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Microservice and Persistent Volumes
Model of PV Usage
Glenn West
gwest@redhat.com
Microservice Architecture Intro
Large Applications are broken into many
microservices.
A microservice is designed to be ab...
Concepts for large applications (Financial Focus)
To Make microservices small and focused, we
create a microservice per te...
Terms and Concepts - POV
POV - Point Of View
Time of Day
End of Quarter
Tax Seasons
First Monday
Second Tuesday
End Of Day...
Genetic Algorithm
We are solving a fuzzy problem, in the real-
world you have different people that are domain
experts, an...
Types of Data
MariaDB
MongoDB
RockdB
SQLite
Small record size
Fast Access
Generally need fast access - SSD
Time = Money Lo...
uThought - Types of Containers
Stock Dicovery Service - One Per Exchange / Data
Provider
Stock History Retrieval Service -...
uThought Sizing Base
One Stock Exchange
Small POV Count
Algorithms based on publically available DLL
Contains per Host / M...
PV Analysis
Summary
Its useful to have small PV Volume - to support
easy update, without loss of previous state
99% of containers find...
Resources
High Level Overview Slides http://www.slideshare.net/glennswest/uthought-executive-overview
LinkedIn Intro to uT...
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MicroserviceAndPersistent Volumes

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MicroserviceAndPersistent Volumes

  1. 1. Microservice and Persistent Volumes Model of PV Usage Glenn West gwest@redhat.com
  2. 2. Microservice Architecture Intro Large Applications are broken into many microservices. A microservice is designed to be able to be developed by a very small team Rapid Development Self-contained Well Defined API Local DB No Central DB - Dependency issues Application is intended to degrade slowly with loss of any single microservice Data and Code should be separated, easy to upgrade restart and replace Better to duplicate code/data than wait on other
  3. 3. Concepts for large applications (Financial Focus) To Make microservices small and focused, we create a microservice per tenant/data set. Example, in uThought, while we are working across a whole market (NASDDAQ), a microservice typically will only deal with a single share, or even a single POV of a share. Genetic Algorithms/Big Data Considerations - Multiple algorithms may be used even on a single POV, futher increases number of microservices. Dataset size of a single microservice can be quite small. (1 Gigabyte) Due to the numbers of PV, each PV should be thin-provisioned. While stateless is best, vast majority of services need some minimal data kept.
  4. 4. Terms and Concepts - POV POV - Point Of View Time of Day End of Quarter Tax Seasons First Monday Second Tuesday End Of Day Intended to look at time/spatial legal concepts and causality with stock price
  5. 5. Genetic Algorithm We are solving a fuzzy problem, in the real- world you have different people that are domain experts, and you often consult a few in important decisions. In Deep Learning, there are multiple Deep Learn Algorithms, the best one is highly variable depending on the Data In NodeJS, there is current 15 of these available. Best practice, run all, and determine dynamically what is the best for a POV/Equity
  6. 6. Types of Data MariaDB MongoDB RockdB SQLite Small record size Fast Access Generally need fast access - SSD Time = Money Lost
  7. 7. uThought - Types of Containers Stock Dicovery Service - One Per Exchange / Data Provider Stock History Retrieval Service - One Per Equity Stock History Rest - One Per Equity Pov Manager Service - One Per Equity Pov Spliter Service - One Per Equity System KV Store - One Per System Equity KV Store - Three Per Equity Pov Service Manager - One Per System Deep Neural Net - POV x Equity AlorithmProfiler - One Per Equity EquityRanker - One Per System EquityUI - One Per Equity BuyEVAL - One Per System GlobalUserInterface - One Per Equity
  8. 8. uThought Sizing Base One Stock Exchange Small POV Count Algorithms based on publically available DLL Contains per Host / Max
  9. 9. PV Analysis
  10. 10. Summary Its useful to have small PV Volume - to support easy update, without loss of previous state 99% of containers find small PV Storage Useful In this app, while indidual apps could re-learn, based on replay, startup times get problematic Storage needs to be close to container cluster May not need multi-region, depending on how app degrades Backup of PV to object store needs to be thought thru Thin Provisioning is important Automation Automation Automation
  11. 11. Resources High Level Overview Slides http://www.slideshare.net/glennswest/uthought-executive-overview LinkedIn Intro to uThought https://www.linkedin.com/pulse/using-containers-docker-change-world-glenn-west?trk=mp-reader-card uThought Physical Sizing - https://www.linkedin.com/pulse/lots-containers-kubernetes-red-hat-openstack-platform-glenn-west?trk=mp- reader-card

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