQL database servers have a common set of problems that they encounter as their usage gets heavier and requirements get more demanding. Peek into the future of your PostgreSQL 10 database's problems today. Know the warning signs to look for and how to avoid the most common issues before they even happen. Surprisingly, most PostgreSQL database applications evolve in the same way—choose the right hardware, tune the operating system and server memory use, optimize queries against the database and CPUs with the right indexes, and monitor every layer, from hardware to queries, using tools from inside and outside PostgreSQL. Also, using monitoring insight, PostgreSQL database applications continuously rework the design and configuration. On reaching the limits of a single server, they break things up; connection pooling, caching, partitioning, replication, and parallel queries can all help handle increasing database workloads. By the end of this book, you will have all the knowledge you need to design, run, and manage your PostgreSQL solution while ensuring high performance and high availability
Table of Contents (18 chapters)

Summary

Large deployments of PostgreSQL systems go through several common phases as the number of database clients increases. You're likely to run into disk bottlenecks initially. These can sometimes be bypassed by reorganizing the system so more of the active data is in RAM. Once that's accomplished, and the system is sized properly so the database is mainly returning information that's in fast memory, it's quite easy to move onto a new bottleneck. One possibility is that you might then be limited by the relatively high overhead of creating a database connection and asking it for data.

When reaching that point, there are two major approaches to consider. You can reuse database connections with pooling, or try and cache database activity outside of the database. The best part is that these two approaches both stack on top of one another. You can, for example...