Keynote: From RAP to Snowflake - A Look at 50 Years of SQL DB Scalability
summary
A 50‑year retrospective traces early database machines (RAP, Chasm, Raries) as academic prototypes that mostly fail, the first parallel systems (Direct runs but hits n^2 messaging), and Gamma which applies Grace hash join to parallel shuffles and achieves real speedup (by 1986 joins of ~1,000,000×100,000 rows). Teradata becomes a successful appliance (customers storing ~10 PB on 256 nodes by 2011), the shared‑nothing era commoditizes scale, and the cloud brings Snowflake separation of storage and compute, Redshift lift‑and‑shift, Databricks Photon (3×–10×) and adaptive query execution.
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chapters (29)
- 0:00Introduction and speaker background
- 1:58Retirement and consulting work
- 3:20Three eras of SQL database scalability
- 4:42Relational model debate and early systems
- 6:05Database machines and custom hardware
- 9:16Failure of database machines and Direct project
- 11:25Wisconsin benchmark and early performance results
- 14:13First appliances: Britton-Lee and Teradata
- 15:39Shared nothing architecture explained
- 17:27Partitioning strategies and skew handling
- 19:31Teradata success and custom hardware details
- 21:42Roll your own era begins
- 22:25Evolution of join algorithms
- 23:53Grace hash join and its impact
- 26:46Parallel joins in shared nothing systems
- 29:15Gamma project and hardware setup
- 31:21Mike Stonebraker and shared nothing revolution
- 33:11Critique of MapReduce and big data paradigms
- 34:59Cloud computing paradigm shift
- 36:24Pre-cloud cost and complexity comparison
- 37:50Cloud advantages and time to insight
- 39:18Cloud database systems overview
- 40:23Amazon Redshift architecture and features
- 42:53Snowflake architecture and innovation
- 46:08Apache Spark as a game changer
- 47:51Spark performance improvements with Photon
- 48:34Query optimization challenges and solutions
- 50:23Adaptive query execution and future trends
- 51:2950 years of SQL scalability and closing remarks
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