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Displaying posts with tag: Hardware and Storage (reset)
Scaling IO-Bound Workloads for MySQL in the Cloud

Is increasing GP2 volumes size or increasing IOPS for IO1 volumes a valid method for scaling IO-Bound workloads? In this post I’ll focus on one question: how much can we improve performance if we use faster cloud volumes? This post is a continuance of previous cloud research posts:

To recap, in Amazon EC2 we can use gp2 and io1 volumes. gp2 performance can be scaled with size, i.e for gp2 volume size of 500GB we get 1500 iops; size 1000GB – 3000 iops; and for 3334GB – 10000 iops (maximal …

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About ZFS Performance

If you are a regular reader of this blog, you likely know I like the ZFS filesystem a lot. ZFS has many very interesting features, but I am a bit tired of hearing negative statements on ZFS performance. It feels a bit like people are telling me “Why do you use InnoDB? I have read that MyISAM is faster.” I found the comparison of InnoDB vs. MyISAM quite interesting, and I’ll use it in this post.

To have some data to support my post, I started an AWS i3.large instance with a 1000GB gp2 EBS volume. A gp2 volume of this size is interesting because it is above the burst IOPS level, so it offers a constant 3000 IOPS performance level.

I used sysbench to create a table of 10M rows and then, using export/import tablespace, I copied it 329 times. I ended up with 330 tables for a total size of about 850GB. The dataset generated by sysbench is not very compressible, so I used lz4 compression in ZFS. …

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How to Restore MySQL Logical Backup at Maximum Speed

The ability to restore MySQL logical backups is a significant part of disaster recovery procedures. It’s a last line of defense.

Even if you lost all data from a production server, physical backups (data files snapshot created with an offline copy or with Percona XtraBackup) could show the same internal database structure corruption as in production data. Backups in a simple plain text format allow you to avoid such corruptions and migrate between database formats (e.g., during a software upgrade and downgrade), or even help with migration from completely different database solution.

Unfortunately, the restore speed for logical backups is usually bad, and for a big database it could require days …

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Archiving MySQL Tables in ClickHouse

In this blog post, I will talk about archiving MySQL tables in ClickHouse for storage and analytics.

Why Archive?

Hard drives are cheap nowadays, but storing lots of data in MySQL is not practical and can cause all sorts of performance bottlenecks. To name just a few issues:

  1. The larger the table and index, the slower the performance of all operations (both writes and reads)
  2. Backup and restore for terabytes of data is more challenging, and if we need to have redundancy (replication slave, clustering, etc.) we will have to store all the data N times

The answer is archiving old data. Archiving does not necessarily mean that the data will be permanently removed. Instead, the archived data can be placed into long-term storage (i.e., AWS S3) or loaded into a …

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Fsync Performance on Storage Devices

While preparing a post on the design of ZFS based servers for use with MySQL, I stumbled on the topic of fsync call performance. The fsync call is very expensive, but it is essential to databases as it allows for durability (the “D” of the ACID acronym).

Let’s first review the type of disk IO operations executed by InnoDB in MySQL. I’ll assume the default InnoDB variable values.

The first and most obvious type of IO are pages reads and writes from the tablespaces. The pages are most often read one at a time, as 16KB random read operations. Writes to the tablespaces are also typically 16KB random operations, but they are done in batches. After every batch, fsync is called on the tablespace file handle.

To avoid partially written pages in the tablespaces (a source of data corruption), InnoDB performs a doublewrite. During a doublewrite operation, a batch of dirty pages, from 1 to about 100 pages, is …

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Hands-On Look at ZFS with MySQL

This post is a hands-on look at ZFS with MySQL.

In my previous post, I highlighted the similarities between MySQL and ZFS. Before going any further, I’d like you to be able to play and experiment with ZFS. This post shows you how to configure ZFS with MySQL in a minimalistic way on either Ubuntu 16.04 or Centos 7.

Installation

In order to be able to use ZFS, you need some available storage space. For storage – since the goal here is just to have a hands-on experience – we’ll use a simple file as a storage device. Although simplistic, I have now been using a similar setup on my laptop for nearly three years (just can’t get rid of it, it is too useful). For simplicity, I suggest you use a small Centos7 or Ubuntu 16.04 VM with one core, 8GB of disk and 1GB of RAM.

First, you need to install …

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ZFS from a MySQL perspective

Since the purpose of a database system is to store data, there is close relationship with the filesystem. As MySQL consultants, we always look at the filesystems for performance tuning opportunities. The most common choices in term of filesystems are XFS and EXT4, on Linux it is exceptional to encounter another filesystem. Both XFS and EXT4 have pros and cons but their behaviors are well known and they perform well. They perform well but they are not without shortcomings.

Over the years, we have developed a bunch of tools and techniques to overcome these shortcomings. For example, since they don’t allow a consistent view of the filesystem, we wrote tools like Xtrabackup to backup a live MySQL database. Another example is the …

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Looking at Disk Utilization and Saturation

In this blog post, I will look at disk utilization and saturation.

In my previous blog post, I wrote about CPU utilization and saturation, the practical difference between them and how different CPU utilization and saturation impact response times. Now we will look at another critical component of database performance: the storage subsystem. In this post, I will refer to the storage subsystem as “disk” (as a casual catch-all). 

The most common tool for command line IO performance monitoring is

iostat

, which shows information like this:

root@ts140i:~# iostat -x nvme0n1 5
Linux 4.4.0-89-generic (ts140i)         08/05/2017      _x86_64_        (4 CPU)
avg-cpu:  %user   %nice %system %iowait  %steal   %idle
          0.51    0.00    2.00    9.45    0.00   88.04
Device:         rrqm/s …
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Better Than Linear Scaling

In this blog, we’ll look at how to achieve better-than-linear scaling.

Scalability is the capability of a system, network or process to handle a growing amount of work, or its potential to be enlarged to accommodate that growth. For example, we consider a system scalable if it is capable of increasing its total output under an increased load when resources (typically hardware) are added: https://en.wikipedia.org/wiki/Scalability.

It is often accepted as a fact that systems (in particular databases) can’t scale better than linearly. By this I mean when you double resources, the expected performance doubles, at best (and often is less than doubled).  

We can attribute this assumption to Amdahl’s law (https://en.wikipedia.org/wiki/Amdahl%27s_law), and later …

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The Puzzling Performance of the Samsung 960 Pro

In this blog post, I’ll take a look at the performance of the Samsung 960 Pro SSD NVME.

First, I know the Samsung 960 Pro is a consumer SSD NVME drive, not intended for sustained data center workloads. But the AnandTech review looked good enough that I decided to take it for a test spin to see if it would work well with MySQL benchmarks.

Before that, I decided to do a simple sysbench file IO test to see how the drives handled sustained workloads, and if it would start acting up.

My expectation for a consumer SSD drive is that its write consistency will suffer. Many of those drives can sustain high bursts for short periods of time but have to slow down to keep up with write leveling (and other …

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