{"data":{"post":{"fields":{"slug":"/is-mysql-autoincrement-really-monotonic/","date":"2018-04-09"},"html":"

TL;DR: If a race condition between two MySQL transactions appears, the row\nwith ID = N may appear in the database BEFORE another row with ID < N.

\n

The task

\n

Last week, I’ve been asked to build a microservice that will be responsible for\npolling a table for data and pushing it to a Kafka topic. As the table contains\nimmutable events, it’d wise to use some kind of incremental loading.

\n

The tool

\n

Confluent JDBC Connector (see more)\nis a ready-made add-on exploiting Kafka Connect possiblities, it simplifies\nbuilding such a service to few lines of properties files and the data just flows… ;)

\n

However, one needs to set the properties carefully, adjusting each of them\nto the particular use case. One of them is the mode,\nwhich defines how the table should be queried for new rows. By default,\nit adopts the incrementing strategy, which should be fine for most. At least,\nthe docs don’t mention any kind of risks involved here.

\n

Beware, it’s a trap! (At least with MySQL that I had to use here.)

\n

The trap

\n

We need three terminals here, let’s call them A, B and C.

\n

In the terminal A, we create an empty table foo (a very simple one -\nautoincrement primary key + createdat timestamp + some text column value):

\n
create table foo (\n  id int not null auto_increment,\n  value char(30) not null,\n  created_at timestamp default current_timestamp,\n  primary key (id)\n  );\n\nQuery OK, 0 rows affected (0,08 sec)\n\nselect * from foo;\n\nEmpty set (0,00 sec)
\n

OK, done - created and empty. Let’s use the second connection, terminal B, to start\na new transaction and insert a row alfa (I’ll use\nthe Alphabet to maintain a readable order).

\n
start transaction;\ninsert into foo (value) values ('alfa');
\n

Since we’re in the middle on an uncommited transaction (with defualt isolation level),\nalfa in not yet visible for connection A.

\n
select * from foo;\n\nEmpty set (0,00 sec)
\n

Let’s use the last connection, C, to make an insert of bravo row (with autocommit).

\n
insert into foo (value) values ('bravo');\n\nQuery OK, 1 row affected (0,01 sec)
\n

This should be visible from now on for all other connections, that’s what terminal A says:

\n
select * from foo;\n\n+----+-------+---------------------+\n| id | value | created_at          |\n+----+-------+---------------------+\n|  2 | bravo | 2018-04-09 13:13:51 |\n+----+-------+---------------------+\n1 row in set (0,00 sec)
\n

It’s there! As you can notice, it got assigned the ID = 2, because ID = 1\nhad been previously allocated for the alfa row. But hey, where is it?!

\n

Uncommited! Hanging in the air!

\n

Now we commit the connection B transation:

\n
commit;\n\nQuery OK, 0 rows affected (0,02 sec)
\n

…and run a query using connection A:

\n
select * from foo;\n\n+----+-------+---------------------+\n| id | value | created_at          |\n+----+-------+---------------------+\n|  1 | alfa  | 2018-04-09 13:13:21 |\n|  2 | bravo | 2018-04-09 13:13:51 |\n+----+-------+---------------------+\n2 rows in set (0,01 sec)
\n

Now, both alfa and bravo rows are visible.

\n

The issue

\n

During the between-commit phase, the row bravo with ID = 2 was visible\nfor other connections while the row alfa (ID = 1) was still uncommited, thus\nnot visible for others.

\n

This is why I claim that in edge cases, when a race condition between transaction\noccurs, the auto_increment breaks its monotonic property. This is due to the fact\nthat the value is allocated on insert, not on commit.

\n

For the microservice I’m developing, this is a serious threat. Consider that\nwe query the db during this short between-commit phase:

\n
    \n
  1. we retrieve a single row, ID = 2,
    2. \n
  2. we process this batch of one row,
    3. \n
  3. we store the ID = 2 state for the next batch, telling it “you should\nadd where ID > 2 to your query”.
    4. \n
\n

We’ve just ommited the alfa row!

\n

Solution ideas

\n

Here’s a list of proposals that might work (or might not):

\n
    \n
  1. \n

    Exploit the created_at column to improve the situation? I don’t think it’ll help,\nbut that’s one of the modes that Kafka JDBC connector can operate in.

    \n
    2. \n
  2. \n

    Wait X seconds until all transactions with ID < N are commited (e.g. innodblockwait_timeout + 5 seconds).

    \n
    3. \n
  3. \n

    Dump whole table instead of incremental loading.

    \n
    4. \n
  4. \n

    Mark the already pushed rows with a flag, query with where flag = false.

    \n
    5. \n
\n

In case of any questions, comments, don’t hestitate to reach out.

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