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Evolving our datastore
From MySQL on Amazon EC2 to Amazon Aurora.. and more
27.03.2018 - AWS Summit
01
Who we are
• Pixartprinting S.p.A.
• Where:	Quarto	d’Altino,	Venezia
• Type:	Web2Print	of	Cimpress group
• Clients:	600.000
• Employees:	700
• Company	surface:	35.000	MQ
• Work	jobs in	a	day:	10.000
• Packages sent in	1	year:	3M
• Countries reached:	+50
• Product	combinations:	400
• Paper per	year:	11.000	tons
• Ink per	year:	98	tons
02
Datastore - 2016
03
Business Challenges - 2016
• Datastore Business	Challenges:		
• Use	Managed Services
• Add High	availability
• Improve Scalability
04
Datastore - 2016
05
Improvement - 2016
06
Black Friday - 2016
07
Black Friday - 2017
2016 vs 2017
2016 vs 2017
• EC2	+	Percona Server
• Bottleneck of	the	datastore
• Unmanaged load
• Website	not available
• Goal	not reached
• Amazon	Aurora	MySQL
• Autoscaling of	read replica
• Managed load
• Website	available
• Goal	reached:	maximum	sales	in	1	day
08
Monitoring
09
Business Challenges - 2018
• Datastore Business	Challenges:		
• Use	Aurora	Auto	Scaling feature
• Experiment	Aurora	Multi-Master
• Improve Proactive Monitoring

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Evolving our Datastore - 2018-03-27

  • 1. Evolving our datastore From MySQL on Amazon EC2 to Amazon Aurora.. and more 27.03.2018 - AWS Summit
  • 2. 01 Who we are • Pixartprinting S.p.A. • Where: Quarto d’Altino, Venezia • Type: Web2Print of Cimpress group • Clients: 600.000 • Employees: 700 • Company surface: 35.000 MQ • Work jobs in a day: 10.000 • Packages sent in 1 year: 3M • Countries reached: +50 • Product combinations: 400 • Paper per year: 11.000 tons • Ink per year: 98 tons
  • 4. 03 Business Challenges - 2016 • Datastore Business Challenges: • Use Managed Services • Add High availability • Improve Scalability
  • 10. 2016 vs 2017 • EC2 + Percona Server • Bottleneck of the datastore • Unmanaged load • Website not available • Goal not reached • Amazon Aurora MySQL • Autoscaling of read replica • Managed load • Website available • Goal reached: maximum sales in 1 day
  • 12. 09 Business Challenges - 2018 • Datastore Business Challenges: • Use Aurora Auto Scaling feature • Experiment Aurora Multi-Master • Improve Proactive Monitoring