DEVOPS D-DAY #5 Monitoring OVH: 350k servers, 30 DCs… and one Metrics platform Horacio Gonzalez @LostInBrittany DEVOPS D-DAY #5
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Who are we? Introducing myself and introducing OVH OVHcloud
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Horacio Gonzalez @LostInBrittany Spaniard lost in Brittany, developer, dreamer and all-around geek
Flutter
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OVH: A Global Leader on Cloud 250k Private cloud VMs running
1
Dedicated IaaS Europe
30 Datacenters
Own 20Tbps
Hosting capacity : 1.3M Physical Servers 360k Servers already deployed
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Netwok with 35 PoPs
1.3M Customers in 138 Countries
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OVH: Our solutions Cloud
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
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And don’t forget, next week…
OVHcloud Summit https://summit.ovhcloud.com/ DEVOPS D-DAY #5
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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 DEVOPS D-DAY #5
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And as in most tales
It begins with a mission DEVOPS D-DAY #5
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And a band of heroes
Engulfed into the adventure DEVOPS D-DAY #5
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They fight against mishaps
And all kind of foes DEVOPS D-DAY #5
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They build mighty fortresses
Pushing the limits of possible DEVOPS D-DAY #5
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And defend them day after day
Against all odds DEVOPS D-DAY #5
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But we don’t know yet the end
Because this tale isn’t finished yet DEVOPS D-DAY #5
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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 DB App
Slaves
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Moving from monolith to μservices
App App App
Bus
DB App
Slaves
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Moving from monolith to μservices RPXY
LB
Cache
App App App
Bus
DB App
Slaves
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What could go wrong? RPXY
LB
Cache
App App App
Bus
DB App
Slaves
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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 DEVOPS D-DAY #5
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We need to have insights
Observability: How the system works?
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OVH decided go metrics-oriented
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A metrics platform for OVH
For all OVH DEVOPS D-DAY #5
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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 OpenTSBD didn’t scale for us DEVOPS D-DAY #5
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Metrics needs
First need: To be massively scalable
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Analytics is the key to success
Fetching data is only the tip of the iceberg DEVOPS D-DAY #5
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Analysing metrics data
To be scalable, analysis must be done in the database, not in user’s computer DEVOPS D-DAY #5
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Metrics needs
Second need: To have rich query capabilities
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Enter Warp 10… Open-source Time series Database DEVOPS D-DAY #5
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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 DEVOPS D-DAY #5
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Did you say scalability?
From the smallest to the largest…
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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 DEVOPS D-DAY #5
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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
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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)
……..…………………
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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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Our stack overview More than 666 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
1.5M of writes/s 3M of reads/s
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Hadoop need a lot of
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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 What is my application doing?
App
What is my runtime doing?
How many GC triggered?
Run
tim
Is there a hardware failure?
Logs
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How many HTTP calls?
e
Hos t
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How many disk I have left?
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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