### MySQL DB info ###
#import MySQLdb
#conn = MySQLdb.connect(host="localhost",  # your host, usually localhost
#                     user="john",         # your username
#                     passwd="megajonhy",  # your password
#                     db="jonhydb")        # name of the data base


### PostgreSQL DB info ###
import psycopg2
import psycopg2.extras
postgresql_table_name = ""
conn = psycopg2.connect("dbname=<DB_NAME> user=<USER_NAME>" +
                        "password=<USER_PASSWORD> host=<POSTGRESQL_HOST>")

### InfluxDB info ####
from influxdb import InfluxDBClient
influx_db_name = ""
influxClient = InfluxDBClient("<INFLUX_HOST>", "<INFLUX_PORT>")
influxClient.delete_database(influx_db_name)
influxClient.create_database(influx_db_name)

# dictates how columns will be mapped to key/fields in InfluxDB
schema = {
    "time_column": "", # the column that will be used as the time stamp in influx
    "columns_to_fields" : ["",...], # columns that will map to fields
    "columns_to_tags" : ["",...], # columns that will map to tags
    "table_name_to_measurement" : "", # table name that will be mapped to measurement
    }

'''
Generates an collection of influxdb points from the given SQL records
'''
def generate_influx_points(records):
    influx_points = []
    for record in records:
        tags = {}, fields = {}
        for tag_label in schema['columns_to_tags']:
            tags[tag_label] = record[tag_label]
        for field_label in schema['columns_to_fields']:
            fields[field_label] = record[field_label]
        influx_points.append({
            "measurement": schema['table_name_to_measurement'],
            "tags": tags,
            "time": record[schema['time_column']],
            "fields": fields
        })
    return influx_points



# query relational DB for all records
curr = conn.cursor('cursor', cursor_factory=psycopg2.extras.RealDictCursor)
# curr = conn.cursor(dictionary=True)
curr.execute("SELECT * FROM " + schema['table_name_to_measurement'] + "ORDER BY " + schema['column_to_time'] + " DESC;")
row_count = 0
# process 1000 records at a time
while True:
    print("Processing row #" + (row_count + 1))
    selected_rows = curr.fetchmany(1000)
    influxClient.write_points(generate_influx_points(selected_rows))
    row_count += 1000
    if len(selected_rows) < 1000:
        break
conn.close()