Mesos at OpenTable
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Presentation for Mesoscon 2015, Seattle wa http://sched.co/3BZR
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Mesos at OpenTable
- 1. Mesos at OpenTable Pablo Delgado Senior Data Engineer OpenTable @pablete MesosCon 2015, Seattle, WA
- 2. • Over 32,000 restaurants worldwide • more than 760 million diners seated since 1998, representing more than $30 billion spent at partner restaurants • Over 16 million diners seated every month • OpenTable has seated over 190 million diners via a mobile device. Almost 50% of our reservations are made via a mobile device • OpenTable currently has presence in US, Canada, Mexico, UK, Germany and Japan • OpenTable has nearly 600 partners including Facebook, Google, TripAdvisor, Urbanspoon, Yahoo and Zagat. 2 OpenTable the world’s leading provider of online restaurant reservations
- 3. At OpenTable we aim to power the best dining experiences!
- 5. 5 From monolith to microservices
- 6. 6 • Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center PAPER: http://mesos.berkeley.edu/mesos_tech_report.pdf • Omega: flexible, scalable schedulers for large compute clusters PAPER: http://research.google.com/pubs/pub41684.html Apache Mesos
- 7. 7 Apache Mesos • Mesos slaves connect to masters and offer resources like CPU, disk, and memory. • Masters take those offers and make decisions about resource allocation using frameworks like Singularity. • Frameworks in turn choose to use resource offers, and run tasks on slaves.
- 8. 8 Zookeeper Netflix’s Exhibitor Mesos Master Zookeeper Netflix’s Exhibitor Standby Master Zookeeper Netflix’s Exhibitor Standby Master Docker Mesos Slave Docker Mesos Slave Docker Mesos Slave Docker Mesos Slave Docker Mesos Slave Docker Mesos Slave availability zone 2bavailability zone 2a availability zone 2c Apache Mesos
- 10. 10 • Native Docker Support • JSON REST API and Java Client • Fully featured web application (replaces and improves Mesos Master UI) • Deployments, automatic rollbacks, and healthchecks • Configurable email alerts to service owners Singularity Features
- 11. 11 Hubspot’s Singularity Process types: Web Services Workers Scheduled (CRON-type) Jobs On-Demand Processes Slave placement: GREEDY SEPARATE_BY_DEPLOY SEPARATE_BY_REQUEST OPTIMISTIC Executors: Mesos executor Singularity executor Docker executor
- 12. Linux Containers
- 13. 13 Docker • Immutability • Portability • Isolation
- 15. 15 Services no longer live in a well known address/port, so we needed a registry or dynamic way to find them. Also it had to be MESOS agnostic. • Service announce their presence to the Discovery Server • Service subscribe to changes in dependencies announcement • Service un-announce on termination or timeout on crash Service Discovery
- 16. 16 Zookeeper Zookeeper Zookeeper availability zone 2bavailability zone 2a availability zone 2c Service Discovery Discovery Server Discovery Server Discovery Server A A A BB Announce Discover Subscribe
- 18. FrontDoor
- 19. 19 FrontDoor • Route external traffic to internal services • Simple Discovery-aware proxy • Dynamic configuration • Developer friendly configuration via Git repo REQUEST_URI=/api/timezone* passthru timezone
- 20. Monitoring
- 21. 21 Monitoring https://github.com/opentable/mesos_stats • Finds your service name by parsing the task names. • Includes grafana dashboard • Runs inside mesos
- 22. All together
- 24. 24 Github Continuous Integration Singularity Docker Registry Developer’s Concerns • Initialize projects with Continuous integration template • Enable monitoring/logging of application level errors • Build project as an immutable docker image • Deploy to Mesos through singularity using a rest API
- 25. 25 Singularity Discovery Master Zookeeper Master Zookeeper Master Zookeeper Slave Docker Slave Docker Slave Docker Slave Docker Slave Docker Slave Docker FrontDoor Docker Registry Discovery Discovery Operational Concerns • Provide Mesos with resources • Monitor and maintain external traffic routing • Monitor and replace failing resources
- 26. 26 Stateless Mesos Cluster Datastores Caches Stateless Simplicity Other Mysql, PostgreSQL, MongoDB Redis, Memcached Zookeeper, Amazon S3
- 27. 27 US Data Center EU Data Center AWS us-west-2 AWS eu-west-1 AWS us-west-2 PROD PROD PROD PROD QA DATA PROCESSING
- 28. 28 US Data Center EU Data Center AWS us-west-2 AWS eu-west-1 AWS us-west-2 PROD PROD PROD PROD QA DATA PROCESSING Kafka Kafka Kafka Kafka Kafka
- 29. Data Processing
- 30. 30 Distributed Multitenant Data Processing
- 31. 31 Spark’s Approach • Generalize MapReduce in order to support new apps in the same engine • General DAGs and Data Sharing • Unification benefits the engine, which is more efficient, and simple for user • Handles batch, interactive and online processing • API available for Java, Scala, Python, SQL, R
- 32. 32 Spark RDDs Resilient Distributed Datasets (or RDD) are fault-tolerant distributed collections They exists in the form of: • Parallelized Collections • External datasets, distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, Amazon S3, etc.
- 33. 33 HadoopRDD( path(=(hdfs://...( FilteredRDD( func(=(_.contains(…)( shouldCache(=(true( file:% errors:% Partition.level%view:%Dataset.level%view:% Task%1%Task%2% ...% RDD Graph Dataset-level view Partition-level view file RDD errors RDD Task 1 Task 2 Task 3 Task n
- 38. 38 Alternating Least Squares (ALS) in MLlib
- 39. 39 Driver Program SparkContext Cluster Manager Worker Node Executor Task Task Cache Worker Node Executor Task Task Cache Running Spark
- 40. 40 Driver Program SparkContext Cluster Manager Worker Node Executor Task Task Cache Mesos Master Mesos Executor Worker Node Task Task Cache Mesos Executor Framework Mesos Coarse Grained Executor
- 41. 41 Driver Program SparkContext Cluster Manager Worker Node Task Mesos Master Mesos Executor Worker Node Mesos Executor Task Task Task Executor Executor Executor Executor Mesos Fine Grained Framework
- 42. Pull Requests (maybe merged) [SPARK-7373] Add docker support for launching drivers in mesos cluster mode. [SPARK-5338] Add cluster mode support for Mesos [SPARK-5095] Support capping cores and launch mulitple executors in coarse mode [SPARK-6707] Mesos Scheduler should allow the user to specify constraints based on slave attributes [SPARK-6287] Add dynamic allocation to the coarse-grained Mesos scheduler
- 43. 43 Memory-centric distributed storage system (cache) Distributed file system General engine for large-scale data processing Kernel for the datacenter Ideal data processing stack
- 44. 44 Other frameworks • KAFKA on mesos https://github.com/mesos/kafka • SAMZA on mesos https://github.com/banno/samza-mesos • PHOENIX (secor on mesos) https://github.com/stealthly/phoenix • CASSANDRA on mesos https://github.com/mesosphere/cassandra-mesos We are also using: We are considering: • CHRONOS https://github.com/mesos/chronos • MARATHON https://github.com/mesosphere/marathon
- 45. 45 Kafka User Activity backups Query/Processing Layer Spark SQL JSON Data Products ETL Spark MLlib Spark Streaming
- 47. 47 Matrix Factorization. Spark MLlib • Collaborative Filtering • Topic Modeling • Restaurant Demand Analysis
- 48. 48 nigiri sashimi gari maki roku rolls roll godzilla chirashi robata zushi omakase yellowtail unagi samba toro gyoza aburi spider starburst nakazawa shabu sasa katana sake hapa maguro tsunami raku kappo yasuda otoro seki tamari ra teppanyaki caterpillar japan shashimi hamasaku Early explorations with Word2vec: Find synonyms for “Sushi” We use Apache Spark’s Implementation of Word2Vec (skip-gram model)
- 49. 49 Sushi of Gari, Gari Columbus, NYC Masaki Sushi Chicago Sansei Seafood Restaurant & Sushi Bar, Maui A restaurant like your favorite one but in a different city. Find the “synonyms” of the restaurant in question, then filter by location! Akiko’s, SF San Francisco Maui Chicago New York ' Downtown upscale sushi experience with sushi bar