Performance and Evaluation of Concurrency Control in Databases

1. Introduction

Concurrency control is a fundamental concept in database systems, which ensures the correctness and consistency of transactions that access and modify data concurrently. Concurrency control techniques and protocols are designed to prevent or resolve conflicts that may arise when multiple transactions operate on the same data at the same time.

However, concurrency control also has a significant impact on the performance of database systems, as it affects the efficiency and scalability of transaction processing. Therefore, it is important to measure and evaluate the performance of concurrency control techniques and protocols, as well as to optimize and improve them according to the specific requirements and characteristics of different applications and environments.

In this blog, you will learn about the following topics:

• The main concurrency control techniques and protocols, such as locking-based, timestamp-based, validation-based, multiversion, and optimistic concurrency control.
• The performance metrics and evaluation methods for concurrency control, such as throughput, latency, scalability, benchmarking, and simulation.
• The performance optimization and trade-offs for concurrency control, such as lock granularity and escalation, deadlock detection and prevention, snapshot isolation and serializable snapshot isolation, and distributed and parallel concurrency control.

By the end of this blog, you will have a solid understanding of the performance and evaluation of concurrency control in databases, and you will be able to apply the knowledge and skills to your own projects and scenarios.

Are you ready to dive into the world of concurrency control? Let’s get started!

2. Concurrency Control Techniques and Protocols

In this section, you will learn about the main concurrency control techniques and protocols that are used in database systems. Concurrency control techniques and protocols are the methods that ensure the correctness and consistency of transactions that access and modify data concurrently. They are based on different principles and assumptions, and they have different advantages and disadvantages in terms of performance, complexity, and applicability.

The main concurrency control techniques and protocols are:

• Locking-Based Protocols: These protocols use locks to control the access and modification of data by transactions. A lock is a mechanism that grants or denies a transaction the permission to read or write a data item. Locking-based protocols can be classified into two-phase locking (2PL), strict two-phase locking (S2PL), and rigorous two-phase locking (R2PL).
• Timestamp-Based Protocols: These protocols use timestamps to order the execution of transactions and to detect and resolve conflicts. A timestamp is a unique identifier that represents the start time of a transaction. Timestamp-based protocols can be classified into basic timestamp ordering (BTO), conservative timestamp ordering (CTO), and optimistic timestamp ordering (OTO).
• Validation-Based Protocols: These protocols use validation or certification to verify the correctness and consistency of transactions before committing them. Validation or certification is a process that checks if a transaction has violated any concurrency control rules or constraints. Validation-based protocols can be classified into basic validation (BV), pre-declare validation (PDV), and optimistic concurrency control (OCC).
• Multiversion Concurrency Control: This is a technique that maintains multiple versions of data items to allow concurrent transactions to access different versions without conflicts. Multiversion concurrency control can be implemented using multiversion locking (MVL), multiversion timestamp ordering (MVTO), or multiversion validation (MVV).
• Optimistic Concurrency Control: This is a technique that assumes that conflicts are rare and allows transactions to execute without any concurrency control until the commit time, where they are validated and certified. Optimistic concurrency control can be implemented using optimistic timestamp ordering (OTO) or optimistic validation (OV).

Each of these techniques and protocols has its own characteristics, benefits, and drawbacks, which affect the performance of concurrency control in databases. In the following subsections, you will learn more about each of them and how they work.

2.1. Locking-Based Protocols

Locking-based protocols are the most common and widely used concurrency control techniques in database systems. They use locks to control the access and modification of data by transactions. A lock is a mechanism that grants or denies a transaction the permission to read or write a data item. Locks can be either shared or exclusive, depending on the type of operation that the transaction wants to perform. A shared lock allows multiple transactions to read the same data item, while an exclusive lock allows only one transaction to write the data item and prevents any other transaction from accessing it.

Locking-based protocols ensure the correctness and consistency of transactions by enforcing two rules:

• Two-phase locking (2PL) rule: A transaction must acquire all the locks it needs before releasing any lock. This rule ensures that a transaction does not interfere with another transaction that has accessed the same data item.
• Serializability rule: The schedule of transactions must be equivalent to a serial schedule, where transactions are executed one after another. This rule ensures that the outcome of concurrent transactions is the same as if they were executed sequentially.

However, locking-based protocols also have some drawbacks and limitations that affect the performance of concurrency control in databases. Some of these are:

• Deadlocks: A deadlock occurs when two or more transactions are waiting for each other to release the locks they hold. This situation prevents the transactions from making any progress and wastes system resources. Deadlocks can be detected and resolved by using various methods, such as timeout, deadlock prevention, deadlock avoidance, and deadlock detection and recovery.
• Blocking: A blocking occurs when a transaction is waiting for another transaction to release a lock that it needs. This situation reduces the concurrency and throughput of the system, as transactions have to wait for each other to complete. Blocking can be reduced by using various methods, such as lock conversion, lock escalation, and lock de-escalation.
• Starvation: Starvation occurs when a transaction is repeatedly denied access to a data item because of the interference of other transactions. This situation violates the fairness and liveness of the system, as some transactions may never get a chance to execute. Starvation can be prevented by using various methods, such as priority-based scheduling, aging, and wound-wait and wait-die schemes.

In the next subsection, you will learn about another concurrency control technique, which is based on timestamps.

2.2. Timestamp-Based Protocols

Timestamp-based protocols are another concurrency control technique that use timestamps to order the execution of transactions and to detect and resolve conflicts. A timestamp is a unique identifier that represents the start time of a transaction. Timestamps can be either logical or physical, depending on the source of the time value. Logical timestamps are generated by a logical clock, such as a counter or a vector, while physical timestamps are obtained from a physical clock, such as a system clock or a GPS clock.

Timestamp-based protocols ensure the correctness and consistency of transactions by enforcing two rules:

• Timestamp ordering (TO) rule: A transaction must access and modify data items in the order of their timestamps. This rule ensures that a transaction does not overwrite or read a data item that has been updated by a later transaction.
• Serializability rule: The schedule of transactions must be equivalent to a serial schedule, where transactions are executed one after another. This rule ensures that the outcome of concurrent transactions is the same as if they were executed sequentially.

However, timestamp-based protocols also have some drawbacks and limitations that affect the performance of concurrency control in databases. Some of these are:

• Aborts: An abort occurs when a transaction is rolled back and restarted because of a conflict with another transaction. This situation causes the transaction to lose its work and waste system resources. Aborts can be reduced by using various methods, such as wound-wait and wait-die schemes, timestamp modification, and multiversion timestamp ordering.
• Overhead: An overhead occurs when a transaction has to perform additional operations or maintain additional data structures to implement the timestamp-based protocol. This situation increases the complexity and cost of the system, as transactions have to generate, compare, and store timestamps for each data item they access or modify. Overhead can be reduced by using various methods, such as timestamp caching, timestamp compression, and timestamp approximation.
• Synchronization: A synchronization occurs when a transaction has to coordinate its timestamp with other transactions or with a global clock. This situation introduces delays and uncertainties in the system, as transactions have to communicate or synchronize with each other or with a central authority. Synchronization can be improved by using various methods, such as logical clocks, vector clocks, and hybrid clocks.

In the next subsection, you will learn about another concurrency control technique, which is based on validation or certification.

2.4. Multiversion Concurrency Control

Multiversion concurrency control (MVCC) is a technique that maintains multiple versions of data items to allow concurrent transactions to access different versions without conflicts. MVCC is based on the idea that transactions can read the most recent committed version of a data item, rather than the current version that may be modified by other transactions. This way, transactions do not have to wait for locks or abort due to conflicts, which improves the concurrency and the performance of transaction processing.

MVCC works by assigning a version number to each data item, which indicates the transaction that created or modified it. Each transaction also has a start timestamp and a commit timestamp, which indicate the time when the transaction began and ended. When a transaction reads a data item, it checks the version number and the timestamps of the data item and decides which version to read. When a transaction writes a data item, it creates a new version of the data item with a new version number and timestamps.

There are different ways to implement MVCC, such as multiversion locking (MVL), multiversion timestamp ordering (MVTO), or multiversion validation (MVV). In the next subsection, you will learn more about MVTO, which is a popular and widely used MVCC technique.

2.3. Validation-Based Protocols

Validation-based protocols are a type of concurrency control protocols that use validation or certification to verify the correctness and consistency of transactions before committing them. Validation or certification is a process that checks if a transaction has violated any concurrency control rules or constraints, such as serializability, recoverability, or consistency.

Validation-based protocols work in three phases: read phase, validation phase, and write phase. In the read phase, a transaction reads the data items from the database and stores them in a local buffer. In the validation phase, a transaction is validated or certified by a validator or a certifier, which compares the transaction’s read set and write set with those of other concurrent transactions. If the transaction passes the validation or certification, it is allowed to proceed to the write phase. In the write phase, a transaction writes the data items from the local buffer to the database and commits.

The main advantage of validation-based protocols is that they do not require any locking or timestamping mechanisms, which reduces the overhead and complexity of concurrency control. They also allow more concurrency, as transactions can read data without any restrictions. However, the main disadvantage of validation-based protocols is that they may cause a high abort rate, as transactions may fail the validation or certification at the commit time. They also require a large buffer space, as transactions have to store the data items in the local buffer until the commit time.

There are different types of validation-based protocols, such as basic validation (BV), pre-declare validation (PDV), and optimistic concurrency control (OCC). In the next subsection, you will learn more about OCC, which is a popular and widely used validation-based technique.

2.5. Optimistic Concurrency Control

Optimistic concurrency control (OCC) is a technique that assumes that conflicts are rare and allows transactions to execute without any concurrency control until the commit time, where they are validated and certified. OCC is based on the idea that it is better to let transactions proceed optimistically and abort them if necessary, rather than to restrict them pessimistically and incur unnecessary overhead.

OCC consists of three phases: read phase, validation phase, and write phase. In the read phase, a transaction reads the data items it needs and stores them in a private workspace, without acquiring any locks or checking any timestamps. In the validation phase, a transaction checks if it has violated any concurrency control rules or constraints, such as serializability or isolation levels. If the transaction passes the validation, it is certified and can proceed to the write phase. In the write phase, a transaction writes the updated data items from its private workspace to the database, and commits.

OCC has several advantages and disadvantages in terms of performance, complexity, and applicability. Some of the advantages are:

• OCC reduces the locking overhead and the blocking time of transactions, as it does not use any locks or timestamps during the read phase.
• OCC improves the throughput and scalability of transaction processing, as it allows more transactions to execute concurrently and reduces the contention for data items.
• OCC avoids deadlocks, as it does not hold any locks for the duration of a transaction.

Some of the disadvantages are:

• OCC increases the abort rate and the restart cost of transactions, as it may detect conflicts at the commit time and abort transactions that have already executed.
• OCC increases the validation overhead and the complexity of the validation phase, as it requires checking the consistency and correctness of transactions before committing them.
• OCC may not be suitable for applications or environments where conflicts are frequent or where transactions are long and complex, as it may incur more aborts and validations than locking-based or timestamp-based protocols.

OCC can be implemented using different methods, such as optimistic timestamp ordering (OTO) or optimistic validation (OV). OTO uses timestamps to order the transactions and to validate them according to their start times. OV uses validation rules or functions to check the transactions and to certify them according to their isolation levels or serializability criteria.

In the next section, you will learn about the performance metrics and evaluation methods for concurrency control, such as throughput, latency, scalability, benchmarking, and simulation.

3. Performance Metrics and Evaluation Methods

In this section, you will learn about the performance metrics and evaluation methods for concurrency control in databases. Performance metrics are the measures that quantify the effectiveness and efficiency of concurrency control techniques and protocols. Evaluation methods are the techniques that compare and analyze the performance metrics of different concurrency control techniques and protocols.

The main performance metrics for concurrency control are:

• Throughput: This is the measure of the number of transactions that can be processed per unit time by the database system. Throughput reflects the capacity and the productivity of the database system. Higher throughput means better performance of concurrency control.
• Latency: This is the measure of the time it takes for a transaction to complete its execution from start to finish. Latency reflects the speed and the responsiveness of the database system. Lower latency means better performance of concurrency control.
• Scalability: This is the measure of the ability of the database system to handle increasing workloads and demands without compromising the performance. Scalability reflects the adaptability and the robustness of the database system. Higher scalability means better performance of concurrency control.

The main evaluation methods for concurrency control are:

• Benchmarking: This is a technique that uses a standard set of transactions and workloads to test and compare the performance of different concurrency control techniques and protocols. Benchmarking provides a fair and consistent way to measure and evaluate the performance of concurrency control.
• Simulation: This is a technique that uses a mathematical model or a computer program to mimic and analyze the behavior and the performance of different concurrency control techniques and protocols. Simulation provides a flexible and realistic way to measure and evaluate the performance of concurrency control.

By using these performance metrics and evaluation methods, you can assess and improve the performance of concurrency control in databases. In the following subsections, you will learn more about each of these metrics and methods and how they work.

3.1. Throughput

Throughput is one of the most important performance metrics for concurrency control in databases. Throughput measures the number of transactions that can be processed per unit of time by the database system. Throughput reflects the efficiency and scalability of the concurrency control technique or protocol, as well as the workload and the system configuration.

Throughput can be calculated by dividing the total number of committed transactions by the total elapsed time. For example, if the database system can process 100 transactions in 10 seconds, the throughput is 10 transactions per second. Alternatively, throughput can be expressed as the average response time of a transaction, which is the inverse of the throughput. For example, if the throughput is 10 transactions per second, the average response time is 0.1 seconds per transaction.

Throughput can be influenced by many factors, such as the degree of concurrency, the conflict rate, the abort rate, the lock granularity, the buffer size, the CPU speed, the disk speed, the network speed, and the transaction size. Generally, higher throughput means better performance, as it indicates that the database system can handle more transactions in less time. However, throughput is not the only performance metric that matters, as it does not capture the quality of service or the user satisfaction. Therefore, throughput should be considered along with other performance metrics, such as latency and scalability, which will be discussed in the next subsections.

3.2. Latency

Latency is another important performance metric for concurrency control in databases. Latency measures the time it takes for a transaction to complete its execution from the start to the end. Latency reflects the quality of service and the user satisfaction of the concurrency control technique or protocol, as well as the workload and the system configuration.

Latency can be calculated by subtracting the start time of a transaction from its end time. For example, if a transaction starts at 10:00:00 and ends at 10:00:05, the latency is 5 seconds. Alternatively, latency can be expressed as the average waiting time of a transaction, which is the time that a transaction spends waiting for resources, such as locks, buffers, CPU, disk, or network. For example, if a transaction has a latency of 5 seconds and an actual execution time of 2 seconds, the average waiting time is 3 seconds.

Latency can be influenced by many factors, such as the degree of concurrency, the conflict rate, the abort rate, the lock granularity, the buffer size, the CPU speed, the disk speed, the network speed, and the transaction size. Generally, lower latency means better performance, as it indicates that the database system can process transactions faster and more responsively. However, latency is not the only performance metric that matters, as it does not capture the efficiency or the scalability of the concurrency control technique or protocol. Therefore, latency should be considered along with other performance metrics, such as throughput and scalability, which will be discussed in the next subsections.

3.3. Scalability

Scalability is another important performance metric for concurrency control in databases. Scalability refers to the ability of a database system to handle increasing workloads and demands without compromising the quality of service or performance. Scalability is especially relevant for modern applications that require high availability, reliability, and responsiveness.

There are two main types of scalability: vertical scalability and horizontal scalability. Vertical scalability means increasing the capacity and performance of a single server or node by adding more resources, such as CPU, memory, disk, or network. Horizontal scalability means increasing the capacity and performance of a system by adding more servers or nodes that work together as a cluster or a distributed system.

Concurrency control techniques and protocols have different impacts on the scalability of database systems. Some techniques and protocols are more suitable for vertical scalability, while others are more suitable for horizontal scalability. Some factors that affect the scalability of concurrency control are:

• The amount of communication and synchronization required between transactions and servers or nodes.
• The amount of overhead and contention caused by concurrency control mechanisms, such as locks, timestamps, or validation.
• The degree of parallelism and concurrency that can be achieved by transactions and servers or nodes.
• The trade-offs between consistency, availability, and partition tolerance in distributed systems.

In the next subsection, you will learn about the methods and tools that are used to evaluate the scalability of concurrency control techniques and protocols, such as benchmarking and simulation.

3.4. Benchmarking and Simulation

Benchmarking and simulation are two common methods and tools that are used to evaluate the performance and scalability of concurrency control techniques and protocols in databases. Benchmarking and simulation allow you to compare and analyze different concurrency control techniques and protocols under various scenarios and workloads, and to identify the factors that affect their performance and scalability.

Benchmarking is a method that involves running a set of predefined tests or experiments on a real or simulated database system, and measuring the performance and scalability of concurrency control techniques and protocols using metrics such as throughput, latency, and scalability. Benchmarking can be done using standard or customized benchmarks, such as TPC-C, TPC-E, YCSB, or OLTP-Bench. Benchmarking can help you to evaluate the performance and scalability of concurrency control techniques and protocols in a realistic and reproducible way, and to compare them with other techniques and protocols or with the state-of-the-art.

Simulation is a method that involves creating a mathematical or computational model of a database system, and simulating the behavior and performance of concurrency control techniques and protocols using parameters such as transaction arrival rate, transaction mix, data size, data access pattern, and network latency. Simulation can be done using tools such as NS-2, OMNeT++, or SimGrid. Simulation can help you to evaluate the performance and scalability of concurrency control techniques and protocols in a flexible and scalable way, and to explore the effects of various factors and parameters on their performance and scalability.

In the next section, you will learn about the performance optimization and trade-offs for concurrency control, such as lock granularity and escalation, deadlock detection and prevention, snapshot isolation and serializable snapshot isolation, and distributed and parallel concurrency control.

4. Performance Optimization and Trade-offs

In this section, you will learn about the performance optimization and trade-offs for concurrency control in databases. Performance optimization and trade-offs are the methods and strategies that are used to improve the performance and scalability of concurrency control techniques and protocols, as well as to balance the conflicting goals and requirements of different applications and environments.

Some of the performance optimization and trade-offs for concurrency control are:

• Lock Granularity and Escalation: These are the methods that are used to adjust the size and level of locks that are used by locking-based protocols. Lock granularity refers to the amount of data that is locked by a single lock, such as a record, a page, a table, or a database. Lock escalation refers to the process of converting multiple fine-grained locks into a single coarse-grained lock, or vice versa. Lock granularity and escalation affect the performance and scalability of locking-based protocols by influencing the degree of concurrency, contention, and overhead.
• Deadlock Detection and Prevention: These are the methods that are used to handle the situation where two or more transactions are waiting for each other to release locks on data items that they need to access or modify. Deadlock detection involves identifying and resolving deadlocks after they occur, by using techniques such as wait-for graphs, timeouts, or wound-wait schemes. Deadlock prevention involves avoiding deadlocks before they occur, by using techniques such as lock ordering, no-waiting, or wait-die schemes. Deadlock detection and prevention affect the performance and scalability of locking-based protocols by influencing the throughput, latency, and abort rate.
• Snapshot Isolation and Serializable Snapshot Isolation: These are the methods that are used to provide consistency and isolation guarantees for transactions that use multiversion concurrency control or optimistic concurrency control. Snapshot isolation allows transactions to read the latest committed version of data items, and to commit if they do not write to data items that have been modified by other concurrent transactions. Serializable snapshot isolation strengthens snapshot isolation by ensuring that transactions are serializable, meaning that they produce the same result as if they were executed one after another. Snapshot isolation and serializable snapshot isolation affect the performance and scalability of multiversion concurrency control and optimistic concurrency control by influencing the degree of concurrency, consistency, and overhead.
• Distributed and Parallel Concurrency Control: These are the methods that are used to extend and adapt concurrency control techniques and protocols to distributed and parallel database systems, where data and transactions are distributed and executed across multiple servers or nodes. Distributed and parallel concurrency control involve challenges and trade-offs such as data replication, data partitioning, data placement, load balancing, communication, synchronization, fault tolerance, and consistency. Distributed and parallel concurrency control affect the performance and scalability of database systems by influencing the throughput, latency, and scalability.

In the following subsections, you will learn more about each of these performance optimization and trade-offs and how they work.

4.1. Lock Granularity and Escalation

Lock granularity and escalation are two concepts that affect the performance of locking-based concurrency control protocols. Lock granularity refers to the size of the data items that are locked by a transaction, while lock escalation refers to the process of converting multiple fine-grained locks into fewer coarse-grained locks.

The choice of lock granularity and escalation has a trade-off between the locking overhead and the concurrency level. Fine-grained locks, such as record-level or field-level locks, have a higher locking overhead, as they require more lock requests and releases, but they also have a higher concurrency level, as they allow more transactions to access different parts of the same data item. Coarse-grained locks, such as file-level or table-level locks, have a lower locking overhead, as they require fewer lock requests and releases, but they also have a lower concurrency level, as they prevent more transactions from accessing the same data item.

Lock escalation is a technique that aims to reduce the locking overhead by dynamically changing the lock granularity according to the workload and the system resources. For example, if a transaction acquires too many fine-grained locks, the system may decide to escalate them to a coarse-grained lock, to save the lock memory and the lock manager time. However, lock escalation may also reduce the concurrency level, as it may block more transactions that need to access the same data item.

Therefore, the performance of locking-based concurrency control protocols depends on finding the optimal balance between the lock granularity and the lock escalation, which may vary depending on the application and the environment. In the next subsection, you will learn about another performance optimization technique for locking-based concurrency control protocols, which is deadlock detection and prevention.

4.2. Deadlock Detection and Prevention

A deadlock is a situation where two or more transactions are waiting for each other to release locks on data items, and none of them can proceed. Deadlocks can severely affect the performance of concurrency control, as they cause transactions to wait indefinitely or abort unnecessarily.

Therefore, it is important to detect and prevent deadlocks in concurrency control, using different methods and algorithms. In this section, you will learn about the main methods and algorithms for deadlock detection and prevention, such as:

• Deadlock Detection: This is a method that periodically checks for the existence of deadlocks in the system, using a data structure called a wait-for graph. A wait-for graph is a directed graph that represents the dependencies between transactions and locks. A deadlock exists if the wait-for graph contains a cycle. If a deadlock is detected, one or more transactions involved in the cycle are aborted to break the deadlock.
• Deadlock Prevention: This is a method that prevents deadlocks from occurring in the first place, by imposing some restrictions on the transactions and locks. Some of the common deadlock prevention techniques are:
• Wait-Die: This is a technique that assigns a timestamp to each transaction, and uses it to determine the priority of the transaction. If a transaction T1 requests a lock on a data item that is held by another transaction T2, and T1 has an older timestamp than T2, then T1 waits for T2 to release the lock. Otherwise, T1 dies (aborts) and restarts later with the same timestamp.
• Wound-Wait: This is a technique that is similar to wait-die, but with the opposite logic. If a transaction T1 requests a lock on a data item that is held by another transaction T2, and T1 has an older timestamp than T2, then T1 wounds (aborts) T2 and takes the lock. Otherwise, T1 waits for T2 to release the lock.
• No-Wait: This is a technique that does not allow any transaction to wait for a lock. If a transaction T1 requests a lock on a data item that is held by another transaction T2, then T1 dies (aborts) and restarts later with a new timestamp.
• Cautious-Wait: This is a technique that allows a transaction to wait for a lock only if it does not create a cycle in the wait-for graph. If a transaction T1 requests a lock on a data item that is held by another transaction T2, and waiting for T2 would create a cycle in the wait-for graph, then T1 dies (aborts) and restarts later with a new timestamp.

Each of these methods and algorithms has its own advantages and disadvantages in terms of performance, complexity, and applicability. In the following subsections, you will learn more about each of them and how they work.

4.3. Snapshot Isolation and Serializable Snapshot Isolation

Snapshot isolation and serializable snapshot isolation are two concurrency control techniques that use snapshots of the database state to provide consistency and isolation for transactions. A snapshot is a copy of the database state at a certain point in time, which is used by a transaction to read and write data. Snapshot isolation and serializable snapshot isolation differ in the level of isolation they provide and the way they handle conflicts.

Snapshot Isolation: This is a technique that provides a weaker form of isolation than serializability, but allows higher concurrency and performance. Snapshot isolation guarantees that a transaction will see a consistent snapshot of the database state, regardless of the concurrent updates by other transactions. However, snapshot isolation does not prevent the phenomenon of write skew, which occurs when two transactions update different data items based on the same snapshot, and create an inconsistency in the database state. For example, suppose two transactions T1 and T2 read the same snapshot of a bank account balance, and both try to withdraw money from it. If the balance is not enough to cover both withdrawals, snapshot isolation will not detect the conflict and will allow both transactions to commit, resulting in a negative balance.

Serializable Snapshot Isolation: This is a technique that provides the same level of isolation as serializability, but uses snapshots to reduce the locking overhead and improve the performance. Serializable snapshot isolation guarantees that a transaction will see a consistent snapshot of the database state, and also prevents write skew by detecting and resolving conflicts at commit time. For example, suppose two transactions T1 and T2 read the same snapshot of a bank account balance, and both try to withdraw money from it. If the balance is not enough to cover both withdrawals, serializable snapshot isolation will detect the conflict and abort one of the transactions, ensuring the consistency of the balance.

Both snapshot isolation and serializable snapshot isolation are widely used in modern database systems, such as PostgreSQL, Oracle, and SQL Server. They offer a trade-off between isolation, concurrency, and performance, depending on the application and environment requirements. In the following subsections, you will learn more about how they work and how to implement them.

4.4. Distributed and Parallel Concurrency Control

In this section, you will learn about the challenges and solutions of concurrency control in distributed and parallel database systems. Distributed and parallel database systems are systems that store and process data across multiple nodes or processors, either physically or logically. They offer many benefits, such as high availability, scalability, fault tolerance, and performance improvement. However, they also introduce new issues and complexities for concurrency control, such as network communication, data replication, synchronization, and coordination.

The main challenges and solutions of concurrency control in distributed and parallel database systems are:

• Network Communication: This is the challenge of transmitting and receiving messages between nodes or processors in a distributed or parallel system. Network communication can affect the performance of concurrency control, as it introduces delays, failures, and inconsistencies. The solutions for network communication include using reliable and efficient protocols, such as TCP/IP, UDP, and RPC, and using techniques such as buffering, caching, and compression to reduce the network overhead.
• Data Replication: This is the challenge of maintaining multiple copies of data items in a distributed or parallel system. Data replication can improve the availability, reliability, and performance of data access, as it allows transactions to access local copies instead of remote ones. However, data replication can also cause conflicts and inconsistencies, as different copies may have different values or versions. The solutions for data replication include using replication protocols, such as primary copy, quorum consensus, and voting, and using techniques such as eager replication, lazy replication, and conflict resolution to ensure the consistency and correctness of replicated data.
• Synchronization and Coordination: This is the challenge of ensuring the order and consistency of transactions in a distributed or parallel system. Synchronization and coordination can affect the performance of concurrency control, as they require transactions to wait for each other or exchange messages to avoid or resolve conflicts. The solutions for synchronization and coordination include using synchronization protocols, such as two-phase commit (2PC), three-phase commit (3PC), and Paxos, and using techniques such as locking, timestamping, validation, and serialization to control the concurrency and isolation of transactions.

By applying these solutions, you can achieve effective and efficient concurrency control in distributed and parallel database systems. However, you should also be aware of the trade-offs and limitations of these solutions, as they may introduce additional costs, complexities, and risks. For example, network communication may increase the latency and bandwidth consumption, data replication may increase the storage and update overhead, and synchronization and coordination may increase the waiting and blocking time. Therefore, you should carefully choose and design the appropriate solutions for your specific application and environment.

5. Conclusion

In this blog, you have learned about the performance and evaluation of concurrency control in databases. You have explored the main concurrency control techniques and protocols, such as locking-based, timestamp-based, validation-based, multiversion, and optimistic concurrency control. You have also learned about the performance metrics and evaluation methods for concurrency control, such as throughput, latency, scalability, benchmarking, and simulation. Moreover, you have learned about the performance optimization and trade-offs for concurrency control, such as lock granularity and escalation, deadlock detection and prevention, snapshot isolation and serializable snapshot isolation, and distributed and parallel concurrency control.

By reading this blog, you have gained a solid understanding of the concepts, principles, and methods of concurrency control in databases. You have also acquired the skills and knowledge to apply and improve the performance of concurrency control in your own projects and scenarios. You have also learned how to measure and evaluate the performance of concurrency control using various tools and techniques.

We hope you have enjoyed this blog and found it useful and informative. If you have any questions, comments, or feedback, please feel free to leave them in the comment section below. We would love to hear from you and help you with any doubts or queries you may have. Thank you for reading and happy learning!