Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (13)
Similaire à Just Enough DevOps for Data Scientists
Similaire à Just Enough DevOps for Data Scientists (20)
Dernier
Dernier (20)
Just Enough DevOps for Data Scientists
- 2. Just Enough DevOps for Data Scientists abida@salesforce.com @ anyabida1 Anya Bida, SRE at Salesforce
- 3. About Anya Sr. Member of Technical Staff (SRE) Salesforce Production Engineering Salesforce Einstein Platform Co-organizer SF Big Analytics Spark Tuning • Cheat-sheet • Talks Previously at Alpine Data, SRI PhD Mayo Clinic, BS Johns Hopkins @anyabida1
- 4. What I am going to talk about What is DevOps Salesforce Einstein Scales Our goal Top 10 tips What’s next?
- 5. What is DevOps? Software Development Network & SecurityInfrastructure Build & Release
- 6. What is DevOps? Software Development Network & SecurityInfrastructure Build & Release Data Science
- 7. What is DevOps? Software Development Network & SecurityInfrastructure Build & Release Data Science • Awesome library on SparkML • Spark clusters • Microservices • Cluster, Containers
- 8. Fastest Growing Top 5 Enterprise Software Company $5.4B FY15 $4.1B FY14 $3.1B FY13 $6.7B FY16 $2.3B FY12 $1.7B FY11 $2.56BFY18Q2 revenue $8.4BFY17 revenue 2009 • 2010 • 2011 2012 • 2013 • 2014 2015 • 2016 • 2017 September 2016 2011 • 2012 • 2013 2014 • 2015 • 2016 • 2017 The world’s most innovative companies “Innovator of the Decade”
- 10. Our Goal Time Number of Predictions Infrastructure Costs
- 11. Tip 1: Plan for Failure Take off that Data Scientist hat now.
- 12. Simple Dashboard with KPIs Tip 1: Plan for Failure Take off that Data Scientist hat now.
- 13. Tip 1: Plan for Failure Take off that Data Scientist hat now. https://www.slideshare.net/jiboumans/how-to-measure-everything-a-million-metrics-per-second-with-minimal-developer-overhead Simple Dashboard with KPIs • Request & error rates • Longest response times - upper 95th & 99th percentile • Capacity • Events Jos Boumans, Salesforce DMP slides
- 14. Tip 1: Plan for Failure Take off that Data Scientist hat now. https://www.slideshare.net/jiboumans/how-to-measure-everything-a-million-metrics-per-second-with-minimal-developer-overhead Simple Dashboard with KPIs • Request & error rates • Longest response times - upper 95th & 99th percentile • Capacity • Events Collect metrics from every machine. Troubleshoot with all the metrics at your disposal
- 15. Tip 2: Blue Green Deployments https://docs.mobingi.com/official/guide/bg-deploy Blue Machine (old) Green Machine (new) Users
- 16. Tip 3: Assume people make mistakes Technical debt • Every manual change • Duplicate metrics Scale down resources • Terminate unused machines • Janitor Monkey • Understand the cost per job • Jobs should not accumulate files on disk
- 17. Tip 4: Changes should be auditable Schaper - the tool to compare schemas https://www.linkedin.com/in/huqixiu/ Qixiu “Q” Hu
- 18. Tip 4: Changes should be auditable Schaper - the tool to compare schemas https://www.linkedin.com/in/huqixiu/ Qixiu “Q” Hu CREATE TABLE myConferences ( name text , city text, early_bird timeuuid, late_bird timeuuid, PRIMARY KEY ((name, city), early_bird) ) WITH CLUSTERING ORDER BY (early_bird DESC); CREATE TABLE myConferences ( name text , city text, early_bird timeuuid, late_bird timeuuid, PRIMARY KEY ((name, city), early_bird) ) WITH CLUSTERING ORDER BY (early_bird DESC);
- 19. Tip 4: Changes should be auditable Schaper - the tool to compare schemas https://www.linkedin.com/in/huqixiu/ Qixiu “Q” Hu CREATE TABLE myConferences ( name text , city text, early_bird timeuuid, late_bird timeuuid, PRIMARY KEY ((name, city), early_bird) ) WITH CLUSTERING ORDER BY (early_bird DESC); CREATE TABLE myConferences ( name text , city text, early_bird timeuuid, late_bird timeuuid, discount_code string, PRIMARY KEY ((name, city), early_bird) ) WITH CLUSTERING ORDER BY (early_bird DESC);
- 20. Tip 5: Configuration management Network Connectivity • 20 parameters User Access • 50 parameters Deploy cluster (eg Mesos) • 20 non-default parameters Deploy a microservice • 50 parameters Schedule a job • 3 parameters SUM X 3 regions X 20 metrics Approx.6000
- 21. Templates for Automation Service discovery Creating dashboards • Prod, non-prod, … Log queries Cost analysis Tip 6: Pick a naming convention <service>. <environment>. <region>. <hostname>. <metric>
- 22. Tip 7: Permissions Every user, service, & job should have specific, auditable permissions. Cluster Manager Scheduler IAM IAM Roles • User has an IAM Role • Job has an IAM Role • IAM Roles determine read / write access to data IAM Out Logs IAM In
- 23. Understanding Memory Management in Spark For Fun And Profit Shivnath Babu (Duke University, Unravel Data Systems) Mayuresh Kunjir (Duke University) Tip 8: Understand resource allocation Node Memory Container Memory 8Gb Node Memory Container Memory 8Gb
- 24. Node Memory Node Memory Node Memory 4Gb used 8Gb total Can my 8Gb container launch on this cluster? 8Gb
- 25. Tip 9: Monitor multiple viewpoints https://light.co/camera
- 26. Tip 9: Monitor multiple viewpoints Connectivity Viewer https://www.linkedin.com/in/vaibhavt/ Vaibhav Tandon
- 27. Tip 9: Monitor multiple viewpoints Connectivity Viewer https://www.linkedin.com/in/vaibhavt/ Vaibhav Tandon
- 28. Tip 9: Monitor multiple viewpoints Connectivity Viewer https://www.linkedin.com/in/vaibhavt/ Vaibhav Tandon
- 29. Getting started tips: 1. Plan for failure 2. Blue / Green Deployments 3. Assume people make mistakes 4. Changes should be auditable 5. Configuration management 6. Pick a naming convention 7. Permissions • user, service, job 8. Understand resource allocation 9. Monitor multiple viewpoints
- 30. Getting started tips: 1. Plan for failure 2. Blue / Green Deployments 3. Assume people make mistakes 4. Changes should be auditable 5. Configuration management 6. Pick a naming convention 7. Permissions • user, service, job 8. Understand resource allocation 9. Monitor multiple viewpoints 10. Infrastructure as Code
- 31. Did we just automate ourselves out of our jobs? Nope. Now we have time to take on new projects and grow…
- 32. More info: Jos Boumans, Salesforce DMP slides SRE How Google Runs Production Systems book James Ward, Engineering & Open Source Ambassador at Salesforce High Performance spark book
- 33. More info: Real Time ML Pipelines in Multi-Tenant Environments Director of Engineering Karl Skucha & Lead Engineer Yan Yang Introduction to Machine Learning Engineering & Open Source Ambassador James Ward Fantastic ML apps and how to build them Principal Engineer, Matthew Tovbin Fireworks - lighting up the sky with millions of Sparks Director of Engineering Thomas Gerber Functional Linear Algebra in Scala Engineer & Professor Vlad Patryshev Panel: Functional Programming for Machine Learning Saturday @ 2:10pm —Complex Machine Learning Pipelines Made Easy Machine Learning Engineers Till Bergmann & Chris Rupley
- 34. abida@salesforce.com @ anyabida1 Anya Bida, SRE at Salesforce
- 35. Questions?
Notes de l'éditeur
- What DevOps actually IS??? -- cross section of infrastructure, -- here’s all the things data scientists need to support themselves at scale
- What DevOps actually IS??? -- cross section of infrastructure, -- here’s all the things data scientists need to support themselves at scale
- What DevOps actually IS??? -- cross section of infrastructure, -- here’s all the things data scientists need to support themselves at scale
- We need to build an infra that scales at the pace of Salesforce.
- Salesforce Einstein is serving 475 Million predictions per day, and growing. So how do we do this from an infra perspective?
- Even if you do everything right, machines WILL fail.
- Collect metrics by installing statsd on every machine.
- Should I automate the file removal Better: keep your files in a distributed, versioned storage system Infra team will monitor disk usage
- Lets say I have a database with one replica on the east coast, and one replica on the west coast.
- My database schema, here represented as a table, is as follows. Right now my schemas are identical across data centers.
- But if someone changes the schema for one of my replicas, I want to know immediately. So my schemas should be auditable. Q on our SRE team built the tool schaper to compare schemas. Schaper is generic - it supports ElasticSearch, Cassandra, MongoDb, etc., and provides a report when there is a schema change. I NEED TO KNOW when my schema changes. Obviously this could be very important information. Wink, wink. Schaper is also modular - it’s plug-n-play. So this is an example of how we ensure changes are auditable. Cassandra: Keyspaces Database replication Schaper is one example of the type of tools that could be built to audit changes. From the audit, we can automate some action, depending on the particular change or … We haven’t open sourced this tool, yet, just an example
- When to automate? Any task that’s done 10x per year should be automated. IAC should be correct, comprehensible, and composable. How the number of clicks can be so big 20clicks per cluster x 3regions x 20metrics IAC -- networking layer -- provisioning -- build and deploy -- monitoring -- manage
- IAM definition Identity and access management Authorization & Authentication
- Ok, so I’ve got my container, which uses maybe 8Gb of RAM. Now I want to know if my container can launch on my cluster.
- So my cluster has 3 nodes, let’s say, and 8Gb total RAM on each node. CAN MY 8GB CONTAINER LAUNCH ON THIS CLUSTER? Since 4Gb of ram is used on each node, the cluster memory available is 4x3 = 12Gb, so if I only monitor cluster level metrics, then my container will fail to launch.
- The image above shows sample connectivity for development, staging and production environments. It helps us verify there are no unintended rules etc.. Mention the three lone servers - should we review these? Are these supposed to be there? This tool is not open sourced, but just an example of the internal tools we build - and you can too!
- Double clicking a node shows its connectivity. This is useful for debugging issues.
- We can filter by resource type, names, tags etc.
- Taken together, hopefully I’ve convinced you that each piece of your infra should be deployed and managed as code.
- This has been “Just enough devops for data scientists”
- This has been “Just enough devops for data scientists”