how to configure influx properly I am storing some data in influx and it is quite confusing that influx is 4-5 times slow as Mysql. I try to test by inserting 10000 rows in mysql and then in influxdb. 
and the stats are below. 

Mysql

real: 6m 39sec
user: 2.956sec  
sys: 0.504sec

Influxdb

real: 6m 17.193sec
user: 11.860sec
sys: 0.328sec


my code for influx is given below, I used the same pattern to store in mysql. 

#!/usr/bin/env python
# coding: utf-8
import time
import csv
import sys
import datetime
import calendar
import pytz
from influxdb import client as influxdb
from datetime import datetime

host = 'localhost'
port = 8086
user = "admin"
password = "admin"
db_name = "testdatabase"
db = influxdb.InfluxDBClient(database=db_name)


def read_data():
    with open(file) as f:
        reader = f.readlines()[4:]
       for line in reader:
            yield (line.strip().split(','))


fmt = '%Y-%m-%d %H:%M:%S'
file = '/home/rob/mycsvfile.csv'

csvToInflux = read_data()
for metric in csvToInflux:
    timestamp = datetime.strptime(metric[0][1: len(metric[0]) - 1], fmt)

    new_value = float(metric[1])
    influx_metric = [{
        'measurement': 'mytable1',
        'time': timestamp,
        'fields': {
             'col1': metric[1],
             'col2': metric[2],
             'col3': metric[3],
             'col4': metric[4],
             'col5': metric[5],
             'col6': metric[6],
             'col7': metric[7],
             'col8': metric[8],
             'col9': metric[9]
        }
        }]
    db.write_points(influx_metric)


Can someone give me an idea how can I improve it. I think it might be due to cache. is cache option is off by default in Influx db? and can someone guide me to do batch processing in influx. I try to look over SO and google but couldn't solve my problem. I am newbie to influx db. I am trying to make it faster.
InfluxDB:  3% cpu; MySQL: 1%.  Do you have something else to use the CPU for?

Can you get InfluxDB to perform a MySQL LOAD DATA INFILE?  That will do all that work in a single SQL statement, and probably do it faster than 6 minutes elapsed.