1. Introduction
Concurrency control is a technique that ensures the correct and consistent execution of transactions in a database system. Transactions are units of work that access and modify data in a database. Concurrency control ensures that transactions do not interfere with each other and preserve the ACID properties of the database.
There are different methods of concurrency control, such as locking, timestamping, and validation. In this blog, we will focus on validation-based concurrency control, a method that allows transactions to execute without locking, but validates them before committing. We will also examine two variants of validation-based concurrency control: optimistic concurrency control and snapshot isolation.
By the end of this blog, you will learn:
- What is validation-based concurrency control and how it works
- What are the advantages and disadvantages of validation-based concurrency control
- What is optimistic concurrency control and how it differs from validation-based concurrency control
- What is snapshot isolation and how it differs from optimistic concurrency control
- How to implement validation-based concurrency control, optimistic concurrency control, and snapshot isolation in a database system
Are you ready to dive into the world of validation-based concurrency control? Let’s get started!
2. Validation-Based Concurrency Control
Validation-based concurrency control is a method that allows transactions to execute without locking, but validates them before committing. The idea is to let transactions run in parallel and check for conflicts at the end. If there are no conflicts, the transactions can commit. If there are conflicts, some transactions have to abort and restart.
How does validation-based concurrency control work? The basic algorithm consists of three phases for each transaction:
- Read phase: The transaction reads data from the database and performs computations, but does not write anything to the database.
- Validation phase: The transaction checks if it has any conflicts with other transactions that have already committed. A conflict occurs when two transactions access the same data item and at least one of them modifies it. The validation phase uses a set of rules to determine if a transaction can commit or has to abort. We will discuss these rules in more detail later.
- Write phase: If the transaction passes the validation phase, it can write its updates to the database and commit. Otherwise, it has to abort and restart.
Validation-based concurrency control is also known as optimistic concurrency control, because it assumes that conflicts are rare and most transactions can commit successfully. However, this is not always the case, and sometimes validation-based concurrency control can perform poorly. What are the advantages and disadvantages of this method? Let’s find out in the next section.
2.1. Basic Algorithm
In this section, we will explain the basic algorithm of validation-based concurrency control. As we mentioned before, this method consists of three phases for each transaction: read, validation, and write. We will describe each phase in detail and show an example of how it works.
The read phase is the first phase of the transaction. In this phase, the transaction reads data from the database and performs computations, but does not write anything to the database. The transaction also records the data items that it reads and writes in two sets: the read set and the write set. The read set contains the data items that the transaction reads, and the write set contains the data items that the transaction intends to write. The transaction also assigns itself a unique identifier, called the transaction number, and a time interval, called the validation interval. The validation interval is the time period between the start and the end of the validation phase.
The validation phase is the second phase of the transaction. In this phase, the transaction checks if it has any conflicts with other transactions that have already committed. A conflict occurs when two transactions access the same data item and at least one of them modifies it. The validation phase uses a set of rules to determine if a transaction can commit or has to abort. The rules are based on the validation intervals and the read and write sets of the transactions. The rules are as follows:
- If the validation interval of a transaction T overlaps with the validation interval of another transaction T’, and T and T’ have a common data item in their write sets, then T has to abort.
- If the validation interval of a transaction T is after the validation interval of another transaction T’, and T reads a data item that T’ writes, then T has to abort.
- If none of the above rules apply, then T can commit.
The write phase is the third and final phase of the transaction. In this phase, if the transaction passes the validation phase, it can write its updates to the database and commit. Otherwise, it has to abort and restart.
Let’s see an example of how validation-based concurrency control works. Suppose we have two transactions, T1 and T2, that execute the following operations:
T1: R(A), W(A), R(B), W(B) T2: R(B), W(B), R(C), W(C)
The read and write sets of the transactions are:
| Transaction | Read Set | Write Set |
|---|---|---|
| T1 | {A, B} | {A, B} |
| T2 | {B, C} | {B, C} |
The validation intervals of the transactions are:
| Transaction | Validation Interval |
|---|---|
| T1 | [10, 20] |
| T2 | [15, 25] |
Now, let’s apply the validation rules to see if the transactions can commit or have to abort. We have the following cases:
- T1 and T2 have overlapping validation intervals, and they have a common data item (B) in their write sets. Therefore, T1 has to abort.
- T2 has a validation interval that is after T1’s validation interval, and T2 reads a data item (B) that T1 writes. Therefore, T2 has to abort.
As a result, both transactions have to abort and restart. This is an example of how validation-based concurrency control can cause high abort rates and low throughput in some scenarios.
2.2. Advantages and Disadvantages
Validation-based concurrency control has some advantages and disadvantages compared to other methods of concurrency control. In this section, we will discuss some of them and see how they affect the performance and correctness of the database system.
One of the main advantages of validation-based concurrency control is that it avoids locking, which can cause deadlock and reduce concurrency. Locking is a technique that prevents transactions from accessing or modifying data items that are already in use by other transactions. Deadlock is a situation where two or more transactions are waiting for each other to release locks, and none of them can proceed. Validation-based concurrency control eliminates the need for locking, and thus avoids deadlock and increases concurrency.
Another advantage of validation-based concurrency control is that it allows transactions to execute in parallel without interference, which can improve the response time and throughput of the system. Response time is the time it takes for a transaction to complete, and throughput is the number of transactions that can be processed per unit of time. Validation-based concurrency control allows transactions to read and write data without blocking or waiting for other transactions, which can reduce the response time and increase the throughput of the system.
However, validation-based concurrency control also has some disadvantages that can limit its applicability and efficiency. One of the main disadvantages is that it can cause high abort rates and low throughput in some scenarios, especially when there are many conflicts or long transactions. Aborts are transactions that fail to commit and have to restart. Validation-based concurrency control can cause aborts when transactions have conflicts with other transactions that have already committed. Conflicts are more likely to occur when there are many transactions accessing the same data items, or when transactions are long and span a large portion of the database. Aborts can reduce the throughput and waste the resources of the system.
Another disadvantage of validation-based concurrency control is that it requires additional overhead for validation and restart. Validation is the process of checking for conflicts and applying the validation rules. Restart is the process of aborting and re-executing a transaction. Validation and restart require extra time and space, which can affect the performance and scalability of the system.
As you can see, validation-based concurrency control has some trade-offs between the benefits of avoiding locking and the costs of handling conflicts. Depending on the characteristics of the workload and the database system, validation-based concurrency control may or may not be suitable for achieving efficient and correct concurrency control. In the next section, we will explore a variant of validation-based concurrency control that tries to overcome some of its limitations: optimistic concurrency control.
3. Optimistic Concurrency Control
Optimistic concurrency control is a variant of validation-based concurrency control that tries to reduce the abort rate and improve the throughput of the system. Optimistic concurrency control is based on the assumption that conflicts are rare and most transactions can commit successfully. Therefore, it optimizes the validation phase to minimize the chances of aborts and maximize the chances of commits.
How does optimistic concurrency control differ from validation-based concurrency control? The main difference is in the way the transactions assign their validation intervals. In validation-based concurrency control, the validation interval is the time period between the start and the end of the validation phase. In optimistic concurrency control, the validation interval is the time period between the end of the read phase and the end of the validation phase. This means that the validation interval of a transaction in optimistic concurrency control is shorter than the validation interval of the same transaction in validation-based concurrency control.
Why does this make a difference? The shorter the validation interval, the less likely it is that a transaction will have a conflict with another transaction. This is because the validation rules are based on the validation intervals and the read and write sets of the transactions. By reducing the validation interval, optimistic concurrency control reduces the possibility of overlapping or succeeding validation intervals, which are the main sources of conflicts. As a result, optimistic concurrency control can increase the commit rate and the throughput of the system.
However, optimistic concurrency control is not a perfect solution. It still has some disadvantages that can affect its performance and correctness. One of the main disadvantages is that it requires additional overhead for maintaining the read and write sets of the transactions. The read and write sets are used to check for conflicts and apply the validation rules. Optimistic concurrency control requires the transactions to keep track of all the data items that they read and write, which can consume a lot of memory and processing power. This can affect the scalability and efficiency of the system.
Another disadvantage of optimistic concurrency control is that it can cause cascading aborts in some scenarios. Cascading aborts are situations where one transaction aborts and causes other transactions to abort as well. This can happen when a transaction reads a data item that is written by another transaction that later aborts. In this case, the first transaction has to abort as well, because it has read an invalid value. Cascading aborts can reduce the throughput and waste the resources of the system.
As you can see, optimistic concurrency control has some advantages and disadvantages compared to validation-based concurrency control. Depending on the characteristics of the workload and the database system, optimistic concurrency control may or may not be suitable for achieving efficient and correct concurrency control. In the next section, we will explore another variant of validation-based concurrency control that tries to overcome some of its limitations: snapshot isolation.
3.1. Basic Algorithm
Optimistic concurrency control is a variant of validation-based concurrency control that uses timestamps to order transactions and validate them. Timestamps are logical or physical values that indicate when a transaction starts or finishes. Optimistic concurrency control assigns a unique timestamp to each transaction and uses them to determine if a transaction can commit or has to abort.
How does optimistic concurrency control work? The basic algorithm consists of three phases for each transaction:
- Read phase: The transaction reads data from the database and performs computations, but does not write anything to the database. The transaction also records the timestamps of the data items it reads in a read set.
- Validation phase: The transaction checks if it has any conflicts with other transactions that have already committed. A conflict occurs when two transactions access the same data item and at least one of them modifies it. The validation phase uses the timestamps of the transactions and the data items to determine if a transaction can commit or has to abort. The validation phase also assigns a commit timestamp to the transaction.
- Write phase: If the transaction passes the validation phase, it can write its updates to the database and commit. The transaction also updates the timestamps of the data items it writes in a write set. Otherwise, it has to abort and restart.
Optimistic concurrency control is based on the assumption that conflicts are rare and most transactions can commit successfully. However, this is not always the case, and sometimes optimistic concurrency control can perform poorly. What are the advantages and disadvantages of this method? Let’s find out in the next section.
3.2. Advantages and Disadvantages
Optimistic concurrency control has some advantages and disadvantages compared to validation-based concurrency control. In this section, we will discuss the pros and cons of this method and when it is suitable to use it.
One of the main advantages of optimistic concurrency control is that it avoids locking, which can reduce the overhead and improve the performance of the database system. Locking can cause problems such as deadlock, starvation, and blocking, which can affect the concurrency and availability of the database. Optimistic concurrency control eliminates these problems by allowing transactions to execute without interference and validating them at the end.
Another advantage of optimistic concurrency control is that it can handle long-running transactions better than validation-based concurrency control. Long-running transactions are transactions that take a long time to complete, such as complex queries or batch updates. Validation-based concurrency control can cause long-running transactions to abort frequently, because they have a higher chance of conflicting with other transactions. Optimistic concurrency control can reduce the abort rate of long-running transactions, because it uses timestamps to order transactions and validate them.
However, optimistic concurrency control also has some disadvantages. One of the main disadvantages is that it can cause a high abort rate when the conflict rate is high. A high conflict rate means that many transactions access and modify the same data items, which can cause them to fail the validation phase and abort. Optimistic concurrency control assumes that conflicts are rare, but this is not always the case in some applications, such as online banking or reservation systems. In such cases, optimistic concurrency control can perform poorly and waste resources.
Another disadvantage of optimistic concurrency control is that it can cause inconsistency and anomalies in the database. Inconsistency means that the database does not reflect the correct state of the data, and anomalies mean that the database violates some integrity constraints or rules. Optimistic concurrency control can cause these problems because it does not enforce serializability, which is a property that ensures that the concurrent execution of transactions is equivalent to some serial execution. Serializability is important for maintaining the correctness and consistency of the database.
Therefore, optimistic concurrency control is a trade-off between performance and correctness. It can improve the performance of the database system by avoiding locking, but it can also compromise the correctness of the database by allowing inconsistency and anomalies. Optimistic concurrency control is suitable for applications that have a low conflict rate and can tolerate some inconsistency, such as data analysis or scientific computing. However, it is not suitable for applications that have a high conflict rate and require strict consistency, such as online banking or reservation systems.
4. Snapshot Isolation
Snapshot isolation is another variant of validation-based concurrency control that uses snapshots to execute and validate transactions. Snapshots are consistent views of the database that reflect the state of the data at a certain point in time. Snapshot isolation assigns a unique snapshot to each transaction and uses them to isolate transactions from each other and prevent conflicts.
How does snapshot isolation work? The basic algorithm consists of three phases for each transaction:
- Read phase: The transaction reads data from the snapshot assigned to it and performs computations, but does not write anything to the database. The snapshot is created when the transaction starts and does not change during the transaction.
- Validation phase: The transaction checks if it has any conflicts with other transactions that have already committed. A conflict occurs when two transactions modify the same data item. The validation phase uses the snapshots of the transactions and the data items to determine if a transaction can commit or has to abort.
- Write phase: If the transaction passes the validation phase, it can write its updates to the database and commit. Otherwise, it has to abort and restart.
Snapshot isolation is based on the assumption that conflicts are rare and most transactions can commit successfully. However, this is not always the case, and sometimes snapshot isolation can cause problems. What are the advantages and disadvantages of this method? Let’s find out in the next section.
4.1. Basic Algorithm
Snapshot isolation is another variant of validation-based concurrency control that uses snapshots to execute and validate transactions. Snapshots are consistent views of the database that reflect the state of the data at a certain point in time. Snapshot isolation assigns a unique snapshot to each transaction and uses them to isolate transactions from each other and prevent conflicts.
How does snapshot isolation work? The basic algorithm consists of three phases for each transaction:
- Read phase: The transaction reads data from the snapshot assigned to it and performs computations, but does not write anything to the database. The snapshot is created when the transaction starts and does not change during the transaction.
- Validation phase: The transaction checks if it has any conflicts with other transactions that have already committed. A conflict occurs when two transactions modify the same data item. The validation phase uses the snapshots of the transactions and the data items to determine if a transaction can commit or has to abort.
- Write phase: If the transaction passes the validation phase, it can write its updates to the database and commit. Otherwise, it has to abort and restart.
Snapshot isolation is based on the assumption that conflicts are rare and most transactions can commit successfully. However, this is not always the case, and sometimes snapshot isolation can cause problems. What are the advantages and disadvantages of this method? Let’s find out in the next section.
4.2. Advantages and Disadvantages
Snapshot isolation is a variant of validation-based concurrency control that provides a higher level of consistency and isolation for transactions. However, it also has some drawbacks that limit its applicability and performance. In this section, we will discuss the advantages and disadvantages of snapshot isolation.
One of the main advantages of snapshot isolation is that it prevents two common anomalies that can occur in concurrent transactions: dirty reads and non-repeatable reads. A dirty read occurs when a transaction reads a data item that has been modified by another transaction that has not yet committed. A non-repeatable read occurs when a transaction reads the same data item twice and gets different values, because another transaction has modified and committed the data item in between. Snapshot isolation prevents these anomalies by ensuring that each transaction sees a consistent snapshot of the database as of the time it started. This means that transactions do not see the effects of other transactions that are running concurrently or have committed after them.
Another advantage of snapshot isolation is that it reduces the amount of locking and blocking that occurs in the database system. Since transactions do not have to acquire locks on the data items they access, they can run faster and with less contention. This also reduces the risk of deadlock, a situation where two or more transactions are waiting for each other to release locks and cannot proceed. Snapshot isolation avoids deadlock by using a validation phase to detect conflicts and abort transactions that violate the isolation level.
However, snapshot isolation also has some disadvantages that make it unsuitable for some applications and scenarios. One of the main disadvantages of snapshot isolation is that it does not prevent a serious anomaly called write skew. Write skew occurs when two transactions read the same data items and update different data items based on the values they read, resulting in an inconsistent state of the database. For example, suppose two transactions are trying to book seats on a flight that has only one seat left. Both transactions read the number of available seats and see that there is one seat left. Then, both transactions update the seat reservation table and the passenger information table with their own booking details. As a result, the database ends up with two bookings for the same seat, which is an inconsistency. Snapshot isolation does not prevent write skew because it only checks for conflicts on the data items that are modified by the transactions, not the data items that are read by the transactions.
Another disadvantage of snapshot isolation is that it requires more storage space and processing power than other methods of concurrency control. Since snapshot isolation maintains multiple versions of each data item, it needs more space to store the old versions and more processing power to manage them. Moreover, snapshot isolation needs to keep track of the start and commit times of each transaction, which adds to the overhead of the system. Snapshot isolation also needs to perform a validation phase for each transaction, which can be costly and time-consuming, especially if there are many transactions running concurrently or if the transactions are long and complex.
In summary, snapshot isolation is a method of validation-based concurrency control that provides a high level of consistency and isolation for transactions, but also has some drawbacks that limit its applicability and performance. In the next section, we will conclude this blog and review the main points we have learned.
5. Conclusion
In this blog, we have learned about validation-based concurrency control, a technique that allows concurrent transactions to execute without locking, but validates them before committing. We have also explored two variants of validation-based concurrency control: optimistic concurrency control and snapshot isolation.
We have seen that validation-based concurrency control has some advantages over other methods of concurrency control, such as reducing locking and blocking, preventing dirty reads and non-repeatable reads, and allowing more concurrency and parallelism. However, we have also seen that validation-based concurrency control has some disadvantages, such as not preventing write skew, requiring more storage and processing power, and performing poorly under high contention and conflict rates.
We have also learned how to implement validation-based concurrency control, optimistic concurrency control, and snapshot isolation in a database system. We have discussed the basic algorithms, the validation rules, and the data structures needed for each method. We have also provided some code examples to illustrate how to use these methods in practice.
We hope that this blog has helped you understand the concept and the implementation of validation-based concurrency control and its variants. If you have any questions or feedback, please leave a comment below. Thank you for reading!
