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  • Objectives
  • Overview
    • 1. Locking-Based Protocols
    • 2. Timestamp-Based Protocols
    • 3. Optimistic Concurrency Control
    • 4. Multiversion Concurrency Control (MVCC)
  • Locking-Based Protocols
    • Two-Phase Locking (2PL) Protocol
    • Ensuring Serializability
    • Lock Types and Their Roles
    • Deadlock Handling
    • Optimizations and Variants
    • Lock Manager
    • Lock Implementation
  • Timestamp-Based Protocols
    • Basic Timestamp Ordering Protocol
    • Thomas Write Rule
    • Key Concepts and Mechanisms
    • Advantages and Challenges
  • Optimistic Concurrency Control
    • Key Concepts and Mechanisms
    • Advantages and Challenges
  • Multiversion Concurrency Control
    • Operational Principles of MVCC
    • Key Concepts and Mechanisms
    • Advantages and Challenges
  • Deadlock Detection & Management
    • Deadlock Prevention Techniques
    • Timestamp-Based Deadlock Prevention Techniques
    • Similarities and Distinctions
  • Summary
  • Files & Resources
  • References
  • Glossary of Key Terms
  • Errata

Objectives

Upon completion of this lesson, you will be able to:

  • explain concurrency issues
  • define basic mechanisms of locking and the types of locks
  • appreciate the use of lock-based techniques for concurrency control
  • identify factors affecting the performance of locking
  • use timestamping for concurrency control
  • list methods for handling deadlock

Overview

Concurrency control in database systems is crucial for ensuring data integrity and consistency, especially in environments where multiple transactions are executed concurrently. These techniques are designed to manage the execution of transactions in a way that preserves the ACID (Atomicity, Consistency, Isolation, Durability) properties of a database. Let’s explore the main concurrency control techniques that ensure serializability, which is the gold standard for transaction execution that maintains database consistency as if transactions were executed serially.

1. Locking-Based Protocols

Locking is the most common method of managing concurrent transactions. It involves controlling access to data items by transactions through locks, which can be either exclusive or shared. Exclusive locks prevent other transactions from reading or writing the locked data item, while shared locks allow multiple transactions to read the same data item but not write to it.

  • Two-Phase Locking (2PL): This protocol ensures serializability by requiring each transaction to go through two distinct phases: the growing phase, where it can acquire locks but cannot release any, and the shrinking phase, where it can release locks but cannot acquire any new ones. This protocol prevents the occurrence of cycles in the precedence graph of transactions, which in turn ensures serializability.

2. Timestamp-Based Protocols

Timestamp ordering uses a logical ordering of transactions to manage concurrency, based on the assumption that each transaction has a unique timestamp.

  • Basic Timestamp Ordering: Transactions are ordered based on their timestamps. If a transaction wants to perform an operation that conflicts with this order, the operation is either delayed or the transaction is rolled back and restarted with a new timestamp.
  • Thomas Write Rule: A variation of timestamp ordering that allows for more concurrency by ignoring write operations that would be overwritten by a later transaction.

3. Optimistic Concurrency Control

Optimistic concurrency control (OCC) assumes that conflicts among transactions are rare and lets transactions execute without acquiring locks. At the end of a transaction, a validation phase checks for conflicts:

  • Read Phase: The transaction executes and records all data items it reads and writes.
  • Validation Phase: The system checks if other transactions have modified the data items that were read during the transaction. If so, the transaction is rolled back.
  • Write Phase: If the validation succeeds, the transaction’s changes are applied to the database.

4. Multiversion Concurrency Control (MVCC)

MVCC maintains multiple versions of a data item to allow for more concurrency. This technique lets read-only transactions access older versions of data items while write transactions create new versions.

  • Versioning: Each write operation creates a new version of a data item, along with a timestamp or transaction ID.
  • Read and Write Rules: Read operations can access the version of the data item that was the latest at the transaction’s start time, while write operations create new versions.

These concurrency control techniques offer different trade-offs between strictness of isolation and level of concurrency (or performance). Locking-based protocols are straightforward but can lead to deadlocks. Timestamp-based protocols avoid deadlocks but can suffer from increased abort rates due to timestamp conflicts. Optimistic concurrency control provides high levels of concurrency but at the risk of higher abort rates under heavy contention. MVCC allows for high concurrency with fewer conflicts but requires additional storage for multiple versions and overhead for version management. The choice of concurrency control technique depends on the specific requirements and characteristics of the database system and its workload.

Let’s delve into the details of Locking-Based Protocols, particularly focusing on the Two-Phase Locking (2PL) protocol, to provide a clearer and more detailed explanation suitable for graduate students in computer science.

Locking-Based Protocols

Locking mechanisms are foundational in managing concurrent transactions in database systems. They are designed to ensure that only one transaction at a time can modify a data item, thereby preventing inconsistent data states. The essence of locking protocols lies in their ability to maintain the isolation property of transactions, ensuring that the execution of transactions concurrently does not lead to data anomalies.

Two-Phase Locking (2PL) Protocol

The Two-Phase Locking protocol is a cornerstone concept in concurrency control that guarantees conflict serializability. It operates under a simple yet powerful principle divided into two distinct phases:

  1. Growing Phase: During this phase, a transaction can acquire any number of locks it needs but cannot release any. The acquisition of locks follows a specific order to prevent deadlocks, although 2PL itself does not inherently prevent deadlocks. This phase continues until the transaction acquires all the locks it requires.

  2. Shrinking Phase: Once a transaction has acquired all the necessary locks and begins to release them, it enters the shrinking phase. During this phase, the transaction can no longer acquire any new locks; it can only release the locks it holds. This ensures that the lock-holding period is confined, reducing the window for potential conflicts with other transactions.

Ensuring Serializability

The key to 2PL’s effectiveness is its strict division into two phases, which inherently ensures that transactions are serializable. The protocol enforces a strict ordering of lock acquisition and release, which prevents cycles in the precedence graph of transactions. A cycle in the precedence graph would indicate a deadlock or the potential for non-serializable behavior. By preventing these cycles, 2PL ensures that the transactions can be serialized in the order of their lock acquisition, thus maintaining database consistency.

Lock Types and Their Roles

  • Exclusive Locks (X-Locks): Used when a transaction intends to write to a data item. An exclusive lock ensures that no other transaction can read or write the data item until the lock is released.

  • Shared Locks (S-Locks): Used when a transaction only needs to read a data item. Multiple transactions can hold shared locks on the same data item concurrently, provided no exclusive locks are held.

The table below shows lock compatibility between different types of locks for the two lock types above. The compatibility between these locks determines whether different transactions can access the same data item concurrently without leading to inconsistency or data integrity issues.

Requested  Held Shared Lock (S) Exclusive Lock (X)
Shared Lock (S) Compatible Incompatible
Exclusive Lock (X) Incompatible Incompatible

Lock Types and Their Roles

  • Shared Lock (S-Lock): A shared lock allows a transaction to read a data item but not modify it. Multiple transactions can hold shared locks on the same data item simultaneously, as long as no exclusive lock is held on that item. This lock is compatible with other shared locks but not with exclusive locks.

  • Exclusive Lock (X-Lock): An exclusive lock allows a transaction to both read and write a data item. If a transaction holds an exclusive lock on a data item, no other transaction can hold either a shared or exclusive lock on that item. Exclusive locks are incompatible with both shared and exclusive locks held by other transactions.

Understanding Lock Compatibility

  • Compatible: Two locks are compatible if they can be held simultaneously by different transactions on the same data item without leading to conflict or inconsistency.

  • Incompatible: Two locks are incompatible if their simultaneous presence on the same data item would lead to conflict, inconsistency, or violation of data integrity. In such cases, the requesting transaction must wait until the lock it conflicts with is released.

Deadlock Handling

While 2PL ensures serializability, it does not inherently solve the problem of deadlocks. Deadlocks occur when two or more transactions hold locks that the other transactions need, creating a cycle of dependencies that cannot be resolved without aborting one of the transactions. Deadlock detection and resolution mechanisms, such as wait-for graphs or timeout policies, are often implemented alongside 2PL to handle potential deadlocks.

Optimizations and Variants

To improve performance and reduce the likelihood of conflicts and deadlocks, several optimizations and variants of 2PL have been developed:

  • Strict 2PL: Ensures that all locks held by a transaction are released only when the transaction commits or aborts. This variant strengthens the consistency guarantees by preventing uncommitted data from being read.

  • Conservative 2PL (Static 2PL): Requires a transaction to obtain all the locks it might need before it begins execution. This approach can reduce the likelihood of deadlocks but requires upfront knowledge of the data items a transaction will access.

By ensuring that transactions adhere to a structured locking discipline, 2PL plays a pivotal role in maintaining data integrity and consistency in the face of concurrent accesses. The study of 2PL and its implications on database performance, deadlock handling, and system design offers valuable insights into the complexities of building robust and efficient database management systems.

Lock Manager

Locking mechanisms within a database system are facilitated by a specialized component known as the lock manager. This component plays a critical role in managing the access and modification of data items by transactions, ensuring data consistency and integrity. Each lock managed by the lock manager serves as a control block, encapsulating key information such as the nature of the lock (whether it is for reading or writing purposes) and the identity of the transaction that holds the lock.

The lock manager operates by processing requests from transactions that wish to either acquire or release locks on data items. When a transaction requests a lock, the lock manager evaluates the request based on the current lock status of the data item and the type of lock requested. It responds in one of two ways:

  1. Lock Grant Message: If the lock can be safely granted without leading to data inconsistency or conflict, the lock manager approves the request, allowing the transaction to proceed with its operation on the data item.

  2. Rollback Message: In scenarios where granting the lock would result in a deadlock or violate data integrity, the lock manager may deny the request and send a rollback message to the transaction, instructing it to undo its operations and restart or terminate.

Upon receiving an unlock request from a transaction, the lock manager simply acknowledges the request and releases the lock. This action may also trigger the lock manager to grant locks to other transactions waiting for the same data item, based on a predetermined priority scheme.

Priority and Avoiding Starvation

To prevent transaction starvation, where a transaction indefinitely waits for a lock, the lock manager employs a priority technique for selecting which transaction from the waiting list should be granted the lock next. Consider the following example to illustrate this concept:

  • Transaction Ti holds a shared lock on data item Q.
  • Transaction Tj requests an exclusive lock on Q but must wait because Ti currently holds a shared lock.
  • While Tj is waiting, transaction Tk requests a shared lock on Q, which is compatible with Ti’s lock, and is immediately granted.

In this situation, if the lock manager continues to grant shared locks to subsequent transactions requesting access to Q, Tj could potentially wait indefinitely for its exclusive lock, leading to starvation. To avoid this, the lock manager can queue Tk’s request behind Tj’s, even though Tk’s request is compatible with the currently held shared lock. This ensures that Tj does not suffer from starvation and will eventually obtain the exclusive lock on Q after Ti releases its lock.

The lock manager’s responsibility to balance efficient data access with fairness among transactions underscores the complexity of concurrency control in database systems. By judiciously granting and queuing lock requests, the lock manager ensures that all transactions can progress without undue delay while maintaining the overall integrity and performance of the database system. This example highlights the importance of a well-designed lock management strategy to prevent scenarios like transaction starvation, ensuring equitable access to shared resources in a concurrent environment.

Lock Implementation

For a database system to maintain consistency and prevent conflicts among concurrent transactions, the operations to lock and unlock data items must be executed as atomic operations. This requirement ensures that each lock or unlock action is completed fully before another begins, thereby eliminating the risk of inconsistencies that could arise from partial or overlapping operations.

To achieve this level of atomicity, the lock manager, a critical component within the database system responsible for managing locks, may operate through multiple instances concurrently. These instances handle incoming lock and unlock requests from transactions. Despite the concurrent handling of requests, access to the central lock table — the data structure that records the status of locks on data items — must be strictly controlled. This is achieved through synchronization mechanisms, such as semaphores, which ensure that only one instance of the lock manager or transaction can modify the lock table at any given time, thereby preserving the atomic nature of lock operations.

To address and prevent the starvation problem, where certain transactions could indefinitely wait for a lock due to continuously incoming requests for the same lock, the lock manager employs a structured approach. It maintains a linked list of records for each data item that is currently locked. This list is organized in the order that lock requests are received, ensuring a fair and orderly queue for access to locked resources.

Each record in the linked list contains several pieces of vital information: - Transaction Identifier: Unique identifier of the transaction that made the lock or unlock request. - Lock Mode: Specifies whether the lock requested is for reading (shared lock) or writing (exclusive lock) purposes. - Request Status: Indicates whether the lock request has been granted or is still pending, providing clarity on the current state of each request.

To efficiently manage and locate these linked lists for any given data item, the lock manager utilizes a hash table known as the lock table. This table is indexed by the data item identifiers, allowing quick retrieval of the linked list associated with any specific data item. This structure not only optimizes the lock management process but also ensures transparency and fairness in how lock requests are handled.

The atomic implementation of lock and unlock requests, coupled with a well-organized system of linked lists and a hash table for managing these requests, forms the backbone of effective concurrency control in database systems. By ensuring atomic operations and employing a fair queuing mechanism, the lock manager plays a crucial role in maintaining the integrity and consistency of the database, preventing conflicts among transactions, and ensuring that no transaction suffers from starvation. This structured approach highlights the sophisticated mechanisms required to manage access to shared resources in a concurrent computing environment.

Timestamp-Based Protocols

Timestamp-based concurrency control assigns a unique timestamp to each transaction at the moment of its creation. This timestamp reflects the transaction’s logical start time and is used to order transactions in a way that ensures serializability. The main principle behind timestamp-based protocols is to enforce a global order of transactions based on their timestamps, thus preventing the anomalies associated with concurrent executions.

Basic Timestamp Ordering Protocol

The Basic Timestamp Ordering Protocol uses timestamps to resolve conflicts by ensuring that transactions are executed in timestamp order with respect to each data item they access. Here’s how it works:

  • Write Operations: If a transaction Ti wants to write to a data item x, but there’s already a read or write timestamp on x that is greater than Ti’s timestamp, Ti is rolled back. This is because a later transaction has already read or written x, and allowing Ti to proceed would violate serializability.

  • Read Operations: If a transaction Ti wants to read a data item x, but there’s a write timestamp on x that is greater than Ti’s timestamp, Ti is rolled back. This ensures that Ti does not see the effects of a transaction that, according to the timestamps, hasn’t occurred yet.

Thomas Write Rule

The Thomas Write Rule is an optimization over the basic timestamp ordering protocol that increases concurrency. It specifically addresses the situation where a write operation by a transaction Ti on a data item x can be ignored because a later transaction Tj has already written to x. The rule allows such write operations by Ti to be skipped rather than rolling back Ti, under the rationale that Tj’s write supersedes Ti’s write. This rule is particularly useful in reducing unnecessary rollbacks, thereby improving system throughput.

Key Concepts and Mechanisms

  • Timestamp Generation: The mechanism for generating timestamps is critical. It must ensure uniqueness and reflect the transaction’s start time. Common approaches include using the system clock or a logical counter.

  • Handling Read and Write Timestamps: Each data item in the database is associated with two timestamps: the read timestamp (the latest time at which it was read) and the write timestamp (the latest time at which it was written). These timestamps are crucial for determining the validity of future read and write operations by transactions.

  • Conflict Resolution: The primary method of conflict resolution in timestamp-based protocols is through transaction rollback. If a transaction’s operation violates the timestamp ordering rules, it is rolled back and may be restarted with a new timestamp.

Advantages and Challenges

Advantages: - Deadlock-Free: Unlike locking protocols, timestamp-based protocols do not suffer from deadlocks, as transactions are rolled back based on their timestamps rather than waiting for locks. - Non-Blocking Reads: Read operations are generally non-blocking, which can lead to higher levels of concurrency, especially for read-heavy workloads.

Challenges: - Starvation: Transactions that are repeatedly rolled back due to timestamp conflicts may suffer from starvation, never completing their execution. - Overhead: Managing timestamps and checking for conflicts introduces overhead, especially in systems with high levels of contention.

Timestamp-based concurrency control provides a logical and effective means of ensuring serializability in database systems. By leveraging the temporal aspect of transactions, these protocols offer a compelling alternative to locking mechanisms, especially in scenarios where deadlocks and lock contention are significant concerns. However, the challenges of starvation and overhead must be carefully managed to ensure system efficiency and fairness. Understanding both the theoretical underpinnings and practical implications of timestamp-based protocols is essential for designing and optimizing database systems in the context of concurrency control.

Continuing our exploration of concurrency control techniques, we now turn our attention to Optimistic Concurrency Control (OCC). This method represents a fundamentally different approach from locking and timestamp-based protocols, relying on the assumption that conflicts between transactions are rare. Understanding OCC is crucial for graduate students in computer science because it illustrates an alternative strategy for achieving serializability and maintaining database consistency without the need for preemptive locking or strict timestamp ordering.

Optimistic Concurrency Control

Optimistic Concurrency Control is based on the optimistic assumption that most transactions can complete without interfering with each other. Therefore, instead of preventing conflicts through locks or timestamps, OCC allows transactions to proceed without restrictions during their execution phase and checks for conflicts only when the transactions are about to commit. This approach consists of three distinct phases:

  1. Read Phase: During this initial phase, a transaction executes and makes all its updates to a private version of the database. It records both the old and new values of any data it reads or writes, without applying these changes to the actual database. This phase allows the transaction to operate as if it has exclusive access to the database.

  2. Validation Phase: Once a transaction completes its execution, it enters the validation phase. This critical step involves checking whether the transaction’s execution has conflicted with any other transactions that have committed since the transaction began. The validation criteria typically involve comparing the read and write sets of the committing transaction against those of transactions that have committed during its execution.

  3. Write Phase: If the transaction passes the validation phase without detecting any conflicts, it proceeds to the write phase. In this phase, the transaction applies its changes to the actual database. Since the transaction has already been validated, this step can often be performed without further conflict checks.

Key Concepts and Mechanisms

  • Read and Write Sets: A fundamental concept in OCC is the distinction between the read set (the data items the transaction reads during its execution) and the write set (the data items it intends to update). These sets are crucial for detecting potential conflicts during the validation phase.

  • Validation Criteria: The validation phase can employ various criteria to detect conflicts. A common approach is to check if any data items in the transaction’s read set have been modified by other transactions that committed during its execution period. If such modifications are detected, the transaction must be rolled back to maintain consistency.

  • Versioning: OCC can be combined with versioning techniques to maintain multiple versions of data items. This allows read-only transactions to access stable data snapshots while update transactions proceed, potentially reducing the need for rollbacks.

Advantages and Challenges

Advantages: - High Concurrency: OCC is well-suited to environments where conflicts are infrequent, allowing a high degree of concurrency with minimal overhead for lock management. - Deadlock Avoidance: Since transactions do not acquire locks, there is no risk of deadlock, which can be a significant advantage in highly concurrent systems.

Challenges: - Rollback and Starvation: Transactions that fail the validation phase must be rolled back, which can lead to wasted work, especially in environments with high contention. Repeated rollbacks can also cause starvation for some transactions. - Overhead of Validation: The validation phase can introduce significant overhead, particularly when transactions have large read or write sets or when the system experiences high levels of concurrency.

Optimistic Concurrency Control offers a compelling alternative to locking and timestamp-based protocols, especially in scenarios where the likelihood of conflict is low. Its ability to allow transactions to execute uninhibited, with conflict detection deferred to the commit stage, can lead to higher levels of concurrency and system throughput. However, the success of OCC depends on the workload characteristics and the system’s ability to efficiently manage the validation and rollback processes. Understanding the principles and trade-offs of OCC is essential for designing and evaluating database systems that effectively balance concurrency, performance, and consistency.

Multiversion Concurrency Control

MVCC is a concurrency control mechanism that maintains multiple versions of database objects, enabling read-only transactions to access older versions of objects while write transactions create new versions. This approach ensures that readers do not block writers and vice versa, thereby increasing the system’s overall throughput and efficiency. MVCC is particularly noteworthy for its ability to allow multiple transactions to access different versions of the same data concurrently, significantly enhancing read efficiency and minimizing lock contention.

Operational Principles of MVCC

  1. Version Creation: Whenever a transaction updates a data item, instead of overwriting the existing data, MVCC creates a new version of the item. Each version is tagged with a transaction ID or timestamp, indicating when it was created.

  2. Version Access: Read operations within a transaction are directed to the versions of data items that were “current” at the transaction’s start time. This ensures that the transaction sees a consistent snapshot of the database, unaffected by concurrent updates.

  3. Commit Mechanism: When a transaction commits, its changes become permanent and a new “current” version of the updated data items. The system maintains a record of active transactions to determine which versions of data items are visible to which transactions.

  4. Garbage Collection: To manage storage and maintain efficiency, MVCC systems periodically purge old data versions that are no longer accessible to any transaction. This garbage collection process is critical to prevent uncontrolled growth of the database storage.

Key Concepts and Mechanisms

  • Snapshot Isolation: MVCC often implements a level of isolation known as snapshot isolation, where each transaction operates on a snapshot of the database as it existed at a certain point in time. This isolation level avoids many concurrency issues, such as non-repeatable reads and phantom reads.

  • Concurrency and Performance: By allowing multiple versions of data to exist concurrently, MVCC significantly reduces the need for locking. Read transactions can proceed without being blocked by write transactions, leading to improved system performance and concurrency.

  • System Complexity and Storage Overhead: The main challenges of MVCC include the increased complexity of managing multiple versions of data and the additional storage overhead required to keep these versions. Efficient implementation of MVCC requires sophisticated version management and garbage collection mechanisms.

Advantages and Challenges

Advantages: - Improved Read Performance: MVCC enables high levels of concurrency, particularly benefiting read-heavy workloads by allowing readers to access consistent data snapshots without waiting for ongoing write operations. - Reduced Lock Contention: The separation of read and write operations onto different versions of data reduces the need for locks, thereby decreasing lock contention and the potential for deadlock situations.

Challenges: - Storage and Performance Overhead: Maintaining multiple versions of data increases storage requirements and can lead to performance overhead, especially in terms of garbage collection and version management. - Complexity in Implementation: Designing and maintaining an MVCC system involves complex mechanisms for version control, transaction management, and garbage collection, requiring careful implementation and tuning.

MVCC offers a flexible approach to concurrency control in database systems, providing significant advantages in terms of concurrency and performance, especially for read-intensive applications. However, the benefits of MVCC come with the trade-offs of increased storage requirements and system complexity. Understanding MVCC is crucial for designing and optimizing database systems that require high levels of concurrency and isolation.

Deadlock Detection & Management

Deadlock is a critical situation in database systems where a set of two or more transactions are all waiting for each other to release locks on data items they need. This standstill prevents any of the transactions from proceeding. To resolve deadlocks, the system may need to rollback one or more transactions involved. This section elaborates on the strategies the system can employ to handle or prevent deadlocks.

Deadlock Prevention Techniques

Deadlock prevention aims to design the system in a way that deadlocks are impossible. This approach is particularly useful when the likelihood of encountering deadlocks is high. The strategies include:

  1. Conservative Two-Phase Locking (Conservative 2PL): This method requires transactions to lock all the data items they will need throughout their execution before starting. This preemptive locking strategy minimizes the chance of deadlocks but requires prior knowledge of data item usage.

  2. Ordering Data Items: By imposing an order on all data items and ensuring that each transaction locks these items following this sequence, the system can avoid the circular wait condition, thereby preventing deadlocks.

  3. Timestamps with Locking: Transactions are assigned priorities based on timestamps, with the system deciding whether a transaction should wait or be rolled back based on these priorities. Transactions with earlier timestamps have higher priority.

While the first two approaches are straightforward, they assume that transactions can predict all the data items they need beforehand, which is often impractical. Moreover, these strategies can severely limit concurrency by locking data items that may not be used for extended periods. Therefore, the timestamp-based approach is more commonly adopted for deadlock prevention.

Timestamp-Based Deadlock Prevention Techniques

To illustrate the timestamp approach, consider two transactions: Ti, which holds a lock on data item Q, and Tj, which requests an incompatible lock on Q. Two specific techniques are employed:

  • Wait-Die: If Ti has an earlier timestamp (higher priority), it is allowed to wait; otherwise, Ti is rolled back (“dies”).

  • Wound-Wait: If Ti has an earlier timestamp (higher priority), Tj is rolled back (“wounded” by Ti); otherwise, Ti waits.

Transactions that are rolled back are restarted with the same timestamp, preserving their priority.

Similarities and Distinctions

Both the wait-die and wound-wait methods are designed to prevent starvation. They ensure that a transaction with the earliest timestamp is never rolled back. New transactions are assigned later timestamps than existing ones, meaning a repeatedly rolled-back transaction will eventually become the oldest and gain the highest priority. This guarantees that it will not be rolled back again and will eventually acquire all requested locks.

Note that a lock request conflicting with another transaction does not necessarily mean a deadlock is imminent. Thus, the wait-die and wound-wait methods may lead to unnecessary rollbacks in their effort to preempt deadlocks.

Deadlock prevention strategies are essential for maintaining the efficiency and reliability of database systems. While conservative 2PL and ordering data items offer straightforward solutions, they are often impractical due to their restrictive nature and assumption of prior knowledge. The timestamp-based methods, wait-die and wound-wait, provide more flexible and dynamic solutions to prevent deadlocks and ensure fairness among transactions. However, the potential for unnecessary rollbacks highlights the need for careful implementation and management of these techniques.

Summary

In this lesson, the complexities of concurrency control and deadlock management in database systems were explored, highlighting their importance for ensuring data integrity and system efficiency in environments with concurrent transaction execution. Various concurrency control techniques were examined, including locking protocols such as Two-Phase Locking (2PL) and Conservative 2PL, timestamp-based protocols, Optimistic Concurrency Control (OCC), and Multiversion Concurrency Control (MVCC). Each technique presents a unique approach to managing concurrent transactions, balancing the trade-offs between data consistency and system performance optimization.

The lesson also covered deadlock scenarios, where transactions indefinitely wait for others to release locks, and discussed strategies for their prevention and resolution. Techniques like wait-die and wound-wait, which utilize timestamps to prioritize transactions and decide whether a transaction should wait or be rolled back, were identified as effective methods for avoiding deadlocks.


Files & Resources

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All Files for Lesson 60.706

References

None.

Glossary of Key Terms

  • Atomic Operation: An operation that is completed in its entirety without interruption or interference, ensuring data integrity and consistency.

  • Concurrency Control: Mechanisms implemented in database systems to ensure that transactions are executed safely in a concurrent environment, maintaining data integrity and consistency.

  • Conservative Two-Phase Locking (Conservative 2PL): A locking protocol where a transaction locks all the resources it needs before it begins execution to prevent deadlocks.

  • Data Item: A unit of data stored in a database, such as a record or row, that can be accessed or manipulated by transactions.

  • Deadlock: A situation where two or more transactions are waiting indefinitely for one another to release locks on resources, preventing them from proceeding.

  • Exclusive Lock (X-Lock): A lock that grants a transaction exclusive access to a data item for writing purposes, preventing other transactions from reading or writing the data item.

  • Lock Manager: A subsystem of the database that manages the locking and unlocking of data items, handling requests from transactions to ensure data consistency and prevent conflicts.

  • Lock Table: A data structure used by the lock manager to keep track of all locks currently held and requested by transactions, facilitating lock management and deadlock detection.

  • Multiversion Concurrency Control (MVCC): A concurrency control method that maintains multiple versions of data items to allow concurrent reads and writes without interference.

  • Optimistic Concurrency Control (OCC): A concurrency control strategy that assumes transactions can complete without conflict and verifies this assumption at the end of each transaction.

  • Semaphore: A synchronization mechanism used to control access to a common resource by multiple processes in a concurrent system, preventing data inconsistencies.

  • Shared Lock (S-Lock): A lock that allows a transaction to read a data item but not write to it, permitting other transactions to also read the data item simultaneously.

  • Starvation: A condition where a transaction is perpetually delayed or prevented from proceeding because it continuously loses out on acquiring necessary resources or locks.

  • Timestamp-Based Protocols: Concurrency control protocols that use timestamps to order transactions chronologically, resolving conflicts based on the age of each transaction.

  • Two-Phase Locking (2PL): A locking protocol that divides the execution of a transaction into a growing phase, where locks are acquired, and a shrinking phase, where locks are released, to ensure serializability.

  • Wait-Die and Wound-Wait: Timestamp-based deadlock prevention techniques that determine whether a transaction should wait for a lock or be rolled back based on the relative ages (timestamps) of the transactions involved.


Errata

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