Concurrency control
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Concurrency control protocols control simultaneous transactions in a database. There are two main categories: lock-based and timestamp-based. Lock-based protocols use locks to control access to data, which can be in exclusive or shared mode. Timestamp-based protocols assign timestamps to transactions and data items to determine if operations can be executed or must be rejected. Optimistic concurrency control validates transactions in three phases: execution, validation, and commit, enforcing rules to ensure serializability.
Concurrency control
- 2. In a multiprogramming environment where multiple transactions can be executed simultaneously, it is highly important to control the concurrency of transactions.
- 3. Concurrency control protocols can be broadly divided into two categories : A. Lock-based protocols B. Time-stamp based protocols Lock-based Protocols : Database systems equipped with lock-based protocols use a mechanism by which any transaction cannot read or write data until it acquires an appropriate lock on it
- 4. Data items can be locked in two modes : a) exclusive (X) mode. Data item can be both read as well as written. X-lock is requested using lock-X instruction. b) shared (S) mode. Data item can only be read. S-lock is requested using lock-S instruction.
- 5. Lock-compatibility matrix If a lock cannot be granted, the requesting transaction is made to wait till all incompatible locks held by other transactions have been released. The lock is then granted.
- 6. Example of a transaction performing locking: T2: lock-S(A); read (A); unlock(A); lock-S(B); read (B); unlock(B); display(A+B)
- 7. Each transaction is issued a timestamp when it enters the system. If an old transaction Ti has time-stamp TS(Ti), a new transaction Tj is assigned time-stamp TS(Tj) such that TS(Ti) <TS(Tj). The timestamp of transaction Ti is denoted as TS(Ti). Read time-stamp of data-item X is denoted by R-timestamp(X). Write time-stamp of data-item X is denoted by W-timestamp(X).
- 8. Timestamp ordering protocol works as follows − If a transaction Ti issues a read(X) operation − ◦ If TS(Ti) < W-timestamp(X) Operation rejected. ◦ If TS(Ti) >= W-timestamp(X) Operation executed. ◦ All data-item timestamps updated. If a transaction Ti issues a write(X) operation − ◦ If TS(Ti) < R-timestamp(X) Operation rejected. ◦ If TS(Ti) < W-timestamp(X) Operation rejected and Ti rolled back. ◦ Otherwise, operation executed.
- 9. Optimistic concurrency control (OCC) is a concurrency control method applied to transactional systems such as relational database management systems and software transactional memory. OCC assumes that multiple transactions can frequently complete without interfering with each other.
- 10. In this approach, a transaction’s life cycle is divided into the following three phases − 1. Execution Phase − A transaction fetches data items to memory and performs operations upon them. 2. Validation Phase − A transaction performs checks to ensure that committing its changes to the database passes serializability test. 3. Commit Phase − A transaction writes back modified data item in memory to the disk.
- 11. This algorithm uses three rules to enforce serializability in validation phase − Rule 1 − Given two transactions Ti and Tj, if Ti is reading the data item which Tj is writing, then Ti’s execution phase cannot overlap with Tj’s commit phase. Tj can commit only after Ti has finished execution.
- 12. Rule 2 − Given two transactions Ti and Tj, if Ti is writing the data item that Tj is reading, then Ti’s commit phase cannot overlap with Tj’s execution phase. Tj can start executing only after Ti has already committed. Rule 3 − Given two transactions Ti and Tj, if Ti is writing the data item which Tj is also writing, then Ti’s commit phase cannot overlap with Tj’s commit phase. Tj can start to commit only after Ti has already committed
- 13. Backward Validation (BV) – check against committed transactions Forward Validation (FV) – check against concurrent executing transactions