Last active
August 22, 2019 06:03
-
-
Save shao-wang-me/f995b5beec548ada239b4315aeab736f to your computer and use it in GitHub Desktop.
A script to generate fake Australian demographic data, and index into Elasticsearch.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
"""A script to generate fake Australian demographic data, and index into Elasticsearch. | |
Python requirements: | |
* elasticsearch | |
* faker | |
Elasticsearch requirements: | |
* phonetic plugin | |
* name_synonyms.txt in elasticsearch-<version>/config/ | |
Author: Shao Wang | |
Date: 2019-08-22 | |
""" | |
from datetime import datetime | |
from time import sleep | |
from elasticsearch import Elasticsearch | |
from elasticsearch.helpers import bulk | |
from faker import Faker | |
from faker.providers.address.en_AU import Provider as AddressProvider | |
from faker.providers.person.en import Provider as EnglishPersonProvider | |
class AddressProviderAU(AddressProvider): | |
"""Override street_address_formats to get a single line street address | |
""" | |
street_address_formats = ( | |
'{{building_number}} {{street_name}}', | |
'{{secondary_address}} {{building_number}} {{street_name}}', | |
) | |
def people(population, people_generator, index_name): | |
for _ in range(population): | |
person = { | |
'id': people_generator.random_number(digits=10, fix_len=10), | |
'first_name': people_generator.first_name(), | |
'last_name': people_generator.last_name(), | |
'gender': people_generator.random_element(elements=('M', 'F')), | |
'dob': people_generator.date_of_birth().isoformat(), | |
'address': people_generator.street_address(), | |
'suburb': people_generator.city(), | |
'state': people_generator.state_abbr(), | |
'postcode': people_generator.postcode(), | |
'@timestamp': datetime.now() | |
} | |
yield { | |
'_index': index_name, | |
'_op_type': 'create', | |
'_id': person['id'], | |
'_source': person | |
} | |
if __name__ == '__main__': | |
POPULATION = 5000000 | |
ELASTIC_HOST = 'localhost:9200' | |
INDEX = 'people-aus' | |
# Elasticsearch client | |
es = Elasticsearch(hosts=[ELASTIC_HOST]) | |
# create index | |
mappings = { | |
"mappings": { | |
"properties": { | |
"@timestamp": {"type": "date"}, | |
"address": {"type": "text", "term_vector": "yes", "fields": {"keyword": {"type": "keyword", "ignore_above": 256}}}, | |
"dob": {"type": "date", "fields": {"keyword": {"type": "keyword"}, "text": {"type": "text", "term_vector": "yes"}}}, | |
"gender": {"type": "keyword"}, | |
"id": {"type": "long", "fields": {"keyword": {"type": "keyword"}, "text": {"type": "text", "term_vector": "yes"}}}, | |
"first_name": {"type": "text", "term_vector": "yes", "fields": {"keyword": {"type": "keyword"}, "phones": {"type": "text", "term_vector": "yes", "analyzer": "phonetic"}, "synonym": {"type": "text", "term_vector": "yes", "analyzer": "name_synonym"}}}, | |
"last_name": {"type": "text", "term_vector": "yes", "fields": {"keyword": {"type": "keyword"}, "phones": {"type": "text", "term_vector": "yes", "analyzer": "phonetic"}, "synonym": {"type": "text", "term_vector": "yes", "analyzer": "name_synonym"}}}, | |
"postcode": {"type": "short", "fields": {"keyword": {"type": "keyword"}, "text": {"type": "text", "term_vector": "yes"}}}, | |
"suburb": {"type": "text", "term_vector": "yes", "fields": {"keyword": {"type": "keyword"}}}, | |
"state": {"type": "text", "term_vector": "yes", "fields": {"keyword": {"type": "keyword"}}} | |
} | |
} | |
} | |
settings = { | |
"settings": { | |
"index": { | |
"number_of_shards": "1", | |
"analysis": { | |
"filter": { | |
"name_synonym": {"type": "synonym", "synonyms_path": "name_synonyms.txt", "expand": "true"}, | |
"phonetic": {"type": "phonetic"} | |
}, | |
"analyzer": { | |
"name_synonym": {"filter": ["lowercase", "name_synonym"], "type": "custom", | |
"tokenizer": "standard"}, | |
"phonetic": {"filter": ["lowercase", "phonetic"], "type": "custom", "tokenizer": "standard"} | |
} | |
}, | |
"number_of_replicas": "0", | |
} | |
} | |
} | |
index = {**mappings, **settings} | |
if es.indices.exists(INDEX): | |
res = es.indices.delete(INDEX) | |
print(res) | |
sleep(5) | |
res = es.indices.create(index=INDEX, body=index) | |
print(res) | |
# create Australia demographic data generator | |
au = Faker('en_AU') | |
# use our own derived provider to get street address in a single line | |
au.add_provider(AddressProviderAU) | |
# use faker.providers.person.en.Provider to get more names | |
au.add_provider(EnglishPersonProvider) | |
au.seed(12) | |
# generate Australian people and bulk index them into Elasticsearch | |
res = bulk(es, people(POPULATION, au, INDEX), stats_only=True, raise_on_error=False) | |
print(res) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment