Unit 1: Database and Database Users
Q1: What are the advantages of using a Database Management System (DBMS) over a traditional file system?
Solution
A Database Management System (DBMS) is software that allows users to create, manage, and manipulate databases efficiently.
It provides an organized and systematic way to store, retrieve, and maintain data compared to a traditional file-based system.
The advantages of using a Database Management System (DBMS) over a traditional file system are
Data Redundancy Control:
DBMS minimizes data duplication by maintaining a single data repository shared among multiple applications.
Data Consistency:
Since redundancy is reduced, data remains uniform and accurate across the system.
Data Sharing:
Multiple users and applications can access the same database simultaneously in a controlled manner.
Data Integrity:
DBMS enforces rules and constraints to maintain the correctness and validity of data.
Data Security:
Provides user authentication, access control, and encryption to protect sensitive data.
Backup and Recovery:
Automatic backup and recovery features ensure data safety in case of system failure.
Data Independence:
Changes in data structure do not affect application programs, improving flexibility.
Efficient Query Processing:
Query languages like SQL make data retrieval faster and more convenient.
Concurrency Control:
DBMS manages simultaneous data access without conflicts or data loss.
Reduced Application Development Time:
Centralized management and predefined operations simplify application development.
Q2: What are the characteristics of the database approach?
Solution
The characteristics of the database approach are
Self-describing nature: Database stores both data and metadata together.
Program-data independence: Application programs are independent of data storage structure.
Multiple views: Different users can access customized views of the same data.
Data sharing and concurrency: Allows multiple users to access data simultaneously without conflict.
Centralized control: Ensures consistency, reduced redundancy, and controlled access.
Data integrity and security: Maintains accuracy, validity, and restricted data access.
Query language support: Provides easy data manipulation using SQL.
Data independence: Structure or storage changes do not affect application programs.
Backup and recovery: Protects data from loss due to failures.
Reduced data redundancy: Eliminates unnecessary data duplication across the system.
Q3: Who are the different types of database users ("actors on the scene") and what are their roles?
Solution
The different types of database users ("actors on the scene") and what are their roles are
Database Administrator (DBA): Manages database storage, security, backup, and performance.
Database Designers: Define data models, relationships, and database structure.
End Users: Use applications or query tools to retrieve and manipulate data.
Application Programmers: Develop programs that interact with the database.
System Analysts: Analyze business requirements and design suitable database solutions.
Data Entry Operators: Input and update data in the database through forms or interfaces.
Casual Users: Occasionally access data using ad-hoc queries or reports.
Sophisticated Users: Use advanced query languages and analysis tools for data exploration.
Unit 2: Database System – Concepts and Architecture
Q1: Explain the ANSI/SPARC three-schema architecture with a suitable diagram.
Solution
The ANSI/SPARC three-schema architecture is a framework that separates the database into three levels to provide data abstraction and independence between users and the physical database.
1. Internal Level (Physical Schema):
Describes how data is physically stored in the database.
Deals with file structures, indexing, and access paths.
2. Conceptual Level (Logical Schema):
Represents the entire database structure for the organization.
Describes entities, relationships, and constraints without showing physical details.
3. External Level (View Schema):
Provides individual user views of the database.
Each user sees only the data relevant to them, improving security and simplicity.
Data Independence:
Logical Data Independence: Changes in the conceptual schema don’t affect external views.
Physical Data Independence: Changes in physical storage don’t affect the conceptual schema.
Q2: Define data independence and explain its types (logical and physical).
Solution
Data Independence refers to the ability to modify a schema at one level of the database without affecting the schema at the next higher level.
It ensures that changes in data structure or storage do not affect application programs.
Types of Data Independence:
1. Logical Data Independence:
The ability to change the conceptual schema (like adding/removing fields or tables) without altering the external schema or user views.
Example: Adding a new attribute to a table should not require modifying existing application programs.
2. Physical Data Independence:
The ability to change the internal schema (like file organization, indexing, or storage structure) without affecting the conceptual schema.
Example: Changing from sequential file storage to indexed storage without altering the logical structure of the database.
Q3: Define the terms schema and instance in a DBMS with examples.
Solution
Schema:
A schema is the logical structure or blueprint of a database that defines how data is organized.
It includes definitions of tables, attributes, relationships, and constraints.
Example: In a student database, the schema may define a table Student(RollNo, Name, Age, Address).
Instance:
An instance is the actual content of the database at a particular moment in time.
It represents the current state of data stored according to the schema.
Example: This table content is an instance of the Student schema.
Unit 3: Data Modeling Using the Entity-Relational Model
Q1: Explain specialization and generalization, including constraints like the disjoint constraint.
Solution
Specialization:
It is a top-down approach in ER modeling where a higher-level entity is divided into two or more lower-level entities based on some distinguishing attributes.
Helps in representing subclasses with specific properties.
Generalization:
It is a bottom-up approach where two or more lower-level entities are combined into a higher-level entity based on common attributes.
Reduces redundancy and simplifies the ER model.
Disjoint Constraint:
Ensures that an entity instance can belong to only one of the subclasses in specialization.
Prevents overlapping membership among subclasses.
Example:
Entity Employee can be specialized into Manager and Clerk (disjoint, since one employee cannot be both).
Entities Car and Truck can be generalized into Vehicle (combining common attributes like registration number, manufacturer).
Q2: What do you mean by entity type and entity set? Explain with an example.
Solution
Entity Type:
A collection of entities that share common attributes and have the same properties.
It defines the structure or blueprint for entities in a database.
Example:
Entity Type: Student with attributes Student_ID, Name, Age.
Entity Set:
The collection of all instances of a particular entity type at a given time.
Represents the actual data stored in the database for that entity type.
Entity Set: { (101, "Madhav", 20), (102, "Sita", 19), (103, "Ram", 21) } representing all students currently in the database.
Q3: Construct an ER diagram for an airline ticket booking system. The system should provide discounts based on the number of tickets bought, keep records of buyer visit frequency, and filter unwanted visitors.
Solution
Unit 4: The Relational Data Model and Relational Database Constraints
Q1: What do you mean by referential integrity? Why is it needed?
Solution:
Referential integrity ensures that a foreign key value in one table must match a primary key value in another table or be null.
It maintains consistency and correctness of relationships between tables.
Prevents orphan records, i.e., records referencing non-existent entries in related tables.
Purpose / Importance:
Ensures data consistency across related tables.
Prevents invalid data insertion, deletion, or updates.
Helps maintain accurate relationships in the database.
Supports reliable querying and reporting by preserving data integrity.
Example:
Table Student(StudentID, Name, DeptID)
Table Department(DeptID, DeptName)
DeptID in Student must exist in Department to maintain referential integrity.
Conclusion:
Referential integrity is essential to maintain trustworthy and consistent relational data, avoiding errors caused by invalid references between tables.
Q2: Explain the fundamental characteristics of a relation and define the terms domain, attribute, tuple, and relation.
Solution:
A relation is a table with rows and columns in a relational database.
It organizes data in a structured way for easy access, manipulation, and retrieval.
Relations follow specific rules to maintain data consistency and integrity.
Fundamental Characteristics of a Relation:
Rows are tuples: Each row represents a unique record.
Columns are attributes: Each column represents a property of the entity.
Atomic values: Each cell contains a single, indivisible value.
Unique tuples: No two rows are identical.
Order-independent: The order of rows and columns does not matter.
Domain: The set of all possible values that an attribute can take.
Example: Age attribute → domain = {1, 2, 3, …, 100}
Attribute: A named column in a table that represents a property of an entity.
Example: Name, Age, StudentID in a Student table
Tuple: A single row in a table representing a record of the entity.
Example: (101, "Hari", 20)
Relation: A table consisting of rows (tuples) and columns (attributes) that stores data in a structured way.
Example: Student(StudentID, Name, Age)
Conclusion:
These terms form the basic building blocks of a relational database, ensuring structured, consistent, and organized data storage.
Q3: Explain different types of database integrity.
Solution:
Database integrity ensures that the data stored in a database is accurate, consistent, and reliable. It prevents invalid or inconsistent data from being entered.
Types of Database Integrity:
Entity Integrity:
Ensures that primary key values are unique and not null.
Prevents duplicate or missing records.
Example: In a Student table, StudentID (primary key) cannot be null or repeated.
Referential Integrity:
Ensures that foreign key values match primary key values in the referenced table.
Prevents orphan records.
Example: If a Course table references DeptID in the Department table, each DeptID in Course must exist in Department.
Domain Integrity:
Ensures that values of attributes belong to a predefined domain (valid data type and range).
Example: Age must be between 1 and 100.
User-Defined Integrity:
Rules defined by the user or organization to enforce business policies.
Example: Salary of an employee must be greater than minimum wage.
Conclusion:
Database integrity maintains accuracy, consistency, and reliability, ensuring that the data is trustworthy and meaningful for applications.
Q4: Explain the use of primary and foreign keys in a DBMS and the role of a foreign key.
Solution:
Keys are attributes used to uniquely identify records and maintain relationships between tables in a database.
Primary Key:
A primary key uniquely identifies each tuple (row) in a table.
Characteristics: Must be unique and not null.
Example: StudentID in Student table uniquely identifies each student.
Foreign Key:
A foreign key is an attribute in one table that references the primary key of another table.
Purpose: Maintains referential integrity between tables.
Example: DeptID in Course table references DeptID in Department table.
Role of Foreign Key:
Establishes a relationship between two tables.
Prevents orphan records (records with invalid references).
Ensures consistency and accuracy of data across related tables.
Supports cascading actions like update or delete to maintain integrity.
Conclusion:
Primary and foreign keys are essential for data integrity, uniqueness, and maintaining relationships between tables in a database.
Q5: Differentiate between Integrity and Security with an example.
Solution:
Unit 5: The Relational Algebra and Relational Calculus
Q1: What is tuple relational calculus? Explain with an example.
Solution
Tuple Relational Calculus (TRC) is a non-procedural query language for relational databases.
It specifies what to retrieve rather than how to retrieve it.
Queries are expressed using variables that represent tuples of a relation.
TRC is based on first-order predicate logic.
Uses tuple variables to describe tuples in a relation.
Conditions are applied to select tuples satisfying certain properties.
The result is a set of tuples from the relation that meets the condition.
Provides high-level abstraction, independent of database storage or access methods.
Example:
Let Student(SID, Name, Age, Major) be a relation.
Query: “Find all students majoring in Computer Science.”
TRC Expression:
{ t | t ∈ Student ∧ t.Major = 'Computer Science' }
Meaning: “Retrieve all tuples t from Student where Major = Computer Science.”
Summary:
TRC is a declarative language that tells what to retrieve without specifying the procedure.
It is useful for expressing queries clearly and concisely.
Q2: Explain the relational algebra natural join (*) operation with an example.
Solution
Natural Join (⋈) is a relational algebra operation that combines two relations based on common attributes.
It returns a relation containing all combinations of tuples from both relations where the common attribute values match.
It automatically eliminates duplicate columns from the result.
Natural join is used to retrieve related data from multiple tables.
Combines two relations into a single relation.
Matching is done on all attributes with the same name in both relations.
Only tuples with equal values in the common attributes are included.
Reduces redundancy by removing duplicate columns automatically.
Example:
Unit 6: SQL
Unit 7: Relational Database Design
Unit 8: Introduction to Transaction Processing Concepts and Theory
Q1: What is a schedule? Explain serializability and how you can test for it.
Solution:
A schedule is the sequence of operations (read/write) from multiple transactions that shows how they are executed in a database system.
It defines the order of interleaved operations of concurrent transactions.
The goal of a schedule is to maintain consistency and isolation during concurrent execution.
Types of Schedules:
Serial Schedule:
All transactions are executed one after another, without overlapping.
Always consistent and free from conflicts.
Non-Serial Schedule:
Operations of multiple transactions are interleaved.
May cause conflicts or inconsistency if not properly controlled.
Serializability:
Serializability ensures that a non-serial schedule produces the same result as a serial schedule.
It is the main criterion for correctness in concurrent transaction execution.
Types of Serializability:
Conflict Serializability:
If a schedule can be transformed into a serial schedule by swapping non-conflicting operations, it is conflict-serializable.
Non-conflicting operations:
Read–Read on the same data item.
Conflicting operations:
Read–Write, Write–Read, or Write–Write on the same data item.
View Serializability:
Two schedules are view-equivalent if they produce the same final result and each read operation reads the same data value in both schedules.
The ACID properties guarantee that even in case of errors, failures, or concurrent access, the integrity and reliability of the database are maintained.
Serializability:
Serializability ensures that a non-serial schedule produces the same result as a serial schedule.
It is the main criterion for correctness in concurrent transaction execution.
Types of Serializability:
Conflict Serializability:
If a schedule can be transformed into a serial schedule by swapping non-conflicting operations, it is conflict-serializable.
A schedule is the sequence of operations (read/write) from multiple transactions that shows how they are executed in a database system.
It defines the order of interleaved operations of concurrent transactions.
The goal of a schedule is to maintain consistency and isolation during concurrent execution.
Types of Schedules:
Serial Schedule:
All transactions are executed one after another, without overlapping.
Always consistent and free from conflicts.
Non-Serial Schedule:
Operations of multiple transactions are interleaved.
May cause conflicts or inconsistency if not properly controlled.
Recoverability deals with whether a database can recover to a consistent state after a transaction failure.
Types of Schedules Based on Recoverability:
1. Recoverable Schedule
A schedule is recoverable if no transaction commits until all transactions whose changes it read have committed.
Rule: If T2 reads data written by T1, then T1 must commit before T2 commits.
Example:
2. Cascadeless Schedule (Avoids Cascading Rollback)
A schedule where transactions read only committed data (no dirty reads).
Rule: If T2 reads data written by T1, T1 must commit before T2 reads.
Example:
3. Strict Schedule (Strictest)
A schedule where transactions can neither read nor write data written by uncommitted transactions.
Rule: If T1 writes X, no other transaction can read or write X until T1 commits/aborts.
Example:
Q5: Write a short note on Transaction processing.
Solution:
A transaction is a logical unit of work in a database that consists of one or more operations (like read, write, update, delete) performed as a single sequence.
It transforms the database from one consistent state to another.
A transaction ensures reliable and consistent database operations.
The ACID properties guarantee that even in case of errors, failures, or concurrent access, the integrity and reliability of the database are maintained.
Example:
Transferring money from Account A to Account B involves:
Read(A)
A = A – 100
Write(A)
Read(B)
B = B + 100
Write(B)
If any step fails, the whole transaction must abort and roll back the changes.
Unit 9: Concurrency Control Techniques
Q1: Why do we need concurrency control? Discuss the two-phase locking (2PL) protocol, including its types (basic, conservative, strict, rigorous).
solution:
Concurrency control ensures that multiple transactions can execute simultaneously without conflict or inconsistency.
It maintains the integrity and isolation of data when transactions overlap in time.
Without concurrency control, problems like lost updates, dirty reads, or inconsistent data can occur.
2. Need for Concurrency Control
Prevent Data Inconsistency: Ensures consistent database state during concurrent execution.
Avoid Conflicts: Prevents simultaneous access to the same data item.
Maintain Isolation: Each transaction should appear to execute alone.
Ensure Serializability: The final result should be the same as if transactions executed serially.
3. Two-Phase Locking (2PL) Protocol
A locking protocol that ensures serializability by dividing the transaction execution into two distinct phases.
4. Phases of 2PL
Growing Phase:
A transaction acquires locks but cannot release any.
Shrinking Phase:
A transaction releases locks but cannot acquire new ones.
➡️ Once a transaction releases its first lock, it cannot obtain any new locks.
5. Types of Two-Phase Locking
(a) Basic 2PL
Follows the two-phase rule (growing + shrinking).
Guarantees conflict serializability but may lead to deadlocks.
(b) Conservative (Static) 2PL
All locks are acquired before the transaction starts execution.
Prevents deadlocks, but may reduce concurrency.
(c) Strict 2PL
All exclusive (write) locks are held until commit or abort.
Prevents cascading rollbacks.
Ensures recoverable schedules.
(d) Rigorous 2PL
Both shared (read) and exclusive (write) locks are held until commit or abort.
Ensures strict serializability (the strongest form of isolation).
Q2: Explain deadlock with an example. Discuss different deadlock prevention protocols, deadlock detection, and starvation.
solution:
A deadlock occurs in a database system when two or more transactions are waiting indefinitely for each other to release locks.
It happens when each transaction holds a resource and waits for another resource locked by another transaction.
Deadlocks cause transactions to halt permanently, blocking system progress.
Example of Deadlock
Consider two transactions T1 and T2:
T1 waits for B, while T2 waits for A.
Neither can proceed → Deadlock occurs.
3. Deadlock Prevention Protocols
Deadlock prevention ensures that the system never enters a deadlock state by controlling how locks are acquired.
(a) Wait-Die Scheme (Non-Preemptive)
Uses timestamps to decide which transaction waits or aborts.
If an older transaction requests a lock held by a younger one → it waits.
If a younger transaction requests a lock held by an older one → it dies (aborts and restarts).
(b) Wound-Wait Scheme (Preemptive)
If an older transaction requests a lock held by a younger one → it wounds (forces abort) the younger transaction.
If a younger transaction requests a lock held by an older one → it waits.
(c) Timeout-Based Prevention
If a transaction waits longer than a specified time, it is aborted and restarted.
(d) Resource Ordering
Assign a fixed order to all data items.
Transactions must request locks in that order to avoid circular waits.
4. Deadlock Detection
Deadlock detection allows deadlocks to occur but uses a mechanism to identify and resolve them.
The system constructs a Wait-for Graph (WFG):
Nodes represent transactions.
Edges represent “waiting for” relationships.
If the graph contains a cycle, a deadlock exists.
One transaction in the cycle is aborted to break the deadlock.
5. Deadlock Recovery
Once a deadlock is detected:
Abort one or more transactions (usually the one with least progress).
Rollback the aborted transaction.
Restart the transaction later.
6. Starvation
Starvation occurs when a transaction never gets the required resource because others are repeatedly favored.
Common in deadlock prevention or priority-based systems.
Solution: Use fair scheduling (FIFO order) to ensure every transaction eventually executes.
Unit 10: Database Recovery Techniques
Q1: Why is database recovery essential? Explain the recovery technique based on the immediate update.
Solution:
Database recovery is the process of restoring the database to a consistent state after a failure.
It ensures that all committed transactions are saved and uncommitted ones are undone.
Failures can occur due to system crashes, power loss, or transaction errors.
Recovery maintains the integrity and reliability of the database system.
Need / Importance of Database Recovery:
Ensures Consistency: Restores the database to a consistent state after failures.
Maintains Durability: Ensures that results of committed transactions are permanent.
Protects Data: Prevents loss or corruption of data.
Supports Reliability: Provides confidence that the system can recover from unexpected issues.
Immediate Update Recovery Technique:
In the Immediate Update technique, all changes made by a transaction are immediately written to the database (and log) before the transaction commits.
The Immediate Update technique allows updates before commit but relies on undo/redo logs to ensure the database returns to a consistent state after a crash.
Write-Ahead Logging (WAL):
Every update is first written to a log file before being applied to the database.
Ensures that recovery actions can be performed correctly after a crash.
Types of Actions:
UNDO: If a transaction fails before committing, its changes must be undone using the log.
REDO: If a transaction commits but the system fails before all changes are written to the database, those changes are redone from the log.
Recovery Process:
Identify transactions that were active, committed, or failed at the time of crash.
Undo changes of uncommitted transactions.
Redo changes of committed transactions using the log entries.
Advantages:
Ensures high data reliability even in case of system failure.
Changes can be recovered accurately using the log.
Disadvantages:
Requires frequent disk writes, increasing overhead.
Recovery process may take longer due to both undo and redo operations.
Example:
Suppose Transaction T1 updates Account A from ₹1000 to ₹800 and commits.
If the system crashes after the log is written but before the data is fully saved, recovery uses the log to redo T1’s update.
If T1 had not committed, its partial changes would be undone.
Q2: Why do we need database recovery? Discuss the shadow paging technique.
Solution:
Need / Importance of Database Recovery:
Ensures Consistency: Restores the database to a consistent state after failures.
Maintains Durability: Ensures that results of committed transactions are permanent.
Protects Data: Prevents loss or corruption of data.
Supports Reliability: Provides confidence that the system can recover from unexpected issues.
shadow paging technique
Shadow paging is a database recovery technique that avoids the need for logs.
It maintains two copies (pages) of the database — one current and one shadow.
The shadow copy always represents the consistent state of the database before the transaction starts.
It provides an atomic and crash-safe recovery method.
It is quite similar to the shadow copy technique in shadow copy, we make a copy of the original data, and if anything goes wrong in the database, we use the copy of the database to correct.
But in shadow paging first we change in the clone of the database, if it works then only we use it in the main database.
Q3: Explain the deferred update approach in database recovery.
Solution:
Deferred update is a database recovery technique where updates made by a transaction are not applied to the database until the transaction commits.
All updates are temporarily stored in a log file or buffer.
It follows the principle of "do after commit" — no changes are made to the database until it is certain the transaction will complete successfully.
This approach ensures easy recovery and data consistency.
it is also known as "no update no undo".
Example:
while we are woking on the database or on the coding we do all the code and work and check either it is working or not if it's work then only we save so that the worked done by us is reflected on the database neither it won't, it quit similar example .
Advantages:
Simple recovery — only redo is required, no need for undo.
Ensures data consistency and atomicity.
Useful when failures are frequent, as the database remains safe until commit.
Disadvantages:
Longer transaction time, since updates are delayed until commit.
Requires extra storage for maintaining logs.
Not suitable for real-time applications where immediate updates are needed.
Q4: What is Buffer Management in DBMS? Explain.
Solution:
Buffer Management in DBMS is the process of handling data pages that are temporarily stored in main memory (RAM) while being read from or written to disk.
It acts as a bridge between disk storage and main memory, improving data access speed.
A buffer pool is a reserved area in memory used to hold copies of database pages from disk.
When a query requests data, the DBMS first checks the buffer pool. If the data is found, it is called a buffer hit; otherwise, it’s a buffer miss, and the data is fetched from disk.
It helps in reducing disk I/O operations, which are slower compared to memory access.
The Buffer Manager decides which pages to keep in memory and which to replace when new pages need to be loaded.
Common page replacement algorithms include LRU (Least Recently Used), MRU (Most Recently Used), and the Clock Algorithm.
It ensures data consistency by managing dirty pages (pages that have been modified in memory but not yet written to disk).
Efficient buffer management leads to better system performance, faster query execution, and optimized resource usage.
Q5: What are Checkpoints in database recovery? How do they help?
Solution:
A checkpoint is a mechanism in database recovery that records the current state of the database and transaction log at a specific point in time.
It helps the system reduce recovery time after a failure.
Checkpoints act as a snapshot of the database’s consistent state.
They are created periodically by the DBMS during normal operation.
Purpose of Checkpoints:
To minimize the amount of work needed during recovery.
To mark a safe point from which the system can restart after a crash.
Recovery Using Checkpoints:
During recovery, the system starts from the last checkpoint instead of the beginning of the log.
Only transactions that started after the last checkpoint are checked for undo/redo operations.
Example:
Suppose a checkpoint is created at 10:00 AM.
If a crash occurs at 10:05 AM, recovery starts from the 10:00 AM checkpoint, not from the beginning of the log — saving time and effort.
Q6: What are the different approaches to Database recovery? What should a log file maintain in log-based recovery?
Solution:
Database recovery is the process of restoring the database to a consistent state after a failure.
It ensures that committed transactions are saved and uncommitted ones are undone.
Different recovery approaches use logs, checkpoints, and copies to maintain data integrity.
The choice of approach depends on how and when updates are applied to the database.
1. Deferred Update Approach:
Updates are not applied to the database until the transaction commits.
All updates are stored in the log file first.
On commit → updates are written to the database.
On failure before commit → no undo is required (since the database wasn’t changed).
Only redo is needed for committed transactions.
2. Immediate Update Approach:
Updates are applied to the database immediately, even before the transaction commits.
A log entry is written before updating the database (Write-Ahead Logging).
On failure →
Undo uncommitted transactions.
Redo committed transactions.
Ensures durability and consistency, but increases overhead.
3. Shadow Paging:
Maintains two copies of the database: a shadow copy (stable) and a current copy (active).
All updates are made to the current copy.
On commit → the current copy becomes the new shadow copy.
On failure → the shadow copy is used for recovery (no undo/redo needed).
Fast recovery, but requires extra storage.
4. Checkpoint-Based Recovery:
Periodically saves a snapshot (checkpoint) of the database and transaction log.
On failure → recovery starts from the last checkpoint instead of the beginning of the log.
Reduces recovery time significantly.
Log-Based Recovery
In log-based recovery, every change made to the database is recorded in a log file before it is applied to the database.
The log file helps perform undo and redo operations during recovery.
Log File Should Maintain:
Transaction Identifier (TID):
Unique ID for each transaction to track its operations.
Type of Operation:
Specifies the action, e.g., START TRANSACTION, UPDATE, COMMIT, or ABORT.
Data Item (Object):
The name or address of the data item being modified.
Old Value (Before Image):
The value of the data item before the update (used for undo).
New Value (After Image):
The value of the data item after the update (used for redo).
Timestamps:
Records the time of each operation for sequencing during recovery.