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Thoughts About Normal and Abnormal Data (PyCon ...
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Markus H
October 27, 2017
Technology
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13k
Thoughts About Normal and Abnormal Data (PyCon UK 2017)
Speaker notes at
https://markusholtermann.eu/2017/10/thoughts-about-normal-and-abnormal-data/
Markus H
October 27, 2017
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Transcript
Thoughts About Normal and Abnormal Data Markus Holtermann @m_holtermann markusholtermann.eu
@m_holtermann I am Markus Holtermann • Senior Software Engineer at
LaterPay • Django Core Developer
@m_holtermann How do we store our data?
@m_holtermann Files CC-BY-NC 2.0 by Tim Gee https://flic.kr/p/rZm63
@m_holtermann Document Stores CC-BY-SA 4.0 by Susan Gerbic https://commons.wikimedia.org/wiki/File%3AArchive_Room.JPG
@m_holtermann Copyright Geek Batman https://www.youtube.com/watch?v=gPDx_IwdYMY
@m_holtermann Name Home planet Gender Padmé Naboo Female Luke Tatooine
Male Leia Alderaan, Naboo Female
@m_holtermann First Normal Form (1NF)
@m_holtermann PersonID Name Home planet Gender 1 Padmé Naboo Female
2 Luke Tatooine Male 3 Leia Alderaan Female 3 Leia Naboo Female
@m_holtermann PersonID Name Home planet Gender 3 Leia Alderaan Female
3 Leia Naboo Male Update Anomalies
@m_holtermann Second Normal Form (2NF)
@m_holtermann PersonID Name Home planet Gender 1 Padmé Naboo Female
2 Luke Tatooine Male 3 Leia Alderaan Female 3 Leia Naboo Female
@m_holtermann PersonID Planet Name 1 Naboo 2 Tatooine 3 Alderaan
3 Naboo PersonID Name Gender 1 Padmé Female 2 Luke Male 3 Leia Female
@m_holtermann PersonID Planet Name 1 Naboo 2 Tatooine 3 Alderaan
3 Naboo ??? Dagobah Insert Anomalies
@m_holtermann Deletion Anomalies PersonID Planet Name 1 Naboo 2 Tatooine
3 Alderaan 3 Naboo PersonID Name Gender 1 Padmé Female 2 Luke Male 3 Leia Female
@m_holtermann Third Normal Form (3NF)
@m_holtermann PlanetID Name Water 10 Naboo 85% 11 Tatooine 1%
12 Alderaan 78% 13 Dagobah 88% PersonID Name Gender 1 Padmé Female 2 Luke Male 3 Leia Female PersonID PlanetID 1 10 2 11 3 10 3 12
@m_holtermann Database normalization is great!
@m_holtermann Always?
@m_holtermann Yet Another Wiki
@m_holtermann Page + PageID Name Slug Revision + RevisionID PageID
Text Date Database Schema
@m_holtermann Task 1: Fetch a single page and its current
revision
@m_holtermann Task 2: Fetch all page titles and the date
of their current revision
Task 1: Fetch a single page SELECT * FROM page
INNER JOIN revision ON page.page_id = revision.page_id WHERE page.slug = 'some-slug' ORDER BY revision.date DESC LIMIT 1;
Task 2: Fetch all pages SELECT page.name, last_revs.date FROM page
INNER JOIN ( SELECT revision.page_id, MAX(revision.date) date FROM revision GROUP BY revision.page_id ) last_revs ON page.page_id = last_revs.page_id;
@m_holtermann Benchmark Environment • Intel i7-6600U, 2.60GHz • 8 GB
Memory • PostgreSQL 9.6.5 • 10k pages, 6m revisions
@m_holtermann Task 1: Fetch a single page Concurrent queries 10
Pages per connection 1000 Queries per page 10 Queries total 100000
@m_holtermann Task 2: Fetch all pages Concurrent queries 1 Queries
per connection 10 Queries total 10
@m_holtermann Task 1: Fetch a single page
@m_holtermann Task 2: Fetch all pages
@m_holtermann Rae Knowler https://speakerdeck.com/bellisk/unsafe-at-any-speed-pycon-uk-26th-october-2017
@m_holtermann Database Schema Page + PageID Name Slug LastRevision Revision
+ RevisionID PageID Text Date
Task 1: Fetch a single page SELECT * FROM page
INNER JOIN revision ON page.last_revision_id = revision.revision_id WHERE page.slug = 'some-slug';
Task 2: Fetch all pages SELECT page.name, revision.date FROM page
INNER JOIN revision ON page.last_revision_id = revision.revision_id;
@m_holtermann Task 1: Fetch a single page
@m_holtermann Task 2: Fetch all pages
@m_holtermann Conclusion
Thanks Markus Holtermann @m_holtermann markusholtermann.eu