PostgreSQL Connection Pooling: Part 1 – Pros & Cons

A long time ago, in a galaxy far far away, ‘threads’ were a programming novelty rarely used and seldom trusted. In that environment, the first PostgreSQL developers decided forking a process for each connection to the database is the safest choice. It would be a shame if your database crashed, after all.

Since then, a lot of water has flown under that bridge, but the PostgreSQL community has stuck by their original decision. It is difficult to fault their argument – as it’s absolutely true that:

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AppBackplane - A Framework for Supporting Multiple Application Architectures

Hidden in every computer is a hardware backplane for moving signals around. Hidden in every application are ways of moving messages around and giving code CPU time to process them. Unhiding those capabilities and making them first class facilities for the programmer to control is the idea behind AppBackplane.

This goes directly against the trend of hiding everything from the programmer and doing it all automagically. Which is great, until it doesn't work. Then it sucks. And the approach of giving the programmer all the power also sucks, until it's tuned to work together and performance is incredible even under increasing loads. Then it's great.

These are two different curves going in opposite directions. You need to decide for your application which curve you need to be on.

AppBackplane is an example framework supporting the multiple application architectures we talked about in Beyond Threads And Callbacks. It provides a scheduling system that supports continuous and high loads, meets critical path timing requirements, supports fair scheduling amongst priorities; is relatively easy to program; and supports higher degrees of parallelism than can be supported with a pure tasking model.

It's a bit much for simple applications. But if you are looking to go beyond a basic thread per request model and think of an application as a container where a diverse set of components must somehow all share limited resources to accomplish work, then some of the ideas may prove useful.

In case you are still wondering where the name AppBackplane comes from, it's something I made up a while ago as a takeoff of computer backplane:

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Scaling Memcached: 500,000+ Operations/Second with a Single-Socket UltraSPARC T2

A software-based distributed caching system such as memcached is an important piece of today's largest Internet sites that support millions of concurrent users and deliver user-friendly response times. The distributed nature of memcached design transforms 1000s of servers into one large caching pool with gigabytes of memory per node. This blog entry explores single-instance memcached scalability for a few usage patterns. Table below shows out-of-the-box (no custom OS rewrites or networking tuning required) performance with 10G networking hardware and one single-socket UltraSPARC T2-based server with 8 cores and 8 threads per core (64 threads on a chip)... Object Size / Ops/Sec / Bandwidth 100 bytes / 530,000 / 1.2 Gb/s 2048 bytes / 370,000 / 6.9 Gb/s 4096 bytes / 255,000 / 9.2 Gb/s Check out the link for more details!

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Thread Pool Engine in MS CLR 4, and Work-Stealing scheduling algorithm

I just saw this article in HFadeel blog that spaek about Parallelism in .NET Framework 4, and how Thread Pool work, and the most faoums scheduling algorithm : Work-stealing algorithm. With preisnation to see it in action.

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Scaling MySQL on a 256-way T5440 server using Solaris ZFS and Java 1.7

How to scale MySQL on a 32 core system with 256 threads? Diagonal scalability in a box. An impressive benchmark that achieved more than 79,000 SQL queries per second on a single 4 RU server! Is this real? If so what is the role of good old horizontal scalability? The goals of the benchmark:

1. Reach a high throughput of SQL queries on a 256-way Sun SPARC Enterprise T5440
2. Do it 21st century style i.e. with MySQL and ZFS , not 20th century style i.e with OraSybInf... and VxFS
3. Do it with minimal tuning i.e as close as possible as out-of-the-box

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Sun's High-Performance and Reliable Web Proxy Solution

As individuals and businesses depend on the Web more than ever to conduct business, rapid and reliable content retrieval is critical. Reducing wait time improves productivity and increases user satisfaction. Web proxy technology has emerged as an effective solution to improve performance, help ensure content availability and enhance network security by caching and filtering Web content. The combination of Sun SPARC Enterprise servers with CoolThreads technology and the Sun Java System Web Proxy Server software provides a compelling foundation for a robust Web proxy solution. Sun SPARC Enterprise T1000 and T2000 servers include the UltraSPARC T1 processor with CoolThreads technology, offering six or eight cores with four threads per core. The Sun Java System Web Proxy Server software is highly threaded and takes advantage of the large number of threads supported by Sun UltraSPARC T1 processors with CoolThreads technology. Together, these products provide a highly scalable solution that accommodates a large number of requests, addresses peak loads, and provides future headroom for growth. This document explores the use of a Sun SPARC Enterprise T1000 server and the Sun Java System Web Proxy Server software as a replacement for an existing Web proxy implementation that used the SQUID Web proxy server software deployed on x86 servers.

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An Open Source Web Solution - Lighttpd Web Server and Chip Multithreading Technology

With more users interacting, working, purchasing, and communicating over the network than ever before, Web 2.0 infrastructure is taking center stage in many organizations. Demand is rising, and companies are looking for ways to tackle the performance and scalability needs placed on Web infrastructure without raising IT operational expenses. Today companies are turning to efficient, high-performance, open source solutions as a way to decrease acquisition, licensing, and other ongoing costs and stay within budget constraints.

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