In 2017 we have welcomed many new customers into the Percona fold. It’s always interesting to find out what challenges the Percona team helped them to address and how they chose their relationship with Percona. As unbiased champions of open source database software, our consultancy, support and managed services staff apply their expertise across a wide range of technologies. Here are just a few stories from the past year.

Scaling applications on Amazon RDS the right way

Specializing in on-demand transportation services, Grab needed a high-availability, high performing database engine to serve their rapidly growing application. Grab operates in over 30 densely populated …

I) An upgrade which hasn’t gone as planned

One of our customer needed help after trying to upgrade their slave from Percona Server 5.6 to Percona Server 5.7. (here 5.7.17 is used)
Indeed, from time to time, the replication lag was linearly growing, which could be quite annoying…

(good guess, 5.7 is the red dashed line!)

Their use case is quite interesting :

• a lot of Queries Per Second (read & write, nearly 10k queries/s on this slave)
• a lot of tables (about 600k spread across different databases)
• a lot of writes
• 24 CPUs, and 386G of RAM
• A big buffer pool (270G)
• not that fast SSD disks.

Because we have a high QPS but also a lot of writes, we need to fine tune the innodb settings.
…

As intuitive and streamlined as ecommerce technology might seem from the user's perspective, it involves so much data that engineering ingenuity and smart database management must constantly deliver in order to keep up. At organizations like Shopify—responsible for the easy and reliable transactions at top brands around the world—that excellence of performance involves deep monitoring of their MySQL core and their Redis caching infrastructure, plus insightful query profiling, packet captures, and the admittance of developers to platforms that measure database performance.

Shopify’s motto is “Make commerce better for everyone.” That mantra applies whether the shopping's done online, on mobile, or in-store. For Shopify's engineering team, better means a fast, reliable application that delivers a positive …

I) Why using TokuDB?

I have been playing with TokuDB since 2013. Although it’s a really good storage engine, it’s sometimes full of surprises.

Initially I migrated one of my customer from InnoDB to TokuDB because it was solving all their issues : their DB has been growing to more then 1TB, and they were facing insert performances issues.

After the migration, there DB size dropped to 100GB which was really amazing, without loss in performances compared to InnoDB, and way better insert performances.

II) Read Free Replication

At this time I was using the TokuDB enterprise MariaDB 5.5.41 custom version, with Read Free Replication enabled on a slave.

To enable it, you need to have row-based replication enabled on the master and to set the following variables on the slave :

Shutterfly needed to develop a cloud-based MySQL back end quickly without disrupting their existing customers. They turned to Pythian’s DevOps and MySQL experts who built, tested, and migrated a scalable cloud environment—ideal for web-scale growth—and significantly improved application stability and flexibility. The solution was delivered on time and successfully supported traffic spikes with no downtime during launch.

Shutterfly Case Study

Pythian revamped Chegg’s MySQL architecture and processes and has provided 24×7 operations and incident management support and continual architectural guidance for Chegg’s MySQL, MongoDB,and Cassandra environments for the past five years—prior to, during, and after their IPO. Pythian also spearheaded an implementation on Amazon Web Services’ cloud for the elasticity needed by an educational application’s cyclical peaks. Chegg’s partnership with Amazon and Pythian has allowed for rapid growth and constant evolution without sacrificing performance. Many years later, Chegg still has no in-house DBAs, and relies on Pythian’s expertise for all their data management needs.

Chegg Case Study