A serializable transaction operates in an environment that makes it appear as if no other users were modifying data in the database. To describe consistent transaction behavior when transactions run concurrently, database researchers have defined a transaction isolation model called serializability. Therefore, a multiuser database must provide the following:ĭata concurrency, which ensures that users can access data at the same timeĭata consistency, which ensures that each user sees a consistent view of the data, including visible changes made by the user's own transactions and committed transactions of other users Transactions executing simultaneously must produce meaningful and consistent results. However, in a multiuser database, statements within multiple simultaneous transactions can update the same data. In a single-user database, a user can modify data without concern for other users modifying the same data at the same time. Phantom reads occur when, within a transaction, two identical queries execute, and the rows the second query returns differ from the first.Introduction to Data Concurrency and Consistency SERIALIZABLE prevents a phenomenon known as a phantom read. The resource locks in a way that also prevents you from appending records to the table the transaction operates upon. Here all records accessed within a transaction are locked. This level does not take into account changes to data made by other transactions, regardless of whether or not they have been committed. The snapshot remains in use throughout the entire transaction for the SELECT query. Snapshots taken for the SELECT query are taken the first time the SELECT query runs during the transaction. Here non-repeatable reads are not possible. ![]() As a result, SELECT queries, when run multiple times within the same transaction, can return different result sets. However, at this isolation level SELECT queries use their own snapshots of committed data, that is data committed before the SELECT query executed. ![]() Uncommitted changes remain invisible to other transactions until the transaction commits. Effectively, READ-UNCOMMITTED has no real isolation at all. In other words, transactions can read data that eventually may not exist, given that other transactions can always rollback the changes without commit. Here transactions can see changes to data made by other transactions that are not yet committed. In multi-master mode, however, you can only use the REPEATABLE-READ level. When using Galera Cluster in master-slave mode, all four levels are available to you, to the extend that MySQL supports it. However the client logic may break if it relies on an isolation level which is not not supported in the given configuration. ![]() Isolation level is honored only between transactions issued on the same node and thus should be avoided.ĭata consistency between the nodes is always guaranteed regardless of the isolation level chosen by the client. This makes for different outcomes depending on transaction origin (transaction issued on the same node may succeed, whereas the same transaction issued on another node would fail), but in either case it is no weaker than that isolation level on a standalone MySQL/InnoDB. Overall isolation levels that are supported cluster-wide areįor transactions issued on different nodes, isolation is also strengthened by the “first committer wins” rule, which eliminates the “lost update anomaly” inherent to these levels, whereas for transactions issued on the same node this rule does not hold (as per original MySQL/InnoDB behavior). However isolation level between the nodes in the cluster is affected by replication protocol, so transactions issued on different nodes may not be isolated identically to transactions issued on the same node. Individual cluster nodes can provide any isolation level to the extent it is supported by MySQL/InnoDB. Inter-Node Isolation in Galera Clusterīefore going into details about possible isolation levels which can be set for a client session in Galera Cluster it is important to make a distinction between single node and global cluster transaction isolation. The level of isolation determines how transactions can affect each other. ![]() In a database system, concurrent transactions are processed in “isolation” from each other.
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