If you missed my previous post on Understanding Postgres Performance its a great starting point. On this particular post I’m going to dig in to some real life examples of optimizing queries and indexes.

It all starts with stats

I wrote about some of the great new features in Postgres 9.2 in the recent announcement on support of Postgres 9.2 on Heroku. One of those awesome features, is pg_stat_statements. Its not commonly known how much information Postgres keeps about your database (beyond the data of course), but in reality it keeps a great deal. Ranging from basic stuff like table size to cardinality of joins to distribution of indexes, and with pg_stat_statments it keeps a normalized record of when queries are run.

I’m increasingly encountering users on Heroku that are encountering the need to [shard](http://en.wikipedia.org/wiki/Shard_(database_architecture)) their data. For most users this is something you delay as long as possible as you can generally go for sometime before you have to worry about it. Additionally scaling up your database is often a reasonable approach early on and something I encourage as a starting point as scaling up is easy to do with regards to databases. However, for the 1% of users that do need to shard when the time comes many are left wondering where to start, hence the following guide.

I recently got asked by a friend and former co-worker how I write SQL. At first this caught me by surprise and I assumed there was nothing different, but after a few additional comments on it, it became clear most people have no concept for creating clean readable SQL. So without further adieu here’s how I write SQL, with a built up example query.

A few weeks back I did a brief feature highlight on Postgres arrays. Since that time I’ve found myself using them with increasing regularity on small side projects. Much of this time I’m using Django and of course not opting to write raw SQL to be able to use arrays. Django actually makes it quite simple to work with Arrays in Postgres with a package by Andrey Antukh. Lets get started by installing two libraries:

Yesterday there was a post which hit Hacker News that talked about using SQL to access Mongo. While this is powerful I think much of the true value was entirely missed within the post.

SQL is an expressive language, though people are often okay with accessing Mongo data through its own ORM. The real value is that you could actually query the data from within Postgres then join across your data stores, without having to do some ETL process to move data around. Think… joining sales data from Postgres with user reviews stored in Mongo or searching for visits to a website (retained in redis) against purchases by user in Postgres.

The mechanism pointed out was a MongoDB Foreign Data Wrapper. A Foreign Data Wrapper or FDW essentially lets you connect to an external datastore from within a Postgres database. In addition to the Mongo FDW released the other day there’s many others. For example Postgres 9.0 and up ships with one called db_link, which lets you query and join across two different Postgres databases. Beyond that there’s support for a variety of other data stores including some you may have never expected:

• Redis
• Textfile
• MySQL
• Oracle
• ODBC
• LDAP
• Twitter
• More

Lets look at actually getting the Redis one running then see what some of the power of it really looks like. First we have to get the code then build it:

Postgres out of the box has an abundance of datatypes, from standard numeric datatypes to geometric or even network datatypes. With extensions you can get even more out of it as earlier discussed with hStore. Though with all of the datatypes its easy to miss out on some of them that are there, in fact one of my favorites is often missed entirely. The Array datatype lets you do just as you’d expect, store an array inside Postgres. With this you can often get some of the functionality you’d want in a single table when you might traditionally have expanded to multiple tables.

Earlier this week while I was at DjangoCon EU there seemed to be a surprising amount of talk about MongoDB. My problem with this isn’t with MongoDB, but in the assumption that only Mongo can solve what you’re looking for. By and far the most common feature is people want schemaless. It gives them flexibility in their data model and lets them iterate quickly. While I still opt for relational models that map cleanly to a relational database, there are cases where developers may want schemaless. I gave a quick lightning talk on this with slides here, but it is worth recapping.

This post is a list of many of the reasons to use Postgres, much this content as well as how to use these features will later be curated within PostgresGuide.com. If you need to get started check out Postgres.app for Mac, or get a Cloud instance at Heroku Postgres for free

Last week I did a post on the many reasons to use Postgres. My goal with the post was two fold:

• Call out some of the historical arguments against it that don’t hold any more
• Highlight some of the awesome but more unique features that are less commonly found in databases.

Once I published the post it was clear and was immediately pointed out in the comments and on hacker news that I missed quite a few features that I’d mostly come to take for granted. Perhaps this is due to so much awesomeness existing within Postgres. A large thanks to everyone for calling these out. To help consolidate many of these, here’s a second list of the many reasons to use PostgreSQL:

This post is a list of many of the reasons to use Postgres, much this content as well as how to use these features will later be curated within PostgresGuide.com. If you need to get started check out Postgres.app for Mac, or get a Cloud instance at Heroku Postgres for free

UPDATE: A part 2 has been posted on Why Use Postgres

Very often recently I find myself explaining why Postgres is so great. In an effort to save myself a bit of time in repeating this, I though it best to consolidate why Postgres is so great and dispel some of the historical arguments against it.