Monitoring OVH: 300k servers, 28 DCs… and one Metrics platform Horacio Gonzalez @LostInBrittany #Sunnytech
@LostInBrittany
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Who are we? Introducing myself and introducing OVH
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@LostInBrittany
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Horacio Gonzalez @LostInBrittany Spaniard lost in Brittany, developer, dreamer and all-around geek
Flutter
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@LostInBrittany
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OVH : Key Figures 1.3M Customers worldwide in 138 Countries 1.5 Billions euros investment over five years 28 Datacenters (growing) 350k Dedicated Servers 200k Private cloud VMs running 650k Public cloud Instances created in a month 20TB bandwidth capacity
35 Points of presence 4TB Anti DDoS capacity Hosting capacity : 1.3M Physical Servers
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@LostInBrittany
2 500 Employees in 19 countries
20 Years of Innovation
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OVH: Our solutions Cloud
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Web Hosting
Mobile Hosting
Telecom
VPS
Containers ▪ Dedicated Server
Domain names
VoIP
Public Cloud
Compute ▪ Data Storage
Email
SMS/Fax
Private Cloud
▪ Network and Database
CDN
Virtual desktop
Serveur dédié
Security Object Storage
Web hosting
Cloud HubiC Over theBox
▪ Licences
Cloud Desktop
Securities
MS Office
Hybrid Cloud
Messaging
MS solutions
@LostInBrittany
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Once upon a time… Because I love telling tales
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This talk is about a tale…
A true one nevertheless #Sunnytech
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And as in most tales
It begins with a mission #Sunnytech
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And a band of heroes
Engulfed into the adventure #Sunnytech
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They fight against mishaps
And all kind of foes #Sunnytech
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They build mighty fortresses
Pushing the limits of possible #Sunnytech
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And defend them day after day
Against all odds #Sunnytech
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But we don’t know yet the end
Because this tale isn’t finished yet #Sunnytech
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It begins with a mission Build a metrics platform for OVH
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A long time ago…
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A long time ago…
Monitoring: Does the system works?
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Moving from monolith to μservices
App
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Moving from monolith to μservices App App
App
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Moving from monolith to μservices App App App
App
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Moving from monolith to μservices App App App
App
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Moving from monolith to μservices RPXY
LB
App App App
App
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Cache
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What could go wrong? RPXY
LB
App App App
App
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Cache
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Microservices are a distributed system
GOTO 2017 • Debugging Under Fire: Keep your Head when Systems have Lost their Mind • Bryan Cantrill #Sunnytech
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We need to have insights
Observability : Understand how it works
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OVH decided go metrics-oriented
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A metrics platform for OVH
For all OVH #Sunnytech
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Building OVH Metrics One Platform to unify them all, One Platform to find them, One Platform to bring them all and in the Metrics monitor them
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What is OVH Metrics?
Managed Cloud Platform for Time Series
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OVH monitoring story We had lots of partial solutions…
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OVH monitoring story One Platform to unify them all What should we build it on?
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OVH monitoring story
Including a really big
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OpenTSDB Rowkey design flaws ● .regex. => full table scans ● High cardinality issues (Query latencies)
We needed something able to manage hundreds of millions time series
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OpenTSBD didn’t scale for us @LostInBrittany
Metrics needs
First need: To be massively scalable #Sunnytech
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Analytics is the key to success
Fetching data is only the tip of the iceberg #Sunnytech
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Analysing metrics data
To be scalable, analysis must be done in the database, not in user’s computer #Sunnytech
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Metrics needs
Second need: To have rich query capabilities #Sunnytech
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Enter Warp 10… Open-source Time series Database #Sunnytech
@LostInBrittany
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More than a Time Series DB Warp 10 is a software platform that ● Ingests and stores time series ● Manipulates and analyzes time series
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Manipulating Time Series with Warp 10
A true Time Series analysis toolbox ○ Hundreds of functions ○ Manipulation frameworks ○ Analysis workflow
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Manipulating Time Series with Warp 10
A Time Series manipulation language
WarpScript #Sunnytech
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Did you say scalability?
From the smallest to the largest… #Sunnytech
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More Warp 10 goodness ● Secured & multi tenant
● Synchronous (transactions)
● In memory Index
● Better Performance
● No cardinality issues
● Better Scalability
● Lockfree ingestion
● Versatile
● WarpScript Query Language
(standalone, distributed)
● Support more data types
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Metrics Live In-memory, high-performance Metrics instances
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In-memory: Metrics live
millions of writes/s #Sunnytech
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In-memory: Metrics live
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In-memory: Metrics live
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Monitoring is only the beginning OVH Metrics answer to many other use cases
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Graveline rack’s temperature
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Even medical research…
Metrics’ Pattern Detection feature helped Gynaecology Research to prove patterns on perinatal mortality #Sunnytech @LostInBrittany
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Use cases families • • • •
Billing
Monitoring IoT
(e.g. bill on monthly max consumption)
……………………………………………..…….
(APM, infrastructure,appliances,…)
…..……………………………
(Manage devices, operator integration, …)
…………………………………………….………………….
Geo Location
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(Manage localized fleets)
……..…………………
@LostInBrittany
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Use cases • • • • • •
DC Temperature/Elec/Cooling map Pay as you go billing (PCI/IPLB) GSCAN Monitoring ML Model scoring (Anti-Fraude) Pattern Detection for medical applications
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SREing Metrics With a great power comes a great responsibility
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Metrics’s metrics
432.000.000.000 datapoints / day
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70
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Our stack overview More than 650 machines operated by 5 people >95% dedicated servers No Docker, only SystemD Running many Apache projects: ○ Hadoop ○ HBase ○ Zookeeper ○ Flink ● And Warp 10 ● ● ● ●
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Our biggest Hadoop cluster
200 datanodes
60k regions of 10Gb
~
2.3 PB of capacity 8.5Gb/s of bandwidth
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1.5M of writes/s 3M of reads/s
Hadoop nodes Most of the nodes are the following: ● ● ●
16 to 32 cores 64 to 128 GB of RAM 12 to 16 TB
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But, we also have some huge nodes: ● ● ●
2x 20 cores (xeon gold) 320 GB of RAM 12x 4TB of Disk
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Warp10 nodes Ingress (cpu-bound): ● ●
32 cores 128 GB of RAM
Egress (cpu-bound): ● ●
32 cores 128 GB of RAM
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Directory (ram-bound): ● ●
48 cores 512 GB of RAM
Store (cpu-bound): ● ●
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32 cores 128 GB of RAM
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Why you should care?
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Why you should care? (>30s)
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The only way to optimize: measure
Logs
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Metrics
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Monitoring JVM with metrics
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Monitoring JVM with metrics
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Monitoring JVM with metrics
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Monitoring JVM with metrics
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Monitoring JVM with metrics
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Tuning G1 is hard
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Tuning G1 is hard
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Our programming stack ● We mostly use garbage collected languages as ○ Go ○ Java ○ JavaScript
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Our programming stack However, we are using non-garbage collected languages as Rust when needed
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Our friends for µservices
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We
open-source
Code contribution: ● ● ● ● ● ●
https://github.com/ovh/beamium https://github.com/ovh/noderig https://github.com/ovh/tsl https://github.com/ovh/ovh-warp10-datasource https://github.com/ovh/ovh-tsl-datasource …
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Involved in: ● ● ● ●
Warp10 community Apache Hbase/Flink development Prometheus/InfluxData discussions TS Query Language Working group
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Conclusion That’s all folks!
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@LostInBrittany