Today I Learned

TIL, 2018-04-23, AI, Postgres Vacuum, Node use cases

Musings, Front-end

  • Weird caching thing with Github.
  • Github uses Akamai CDN for caching and services. Also, no modifying of .htaccess.
  • RequireJS: A bit older, replaced by Webpack. Not that compatible with npm.
  • CommonJS: A way to include JS modules within the current scope and keeps the global scope from being polluted. This reduces the chance of naming collisions and keeps code organized.
  • Passing a function with parameters through props: use <Button onClick={() => this.props.myFunction(param)} />. Reference
  • On parameterized event handlers: do not curry it, some binding thing can be done. Reference

Musings, Postgres:

  • Multiversion Concurrency Control (MVCC): Each SQL statement sees a snapshot of data as it was some time ago, regardless of the current state of the underlying data.
    • Contrast this to locking.
  • What’s the difference between a tuple and a row in Postgres?
    • Tuple is the abstract term, row is the concrete implementation.
    • n_live_tup in the pg_stat_user_tables to figure out how many tuples you have.
    • Reclaims storage occupied by dead tuples.
    • Tuples that are updated or deleted are not physically removed from the table, they remain present until a VACUUM is done. So it’s necessary to do VACUUM periodically, especially on frequently-updated tables.

Musings, Ruby, Node

  • Database Outage on 2016/11/28 when project_authorizations had too much bloat
    • Cool article on what they did to solve a problem with the site going down.
    • Killing queries.
    • Use htop to figure out memory usage.
    • Check dead tuples for the tables.
    • VACUUM FULL on offending table.
    • pg_repack lets you remove bloat from tables and indexes, and optionally restore the physical order of clustered indexes.
  • I/O bound: The time it takes to complete a computation is determined by waiting for I/O operations to be completed, opposite of CPU bound (where computations are the ones that take longer).
  • Artificial Intelligence — The Revolution Hasn’t Happened Yet
    • Distraction: the idea of having an intelligence that rivals our own.
    • Ex: the medical system that can misdiagnose unborn babies for having down syndrome, due to some error in white noise.
    • Provenance: where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation?
    • There is a need to develop AI systems for the medical, commerce, transportation, and education domains.
    • We are now building societal-scale decision-making systems that involve machines, humans, and the environment.
    • ML in the 90s: fraud detection, logistics-chain prediction, recommendation systems. Data science: combining ML with database experts to build scalable/robust ML systems.
    • Trend: intelligence augmentation (computation/data to augment human intelligence and creativity).
    • Intelligent infrastructure: computation + data + physical entities to make human environments more supportive, interesting, and safe.
    • Ex: data flows/data analysis flows to aid human diagnoses and providing care.
    • The scope of AI is not about science-fiction dreams or nightmares, but the need for humans to understand/shape technology as it becomes more present in our lives.

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