We currently have a 45mb csv file that we're going to be loading into a Splunk kvstore. I want to be able to accomplish this via the python SDK but I'm running into a bit of trouble loading the records.
The only way I can find to update a kvstore is the service.collection.insert() function which as far as I can tell only accepts 1 row at a time. Being that we have 250k rows in this file, I can't afford to wait for all lines to upload every day.
This is what I have so far:
from splunklib import client, binding
import json, pandas as pd
from copy import deepcopy
data_file = '/path/to/file.csv'
username = 'user'
password = 'splunk_pass'
connectionHandler = binding.handler(timeout=12400)
connect_kwargs = {
'host': 'splunk-host.com',
'port': 8089,
'username': username,
'password': password,
'scheme': 'https',
'autologin': True,
'handler': connectionHandler
}
flag = True
while flag:
try:
service = client.connect(**connect_kwargs)
service.namespace['owner'] = 'Nobody'
flag = False
except binding.HTTPError:
print('Splunk 504 Error')
kv = service.kvstore
kv['test_data'].delete()
df = pd.read_csv(data_file)
df.replace(pd.np.nan, '', regex=True)
df['_key'] = df['key_field']
result = df.to_dict(orient='records')
fields = deepcopy(result[0])
for field in fields.keys():
fields[field] = type(fields[field]).__name__
df = df.astype(fields)
kv.create(name='test_data', fields=fields, owner='nobody', sharing='system')
for row in result:
row = json.dumps(row)
row.replace("nan", "'nan'")
kv['learning_center'].data.insert(row)
transforms = service.confs['transforms']
transforms.create(name='learning_center_lookup', **{'external_type': 'kvstore', 'collection': 'learning_center', 'fields_list': '_key, userGuid', 'owner': 'nobody'})
# transforms['learning_center_lookup'].delete()
collection = service.kvstore['learning-center']
print(collection.data.query())
In addition to the problem of taking forever to load a quarter million records, it keeps failing on a row with nan as the value, and no matter what I put in there to try to deal with the nan, it persists in the dictionary value.
