def test_parse_investor_names():
examples = [
'{First,Second,"Third",Fourth,"Fifth","Sixth","Seventh"}',
'{"First",Second,"Third",Fourth,"Fifth","Sixth","Seventh"}',
'{"First",Second,Third,Fourth,Fifth,Sixth,Seventh}',
'{First,Second,Third,Fourth,Fifth,Sixth,Seventh}',
'{"First","Second","Third","Fourth","Fifth","Sixth","Seventh"}'
]
expected = sorted(
["First", "Second", "Third", "Fourth", "Fifth", "Sixth", "Seventh"])
for eg in examples:
result = parse_investor_names(eg)
assert sorted(result) == expected
def run():
test = literal_eval(os.environ["BATCHPAR_test"])
bucket = os.environ['BATCHPAR_bucket']
batch_file = os.environ['BATCHPAR_batch_file']
db_name = os.environ["BATCHPAR_db_name"]
es_host = os.environ['BATCHPAR_outinfo']
es_port = int(os.environ['BATCHPAR_out_port'])
es_index = os.environ['BATCHPAR_out_index']
es_type = os.environ['BATCHPAR_out_type']
entity_type = os.environ["BATCHPAR_entity_type"]
aws_auth_region = os.environ["BATCHPAR_aws_auth_region"]
# database setup
engine = get_mysql_engine("BATCHPAR_config", "mysqldb", db_name)
static_engine = get_mysql_engine("BATCHPAR_config", "mysqldb",
"static_data")
states_lookup = {
row['state_code']: row['state_name']
for _, row in pd.read_sql_table('us_states_lookup',
static_engine).iterrows()
}
states_lookup["AE"] = "Armed Forces (Canada, Europe, Middle East)"
states_lookup["AA"] = "Armed Forces (Americas)"
states_lookup["AP"] = "Armed Forces (Pacific)"
states_lookup[None] = None # default lookup for non-US countries
# Get continent lookup
url = "https://nesta-open-data.s3.eu-west-2.amazonaws.com/rwjf-viz/continent_codes_names.json"
continent_lookup = {
row["Code"]: row["Name"]
for row in requests.get(url).json()
}
continent_lookup[None] = None
# es setup
field_null_mapping = load_json_from_pathstub("tier_1/field_null_mappings/",
"health_scanner.json")
strans_kwargs = {
'filename': 'crunchbase_organisation_members.json',
'from_key': 'tier_0',
'to_key': 'tier_1',
'ignore': ['id']
}
es = ElasticsearchPlus(
hosts=es_host,
port=es_port,
aws_auth_region=aws_auth_region,
no_commit=("AWSBATCHTEST" in os.environ),
entity_type=entity_type,
strans_kwargs=strans_kwargs,
field_null_mapping=field_null_mapping,
null_empty_str=True,
coordinates_as_floats=True,
country_detection=True,
listify_terms=True,
terms_delimiters=("|", ),
null_pairs={"currency_of_funding": "cost_of_funding"})
# collect file
nrows = 20 if test else None
s3 = boto3.resource('s3')
obj = s3.Object(bucket, batch_file)
org_ids = json.loads(obj.get()['Body']._raw_stream.read())
logging.info(f"{len(org_ids)} organisations retrieved from s3")
org_fields = set(c.name for c in Organization.__table__.columns)
geo_fields = [
'country_alpha_2', 'country_alpha_3', 'country_numeric', 'continent',
'latitude', 'longitude'
]
# First get all funders
investor_names = defaultdict(list)
with db_session(engine) as session:
rows = (session.query(Organization, FundingRound).join(
FundingRound, Organization.id == FundingRound.company_id).filter(
Organization.id.in_(org_ids)).all())
for row in rows:
_id = row.Organization.id
_investor_names = row.FundingRound.investor_names
investor_names[_id] += parse_investor_names(_investor_names)
# Pipe orgs to ES
with db_session(engine) as session:
rows = (session.query(Organization, Geographic).join(
Geographic, Organization.location_id == Geographic.id).filter(
Organization.id.in_(org_ids)).limit(nrows).all())
for count, row in enumerate(rows, 1):
# convert sqlalchemy to dictionary
row_combined = {
k: v
for k, v in row.Organization.__dict__.items()
if k in org_fields
}
row_combined[
'currency_of_funding'] = 'USD' # all values are from 'funding_total_usd'
row_combined.update({
k: v
for k, v in row.Geographic.__dict__.items() if k in geo_fields
})
row_combined['investor_names'] = list(
set(investor_names[row_combined['id']]))
# reformat coordinates
row_combined['coordinates'] = {
'lat': row_combined.pop('latitude'),
'lon': row_combined.pop('longitude')
}
# iterate through categories and groups
row_combined['category_list'] = []
row_combined['category_group_list'] = []
for category in (session.query(CategoryGroup).select_from(
OrganizationCategory).join(CategoryGroup).filter(
OrganizationCategory.organization_id ==
row.Organization.id).all()):
row_combined['category_list'].append(category.category_name)
row_combined['category_group_list'] += [
group
for group in str(category.category_group_list).split('|')
if group is not 'None'
]
# Add a field for US state name
state_code = row_combined['state_code']
row_combined['placeName_state_organisation'] = states_lookup[
state_code]
continent_code = row_combined['continent']
row_combined[
'placeName_continent_organisation'] = continent_lookup[
continent_code]
row_combined['updated_at'] = row_combined['updated_at'].strftime(
'%Y-%m-%d %H:%M:%S')
uid = row_combined.pop('id')
_row = es.index(index=es_index,
doc_type=es_type,
id=uid,
body=row_combined)
if not count % 1000:
logging.info(f"{count} rows loaded to elasticsearch")
logging.warning("Batch job complete.")