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World Congress 2023 • Oct 6, 2023

How building an industry DBMS differs from building a research one

Markus Dreseler

Building a planetary-scale DBMS requires radically different engineering than academic research. Learn why defensive programming, petabyte-scale telemetry, and obsessive reliability beat rapid prototyping in the real world.

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#1 about 3 min

Building a research database prototype from scratch

Developing an end-to-end open-source in-memory database enables unobstructed academic experimentation.

#2 about 4 min

Understanding decoupled compute and central storage architecture

Separating central storage from an independent compute layer scales resources efficiently without hardware constraints.

#3 about 3 min

Comparing textbook query planning to industry reality

Examining how commercial databases parse, optimize, and execute logical query plans mirrors academic prototypes.

#4 about 4 min

Implementing complex customer requirements and obscure features

Handling collations, evolving time zones, and niche operations like match recognize introduces significant engineering overhead.

#5 about 6 min

Leveraging telemetry to target query performance improvements

Running widespread execution profilers extracts actionable production trends instead of relying on artificial benchmarks.

#6 about 4 min

Implementing extensive automated testing for query correctness

Guaranteeing result consistency requires continuous static analysis, query permutation, and historical query re-execution checks.

#7 about 3 min

Safeguarding code deployments via granular parameter protection

Isolating new code paths with internal feature parameters mitigates release rollbacks and enables progressive rollouts.

#8 about 5 min

Managing query edge cases and hardware failures

Rotating engineers onto support reveals nondeterministic queries, distributed race conditions, and hidden hardware degradation.

#9 about 3 min

Reconciling development speed with huge operational impact

While rigorous safety mechanisms prevent quick iteration, operating at massive scale compounds the value of optimizations.

Matching moments

2:36 min

Modern application stacks and real-time data requirements

Tim Faulkes · LIVE

7:52 min

Audience questions on database performance, deployments, and data migrations

Gregor Bauer Gregor Bauer · LIVE

2:14 min

Solving complex platform architecture challenges at an enterprise scale

Maria Apazoglou · Coffee With Developers

3:16 min

Q&A on database vendor lock-in and alternative architectural choices

George Asafev · WWC 2023

10:58 min

Exploring query boundaries, data storage, and architecture limits

Denis Washington +1 · WWC 2021

1:50 min

Prioritizing technical trade-offs over widespread industry misconceptions

Josip Stuhli Josip Stuhli · Coffee With Developers

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