PostgreSQL Performance Tuning
PostgreSQL is an object-relational database developed on the Internet by a group of developers spread across the globe. It is an open-source alternative to commercial databases like Oracle and Informix.
PostgreSQL was originally developed at the University of California, Berkeley. In 1996, a group began development of the database on the Internet. They used e-mail to share ideas and file servers to share code. PostgreSQL is now comparable to proprietary databases in terms of features, performance and reliability. It has transactions, views, stored procedures and referential integrity constraints. It supports a large number of programming interfaces, including ODBC, Java (JDBC), Tcl/Tk, PHP, Perl and Python. PostgreSQL continues to improve at a tremendous pace thanks to a talented pool of internet developers.
There are two aspects of database-performance tuning. One is improving the database's use of the CPU, memory and disk drives in the computer. The second is optimizing the queries sent to the database. This article talks about the hardware aspects of performance tuning. The optimization of queries is done using SQL commands like CREATE INDEX, VACUUM, VACUUM ANALYZE, CLUSTER and EXPLAIN. These are discussed in my book, PostgreSQL: Introduction and Concepts at www.postgresql.org/docs/awbook.html [see also Stephanie Black's review on page 76].
To understand hardware performance issues, it is important to understand what is happening inside the computer. For simplicity, a computer can be thought of as a central processing unit (CPU) surrounded by storage. On the same chip with the CPU are several CPU registers, which store intermediate results and various pointers and counters. Surrounding this is the CPU cache, which holds the most recently accessed information. Beyond the CPU cache is a large amount of random-access main memory (RAM), which holds executing programs and data. Beyond this main memory are disk drives, which store even larger amounts of information. Disk drives are the only permanent storage area, so anything to be kept when the computer is turned off must be placed there (see Table 1). Figure 1 shows the storage areas surrounding the CPU.
You can see that storage areas increase in size as they get farther from the CPU. Ideally, a huge amount of permanent memory could be placed right next to the CPU, but this would be too slow and expensive. In practice, the most frequently used information is stored next to the CPU, and less frequently accessed information is stored farther away and brought to the CPU as needed.
Moving information between various storage areas happens automatically. Compilers determine which information should be stored in registers. CPU chip logic keeps recently used information in the CPU cache. The operating system controls which information is stored in RAM and shuttles it back and forth from the disk drive.
CPU registers and the CPU cache cannot be tuned effectively by the database administrator. Effective database tuning involves increasing the amount of useful information in RAM, thus preventing disk access where possible.
You might think this is easy to do, but it is not. A computer's RAM contains many things, including executing programs, program data and stack, PostgreSQL shared buffer cache and kernel disk buffer cache. Proper tuning involves keeping as much database information in RAM as possible while not adversely affecting other areas of the operating system.
PostgreSQL does not directly change information on disk. Instead, it requests data be read into the PostgreSQL shared buffer cache. PostgreSQL backends then read/write blocks, and finally flush them back to disk. Backends that need to access tables first look for needed blocks in this cache. If they are already there, they can continue processing right away. If not, an operating system request is made to load the blocks. The blocks are loaded either from the kernel disk buffer cache or from disk. These can be expensive operations.
The default PostgreSQL configuration allocates 64 shared buffers. Each buffer is eight kilobytes. Increasing the number of buffers makes it more likely that backends will find the information they need in the cache, thus avoiding an expensive operating system request.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide