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Difference between revisions of "Meetings/Storlets"

(Agenda:)
(Agenda:)
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=Agenda:=
 
=Agenda:=
 
* Prioritize patches (suggestion):
 
* Prioritize patches (suggestion):
* COPY functionality - Still need to post the patch
+
** https://review.openstack.org/#/c/283486/ (COPY functionality)
* https://review.openstack.org/#/c/276545/ (Improve timeout handling)
+
** https://review.openstack.org/#/c/276545/ (Improve timeout handling)
* https://review.openstack.org/#/c/282135/ (Refactor unit tests about storlet docker gateway)
+
** https://review.openstack.org/#/c/282135/ (Refactor unit tests about storlet docker gateway)
* https://review.openstack.org/#/c/280570/ (waiting 282135)
+
** https://review.openstack.org/#/c/280570/ (waiting 282135)
* https://review.openstack.org/#/c/285947/ (Refactor directory structure about python modules): Should be rebased after merging other patches
+
** https://review.openstack.org/#/c/285947/ (Refactor directory structure about python modules): Should be fixed
* Putting the last two after COPY does not mean I will not review them before.
+
*** We should decide when we deal with directory refactoring, to avoid many merge conflicts caused by that change. [takashi]
 +
 
  
 
* Storlet in Hackathon etherpad
 
* Storlet in Hackathon etherpad

Revision as of 07:10, 7 March 2016

Meeting Time: Every Wednesday at 13:00 UTC in #openstack-storlets

Agenda:


  • Storlet in Hackathon etherpad
    • I would like to add another analytics related question.
    • What timeframe do we think we need?

Austin:

  • We currently have the following abstract
    • Title: From Analytic to Image Processing in Swift with Openstack Storlets
    • Openstack Stolets is an emerging Openstack project aimed at pushing down data centric compute jobs to the object store. Storlets offer a server-less programming model enabling developers to quickly develop and deploy data path computations without worrying about the underlying platform details.
    • Storlets facilitate several interesting use cases such as:
      • Performing image processing in the storage, allowing to process large objects without downloading it.
      • Spark analytic push down to the store, saving on both bandwidth and memory in the analytic cluster.
      • Data obfuscation and anonymization at the source. Thus, preventing sensitive data from ever leaving the store.
    • In this talk we will present the project, discuss and demonstrate the use cases and present a PoC about an effective architecture for image processing using Storlets done at NTT.

Swift Hackathon

Bugs