Flink SQL: Unified Streaming and batch for hive integration
summary
Flink 1.11 adds Hive metastore integration so Kafka-streamed tables persist to HDFS-backed Hive tables with exactly-once guarantees via the streaming file sink and checkpoint-based writes. Partition commits support a partition-time commit policy (demo uses 30s checkpoints and 1‑minute partition delay), and temporal joins let streaming jobs enrich events from Hive dimension tables with cache TTL. Uber jars simplify connector setup. Tests show planned 1.12 file-merge work can cut checkpoint needs from 10 minutes to ~5 seconds to reduce small-file churn.
AI-written summary · about
chapters (10)
- 0:00Intro
- 0:55Typical Offline Data Warehouse
- 2:34Warehouse tools
- 4:26Real-time Data Warehouse
- 5:48Lambda architecture
- 8:35Unify Computing engine
- 12:43Details of Streaming File Sink
- 17:29Small Files Compaction (Flink 1.12)
- 22:58Demo - Streaming Write/Read
- 30:27Demo - Temporal Join Hive Table
related talks
35mProduction-Ready Flink and Hive Integration - what story you can tell now?
46mFlink SQL in 2020: Time to show off!
40mUnify Enterprise Data Processing System: Platform-level integration of Flink and Hive
37mSQL Ask-Me-Anything
41mBeyond the Hype: When Apache Flink Solves Real Problems
38mApache Flink: A Very Quick Guide
42mApache Flink deep-dive #bbuzz
23mBetter, Faster, Stronger Streaming: Your First Dive into Flink SQL
