Art Van Scheppingen - vidaXL & Bart Oles - Severalnines AB
Over the past few years, VidaXL has become a European market leader in the online retail of slow moving consumer goods. When a company achieved over 50% year over year growth for the past 9 years, there is hardly enough time to overhaul existing systems. This means existing systems will be stretched to the maximum of their capabilities, and often additional performance will be gained by utilizing a large variety of datastores.
Polyglot persistence reigns in rapidly growing environments and the traditional one-size-fits-all strategy of monoglots is over.
VidaXL has a broad landscape of datastores, ranging from traditional SQL data stores, like MySQL or PostgreSQL alongside more recent load balancing technologies such as ProxySQL, to document stores like MongoDB and search engines such as SOLR and Elasticsearch.
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Polyglot Persistence Utilizing Open Source Databases as a Swiss Pocket Knife
1. Art van Scheppingen - Senior (No)SQL DBA @ VidaXL
Bart OleĹ - Senior Support Engineer @ Severalnines
Polyglot persistence: utilizing
open source databases as a
Swiss pocket knife
3. VidaXL Company Stats
⢠Online retailer in (mostly) slow moving goods
⢠Founded 2008
⢠350M turnover, 40% growth yearly
⢠1500 employees (US, CN, AU, IN, RO, UA)
⢠HQ in the Netherlands
⢠4 warehouses worldwide (NL, US and AU)
4. How does VidaXL sell its goods?
⢠Own webshop platform in EU, US and AU
⢠Warehouses in NL, US and AU
⢠Selling on other platforms, e.g. Amazon, eBay
⢠Allow selling on our own platform using Mirakl
⢠B2B drop-shipments
5. VidaXL Technical Foundations
⢠SAP as ERP system
⢠Genesys as CS system
⢠Webshop
⢠Open source web-based development strategy
⢠PHP / NodeJS
⢠Docker
⢠Cloudflare workers
7. What is Polyglot Persistence?
Using multiple specialized persistent stores rather than one single general-purpose database
8. Where does the term come from?
⢠The way we work is changing
⢠Enterprise applications are becoming more complex
⢠Separate (devops/agile) teams
⢠Ownership of applications
⢠(Micro)services
⢠Everyone has their preference
⢠Various programming languages
⢠Various storage systems
9. Where does the term come from?
⢠Monoglot Programming
⢠Only one programming language allowed
⢠Readability
⢠All code is in the same language
⢠Support
⢠One platform to support
⢠Knowledge
⢠Everybody is an expert
⢠Is there a jack-of-all-trades language?
10. Where does the term come from?
⢠Monoglot Programming
⢠Only one programming language allowed
⢠Readability
⢠All code is in the same language
⢠Support
⢠One platform to support
⢠Knowledge
⢠Everybody is an expert
⢠Is there a jack-of-all-trades language?
13. Polyglot Programming
⢠Polyglot Programming
⢠Use programming languages for what they are good at
⢠Flexibility
⢠Use Java for a secure API
⢠Use Scala for real time stream processing
⢠Use Python for text analysis
⢠Tie everything together using AngularJS
⢠Knowledge
⢠Everybody is expert at one or more languages
16. Data storage landscape changes
⢠Relational data stores (RDBMS)
⢠Key-Value data stores (âNoSQLâ)
⢠Columnar data stores (OLAP)
⢠Document data stores (NoSQL)
⢠Graph data stores (GDB)
⢠Timeseries data stores
⢠Big Data
17. Data storage landscape changes
Software
RDBMS Oracle, MySQL,
PostgreSQL
Key-Value Redis, Riak
Columnar InfiniDB, Clickhouse
Document MongoDB,
Couchbase
Graph Neo4J, Janusgraph
Big Data Hadoop
20. Polyglot Persistence
⢠Complex problems require different storage systems
⢠Use the right tool for the job, for example
⢠Use PostgreSQL for financial data
⢠Use MySQL for website contents
⢠Use MongoDB for user profiles
⢠Use Cassandra for real time streams
⢠Use Neo4J for recommendation analysis
23. Quick recap on our data stores
⢠MySQL
⢠MariaDB (Galera) clusters
⢠MySQL replication
⢠ProxySQL
⢠PostgreSQL
⢠SOLR
⢠Elasticsearch
⢠ELK
⢠MongoDB
⢠Couchbase
⢠(RabbitMQ)
⢠Prometheus
24. How did this happen?
⢠Continuous growth
⢠Hardly any time to overhaul existing systems
⢠Transition from monolith to microservice architecture
⢠For each microservice the most optimal solution has been chosen
⢠Early adopters of new technology
⢠Gaining advantage over competition
31. What were the challenges?
⢠Automation
⢠Increased complexity
⢠Systems monitoring
⢠Multiple integrations
⢠Maintenance becomes more difficult
⢠Backups
⢠Scaling
⢠Software updates
⢠DevOps are not a DBA
32. What were the solutions?
⢠Invest in automation
⢠Never perform any (large) task thrice
⢠Increase tooling
⢠Build it ourselves costs time
⢠Buying/licensing tools costs money
⢠Keeping the headcount low saves money
⢠Focus on systems that matter most
⢠Get (exteneral) help
⢠Hire DBAs! ;)
35. Copyright 2017 Severalnines AB
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