Aggregator Leaf Tailer: Bringing Data to Your Users with Ultra Low Latency
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The document discusses the aggregator, leaf, and tailer architecture, which aims to provide low-latency data aggregation and operational simplicity. It outlines the goals and constraints of this architecture, including scalability and ease of maintenance, while contrasting it with traditional databases and NoSQL solutions. The document also emphasizes performance tips and future improvements for enhancing the system's efficiency.
![Aggregator, Leaf, Tailer
■ Term started getting use in 2019
■ Largely promoted by Rockset
● Rockset Concepts, Design & Architecture [1]
● Aggregator Leaf Tailer: An Alternative to Lambda Architecture for Real-Time Analytics [2]
● Rockset’s Aggregator-Leaf-Tailer Architecture for SQL on semi structured data [3]
■ Prior Art
● Facebook: Science and the Social Graph (2008) [4]
● Serving Facebook Multifeed: Efficiency, performance gains through redesign (2015) [5]
● FollowFeed: LinkedIn's Feed Made Faster and Smarter (2016) [6]](https://image.slidesharecdn.com/jefferyutterp99conf-221012154740-b696b071/85/Aggregator-Leaf-Tailer-Bringing-Data-to-Your-Users-with-Ultra-Low-Latency-5-320.jpg)
Aggregator Leaf Tailer: Bringing Data to Your Users with Ultra Low Latency
- 1. Brought to you by Aggregator Leaf Tailer: Bringing Data to Your Users with Ultra Low Latency Jeffery Utter Staff Developer at theScore
- 2. Jeffery Utter Staff Developer, theScore ■ Built half of a distributed database ■ P99.*s matter to 100% of users ■ Wrote my first line of Java at age 30-something ■ I lead a double-life as a double-bassist
- 3. Table of Contents ■ What is Aggregator, Leaf, Tailor: ■ Goals and Constraints ■ Why not < insert your favorite (distributed) database > ■ Datadex ■ Performance Tips ● Java ● RocksDB ■ Overview ● Architecture ● Future ■ Conclusion
- 5. Aggregator, Leaf, Tailer ■ Term started getting use in 2019 ■ Largely promoted by Rockset ● Rockset Concepts, Design & Architecture [1] ● Aggregator Leaf Tailer: An Alternative to Lambda Architecture for Real-Time Analytics [2] ● Rockset’s Aggregator-Leaf-Tailer Architecture for SQL on semi structured data [3] ■ Prior Art ● Facebook: Science and the Social Graph (2008) [4] ● Serving Facebook Multifeed: Efficiency, performance gains through redesign (2015) [5] ● FollowFeed: LinkedIn's Feed Made Faster and Smarter (2016) [6]
- 6. ■ Aggregator — Low latency aggregation of data stored in one or more Leaf ■ Leaf — All data stored and indexed in one or more leaf ■ Tailer — Pulls new data from various sources and inserts it into the leaves Aggregator, Leaf, Tailer
- 8. Goals & Constraints ■ Low latency ■ Low operational complexity ● Ease of maintenance ● Ease of deployment (in all geolocations) ■ Developer ergonomics ■ Scalability
- 9. Why Not …?
- 10. Traditional RDBMS (Postgres) ■ Duplication of effort to populate databases ■ Operational overhead - database setup, maintenance, scaling
- 11. ■ Implicit shared “schema” ■ Good scalability via hosted offerings ■ Operational overhead for on-prem Cloud NoSQL (MongoDB)
- 12. Kafka-native (Rockset/kSQL) ■ Not an exact match with our querying needs (kSQL has no secondary indexes) ■ Both seem geared towards analytic workflows ■ Operational overhead for on-prem
- 13. Datadex
- 14. ■ Aggregator — Low latency aggregation of data stored in one or more Leaf ■ Leaf — All data stored and indexed in one or more leaf ■ Tailer — Pulls new data from various sources and inserts it into the leaves Aggregator, Leaf, Tailer
- 15. Aggregator, Leaf, Tailer ■ Aggregator — gRPC Java ■ Leaf — RocksDB ■ Tailer — Kafka
- 16. ■ zGC ■ Careful memory allocations ■ GRPC Streaming ■ Double-edged sword ■ “Bypasses” service mesh Low Latency
- 17. ■ Single codebase ■ Single deployable unit (for now) ■ Instances managed by Kubernetes Operator ■ Deploy / Release / Upgrade cycle similar to other backend applications Low Operational Complexity
- 18. ■ Simple configuration through CRD ■ Elixir Client library ■ Simple query “language” ■ “Watch” feature to stream updates to downstream services Developer Ergonomics
- 19. Scalability ■ Fast scale-out through snapshot/backup/restore mechanism ■ Future improvements to independently scale Aggregator/Leaf/Tailer
- 20. Performance Tips
- 21. Java
- 22. Minimize Allocations Ops/sec Error Before 3,053.990 ± 742.316 After 3,964.574 ± 240.020 ~ 30% Increase Throughput ■ Re-use buffers for key serialization/deserialization ■ Re-use buffer for reading values - up to a certain size (fastGet)
- 23. RocksDB
- 25. Resources 1. Rockset Concepts, Design & Architecture: https://rockset.com/Rockset_Concepts_Design_Architecture.pdf 2. Aggregator Leaf Tailer: An Alternative to Lambda Architecture for Real-Time Analytics: https://rockset.com/blog/aggregator-leaf-tailer-an-architecture-for-live-analytics-on-event-streams/ 3. Rockset’s Aggregator-Leaf-Tailer Architecture for SQL on semi structured data: http://www.hpts.ws/papers/2019/RocksetHPTS19.pdf 4. Facebook: Science and the Social Graph https://www.infoq.com/presentations/Facebook-Software-Stack/ (about 53 minutes in) 5. Serving Facebook Multifeed: Efficiency, performance gains through redesign: https://engineering.fb.com/2015/03/10/production-engineering/serving-facebook-multifeed-efficiency-performance-gains- through-redesign/ 6. FollowFeed: LinkedIn's Feed Made Faster and Smarter: https://engineering.linkedin.com/blog/2016/03/followfeed--linkedin-s-feed-made-faster-and-smarter Realtime Indexing for Fast Queries on Massive Semi-Structured Data: https://www.p99conf.io/session/realtime-indexing-for-fast-queries-on-massive-semi-structured-data/
- 26. Brought to you by Jeffery Utter jeff@jeffutter.com @jeffutter