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Google Tech Talk with Dr. Eric Brewer in Korea Apr.27.2015
This document summarizes a Google Tech Talk given by Dr. Eric Brewer on containers. The talk discussed how Google has been using containers for over 10 years to manage applications, with over 2 billion containers launched per week. Containers were described as providing simplification of management, performance isolation, and efficiency. Docker and Linux containers were discussed as merging the packaging benefits of Docker with the isolation capabilities of Linux containers. Kubernetes, an open source container orchestration system inspired by Google's internal systems, was also summarized.
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Similar to Google Tech Talk with Dr. Eric Brewer in Korea Apr.27.2015
Google Tech Talk with Dr. Eric Brewer in Korea Apr.27.2015
- 1. Google Tech Talk. Containers:What, Why and How Google Cloud Innovation Dr. Eric Brewer VP of Infrastructure April 27, 2015
- 2. Google confidential │Do not distribute
- 3. Google confidential │Do not distribute
- 4. Google confidential │Do not distribute
- 5. Google confidential │Do not distribute Management MobileDeveloper Tools Compute Networking Big Data Storage
- 6. Google confidential │Do not distribute “Cloud Native” Applications Middle of a great transition • unlimited “ethereal” resources in the Cloud • an environment of services not machines • thinking in APIs and co-designed services • high availability offered and expected (this is how Google already works internally)
- 7. Google confidential │Do not distribute Google has been developing and using containers to manage our applications for over 10 years. Images by Connie Zhou 2B launched per week ● simplifies management ● performance isolation ● efficiency
- 8. Google confidential │Do not distributeGoogle confidential │ Do not distribute What are containers?
- 9. Google confidential │Do not distribute VMs vs. Containers Physical Processor Virtual Processor Operating System Libraries User Code Private Copy Shared Virtual Machines Physical Processor Virtual Processor Operating System Libraries User Code Containers ISA syscall Containers: less overhead, enable more “magic”
- 10. Google confidential │Do not distribute Merging Two Kinds of Containers Docker • It’s about packaging • Control: • packages • versions • (some config) • Layered file system • => Prod matches testing Linux Containers • It’s about isolation … performance isolation • not security isolation … use VMs for that • Manage CPUs, memory, bandwidth, … • Nested groups
- 11. Google confidential │Do not distribute Google Platform Layering Infrastructure: Machines App Engine: Language-based Containers: Process-based GCE Kubernetes GKE GAE Easy to use, Flexible
- 12. Google confidential │Do not distributeGoogle confidential │ Do not distribute Why do we use them?
- 13. Google confidential │Do not distribute Why Containers Ease of development: • User makes jobs based on containers; we schedule those jobs for them • They don’t worry about machines or the OS Utilization (much more efficient) • We pack many containers per machine • Mix or live services and batch workloads • Batch fills in the “holes” Ease of Operation: fewer staff per job • E.g. all jobs use latest security patches • More shared code among projects (shared services and code)
- 14. Google confidential │Do not distributeGoogle confidential │ Do not distribute How do we use them?
- 15. Google confidential │Do not distribute Services Model Each app lives in an environment of shared services • storage, monitoring, logging • master election, deployment, testing Services are updated independently • Multiple versions running at a time • Required for “canary” testing -- deploy new version a little bit • => Need version numbers • Usually new versions are backward compatible • Sometimes not => must eventually update ALL clients • Running multiple versions gives clients some time to update
- 16. Google confidential │Do not distribute Borg Scheduler Borg is our primary scheduler for the last 10 years • Schedules over a single very large cluster (10,000+ machines) • Starts, moves, restarts container-based jobs • Also handles some logging, monitoring • Users don’t worry about where their jobs run Recent paper on Borg: https://research.google.com/pubs/pub43438.html
- 17. Google confidential │Do not distribute Kubernetes • Container orchestrator • Runs Docker containers • Supports multiple cloud and bare-metal environments • Inspired and informed by Google’s experiences and internal systems • Open source, written in Go Manage applications, not machines κυβερνήτης: Greek for “pilot” or “helmsman of a ship” the open source cluster manager from Google
- 18. Google confidential │Do not distribute Kubernetes: Higher level of Abstraction Don’t Worry About • OS details • Packages — no conflicts • Machine sizes (much) • Mixing languages • Port conflicts Think About • Composition of services • Load-balancing • Names of services • State management • Monitoring and Logging • Upgrading
- 19. Google confidential │Do not distribute Kubernetes Partners
- 20. Google confidential │Do not distribute Open sourced in June 2014 Google launched Google Container Engine (GKE) • Full managed and hosted Kubernetes • https://cloud.google.com/container-engine/ Roadmap: • https://github.com/GoogleCloudPlatform/kubernetes/blob/master/docs/roadmap.md Working towards v1.0 over next few months • Key use cases: Redis, Memcache, MySQL, Mongo, Cassandra Kubernetes Status & Plans
- 21. Google confidential │Do not distribute Evolution is the Real Value Apps Structured as Independent Microservices ● Encapsulated state with APIs (like “objects”) ● Mixture of languages ● Mixture of teams Services are Abstract ● A “Service” is just a long-lived abstract name ● Varied implementations over time (versions) ● Kubernetes routes to the right implementation
- 22. Google confidential │Do not distribute Pod /data Containers: • Handle package dependencies • Different versions, same machine • No “DLL hell” Managing Dependencies python 3.4.2 glibc 2.21 MyService python 2.7.9 glibc 2.19 MySQL Pods: • Co-locate containers • Shared volumes • IP address, independent port space • Unit of deployment, migration
- 23. Google confidential │Do not distribute Dependencies: Services Services: • Replicated pods • Source pod is a template • Auto-restart member pods • Abstract name (DNS) • IP address for the service • in addition to the members • Load balancing among replicas Load Balancer Service IP
- 24. Google confidential │Do not distribute Example: Rolling Upgrade with Labels Servers: Labels: frontend v1.2 frontend v1.2 frontend v1.2 frontend v1.2 frontend v1.3 frontend v1.3 frontend v1.3 frontend v1.3 frontend Replication Controller replicas: 4 v1.2 Replication Controller replicas: 1 v1.3 replicas: 3 replicas: 2replicas: 3replicas: 2replicas: 1 replicas: 4replicas: 0
- 25. Google confidential │Do not distribute Summary A new path for Cloud Native applications: • Collection of independent (micro) services • Each service evolves on its own • Scale as needed • Update as needed • Mix versions as needed • Pods are a building block • Template for service members • Group containers and volumes • Dedicated IP and thus port space • Containers simplify deployment
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- 27. 2012 20132002 20042006 2008 2010 Cloud Dataflow History of Big Data at Google Why Cloud Dataflow? MapReduce GFS Big Table Dremel Pregel Flume Colossus Spanner MillWheel
- 28. Time to answersome Big Data questions 1M Devices 16.6K Events/sec 43B Events/month 518B Events/year What was the average viewing time over the past 7 days, compared to the last year? How many active viewers did I have in the last minute? How many sales were made in the last hour due to advertising conversion?
- 29. “One” approach 1M Devices 16.6K Events/sec 43BEvents/month 518B Events/month
- 30. Big Data onGoogle Cloud BigQuery High throughput ingestion, store and query petabytes Dataflow Stream & batch processing, unified and simplified Pub/Sub Scalable, flexible, and globally available messaging Fully Managed, No-Ops Services Capture Process Analyze
- 31. Cloud Dataflow Cloud Dataflowis a collection of SDKs for building batch or streaming parallelized data processing pipelines. Cloud Dataflow is a fully managed & elastic service for executing optimized parallelized data processing pipelines.
- 32. Benefits of CloudDataflow ❯ No Ops - truly elastic data processing for the cloud • On demand resource allocation w/intelligent auto-scaling • Automated worker optimization ❯ Unified model for batch & stream based processing • Flexible I/O support • Fine grained correctness primitives ❯ Open sourced SDK @ github • Java 7 today @ /GoogleCloudPlatform/DataflowJavaSDK • Python 2 in progress • Scala @/darkjh/scalaflow & /jhlch/scala-dataflow-dsl • Spark runner@ /cloudera/spark-dataflow • Flink runner @ /dataArtisans/flink-dataflow
- 33. Optimizing Your TimeTo Answer More time to dig into your data Programming Resource provisioning Performance tuning Monitoring Reliability Deployment & configuration Handling Growing Scale Utilization improvements Data Processing with Cloud DataflowTypical Data Processing Programming
- 34. • Movement • Filtering •Enrichment • Shaping • Reduction • Batch computation • Continuous computation • Composition • External orchestration • Simulation Where might you use Cloud Dataflow? AnalysisETL Orchestration
- 35. The tension andpolarity of Big Data AccuracySpeed Cost control Complexity Time to answer
- 36. Let’s build something 1M Devices 16.6KEvents/sec 43B Events/month 518B Events/month Cloud Pub/Sub Cloud Dataflow BigQuery How many active viewers did I have in the last minute?
- 37. Let’s build something 1M Devices 16.6KEvents/sec 43B Events/month 518B Events/month Cloud Dataflow BigQuery How many active viewers did I have in the last minute?
- 38. Let’s build something 1M Devices 16.6KEvents/sec 43B Events/month 518B Events/month Cloud Dataflow How many active viewers did I have in the last minute?
- 39. Let’s build something 1M Devices 16.6KEvents/sec 43B Events/month 518B Events/month Cloud Dataflow SDK + How many active viewers did I have in the last minute?
- 40.