HTAP Workloads: Challenges and Solutions
summary
Compares concrete solutions and tradeoffs: persistent-parallel trees give lockfree reads by versioning the whole index but CAS-based commits serialize writers unless parallel batching or combining is used to restore throughput, fork+COW (HyPer) achieves fast snapshots via process fork and copy on write, MVCC creates per-row versions with start/commit timestamps providing snapshot isolation while allowing writers to block long-running readers, and HTAP-aware LSM (LASER) places rows in top levels for OLTP and column or column-family layouts in lower levels for OLAP, with log shipping or WAL replication used to keep OLAP replicas current.
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chapters (32)
- 0:00HTAP workloads intro
- 1:25Database workload types
- 4:54Concurrency challenges in HTAP
- 8:48OLTP vs OLAP conflicts
- 9:52Lock-free reads and consistency
- 12:02Persistent data structures
- 14:48Universal construction limits
- 16:35Parallel batching technique
- 19:43Latency vs throughput trade-off
- 21:09Persistent parallel trees reading list
- 21:30Multiversion concurrency control basics
- 23:15MVCC read handling
- 24:40MVCC transaction commit
- 26:05MVCC writer blocks reader
- 27:30MVCC past read timestamps
- 28:53MVCC garbage collection
- 29:58Snapshot isolation guarantees
- 30:41MVCC in modern DBs
- 31:23Row vs column storage
- 33:35OLAP with row storage
- 34:41OLAP with column storage
- 37:11Column families for mixed workloads
- 38:34Separate OLTP/OLAP storage
- 40:20Leader-follower replication
- 41:23Log shipping and CDC
- 43:11LSM-trees explained
- 47:27LSM-trees in modern DBs
- 48:52Merging blocks in LSM
- 51:01Time-based LSM partitioning
- 53:50Storage layout per level
- 56:21HTAP storage solutions reading list
- 57:25Q&A
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