Bringing Cypher to Apache Flink
summary
Implements a backend so Cypher queries run on Flink by translating Cypher to relational operators over an abstract table, shipping a working OKAPI backend that plugs into Flink Table + Calcite. Shows an end-to-end implementation, integration issues with planner rules, state, checkpoints and offsets, and benchmarking methodology that compares throughput and latency against Neo4j Morpheus on Spark, reporting concrete performance trade‑offs and practical tuning lessons.
AI-written summary · about
chapters (10)
- 0:00Introduction to Flink in production
- 2:13Service level agreements in Flink
- 5:04Integrating Kafka with Flink
- 7:30Handling Kafka offsets and checkpoints
- 10:23Parallel Flink jobs for offset management
- 12:07Debugging and profiling Flink jobs
- 14:35Visualizing Flink job metrics
- 15:42Flink extensions and scalability
- 16:24Common Flink problems and solutions
- 17:04Conclusion and Q&A
related talks
41mCypher-based Graph Pattern Matching in Apache Flink
47mTaking a look under the hood of Apache Flink’s relational APIs
32mKeynote: Apache Flink - Completing Cloudera’s End to End Streaming Platform
43mUnified stream and batch processing with Apache Flink’s relational APIs
38mApache Flink: A Very Quick Guide
15mPowering OLAP at Uber using Apache Flink
43mLeverage the power and simplicity of SQL on Apache Flink
40mIt’s finally here: Python on Flink & Flink on Zeppelin
