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Large Scale Processing with Django
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A short presentation for PyWeb-IL 8th meeting.
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Large Scale Processing with Django
1.
Large-scale processing
using Django Mashing clouds, queues & workflows PyWeb-IL 8 th meeting Udi h Bauman (@dibau_naum_h) Tikal Knowledge (http://tikalk.com)
2.
3.
Addressing Scalability
4.
Experience with Django
5.
Use-case 1: automated
data integration service
6.
Use-case 2: social
media analysis service
7.
Recommendations
8.
Links
9.
10.
Web back-ends become
thinner - just services
11.
Applications become service
providers, usually over HTTP
12.
All reasons for
using Django for almost any back-end offering services
13.
14.
Usually involve long
processing
15.
May involve continuous
communications, & not just request-response
16.
Reliability & high-availability
are usually more important with non-human users
17.
Lots of communication
with other back-ends
18.
19.
Workflow Engines manage
long processing
20.
Continuous communication (e.g.,
TCP-based) is possible, can be abstracted with XMPP
21.
Clouds & auto-scaling
enable high-availability
22.
Can use SOAP/REST
for protocols against other back-ends
23.
24.
Even when processing
took days to complete, Django was 100% robust
25.
26.
Large data
27.
Protocols against other
back-ends
28.
29.
Integrating data &
services across several back-end systems
30.
31.
32.
Used a great
library called Suds
33.
34.
35.
36.
Used REST interface
to push & pull messages with message queues, such as ActiveMQ
37.
Used Celery for
AMQP-based message queues
38.
39.
According to metadata,
different format handlers were invoked
40.
41.
42.
43.
44.
Exports logic to
Java-based scripts
45.
46.
Used Nicolas Toll's
workflow engine, which allows users to define & manage complex workflows
47.
48.
Added a graphical
dashboard
49.
50.
51.
Used task-names for
flexible handlers invocation
52.
Used periodic tasks
for driving the workflow engine
53.
54.
Horizontal & vertical
scaling
55.
Reliable & easy
to manage
56.
Message queues allow
distributing load horizontally
57.
Used script-based auto-scaling
– starting new instances based on load
58.
59.
60.
61.
62.
63.
Recommend people to
follow based on interests
64.
65.
Objects are organized
in hierarchical layers
66.
Objects have autonomous
micro behavior aggregating to the macro behavior of the system
67.
Layers are organized
in spatial grids, which enable easy sharding & parallel processing
68.
69.
Dbpedia
70.
ConceptNet
71.
72.
73.
Celery & RabbitMQ
are used
74.
JSON is used
as message payload
75.
76.
most of the
data processing
77.
Very intensive
78.
79.
Not always-on
80.
Requires 3 rd
party libraries, not limited to Python
81.
82.
83.
Can be started
just upon need
84.
85.
86.
87.
Frameworks such as
Fuzed can be integrated with Python/Django
88.
We're working on
it as a coding session & hope to deliver a prototype soon
89.
90.
Suds
91.
Workflow
92.
django-xmpp
93.
Fuzed
94.
Google Chart API
95.
Talend
96.
Thanks! @dibau_naum_h
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