Chasing unicorns!

0 dataloss and 99.999% availability

Telegram PostgreSQL Group Meeting
2020-04-08

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Who am I

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Chasing unicorns

  • About dataloss and downtime
  • CAP and PACELC theorem
  • Some typical architectures
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0 dataloss

  • Does not exist
  • Sorry :-)
  • Different meanings
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Allowed failure time

Availability needed daily weekly yearly
100% 0 0 0
99.999% <0.9s 6s <5.5min
99.99% <9s 1min 2d
2 hours by night 2h 14h 30d
"day" only 12h 3,5d >180d
99.999% during day <0.4s <3s <1.5min
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Anythink can break

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Anythink can break

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Anythink can break

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CAP theorem

When designing distributed web services, there are three properties that are commonly desired: consisteny, availability, and partition tolerance. It is impossible to achieve all three.
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CAP theorem

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Consistency

Each node will see the last (committed) version of the data.
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Availability

The system will always answers something different than an error message.
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Network partitioning

The network will be allowed to lose arbitrarily many messages sent from one node to another without altering the system answers.
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CAP theorem

  • impossibility to have all 3
  • you can secure 2
  • then stretch to the third
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RDBMS are CA
⟶ True and False
RDBMS in single node configuration are CA
RDBMS with asynchronous streaming replication are AP
RDBMS with synchronous streaming replication are CP
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  • No network...
  • ... No network partitioning !

➠ CA
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  • Risk of no read consistency

➠ AP
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  • Risk of no read consistency

➠ AP
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  • Princess acknowledges each commit
  • If princess fails, no writes on Queen

➠ CP
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PACELC theorem

In case of network partitioning (P) in a distributed computer system, one has to choose between availability (A) and consistency (C),
but else (E), even when the system is running normally in the absence of partitions, one has to choose between latency (L) and consistency (C).
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Latency

Latency is the amount of time the system needs to answer when it's smaller than a request timeout.
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Blueprints

  • PostgreSQL
  • Minimal configuration
  • Be aware of the weakest link
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Near 0 dataloss

  • 1 queen
  • 1 (synchronous) princess
  • Don't forget (tested) backups
  • CP for cap theorem
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99.999% availability


  • 1 queen A
  • 1 queen B
  • logical replication
  • AP for cap theorem
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Less availability, more data consistency


  • 1 queen
  • quorum of princesses
  • CP for cap theorem
Image by Reimund Bertrams from Pixabay
  • Database is NOT the single point of failure
  • You're NOT Google
  • Quantify your manageable dataloss
  • Quantify your manageable downtime
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Unicorns are more real than 0-dataloss and 99.999% availability.
Gülçin Yıldırım Jelínek, Prague, 2019-08-27
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Questions?
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