Table Does Not Exist Error When Using write_pandas - pandas

I have a very frustrating issue. At the bottom of this post is a function I created to (1) create a table in snowflake and (2) store a dataframe to that table.
The creation of the table is work fine. The issue is happening specifically with writepandas the code snippet:
write_pandas(
conn=conn,
df=df,
table_name=table_name,
database=database,
schema=schema
)
I keep getting an error that the table I created "doesn't exist" because the naming convention is off .. for instance in the database the table is created as "DATABASE"."SCHEMA"."TABLE" but the error message says 'DATABASE.SCHEMA."TABLE"' does not exist
I know this is a simple issue but Im stuck for the moment. Any help would be appreciated.
from datetime import datetime, timedelta, date
from airflow import DAG
from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator
from sqlalchemy import create_engine
import requests
from pandas.io.json import json_normalize
import numpy as np
from sqlalchemy.types import Integer, Text, String, DateTime
from IPython.display import display, HTML
from flatten_json import flatten
from snowflake.connector import connect
from snowflake.connector.pandas_tools import write_pandas
from airflow.operators.python_operator import PythonOperator
import os
from airflow.providers.snowflake.hooks.snowflake import SnowflakeHook
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
def create_store_snowflake(df,table):
#quick transforms
df = df.rename(columns=str.upper)
df.columns = df.columns.str.replace('[-,/]','')
#Define the table name, schema, and database you want to write to
#Note: the table, schema, and database need to already exist in Snowflake
#Define the table name, schema, and database you want to write to
table_name = table
schema = 'schema'
database = 'database'
#Connect to Snowflake using the required user
conn = connect(
user="user",
password="password",
account="account",
role="role",
database = "database",
schema = 'schema'
)
#reroute raw data to dataframe variable
dataframe = df
#Create the SQL statement to create or replace the table
create_tbl_statement = "CREATE OR REPLACE TABLE " + database + "." + schema + "." + table_name + " (\n"
# Loop through each column finding the datatype and adding it to the statement
for column in dataframe.columns:
if (
dataframe[column].dtype.name == "int"
or dataframe[column].dtype.name == "int64"
):
create_tbl_statement = create_tbl_statement + column + " int"
elif dataframe[column].dtype.name == "object":
create_tbl_statement = create_tbl_statement + column + " varchar(16777216)"
elif dataframe[column].dtype.name == "datetime64[ns]":
create_tbl_statement = create_tbl_statement + column + " datetime"
elif dataframe[column].dtype.name == "float64":
create_tbl_statement = create_tbl_statement + column + " float8"
elif dataframe[column].dtype.name == "bool":
create_tbl_statement = create_tbl_statement + column + " boolean"
else:
create_tbl_statement = create_tbl_statement + column + " varchar(16777216)"
# If column is not last column, add comma, else end sql-query
if dataframe[column].name != dataframe.columns[-1]:
create_tbl_statement = create_tbl_statement + ",\n"
else:
create_tbl_statement = create_tbl_statement + ")"
#Execute the SQL statement to create the table
conn.cursor().execute(create_tbl_statement)
print(f"{table_name} created!")
#write df to created table
write_pandas(
conn=conn,
df=df,
table_name=table_name,
database=database,
schema=schema
)
print(df.shape[0],f"rows written to {table_name} in Snowflake")

just had to make sure the tablename was CAPITALIZED as everything stored to Snowflake is apparently capitalized ::face-palm:: instead of create_store_snowflake(df,'mynewtable') it has to be create_store_snowflake(df,'MYNEWTABLE')

When the table identifier is wrapped with " during creation the followin rules applies:
create_tbl_statement= "CREATE OR REPLACE TABLE " + database + "." + schema + "." + table_name
Double-quoted Identifiers:
Delimited identifiers (i.e. identifiers enclosed in double quotes) are case-sensitive and can start with and contain any valid characters
Important
If an object is created using a double-quoted identifier, when referenced in a query or any other SQL statement, the identifier must be specified exactly as created, including the double quotes. Failure to include the quotes might result in an Object does not exist error (or similar type of error).

Related

ProgrammingError when trying to skip duplicate data in postgres sql

PostGres SQL will not accept data which is in violation of primary key. To ignore the duplicate data, I have this code:
import pandas as pd
import psycopg2
import os
import matplotlib
from sqlalchemy import create_engine
from tqdm import tqdm_notebook
from pandas_datareader import data as web
import datetime
from dateutil.relativedelta import relativedelta
db_database = os.environ.get('123')
engine = create_engine('postgresql://postgres:{}#localhost:5433/stockdata'.format(123))
def import_data(Symbol):
df = web.DataReader(Symbol, 'yahoo',start=datetime.datetime.now()-relativedelta(days=3), end= datetime.datetime.now())
insert_init = """INSERT INTO stockprices
(Symbol, Date, Volume, Open, Close, High, Low)
VALUES
"""
vals = ",".join(["""('{}','{}','{}','{}','{}','{}','{}')""".format(
Symbol,
Date,
row.High,
row.Low,
row.Open,
row.Close,
row.Volume,
) for Date, row in df.iterrows()])
insert_end ="""ON CONFLICT (Symbol, Date) DO UPDATE
SET
Volume = EXCLUDED.Volume,
Open = EXCLUDED.Open,
Close = EXCLUDED.Close,
Low = EXCLUDED.Low,
High = EXCLUDED.High
"""
query = insert_init + vals + insert_end
engine.execute(query)
import_data('aapl')
I am getting this error:
ProgrammingError: (psycopg2.errors.UndefinedColumn) column "symbol" of relation "stockprices" does not exist
LINE 2: (Symbol,Date, Volume, Open, Close, H...
^
[SQL: INSERT INTO stockprices
Could you please advise as to what does this error mean? I got rid of all the double quotes as advised in the comment.
I had used this code to create the table:
def create_price_table(symbol):
print(symbol)
df = web.DataReader(symbol, 'yahoo', start=datetime.datetime.now()-relativedelta(days=7), end= datetime.datetime.now())
df['Symbol'] = symbol
df.to_sql(name = "stockprices", con = engine, if_exists='append', index = True)
return 'daily prices table created'
create_price_table('amzn')
Also as was mentioned in the comment. I used this to check the table name:
SELECT table_name
FROM information_schema.tables
WHERE table_schema='public'
AND table_type='BASE TABLE';
Edit 1:
I changed the code as suggested in the comment, now the column name is in small case. Below is the code:
import pandas as pd
import psycopg2
import os
import matplotlib
from sqlalchemy import create_engine
from tqdm import tqdm_notebook
from pandas_datareader import data as web
import datetime
from dateutil.relativedelta import relativedelta
db_database = os.environ.get('123')
engine = create_engine('postgresql://postgres:{}#localhost:5433/stockdata'.format(123))
def create_price_table(symbol):
print(symbol)
df = web.DataReader(symbol, 'yahoo', start=datetime.datetime.now()-relativedelta(days=7), end= datetime.datetime.now())
df['symbol'] = symbol
df = df.rename(columns= {'Open':'open'})
df = df.rename(columns= {'Close':'close'})
df = df.rename(columns= {'High':'high'})
df = df.rename(columns= {'Low':'low'})
df = df.rename(columns= {'Volume':'volume'})
df = df.rename(columns= {'Adj Close':'adj_close'})
df.index.name ='date'
df.to_sql(name = "stockprices", con = engine, if_exists='append', index = True)
return 'daily prices table created'
# create_price_table('amzn')
def import_data(Symbol):
df = web.DataReader(Symbol, 'yahoo', start=datetime.datetime.now()-relativedelta(days=3), end= datetime.datetime.now())
insert_init = """INSERT INTO stockprices
(symbol, date, volume, open, close, high, low)
VALUES
"""
vals = ",".join(["""('{}','{}','{}','{}','{}','{}','{}')""".format(
Symbol,
Date,
row.High,
row.Low,
row.Open,
row.Close,
row.Volume,
) for Date, row in df.iterrows()])
insert_end ="""ON CONFLICT (Symbol, Date) DO UPDATE
SET
Volume = EXCLUDED.Volume,
Open = EXCLUDED.Open,
Close = EXCLUDED.Close,
Low = EXCLUDED.Low,
High = EXCLUDED.High
"""
query = insert_init + vals + insert_end
engine.execute(query)
import_data('aapl')
This code however is producing a new error:
DataError: (psycopg2.errors.InvalidTextRepresentation) invalid input syntax for type bigint: "166.14999389648438"
LINE 4: ('aapl','2022-02-23 00:00:00','166.14999...
^
Per my comment you have two issues:
You are trying to INSERT a float value(166.14999389648438) into an integer field. First thing to figure out is why the mismatch? Do really want the database field to be an integer? Second thing is that trying to force a float into an integer will work if the value is being entered as a float/numeric:
select 166.14999389648438::bigint; 166
Though as you see it gets truncated.
It will not work if entered as a string:
ERROR: invalid input syntax for type bigint: "166.14999389648438"
Which is what you are doing. This leads to the second issue below.
You are not using proper Parameter passing as shown in the link. Where among other things is the warning:
Warning
Never, never, NEVER use Python string concatenation (+) or string parameters interpolation (%) to pass variables to a SQL query string. Not even at gunpoint.
For the purposes of this question the important part is that using parameter passing will result in proper type adaptation.

Creating sqlite db in Python 3 with constructed f string and receiving: sqlite3.OperationalError: near "(": syntax error

I am trying to create a sqlite3 db table using a constructed f string in python 3, however I am receiving the below error:
sqlite3.OperationalError: near "(": syntax error
I had hoped that I wouldn't need to ask here for a syntax error but I have been searching on stackoverflow as well as generally online to identify the issue with no success.
I have compared the code to other samples and equally do not see any difference to the construction, except for that it doesn't appear to be common to use f strings.
I have read the pros/cons of passing parameters and would prefer this f string unless it is the root cause.
I expect the issue might be obvious, however any pointers would be greatly appreciated.
Below is the full code:
import sqlite3
import pandas as pd
db_path = [PATH TO DATABASE]
db_table_name = [TABLE NAME]
header_source = [PATH TO .XLSX]
def ReadHeaders():
df = pd.read_excel(header_source)
col_list = list(df.columns.values)
prep_col_list = [item.replace(" ", "_") for item in col_list]
col_string = " TEXT, _".join(prep_col_list)
final_col_string = col_string.replace("Primary_ID TEXT", "Primary_ID PRIMARY KEY")
return final_col_string
def CreateSQLdb():
cols = ReadHeaders()
conn = sqlite3.connect(db_path)
c = conn.cursor()
c.execute(f""" CREATE TABLE IF NOT EXISTS {db_table_name} ({cols}) """)
conn.commit()
conn.close()
A sample of the string that is created for the table headers is:
_link TEXT, _Primary_ID PRIMARY KEY, _Status_Description TEXT, _Price_List_Status TEXT, _Brand TEXT, _36_Character_Description TEXT
Solved
After breaking everything down, the root cause was the constructed string. I was able to identify it when trying to export the constructed string to a .txt file and received a unicode error.
Code before:
return final_col_string
Code after:
return final_col_string.encode(encoding="utf-8")
I also added a simple check of the table info for confirmation
def ShowTable(c):
c.execute(f"PRAGMA table_info({db_table_name})")
print (c.fetchall())
Complete code encase anyone else comes across this issue:
import sqlite3
import pandas as pd
db_path = [PATH TO DATABASE]
db_table_name = [TABLE NAME]
header_source = [PATH TO .XLSX]
def ReadHeaders():
df = pd.read_excel(header_source)
col_list = list(df.columns.values)
prep_col_list = [item.replace(" ", "_") for item in col_list]
col_string = " TEXT, _".join(prep_col_list)
final_col_string = col_string.replace("Primary_ID TEXT", "Primary_ID PRIMARY KEY")
return final_col_string.encode(encoding="utf-8")
def CreateSQLdb():
cols = ReadHeaders()
conn = sqlite3.connect(db_path)
c = conn.cursor()
c.execute(f""" CREATE TABLE IF NOT EXISTS {db_table_name} ({cols}) """)
conn.commit()
conn.close()
def ShowTable(c):
c.execute(f"PRAGMA table_info({db_table_name})")
print (c.fetchall())
if __name__ == "__main__":
CreateSQLdb()

Inserting dictionary from data in Postgresql

I have followed other posts related to inserting a dictionary into a Postgresql. The syntax is as follows:
sql_insert = 'INSERT INTO table_results (id, name, state, response, duration_time) VALUES (%(id)s, %(name)s, %(response)s, %(duration_time)s);'
The dictionary has the following data:
data= {'id': 29, 'name': "ABC'S KINDERGARDEN", 'response': 'OK', 'duration_time': 60}
Then, I can execute the insert statement:
db.session.execute(sql_insert,data_dict )
However, the previous throws the next error:
(psycopg2.errors.SyntaxError) syntax error at or near "%"
LINE 1: ...1, result_response, result_duration_secs) VALUES (%(id)s, %(...
^
[SQL: INSERT INTO table_results (id, name, state, response, duration_time) VALUES (%%(id)s, %%(name)s, %%(state)s, %%(response)s, %%(duration_time)s);]
(Background on this error at: http://sqlalche.me/e/13/f405)
I have tried several ways with this as the most general accepted format.
The following snippet includes the import and code:
import psycopg2
from flask_sqlalchemy import SQLAlchemy
db = SQLAlchemy()
sql_insert = "INSERT INTO table_results (" + ", ".join(data_dict.keys()) + ") VALUES (" + ", ".join(["%("+k+")s" for k in data_dict]) + ");"
db.session.execute(sql_insert,data_dict )
db.session.commit()
Thanks

Writing Data from pandas dataframe to PostgreSQL gives error of 'DataFrame' objects are mutable, thus they cannot be hashed

i am trying to save a data frame which was first imported in pandas from postgresql as dfraw and then do some manipulation and create another dataframe as df and save it back in postgresql same database using sql alchemy. but when i am trying to save it back its giving error of 'DataFrame' objects are mutable, thus they cannot be hashed
PFB code below
import psycopg2
import pandas as pd
import numpy as np
import sqlalchemy
from sqlalchemy import create_engine
# connect the database to python
# Update connection string information
host = "something.something.azure.com"
dbname = "abcd"
user = "abcd"
password = "abcd"
sslmode = "require"
schema = 'xyz'
# Construct connection string
conn_string = "host={0} user={1} dbname={2} password={3} sslmode={4}".format(host, user, dbname, password, sslmode)
conn = psycopg2.connect(conn_string)
print("Connection established")
cursor = conn.cursor()
# Fetch all rows from table
cursor.execute("SELECT * FROM xyz.abc;")
rows = cursor.fetchall()
# Convert the tuples in dataframes
dfraw = pd.DataFrame(rows, columns =["ID","Timestamp","K","S","H 18","H 19","H 20","H 21","H 22","H 23","H 24","H 2zzz","H zzz4","H zzzzzz","H zzz6","H zzz7","H zzz8","H zzz9","H 60","H zzz0","H zzz2"])
dfraw[["S","H 18","H 19","H 20","H 21","H 22","H 23","H 24","H 2zzz","H zzz4","H zzzzzz","H zzz6","H zzz7","H zzz8","H zzz9","H 60","H zzz0","H zzz2"]] = dfraw[["S","H 18","H 19","H 20","H 21","H 22","H 23","H 24","H 2zzz","H zzz4","H zzzzzz","H zzz6","H zzz7","H zzz8","H zzz9","H 60","H zzz0","H zzz2"]].apply(pd.to_numeric)
dfraw[["Timestamp","K"]]=dfraw[["Timestamp","K"]].apply(pd.to_datetime)
# Creating temp files
temp1 = dfraw
dfraw = temp1
# creating some fucntions for data manipulation and imputations
def remZero(df,dropCol):
for k in df.drop(dropCol,axis=1):
if all(df[k] == 0):
continue
if any(df[k] == 0):
print(k)
df[k] = df[k].replace(to_replace=0, method='ffill')
return df
# Drop Columns function
dropCol = ['Timestamp','K','ID','H','C','S']
dropCol2 = ['Timestamp','K','ID','Shift']
df = remZero(dfraw,dropCol)
from sqlalchemy import create_engine
engine = create_engine('postgresql://abcd:abcd#something.something.azure.com:5432/abcd')
df.to_sql(name = df,
con=engine,
index = False,
if_exists= 'replace'
)
Error Message
Found basic error in the code I just missed putting the inverted comma before the data frame name to be published. The basic hygiene was missed
df.to_sql(name = "df",
con=engine,
index = False,
if_exists= 'replace'
)

I just want to load 5GB from MySql into BigQuery

Long time no see. I'd want to get 5GB of data from MySql into BigQuery. My best bet seems to be some sort of CSV export / import. Which doesn't work for various reasons, see:
agile-coral-830:splitpapers1501200518aa150120052659
agile-coral-830:splitpapers1501200545aa150120055302
agile-coral-830:splitpapers1501200556aa150120060231
This is likely because I don't have the right MySql incantation able to generate perfect CSV in accordance with RFC 4180. However, instead of arguing RFC 4180 minutia, this whole load business could be solved in five minutes by supporting customizable multi-character field separators and multi-character line separators. I'm pretty sure my data doesn't contain either ### nor ###, so the following would work like a charm:
mysql> select * from $TABLE_NAME
into outfile '$DATA.csv'
fields terminated by '###'
enclosed by ''
lines terminated by '###'
$ bq load --nosync -F '###' -E '###' $TABLE_NAME $DATA.csv $SCHEMA.json
Edit: Fields contain '\n', '\r', ',' and '"'. They also contain NULLs, which MySql represents as [escape]N, in the example "N. Sample row:
"10.1.1.1.1483","5","9074080","Candidate high myopia loci on chromosomes 18p and 12q do not play a major role in susceptibility to common myopia","Results
There was no strong evidence of linkage of common myopia to these candidate regions: all two-point and multipoint heterogeneity LOD scores were < 1.0 and non-parametric linkage p-values were > 0.01. However, one Amish family showed slight evidence of linkage (LOD>1.0) on 12q; another 3 Amish families each gave LOD >1.0 on 18p; and 3 Jewish families each gave LOD >1.0 on 12q.
Conclusions
Significant evidence of linkage (LOD> 3) of myopia was not found on chromosome 18p or 12q loci in these families. These results suggest that these loci do not play a major role in the causation of common myopia in our families studied.","2004","BMC MEDICAL GENETICS","JOURNAL","N,"5","20","","","","0","1","USER","2007-11-19 05:00:00","rep1","PDFLib TET","0","2009-05-24 20:33:12"
I found loading through a CSV very difficult. More restrictions and complications. I have been messing around this morning with moving data from MySQL to BigQuery.
Bellow is a Python script that will build the table decorator and stream the data directly into the BigQuery table.
My db is in the Cloud so you may need to change the connection string. Fill in the missing values for your particular situation then call it by:
SQLToBQBatch(tableName, limit)
I put the limit in to test with. For my final test I sent 999999999 for the limit and everything worked fine.
I would recommend using a backend module to run this over 5g.
Use "RowToJSON" to clean up and invalid characters (ie anything non utf8).
I haven't tested on 5gb but it was able to do 50k rows in about 20 seconds. The same load in CSV was over 2 minutes.
I wrote this to test things, so please excuse the bad codding practices and mini hacks. It works so feel free to clean it up for any production level work.
import MySQLdb
import logging
from apiclient.discovery import build
from oauth2client.appengine import AppAssertionCredentials
import httplib2
OAUTH_SCOPE = 'https://www.googleapis.com/auth/bigquery'
PROJECT_ID =
DATASET_ID =
TABLE_ID =
SQL_DATABASE_NAME =
SQL_DATABASE_DB =
SQL_USER =
SQL_PASS =
def Connect():
return MySQLdb.connect(unix_socket='/cloudsql/' + SQL_DATABASE_NAME, db=SQL_DATABASE_DB, user=SQL_USER, passwd=SQL_PASS)
def RowToJSON(cursor, row, fields):
newData = {}
for i, value in enumerate(row):
try:
if fields[i]["type"] == bqTypeDict["int"]:
value = int(value)
else:
value = float(value)
except:
if value is not None:
value = value.replace("\x92", "'") \
.replace("\x96", "'") \
.replace("\x93", '"') \
.replace("\x94", '"') \
.replace("\x97", '-') \
.replace("\xe9", 'e') \
.replace("\x91", "'") \
.replace("\x85", "...") \
.replace("\xb4", "'") \
.replace('"', '""')
newData[cursor.description[i][0]] = value
return newData
def GetBuilder():
return build('bigquery', 'v2',http = AppAssertionCredentials(scope=OAUTH_SCOPE).authorize(httplib2.Http()))
bqTypeDict = { 'int' : 'INTEGER',
'varchar' : 'STRING',
'double' : 'FLOAT',
'tinyint' : 'INTEGER',
'decimal' : 'FLOAT',
'text' : 'STRING',
'smallint' : 'INTEGER',
'char' : 'STRING',
'bigint' : 'INTEGER',
'float' : 'FLOAT',
'longtext' : 'STRING'
}
def BuildFeilds(table):
conn = Connect()
cursor = conn.cursor()
cursor.execute("DESCRIBE %s;" % table)
tableDecorator = cursor.fetchall()
fields = []
for col in tableDecorator:
field = {}
field["name"] = col[0]
colType = col[1].split("(")[0]
if colType not in bqTypeDict:
logging.warning("Unknown type detected, using string: %s", str(col[1]))
field["type"] = bqTypeDict.get(colType, "STRING")
if col[2] == "YES":
field["mode"] = "NULLABLE"
fields.append(field)
return fields
def SQLToBQBatch(table, limit=3000):
logging.info("****************************************************")
logging.info("Starting SQLToBQBatch. Got: Table: %s, Limit: %i" % (table, limit))
bqDest = GetBuilder()
fields = BuildFeilds(table)
try:
responce = bqDest.datasets().insert(projectId=PROJECT_ID, body={'datasetReference' :
{'datasetId' : DATASET_ID} }).execute()
logging.info("Added Dataset")
logging.info(responce)
except Exception, e:
logging.info(e)
if ("Already Exists: " in str(e)):
logging.info("Dataset already exists")
else:
logging.error("Error creating dataset: " + str(e), "Error")
try:
responce = bqDest.tables().insert(projectId=PROJECT_ID, datasetId=DATASET_ID, body={'tableReference' : {'projectId' : PROJECT_ID,
'datasetId' : DATASET_ID,
'tableId' : TABLE_ID},
'schema' : {'fields' : fields}}
).execute()
logging.info("Added Table")
logging.info(responce)
except Exception, e:
logging.info(e)
if ("Already Exists: " in str(e)):
logging.info("Table already exists")
else:
logging.error("Error creating table: " + str(e), "Error")
conn = Connect()
cursor = conn.cursor()
logging.info("Starting load loop")
count = -1
cur_pos = 0
total = 0
batch_size = 1000
while count != 0 and cur_pos < limit:
count = 0
if batch_size + cur_pos > limit:
batch_size = limit - cur_pos
sqlCommand = "SELECT * FROM %s LIMIT %i, %i" % (table, cur_pos, batch_size)
logging.info("Running: %s", sqlCommand)
cursor.execute(sqlCommand)
data = []
for _, row in enumerate(cursor.fetchall()):
data.append({"json": RowToJSON(cursor, row, fields)})
count += 1
logging.info("Read complete")
if count != 0:
logging.info("Sending request")
insertResponse = bqDest.tabledata().insertAll(
projectId=PROJECT_ID,
datasetId=DATASET_ID,
tableId=TABLE_ID,
body={"rows":data}).execute()
cur_pos += batch_size
total += count
logging.info("Done %i, Total: %i, Response: %s", count, total, insertResponse)
if "insertErrors" in insertResponse:
logging.error("Error inserting data index: %i", insertResponse["insertErrors"]["index"])
for error in insertResponse["insertErrors"]["errors"]:
logging.error(error)
else:
logging.info("No more rows")
• Generate google service account key
o IAM & Admin > Service account > create_Service_account
o Once created then create key , download and save It to the project folder on local machine – google_key.json
• Run the code in pycharm environment after installing the packages.
NOTE : The table data in mysql remains intact. Also , if one uses preview in BQ to see that you won’t see. Go to console and fire the query.
o CODE
o import MySQLdb
from google.cloud import bigquery
import mysql.connector
import logging
import os
from MySQLdb.converters import conversions
import click
import MySQLdb.cursors
from google.cloud.exceptions import ServiceUnavailable
import sys
bqTypeDict = {'int': 'INTEGER',
'varchar': 'STRING',
'double': 'FLOAT',
'tinyint': 'INTEGER',
'decimal': 'FLOAT',
'text': 'STRING',
'smallint': 'INTEGER',
'char': 'STRING',
'bigint': 'INTEGER',
'float': 'FLOAT',
'longtext': 'STRING',
'datetime': 'TIMESTAMP'
}
def conv_date_to_timestamp(str_date):
import time
import datetime
date_time = MySQLdb.times.DateTime_or_None(str_date)
unix_timestamp = (date_time - datetime.datetime(1970, 1, 1)).total_seconds()
return unix_timestamp
def Connect(host, database, user, password):
return mysql.connector.connect(host='',
port='',
database='recommendation_spark',
user='',
password='')
def BuildSchema(host, database, user, password, table):
logging.debug('build schema for table %s in database %s' % (table, database))
conn = Connect(host, database, user, password)
cursor = conn.cursor()
cursor.execute("DESCRIBE %s;" % table)
tableDecorator = cursor.fetchall()
schema = []
for col in tableDecorator:
colType = col[1].split("(")[0]
if colType not in bqTypeDict:
logging.warning("Unknown type detected, using string: %s", str(col[1]))
field_mode = "NULLABLE" if col[2] == "YES" else "REQUIRED"
field = bigquery.SchemaField(col[0], bqTypeDict.get(colType, "STRING"), mode=field_mode)
schema.append(field)
return tuple(schema)
def bq_load(table, data, max_retries=5):
logging.info("Sending request")
uploaded_successfully = False
num_tries = 0
while not uploaded_successfully and num_tries < max_retries:
try:
insertResponse = table.insert_data(data)
for row in insertResponse:
if 'errors' in row:
logging.error('not able to upload data: %s', row['errors'])
uploaded_successfully = True
except ServiceUnavailable as e:
num_tries += 1
logging.error('insert failed with exception trying again retry %d', num_tries)
except Exception as e:
num_tries += 1
logging.error('not able to upload data: %s', str(e))
#click.command()
#click.option('-h', '--host', default='tempus-qa.hashmapinc.com', help='MySQL hostname')
#click.option('-d', '--database', required=True, help='MySQL database')
#click.option('-u', '--user', default='root', help='MySQL user')
#click.option('-p', '--password', default='docker', help='MySQL password')
#click.option('-t', '--table', required=True, help='MySQL table')
#click.option('-i', '--projectid', required=True, help='Google BigQuery Project ID')
#click.option('-n', '--dataset', required=True, help='Google BigQuery Dataset name')
#click.option('-l', '--limit', default=0, help='max num of rows to load')
#click.option('-s', '--batch_size', default=1000, help='max num of rows to load')
#click.option('-k', '--key', default='key.json',help='Location of google service account key (relative to current working dir)')
#click.option('-v', '--verbose', default=0, count=True, help='verbose')
def SQLToBQBatch(host, database, user, password, table, projectid, dataset, limit, batch_size, key, verbose):
# set to max verbose level
verbose = verbose if verbose < 3 else 3
loglevel = logging.ERROR - (10 * verbose)
logging.basicConfig(level=loglevel)
logging.info("Starting SQLToBQBatch. Got: Table: %s, Limit: %i", table, limit)
## set env key to authenticate application
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = os.path.join(os.getcwd(), key)
print('file found')
# Instantiates a client
bigquery_client = bigquery.Client()
print('Project id created')
try:
bq_dataset = bigquery_client.dataset(dataset)
bq_dataset.create()
logging.info("Added Dataset")
except Exception as e:
if ("Already Exists: " in str(e)):
logging.info("Dataset already exists")
else:
logging.error("Error creating dataset: %s Error", str(e))
bq_table = bq_dataset.table(table)
bq_table.schema = BuildSchema(host, database, user, password, table)
print('Creating schema using build schema')
bq_table.create()
logging.info("Added Table %s", table)
conn = Connect(host, database, user, password)
cursor = conn.cursor()
logging.info("Starting load loop")
cursor.execute("SELECT * FROM %s" % (table))
cur_batch = []
count = 0
for row in cursor:
count += 1
if limit != 0 and count >= limit:
logging.info("limit of %d rows reached", limit)
break
cur_batch.append(row)
if count % batch_size == 0 and count != 0:
bq_load(bq_table, cur_batch)
cur_batch = []
logging.info("processed %i rows", count)
# send last elements
bq_load(bq_table, cur_batch)
logging.info("Finished (%i total)", count)
print("table created")
if __name__ == '__main__':
# run the command
SQLToBQBatch()
o Command to run the file : python mysql_to_bq.py -d 'recommendation_spark' -t temp_market_store -i inductive-cocoa-250507 -n practice123 -k key.json