Sqlite analyze big table-CodePudding

i have tables in sqlite size of 649876358 line. I need get all lines with repetitive value in column and write every group in another file but sqlite working very slowing (after 10 minutes not one group not writed). What i have do for faster work?

I used python3 sqlite

db = sql.connect(path_to_db)
    
    with db:
        db_cursor = db.cursor()
    
    countLine = 649876358 #db_cursor.execute("SELECT COUNT(*) FROM 'T'").fetchall()[0][0]
    
    for i in range(1, countLine   1):
        line = db_cursor.execute(f"SELECT * FROM 'T' WHERE ROWID = {i}", ).fetchall()[0]
        
        value = line[0]
        fileName = f"{path_to_save}{value}.json"
        
        if os.path.exists(fileName):
            continue

        allValue = db_cursor.execute(f"SELECT * FROM 'T' WHERE value = '{value}'").fetchall()
        
        if len(allValue) > 1:                        
            with open(fileName, "w") as file:
                json.dump(allValue, file)

CodePudding user response：

There are a few things that would help but first of all I would add an index to your database:

create index your_index_name_here on your_table_name_here (your_column_name_here);

I just created a database with 3 columns id, name, number like this with 1 million lines (you have 600 million lines):

1   R7N47UCYNE  363233
2   BFDXTROHVZ  273710
3   AVN6B8RCS7  949561
4   OHWTUCWDQW  863241
5   7F0B7UDJI4  791392
6   CV5TGBAQBJ  919640
7   PP8UF35C8J  897746
8   CN2J9ZAO63  671596
9   ZPM338I8QN  601521
10  12BFX9LXGE  845189
11  2NFP6DG299  858336
12  T5GH1V609M  692232

Searching for 10 names within my database before the index looked like this:

# (1999841, 'K6IWJK0MNP', 500224)
# (1715030, 'RY0N5VO9UM', 148000)
# (1999694, '474R4EMI3T', 705184)
# (1999539, 'L2YYGSLMUH', 943698)
# (1999425, 'UT352CDTQH', 828853)
# (1714958, 'KKAQ36DZGV', 279517)
# (1714905, 'BZ9CNWL5LS', 320070)
# (1714877, '6X7WCBBIZF', 247905)
# (1714839, '4MSV61HY9I', 263813)
# (1714806, '7NSRANTWEP', 476828)
# Finished in 1.3834 seconds

After the index was created:

# (1999841, 'K6IWJK0MNP', 500224)
# (1715030, 'RY0N5VO9UM', 148000)
# (1999694, '474R4EMI3T', 705184)
# (1999539, 'L2YYGSLMUH', 943698)
# (1999425, 'UT352CDTQH', 828853)
# (1714958, 'KKAQ36DZGV', 279517)
# (1714905, 'BZ9CNWL5LS', 320070)
# (1714877, '6X7WCBBIZF', 247905)
# (1714839, '4MSV61HY9I', 263813)
# (1714806, '7NSRANTWEP', 476828)
# Finished in 0.0006 seconds

Your can also set up a multiple cursors to do the second search rather than do a count/range. Your first query could be:

select_all = "SELECT * FROM your_table"
rows = cursor.execute(select_all).fetchall()
for row in rows:
    value = row[0]
    .....etc etc

Then use the results to generate the a second query:

allValue = cursor2.execute(f"SELECT * FROM 'T' WHERE value = '{value}'").fetchall()

Finally SQLite might not be the best database to work with for this volume of rows. You could use a more performant database and dump the SQLite one to something like MariaDB:

sqlite3 sample.db .dump > dump.sql

Then into your new database with a command like:

mysql -p -u root -h 127.0.0.1 newdb < dump.sql