Distributed Logging Architecture in the Container Era
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Presentation given at LinuxCon Japan 2016 by Satoshi "Moris" Tagomori (@tagomoris), Treasure Data. Describes various strategies for aggregating log data in a microservices architecture using containers, e.g. Docker.
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Distributed Logging Architecture in the Container Era
- 1. Distributed Logging Architecture in Container Era LinuxCon Japan 2016 at Jun 13 2016 Satoshi "Moris" Tagomori (@tagomoris)
- 2. Satoshi "Moris" Tagomori (@tagomoris) Fluentd, MessagePack-Ruby, Norikra, ... Treasure Data, Inc.
- 5. Topics • Microservices and logging in various industries • Difficulties of logging with containers • Distributed logging architecture • Patterns of distributed logging architecture • Case Study: Docker and Fluentd
- 6. Logging
- 7. Logging in Various Industries • Web access logs • Views/visitors on media • Views/clicks on Ads • Commercial transactions (EC, Game, ...) • Data from devices • Operation logs on Apps of phones • Various sensor data
- 8. Microservices and Logging • Monolithic service • a service produces all data about an user's behavior • Microservices • many services produce data about an user's access • it's needed to collect logs from many services to know what is happening Users Service (Application) Logs Users Logs
- 10. Containers: "a must" for microservices • Dividing a service into services • a service requires less computing resources (VM -> containers) • Making services independent from each other • but it is very difficult :( • some dependency must be solved even in development environment (containers on desktop)
- 11. Redesign Logging: Why? • No permanent storages • No fixed physical/network address • No fixed mapping between servers and roles • We should parse/label logs at the source, ship these logs by pushing to destination ASAP
- 12. Containers: immutable & disposable • No permanent storages • Where to write logs? • files in the container → gone w/ container instance 😞 • directories shared from hosts → hosts are shared by many containers/services ☹ • TODO: ship logs from container to anywhere ASAP
- 13. Containers: unfixed addresses • No fixed physical / network address • Where should we go to fetch logs? • Service discovery (e.g., consul) → one more component 😞 • rsync? ssh+tail? or ..? Is it installed in containers? → one more tool to depend on ☹ • TODO: push logs to anywhere from containers
- 14. Containers: instances per roles • No fixed mapping between servers and roles • How can we parse / store these logs? • Central repository about log syntax → very hard to maintain 😞 • Label logs by source address → many containers/roles in a host ☹ • TODO: label & parse logs at source of logs
- 16. Core Architecture • Collector nodes • Aggregator nodes • Destinations Collector nodes (Docker containers + agent) Destinations (Storage, Database, ...) Aggregator nodes
- 17. • Parse/Label (collector) • Raw logs are not good for processing • Convert logs to structured data (key-value pairs) • Split/Sort (aggregator) • Mixed logs are not good for searching • Split whole data stream into streams per services • Store (destination) • Format logs(records) as destination expects Collecting and Storing Data
- 18. Scaling Logging • Network traffic • CPU load to parse / format • Parse logs on each collector (distributed) • Format logs on aggregator (to be distributed) • Capability • Make aggregators redundant • Controlling delay • to make sure when we can know what's happening in our systems
- 19. Patterns
- 20. source aggregation NO source aggregation YES destination aggregation NO destination aggregation YES Aggregation Patterns
- 21. Source Side Aggregation Patterns w/o source aggregation w/ source aggregation collector aggregator / destination aggregate container
- 22. Without Source Aggregation • Pros: • Simple configuration • Cons: • fixed aggregator (endpoint) address • many network connections • high load in aggregator collector aggregator
- 23. With Source Aggregation • Pros: • less connections • lower load in aggregator • less configuration in containers (by specifying localhost) • highly flexible configuration (by deployment only of aggregate containers) • Cons: • a bit much resource (+1 container per host) aggregate container aggregator
- 24. Destination Side Aggregation Patterns w/o destination aggregation w/ destination aggregation aggregator collector destination
- 25. Without Destination Aggregation • Pros: • Less nodes • Simpler configuration • Cons: • Storage side change affects collector side • Worse performance: many small write requests on storage
- 26. With Destination Aggregation • Pros: • Collector side configuration is free from storage side changes • Better performance with fine tune on destination side aggregator • Cons: • More nodes • A bit complex configuration aggregator
- 27. Scaling Patterns Scaling Up Endpoints HTTP/TCP load balancer Huge queue + workers Scaling Out Endpoints Round-robin clients Load balancer Backend nodes Collector nodes Aggregator nodes
- 28. Scaling Up Endpoints • Pros: • Simple configuration in collector nodes • Cons: • Limits about scaling up Load balancer Backend nodes
- 29. Scaling Out Endpoints • Pros: • Unlimited scaling by adding aggregator nodes • Cons: • Complex configuration • Client features for round-robin
- 30. Without Destination Aggregation With Destination Aggregation Scaling Up Endpoints Systems in early stages Collecting logs over Internet or Using queues Scaling Out Endpoints Impossible :( Collector nodes must know all endpoints ↓ Uncontrollable Collecting logs in datacenter
- 31. Case Studies
- 32. Case Study: Docker+Fluentd • Destination aggregation + scaling up • Fluent logger + Fluentd • Source aggregation + scaling up • Docker json logger + Fluentd + Elasticsearch • Docker fluentd logger + Fluentd + Kafka • Source/Destination aggregation + scaling out • Docker fluentd logger + Fluentd
- 33. Why Fluentd? • Docker Fluentd logging driver • Docker containers can send logs to Fluentd directly - less overhead • Pluggable architecture • Various destination systems • Small memory footprint • Source aggregation requires +1 container per host • Less additional resource usage ( < 100MB )
- 34. Destination aggregation + scaling up • Sending logs directly over TCP by Fluentd logger library in application code • Same with patterns of New Relic • Easy to implement - good for startups Application code
- 35. Source aggregation + scaling up • Kubernetes: Json logger + Fluentd + Elasticsearch • Applications write logs to STDOUT • Docker writes logs as JSON in files • Fluentd reads logs from file parse JSON objects writes logs to Elasticsearch • EFK stack (like ELK stack) http://kubernetes.io/docs/getting-started-guides/logging-elasticsearch/ Elasticsearch Application code Files (JSON)
- 36. Source aggregation + scaling up/out • Docker fluentd logging driver + Fluentd + Kafka • Applications write logs to STDOUT • Docker sends logs to localhost Fluentd • Fluentd gets logs over TCP pushes logs into Kafka • Highly scalable & less overhead - very good for huge deployment Kafka Application code
- 37. Application code Source/Destination aggregation + scaling out • Docker fluentd logging driver + Fluentd • Applications write logs to STDOUT • Docker sends logs to localhost Fluentd • Fluentd gets logs over TCP sends logs into Aggregator Fluentd w/ round-robin load balance • Highly flexible - good for complex data processing requirements Any other storages
- 38. What's the Best? • Writing logs from containers: Some way to do it • Docker logging driver • Write logs on files + read/parse it • Send logs from apps directly • Make the platform scalable! • Source aggregation: Fluentd on localhost • Scalable storage: (Kafka, external services, ...) • No destination aggregation + Scaling up • Non-scalable storage: (Filesystems, RDBMSs, ...) • Destination aggregation + Scaling out
- 39. Why OSS Are Important For Logging?
- 40. Why OSS? • Logging layer is interface • transparency • interoperability • Keep the platform scalable • number of nodes • number of types of source/destination
- 41. Use OSS, Make Logging Scalable Thank you!