Book Image

PostgreSQL 9 High Availability Cookbook

By : Shaun Thomas
Book Image

PostgreSQL 9 High Availability Cookbook

By: Shaun Thomas

Overview of this book

Table of Contents (17 chapters)
PostgreSQL 9 High Availability Cookbook
About the Author
About the Reviewers

Saddling up to a SAN

SAN stands for Storage Area Network. Working in the industry, you may have encountered NAS as well. How exactly is that different, and how is it relevant to us?

It's subtle, but important. While both introduce networked storage, only a SAN grants direct block-level access, as if the allocation were raw, unformatted disk space. NAS systems operate one level higher, providing a fully formatted file system such as NFS or CIFS. This means our PostgreSQL database does not have direct control over the filesystem; locks, flushes, allocation, and read cache management are all controlled by a remote server.

When building a highly available server, raw I/O and synchronization messages are very important, and NFS is more for sharing storage than extending the storage capabilities of a server. So what must we consider when deciding on how to best utilize a SAN, and when should we do this instead of using a cheaper solution such as direct attached storage?

We won't be discussing how to evaluate a SAN, which vendors produce the best hardware, or even basic configuration strategies. There are several entire books dedicated to SAN management and evaluation that are far beyond the scope of our overview. For building a highly available PostgreSQL architecture, all we need to consider is the when and why, not the how.

Getting ready

Because we're going to cover both SAN performance and storage allocation, we recommend referring to the Having enough IOPS and Sizing storage recipes. Just like physical disks, we need to know how much space we need, and roughly how fast it should be to fulfill our transaction and query requirements.

Do we need a SAN? We can ask ourselves a few questions:

  • Do our IOPS or storage requirements demand more than 20 hard drives?

  • Will the size of our database reach or exceed 3TB within the next three years?

  • Would the risk to the company be too high if we ever ran out of space?

  • Is there already a SAN available for testing?

If we answer yes to any of these, a SAN might be in our best interests. In that case, we can determine if it would fulfill our needs.

How to do it...

Follow these steps if possible:

  1. Request a LUN from the infrastructure department with the necessary IOPS and storage requirements.

  2. If a SAN isn't available, many SAN vendors will provide testing equipment to encourage purchase. Try to obtain one of these.

  3. Have the infrastructure department format the allocation and attach it to a testing server. Keep note of the path to the storage.

  4. Create a basic PostgreSQL testing database with the following command-line operations as the postgres user:

    createdb pgbench
    pgbench -i -s 4000 pgbench
  5. Drop the system caches as a user capable of performing root-level commands, as follows:

    echo 3 | sudo tee /proc/sys/vm/drop_caches
  6. Test the storage read IOPS with one final command as the postgres user:

    pgbench -S -c 24 -T 60 pgbench

How it works...

The first part of our process is to decide whether or not we actually need a SAN at all. If the database will remain relatively small, capable of residing easily on local hard drives for several years, we don't need a SAN just yet.

While it might seem arbitrary, setting 3 TB as a cutoff for local storage comes with a few justifications. First, consider the local drives. Even if they were capable of saturating a 6 Gbps disk controller, 3 TB would require over an hour to transfer to another local storage device. If that weren't a bottleneck, there is still the network. With a 10 Gbps NIC and assuming no overhead, that's 40 minutes of transfer at full speed.

That directly affects speed of backups, synchronization, emergency data restores, and any number of other critical operations. Some RAID cards also require special configuration when handling over 4 TB of storage, out of which 3 TB is uncomfortably close if we ever need an extension. SAN devices can perform local storage snapshots for nearly instant data copies intended for other servers. If the other server also uses the same SAN, there's no transfer overhead.

And lastly, while RAID devices can be extended when online, there is a limit imposed by how many local disks are available to our server, either directly in the chassis, or from direct attached storage extensions. If there's ever any risk we can reach that maximum, SAN devices do not have any of these inherent limitations, which we can use to our advantage.

If a SAN is ever available for testing, we're still not done. Depending on the speed of configuration of the SAN or the storage allocation itself, performance may not be sufficient, so we should test the claims made by the SAN manufacturer before committing all of our storage to it.

A very easy way to do this is with a basic pgbench test. The pgbench command is provided by the PostgreSQL software, and can test various aspects of a server. For our uses, we want to focus on the disk storage. We start by creating a new pgbench database with createdb, so the pgbench command has somewhere to store its test data. The -i option to pgbench tells it to initialize new test data, and the -s option describes the scale of test data we want. A scale of 4000 creates a database of roughly 60 GB. Feel free to adjust this scale to be larger than the amount of available RAM, which guarantees that the server cannot cache all of the test data and taint our performance results by inflating the numbers.

After initializing a new test database, there is a Linux command that can instruct the server to drop all available cached data. This means none of our test data is in memory before we start the benchmark. Again, we don't want to inflate our results, otherwise the SAN looks more capable than it really is.

The test itself comes from pgbench again, which is instructed to only read the test data with the -S option. Further more, we tell the benchmark to launch 24 clients with the -c parameter, and to run the test for a full minute with the -T option. While we used 24 clients here, consider any amount up to three times the number of available processor cores.

This process should reveal how capable the SAN is, and if our production database will be safe and have good performance while relying on remote storage.

There's more...

Notice how we never ask for a specific number of disks when requesting a SAN allocation. Modern SAN equipment operates on an implied service level agreement based on installed components. In effect, if we need 6,000 IOPS and 10 TB of space, the SAN will combine disks, cache, and even SSDs if necessary, to match those numbers as closely as possible.

This not only reduces the amount of risky micromanagement we perform as DBAs, but acts as an abstraction layer between storage and server. In this case, storage can be modified any number of ways, enhanced, adjusted, or copied, without affecting the database installation itself.

The main problem we encounter when using a SAN instead of several servers configured with local storage, is that the SAN becomes a single point of failure. This is something to keep in mind as our journey to high availability progresses.

See also

Here is a list of several SAN vendors, from well known companies, to companies with great potential:

  • EMC:

  • NetApp:

  • Whiptail:

  • VCE: