Dynomite @ RedisConf 2017
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- Dynomite is a framework that makes non-distributed databases distributed by adding a proxy layer, auto-sharding, replication across datacenters, and more. - Netflix uses Dynomite to provide high availability and scalability for several internal microservices and tools like Conductor, an orchestration engine that uses Redis. - Conductor allows defining workflows as code and executing them in a distributed and scalable way using Dynomite and Dyno Queues.
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Dynomite @ RedisConf 2017
- 1. Making Non-Distributed Databases, Distributed - Ioannis Papapanagiotou, PhD - Gim Mahasintunan - Viren Baraiya
- 3. Dynomite/Redis Ecosystem ● Dynomite - Proxy Layer ● Dyno - Redis based Client ● Dynomite-manager - Ecosystem Orchestrator ● Dynomite-Explorer - Redis based UI ● Dyno-queues - Redis based keys ● Conductor - Workflow engine ● NDBench - Netflix Data Benchmark (for Redis)
- 4. ● Needed a data store: o Scalable & highly available o High throughput, low latency o Netflix use case is active-active ● Master-slave storage engines: o Do not support bi-directional replication o Cannot withstand a Monkey attack o Cannot easily perform maintenance Problems & Observations
- 5. What is Dynomite? ● A framework that makes non-distributed data stores, distributed. Features: highly available, automatic failover, node warmup, tunable consistency, backups/restores
- 6. Dynomite @ Netflix ● Running around 2.5 years in PROD ● 70 clusters (100% Y/Y) ● ~1000 nodes used by internal microservices ● Microservices based on Java, Python, NodeJS
- 7. Pluggable Storage Engines ● Layer on top of a non-distributed key value data store ○ Peer-peer, Shared Nothing ○ Auto Sharding ○ Multi-datacenter ○ Linear scale ○ Replication(Encrypted) ○ Gossiping
- 8. Replication ● A client can connect to any node on the Dynomite cluster when sending requests. o If node owns the data, ▪ data are written in local data-store and asynchronously replicated. o If node does not own the data ▪ node acts as a coordinator and sends the data in the same rack & replicates to other nodes in other racks and DC.
- 9. ● Each rack contains one copy of data, partitioned across multiple nodes in that rack ● Multiple Racks == Higher Availability (HA) Topology
- 10. Dynomite on the Cloud Discovery Service Insights (Metrics) Continuous Delivery Healthcheck Backups & Restores Dynomite Manager RESP = Redis Serialization Protocol REST/HTTP
- 11. Dyno Client - Java API ● Connection Pooling ● Load Balancing ● Effective failover ● Pipelining ● Scatter/Gather ● Metrics, e.g. Netflix Insights
- 12. Dyno Load Balancing ● Dyno client employs token aware load balancing. ● Dyno client is aware of the cluster topology of Dynomite within the region, can write to specific node using consistent hashing.
- 13. Moving across Storage Engines
- 14. Netflix Data Benchmark ● Benchmarking Dynomite with Redis on the Cloud
- 15. Netflix Data Benchmark for Redis ● Dynamically change the benchmark configurations, ○ perform tests along with our production microservices. ● Be able to integrate with platform cloud services ○ dynamic configurations, discovery, metrics, etc. ● Run for an infinite duration in order to introduce failure scenarios ● Provide pluggable patterns and loads. ● Support different client APIs. ● Deploy, manage and monitor multiple instances from a single entry point.
- 17. How everything comes together?
- 18. Netflix Data Explorer - Dynomite ● Exploring Netflix Data Sources ● Providing a UI for Dynomite and Redis
- 19. Netflix Data Explorer - Use Cases Netflix needed a client to satisfy the following requirements: ● Support Redis API ● Avoid blocking calls (e.g. Redis KEYS *) ● UI needs to scale to millions of keys ● Customizable UI ● Ability to share UI components amongst Netflix projects ● Provide extensive logging for audit trail purposes
- 20. Architecture
- 21. Cluster Listing ● Multi-tenant system ● Each application has a dedicated Dynomite cluster ● Access controls restrict cluster visibility
- 23. JSON and XML
- 25. TTL Support
- 26. Audit Trail and Logging eu-west c b a us-east c b a us-west b a Elasticsearch
- 27. Conductor ● Orchestration Engine ● Redis for Storage and Queues ● Open Source (Apache 2.0)
- 28. Orchestration - Use Cases ● Content Ingest & Delivery ● Title Setup ● Studio Deliveries ● Content Quality Checks ● Content Localization
- 29. Once Upon A Time... ● Peer to Peer Messaging ● 10’s MM messages per day ● Process flows embedded in applications ● Lack of control (STOP deployment!) ● Lack of visibility into progress
- 30. Peer to Peer Application C Application BApplication BApplication A Request Content Content Inspection Result Encode Publish Events / API calls Events / API calls Events / API calls
- 31. Peer to Peer Application C Application BApplication BApplication A Request Content Content Inspection Result Encode Publish Events / API calls Events / API calls Events / API calls ● Logical flow is not easily trackable ● Modifying steps is not easy (tightly coupled) ● Controlling flow is not possible
- 32. Conductor ● BYO Task (Reuse existing code) ● REST/HTTP support ● Extensible and Hackable ● JSON based DSL to define blueprint ● Scale Out Horizontally ● Visibility, Traceability & Control
- 33. Same Flow - New Flavor Request Content Content Inspection Result Encode PublishStart Stop Conductor Application A Task Request Content Application B Task Content Inspection Application C Task Encode Application B Task Publish OrchestrationExecution
- 35. High Level Architecture API Workflows Metadata Tasks SERVICE Workflow Service Task Service Decider Service Queue Service STORE Storage (Redis /Dynomite) Start and manage workflows Define blueprints and tasks Gets tasks from queue and execute Index (Elasticsearch)
- 36. Conductor - Scale ● Peer-to-Peer - Scale horizontally ● Stateless server - state is persisted in Redis ● Storage scalability : Dynomite ● Workload scale: Dyno-Queues
- 37. Storage Layer ● Dynomite ○ Generic Dynamo implementation (Redis, Memcache) ○ Multi-datacenter ○ Highly available ○ Peer-to-Peer ● Elasticsearch ○ Indexing workflow and task executions
- 38. Dyno-Queues ● Distributed lock free queues used by Conductor ● OSS ○ Apache 2.0 License ○ https://github.com/Netflix/dyno-queues ● Delayed Queues ● Loose priorities and FIFO
- 39. Dyno-Queues
- 40. Conductor @ Netflix ● In production > 1.5 year ● Used by Content Platform Engineering ○ Content Ingest & Encoding ○ Content Processing ● ~150 Process Flows & ~300 Tasks / Services ● 1+ MM Executions / Month
- 41. More information ● Dynomite Ecosystem: o https://github.com/Netflix/dynomite o https://github.com/Netflix/dyno o https://github.com/Netflix/dyno-queues o https://github.com/Netflix/dynomite-manager ● NDBench: o https://github.com/Netflix/ndbench ● Conductor: o https://github.com/Netflix/Conductor ● Chat: o https://gitter.im/Netflix/dynomite o https://gitter.im/Netflix/conductor
Notas do Editor
- By a show of hands, how many people have seen this? Great. we have 60-90 seconds before you ditch us and do something else. Obviously the choice of content plays a big role, but just as important is a seamless user experience. Netflix has both. Our job is to deliver that to the members and keep them happy and streaming. The use cases for data caching range from session storage, managing viewing history, tracking bookmarks, managing playlist, ratings, and personalized recommendations to name a few.
- Our business use case is to stream movies at any cost. Hence we moved from the SQL to Cassandra in order to have high availability. We are very sensitive to 99th latencies, Cassandra Started Migrating to NoSQL Quickly Became the Defacto standard for data storage Scaled out Cassandra to reduce data per node and reduce latency Definitely Not economical. Needed something in memory to meet the throughput and latency Needed: Typical deployment is in 3 data centers and 3 availability zones in each Redis: Kong exercises: Monkey, Gorilla and Kong
- Two types of use cases : As a Cache and As a datastore
- Use Master branch of github since that is the stable one and thats what we run in production
- Fix errors in arrows - Minh has source :) and rack names
- All nodes know the topology in the system
- Dynomite customer base was growing rapidly and introducing new users to Redis Dynomite supports most native Redis commands Users new to Redis might not follow best practices and perform KEYS *. Scalable UI mandatory. Storing session data is a common use case. Within the Netflix Cloud Engineering organization we have many projects where we try to share Web Components.
- The UI leverages Polymer to build reusable Web Components that can be shared among other Netflix projects. The Server is a Node.js Express app Supports pluggable discovery modules Netflix Discovery Local Redis environment File system based configuration Supports pluggable Authentication using Passport. Netflix uses Meechum for authentication. Can be extended for other passport-based auth like facebook Supports pluggable ACL modules Currently integrates with other Netflix services to get access control information for all Dynomite clusters Currently supports the Redis/Dynomite API
- Typically each application/team has a dedicated Dynomite cluster (confirm with Ioannis). The list of visible clusters is restricted by user group membership
- Hashes are great for representing objects Commands for manipulating hashes: HMSET, HSET, HGET, HGETALL
- As we mentioned, session storage is a common use case for Dynomite. Using Redis’ TTL provides a convenient way to expire data.
- App is deployed in multiple regions and multiple availability zones for resiliency Wanted to consolidate logging across all app instances to provide an audit trail FileBeat runs on each instance and ships logs to an ElasticSearch cluster which allows us to create a variety of dashboards using Kibana.