Which is faster: multiple single INSERTs or one multiple-row INSERT?

Question

I am trying to optimize one part of my code that inserts data into MySQL. Should I chain INSERTs to make one huge multiple-row INSERT or are multiple separate INSERTs faster?

Answer 1

https://dev.mysql.com/doc/refman/8.0/en/insert-optimization.html

The time required for inserting a row is determined by the following factors, where the numbers indicate approximate proportions:

• Connecting: (3)
• Sending query to server: (2)
• Parsing query: (2)
• Inserting row: (1 × size of row)
• Inserting indexes: (1 × number of indexes)
• Closing: (1)

From this it should be obvious, that sending one large statement will save you an overhead of 7 per insert statement, which in further reading the text also says:

If you are inserting many rows from the same client at the same time, use INSERT statements with multiple VALUES lists to insert several rows at a time. This is considerably faster (many times faster in some cases) than using separate single-row INSERT statements.

Answer 2

I know I'm answering this question almost two and a half years after it was asked, but I just wanted to provide some hard data from a project I'm working on right now that shows that indeed doing multiple VALUE blocks per insert is MUCH faster than sequential single VALUE block INSERT statements.

The code I wrote for this benchmark in C# uses ODBC to read data into memory from an MSSQL data source (~19,000 rows, all are read before any writing commences), and the MySql .NET connector (Mysql.Data.*) stuff to INSERT the data from memory into a table on a MySQL server via prepared statements. It was written in such a way as to allow me to dynamically adjust the number of VALUE blocks per prepared INSERT (ie, insert n rows at a time, where I could adjust the value of n before a run.) I also ran the test multiple times for each n.

Doing single VALUE blocks (eg, 1 row at a time) took 5.7 - 5.9 seconds to run. The other values are as follows:

2 rows at a time: 3.5 - 3.5 seconds
5 rows at a time: 2.2 - 2.2 seconds
10 rows at a time: 1.7 - 1.7 seconds
50 rows at a time: 1.17 - 1.18 seconds
100 rows at a time: 1.1 - 1.4 seconds
500 rows at a time: 1.1 - 1.2 seconds
1000 rows at a time: 1.17 - 1.17 seconds

So yes, even just bundling 2 or 3 writes together provides a dramatic improvement in speed (runtime cut by a factor of n), until you get to somewhere between n = 5 and n = 10, at which point the improvement drops off markedly, and somewhere in the n = 10 to n = 50 range the improvement becomes negligible.

Hope that helps people decide on (a) whether to use the multiprepare idea, and (b) how many VALUE blocks to create per statement (assuming you want to work with data that may be large enough to push the query past the max query size for MySQL, which I believe is 16MB by default in a lot of places, possibly larger or smaller depending on the value of max_allowed_packet set on the server.)

Answer 3

A major factor will be whether you're using a transactional engine and whether you have autocommit on.

Autocommit is on by default and you probably want to leave it on; therefore, each insert that you do does its own transaction. This means that if you do one insert per row, you're going to be committing a transaction for each row.

Assuming a single thread, that means that the server needs to sync some data to disc for EVERY ROW. It needs to wait for the data to reach a persistent storage location (hopefully the battery-backed ram in your RAID controller). This is inherently rather slow and will probably become the limiting factor in these cases.

I'm of course assuming that you're using a transactional engine (usually innodb) AND that you haven't tweaked the settings to reduce durability.

I'm also assuming that you're using a single thread to do these inserts. Using multiple threads muddies things a bit because some versions of MySQL have working group-commit in innodb - this means that multiple threads doing their own commits can share a single write to the transaction log, which is good because it means fewer syncs to persistent storage.

On the other hand, the upshot is, that you REALLY WANT TO USE multi-row inserts.

There is a limit over which it gets counter-productive, but in most cases it's at least 10,000 rows. So if you batch them up to 1,000 rows, you're probably safe.

If you're using MyISAM, there's a whole other load of things, but I'll not bore you with those. Peace.

Answer 4

Here are the results of a little PHP bench I did :

I'm trying to insert 3000 records in 3 different ways, using PHP 8.0, MySQL 8.1 (mysqli)

Multiple insert queries, with multiple transaction :

$start = microtime(true);
for($i = 0; $i < 3000; $i++)
{
    mysqli_query($res, "insert into app__debuglog VALUE (null,now(), 'msg : $i','callstack','user','debug_speed','vars')");
}
$end = microtime(true);
echo "Took " . ($end - $start) . " s\n";

Did it 5 times, average : 11.132s (+/- 0.6s)

Multiple insert queries, with single transaction :

$start = microtime(true);
mysqli_begin_transaction($res, MYSQLI_TRANS_START_READ_WRITE);
for($i = 0; $i < 3000; $i++)
{
    mysqli_query($res, "insert into app__debuglog VALUE (null,now(), 'msg : $i','callstack','user','debug_speed','vars')");
}
mysqli_commit($res);
$end = microtime(true);
echo "Took " . ($end - $start) . " ms\n";

Result with 5 tests : 0.48s (+/- 0.04s)

Single aggregated insert query

$start = microtime(true);

$values = "";

for($i = 0; $i < 3000; $i++)
{
    $values .= "(null,now(), 'msg : $i','callstack','user','debug_speed','vars')";
    if($i !== 2999)
        $values .= ",";
}
mysqli_query($res, "insert into app__debuglog VALUES $values");

$end = microtime(true);
echo "Took " . ($end - $start) . " ms\n";

Result with 5 tests : 0.085s (+/- 0.05s)

So, for a 3000 row insert, looks like :

• Using multiple queries in a single write transaction is ~22 times faster than making a multiple queries with multiple transactions for each insert.
• Using a single aggregated insert statement is still ~6 times faster than using multiple queries with a single write transaction

Answer 5

Send as many inserts across the wire at one time as possible. The actual insert speed should be the same, but you will see performance gains from the reduction of network overhead.

Answer 6

In general the less number of calls to the database the better (meaning faster, more efficient), so try to code the inserts in such a way that it minimizes database accesses. Remember, unless your using a connection pool, each databse access has to create a connection, execute the sql, and then tear down the connection. Quite a bit of overhead!

Answer 7

I just did a small benchmark and it appears that for a lot of line it's not faster. Here my result to insert 280 000 rows :

• by 10 000 : 164.96 seconds
• by 5 000 : 37seconds
• by 1000 : 12.56 seconds
• by 600 : 12.59 seconds
• by 500 : 13.81 seconds
• by 250 : 17.96 seconds
• by 400 : 14.75 seconds
• by 100 : 27seconds

It appears that 1000 by 1000 is the best choice.

Answer 8

You might want to :

• Check that auto-commit is off
• Open Connection
• Send multiple batches of inserts in a single transaction (size of about 4000-10000 rows ? you see)
• Close connection

Depending on how well your server scales (its definitively ok with PostgreSQl, Oracle and MSSQL), do the thing above with multiple threads and multiple connections.

Answer 9

In general, multiple inserts will be slower because of the connection overhead. Doing multiple inserts at once will reduce the cost of overhead per insert.

Depending on which language you are using, you can possibly create a batch in your programming/scripting language before going to the db and add each insert to the batch. Then you would be able to execute a large batch using one connect operation. Here's an example in Java.

Answer 10

MYSQL 5.5 One sql insert statement took ~300 to ~450ms. while the below stats is for inline multiple insert statments.

(25492 row(s) affected)
Execution Time : 00:00:03:343
Transfer Time  : 00:00:00:000
Total Time     : 00:00:03:343

I would say inline is way to go :)

Answer 11

It's ridiculous how bad Mysql and MariaDB are optimized when it comes to inserts. I tested mysql 5.7 and mariadb 10.3, no real difference on those.

I've tested this on a server with NVME disks, 70,000 IOPS, 1.1 GB/sec seq throughput and that's possible full duplex (read and write).
The server is a high performance server as well.
Gave it 20 GB of ram.
The database completely empty.

The speed I receive was 5000 inserts per second when doing multi row inserts (tried it with 1MB up to 10MB chunks of data)

Now the clue:
If I add another thread and insert into the SAME tables I suddenly have 2x5000 /sec. One more thread and I have 15000 total /sec

Consider this: When doing ONE thread inserts it means you can sequentially write to the disk (with exceptions to indexes). When using threads you actually degrade the possible performance because it now needs to do a lot more random accesses. But reality check shows mysql is so badly optimized that threads help a lot.

The real performance possible with such a server is probably millions per second, the CPU is idle the disk is idle.
The reason is quite clearly that mariadb just as mysql has internal delays.

Answer 12

I would add the information that too many rows at a time depending on their contents could lead to Got a packet bigger than 'max_allowed_packet'.

Maybe consider using functions like PHP's array_chunk to do multiple inserts for your big datasets.

Answer 13

multiple inserts are faster but it has thredshould. another thrik is disabling constrains checks temprorary make inserts much much faster. It dosn't matter your table has it or not. For example test disabling foreign keys and enjoy the speed:

SET FOREIGN_KEY_CHECKS=0;

offcourse you should turn it back on after inserts by:

SET FOREIGN_KEY_CHECKS=1;

this is common way to inserting huge data. the data integridity may break so you shoud care of that before disabling foreign key checks.