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PostgreSQL 10 High Performance

PostgreSQL 10 High Performance

By : Enrico Pirozzi
2.5 (2)
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PostgreSQL 10 High Performance

PostgreSQL 10 High Performance

2.5 (2)
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 (23 chapters)
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Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

Other query-planning parameters


Now that all of the ways a query executes have been covered, some of the more obscure query-planning parameters can be explained usefully.

effective_cache_size

Defaulting to 128 MB, effective_cache_size is used to represent approximately how much total disk space is available for caching the database. This is normally set to the total of shared_buffers plus the size of the operating system disk buffer cache after the database is started. This turns out to be greater than half of the total system memory on a typical dedicated database server. This setting does not allocate any memory itself; it simply serves as an advisory value for the planner about what should likely be available.

The only thing this is used for is estimating whether an index scan will fit into the memory, with the alternative being a sequential scan. One area that is particularly impacted by this setting is Nested Loop joins that are using an inner Index Scan. As you reduce effective_cache_size...

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