如何使用python更快地将行插入mysql表？

Question

I am trying to find a faster method to insert data into my table, the table should end up with over 100 million rows, I have been running my code for 24 hours nearly and the table currently only has 9 million rows entered and is still in progress.

My code currently reads 300 csv files at a time, and stores the data in a list, it gets filtered for duplicate rows, then I use a for loop to place an entry in the list as a tuple and update the table one tuple at a time. This method just takes too long, is there a way for me to bulk insert all rows? I have tried looking online but the methods I am reading do not seem to help in my situation.

Many thanks,

David

import glob
import os
import csv
import mysql.connector

# MYSQL logon
mydb = mysql.connector.connect(
    host="localhost",
    user="David",
    passwd="Sword",
    database="twitch"
)
mycursor = mydb.cursor()

# list for strean data file names
streamData=[]

# This function obtains file name list from a folder, this is to open files 
in other functions
def getFileNames():
    global streamData
    global topGames

    # the folders to be scanned
    #os.chdir("D://UG_Project_Data")
    os.chdir("E://UG_Project_Data")
    # obtains stream data file names
    for file in glob.glob("*streamD*"):
        streamData.append(file)
    return

# List to store stream data from csv files
sData = []
# Function to read all streamData csv files and store data in a list
def streamsToList():
    global streamData
    global sData

    # Same as gamesToList
    index = len(streamData)
    num = 0
    theFile = streamData[0]
    for x in range(index):
        if (num == 301):
            filterStreams(sData)
            num = 0
            sData.clear()
        try:
            theFile = streamData[x]
            timestamp = theFile[0:15]
            dateTime = timestamp[4:8]+"-"+timestamp[2:4]+"-"+timestamp[0:2]+"T"+timestamp[9:11]+":"+timestamp[11:13]+":"+timestamp[13:15]+"Z"
            with open (theFile, encoding="utf-8-sig") as f:
                reader = csv.reader(f)
                next(reader) # skip header
                for row in reader:
                    if (row != []):
                        col1 = row[0]
                        col2 = row[1]
                        col3 = row[2]
                        col4 = row[3]
                        col5 = row[4]
                        col6 = row[5]
                        col7 = row[6]
                        col8 = row[7]
                        col9 = row[8]
                        col10 = row[9]
                        col11 = row[10]
                        col12 = row[11]
                        col13 = dateTime
                        temp = col1, col2, col3, col4, col5, col6, col7, col8, col9, col10, col11, col12, col13
                        sData.append(temp)
        except:
            print("Problem file:")
            print(theFile)
        print(num)
        num +=1
    return

def filterStreams(self):
    sData = self
    dataSet = set(tuple(x) for x in sData)
    sData = [ list (x) for x in dataSet ]
    return createStreamDB(sData)

# Function to create a table of stream data
def createStreamDB(self):
    global mydb
    global mycursor
    sData = self
    tupleList = ()
    for x in sData:
        tupleList = tuple(x)
        sql = "INSERT INTO streams (id, user_id, user_name, game_id, community_ids, type, title, viewer_count, started_at, language, thumbnail_url, tag_ids, time_stamp) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
        val = tupleList
        try:
            mycursor.execute(sql, val)
            mydb.commit()
        except:
            test = 1
    return

if __name__== '__main__':
    getFileNames()
    streamsToList()
    filterStreams(sData)

Answer 1

If some of your rows succeeds but the some fails, Do you want your database to be left in a corrupt state? if no, try to commit out of the loop. like this:

for x in sData:
    tupleList = tuple(x)
    sql = "INSERT INTO streams (id, user_id, user_name, game_id, community_ids, type, title, viewer_count, started_at, language, thumbnail_url, tag_ids, time_stamp) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
    val = tupleList
    try:
        mycursor.execute(sql, val)
    except:
        # do some thing
        pass
try:
    mydb.commit()
except:
    test = 1

And if you don't. try to load your cvs file into your mysql directly.

LOAD DATA INFILE "/home/your_data.csv"
INTO TABLE CSVImport
COLUMNS TERMINATED BY ','
OPTIONALLY ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 LINES;

Also, to make you more clear. I've define three ways to insert those data, if you insistent to use python, since you have some processing with your data.

Bad way

In [18]: def inside_loop(): 
    ...:     start = time.time() 
    ...:     for i in range(10000): 
    ...:         mycursor = mydb.cursor() 
    ...:         sql = "insert into t1(name, age)values(%s, %s)" 
    ...:         try: 
    ...:             mycursor.execute(sql, ("frank", 27)) 
    ...:             mydb.commit() 
    ...:         except: 
    ...:             print("Failure..") 
    ...:     print("cost :{}".format(time.time() - start)) 
    ...: 

Time cost:

In [19]: inside_loop()                                                                                                                                                                                                                        
cost :5.92155909538269 

Okay way

In [9]: def outside_loop(): 
   ...:     start = time.time() 
   ...:     for i in range(10000): 
   ...:         mycursor = mydb.cursor() 
   ...:         sql = "insert into t1(name, age)values(%s, %s)" 
   ...:         try: 
   ...:             mycursor.execute(sql, ["frank", 27]) 
   ...:         except: 
   ...:             print("do something ..") 
   ...:              
   ...:     try: 
   ...:         mydb.commit() 
   ...:     except: 
   ...:         print("Failure..") 
   ...:     print("cost :{}".format(time.time() - start))

Time cost:

In [10]: outside_loop()                                                                                                                                                                                                                       
cost :0.9959311485290527

Maybe, there are still having some better way, even best. (ie, use pandas to process your data. and try redesign your table ...)

Answer 2

You might like my presentation Load Data Fast! in which I compared different methods of inserting bulk data, and did benchmarks to see which was the fastest method.

Inserting one row at a time, committing a transaction for each row, is about the worst way you can do it.

Using LOAD DATA INFILE is fastest by a wide margin. Although there are some configuration changes you need to make on a default MySQL instance to allow it to work. Read the MySQL documentation about options secure_file_priv and local_infile .

Even without using LOAD DATA INFILE, you can do much better. You can insert multiple rows per INSERT, and you can execute multiple INSERT statements per transaction.

I wouldn't try to INSERT the whole 100 million rows in a single transaction, though. My habit is to commit about once every 10,000 rows.