tpoljak

#!/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()

tpoljak

README is empty

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README is empty

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README is empty

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README is empty

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README is empty

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README is empty

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README is empty

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1. 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&rep

tpoljak

1) Indexing with default settings (automatic index creating and type mapping definition -> default/plain highlighter)

curl -XPUT 'http://localhost:9200/twitter/tweet/1' -d '{
    "user" : "kimchy",
    "post_date" : "2009-11-15T14:12:12",
    "message" : "trying out Elastic and Search"
}'

curl -XPUT 'http://localhost:9200/twitter/tweet/2' -d '{
    "user" : "kimchy",