A beginner’s guide to Read and Write Skew phenomena

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Introduction

In my article about ACID and database transactions, I introduced the three phenomena described by the SQL standard:

dirty read
non-repeatable read
phantom read

While these are good to differentiate the four isolation levels (Read Uncommitted, Read Committed, Repeatable Read and Serializable), in reality, there are more phenomena to take into consideration as well. The 1995 paper (A Critique of ANSI SQL Isolation Levels) introduces the other phenomena that are omitted from the standard specification.

In my High-Performance Java Persistence book, I decided to insist on the Transaction chapter as it is very important for both data-access effectiveness and efficiency.

Domain model

For the following examples, I’m going to use the following two entities:

In our fictional application, when the Post title is changed, the author must be recorded in the associated PostDetails record.

If the read and write skew anomalies are not prevented, this domain model constraint can be compromised, as you will see in the following test cases.

Read skew

Alice selects a Post entity.
Bob sneaks in and updates both the Post and the PostDetails entities.
Alice thread is resumed and she selects the PostDetails record.

If read skew is permitted, Alice sees Bob’s update and she can assume that the previous Post version (that she read at the beginning of her transaction) was issued by Bob, therefore breaking consistency.

Running this test on the four most common relation database systems gives the following results:

Database isolation level	Read skew
Oracle Read Committed	Yes
Oracle Serializable	No
SQL Server Read Uncommitted	Yes
SQL Server Read Committed	Yes
SQL Server Read Committed Snapshot Isolation	Yes
SQL Server Repeatable Read	No
SQL Server Serializable	No
SQL Server Snapshot Isolation	No
PostgreSQL Read Uncommitted	Yes
PostgreSQL Read Committed	Yes
PostgreSQL Repeatable Read	No
PostgreSQL Serializable	No
MySQL Read Uncommitted	Yes
MySQL Read Committed	Yes
MySQL Repeatable Read	No
MySQL Serializable	No

Write skew

Both Alice and Bob select the Post and the PostDetails entities.
Bob modifies the Post title, but, since the PostDetails is already marked as updated by Bob, the dirty checking mechanism will skip updating the PostDetails entity, therefore preventing a redundant UPDATE statement.
Alice wants to update the Post entity, but the entity already has the same value as the one she wants to apply so only the PostDetails record will mark that the latest change is the one proposed by Alice.

If write skew is permitted, Alice and Bob disjoint writes will proceed, therefore breaking the guarantee that Post and PostDetails should always be in sync.

Running this test on the four most common relation database systems gives the following results:

Database isolation level	Write skew
Oracle Read Committed	Yes
Oracle Serializable	Yes
SQL Server Read Uncommitted	Yes
SQL Server Read Committed	Yes
SQL Server Read Committed Snapshot Isolation	Yes
SQL Server Repeatable Read	No
SQL Server Serializable	No
SQL Server Snapshot Isolation	Yes
PostgreSQL Read Uncommitted	Yes
PostgreSQL Read Committed	Yes
PostgreSQL Repeatable Read	Yes
PostgreSQL Serializable	No
MySQL Read Uncommitted	Yes
MySQL Read Committed	Yes
MySQL Repeatable Read	Yes
MySQL Serializable	No

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Conclusion

Write skew is prevalent among MVCC (Multi-Version Concurrency Control) mechanisms and Oracle cannot prevent it even when claiming to be using Serializable, which in fact is just the Snapshot Isolation level.
SQL Server default locking-based isolation levels can prevent write skews when using Repeatable Read and Serializable. Neither one of its MVCC-based isolation levels (MVCC-based) can prevent/detect it instead.
PostgreSQL prevents it by using its more advanced Serializable Snapshot Isolation level.
MySQL employs shared locks when using Serializable so the write skew can be prevented even if InnoDB is also MVCC-based.