tpoljak

## csv2elasticsearch.py
#!/usr/bin/env python3
import argparse
from pathlib import Path
import csv
from elasticsearch import Elasticsearch
from elasticsearch.exceptions import TransportError
from elasticsearch.helpers import bulk, streaming_bulk

parser = argparse.ArgumentParser(description='Simple upload of a CSV to Elasticsearch for analysis')
#group = parser.add_mutually_exclusive_group()

## gist:b94d5ea048ce3bff5490

      
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                tpoljak
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              August 29, 2015 14:12
                — forked from debasishg/gist:8172796
            
          
General Background and Overview


Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&amp;rep
	#!/usr/bin/env python3
	import argparse
	from pathlib import Path
	import csv
	from elasticsearch import Elasticsearch
	from elasticsearch.exceptions import TransportError
	from elasticsearch.helpers import bulk, streaming_bulk

	parser = argparse.ArgumentParser(description='Simple upload of a CSV to Elasticsearch for analysis')
	#group = parser.add_mutually_exclusive_group()