Anomalies in Relational Model Last Updated : 08 Jan, 2025 Comments Improve Suggest changes Like Article Like Report Anomalies in the relational model refer to inconsistencies or errors that can arise when working with relational databases, specifically in the context of data insertion, deletion, and modification. There are different types of anomalies that can occur in referencing and referenced relations which can be discussed as: These anomalies can be categorized into three types:Insertion AnomaliesDeletion AnomaliesUpdate Anomalies.How Are Anomalies Caused in DBMS?Anomalies in DBMS are caused by poor management of storing everything in the flat database, lack of normalization, data redundancy, and improper use of primary or foreign keys. These issues result in inconsistencies during insert, update, or delete operations, leading to data integrity problems. The three primary types of anomalies are:Insertion Anomalies: These anomalies occur when it is not possible to insert data into a database because the required fields are missing or because the data is incomplete. For example, if a database requires that every record has a primary key, but no value is provided for a particular record, it cannot be inserted into the database.Deletion anomalies: These anomalies occur when deleting a record from a database and can result in the unintentional loss of data. For example, if a database contains information about customers and orders, deleting a customer record may also delete all the orders associated with that customer.Update anomalies: These anomalies occur when modifying data in a database and can result in inconsistencies or errors. For example, if a database contains information about employees and their salaries, updating an employee's salary in one record but not in all related records could lead to incorrect calculations and reporting.These anomalies can be removed with the process of Normalization, which generally splits the database which results in reducing the anomalies in the database.STUDENT TableSTUD_NOSTUD_NAMESTUD_PHONESTUD_STATESTUD-COUNTRYSTUD_AGE1RAM9716271721HaryanaIndia202RAM9898291281PunjabIndia193SUJIT7898291981RajasthanIndia184SURESH PunjabIndia21Table 1STUDENT_COURSESTUD_NOCOURSE_NOCOURSE_NAME1C1DBMS2C2Computer Networks1C2Computer Networks Table 2Insertion Anomaly: If a tuple is inserted in referencing relation and referencing attribute value is not present in referenced attribute, it will not allow insertion in referencing relation.ORAn insertion anomaly occurs when adding a new row to a table leads to inconsistencies.Example: If we try to insert a record into the STUDENT_COURSE table with STUD_NO = 7, it will not be allowed because there is no corresponding STUD_NO = 7 in the STUDENT table. Deletion and Updation Anomaly: If a tuple is deleted or updated from referenced relation and the referenced attribute value is used by referencing attribute in referencing relation, it will not allow deleting the tuple from referenced relation.Example: If we want to update a record from STUDENT_COURSE with STUD_NO =1, We have to update it in both rows of the table. If we try to delete a record from the STUDENT table with STUD_NO = 1, it will not be allowed because there are corresponding records in the STUDENT_COURSE table referencing STUD_NO = 1. Deleting the record would violate the foreign key constraint, which ensures data consistency between the two tables.To avoid this, the following can be used in query:ON DELETE/UPDATE SET NULL: If a tuple is deleted or updated from referenced relation and the referenced attribute value is used by referencing attribute in referencing relation, it will delete/update the tuple from referenced relation and set the value of referencing attribute to NULL.ON DELETE/UPDATE CASCADE: If a tuple is deleted or updated from referenced relation and the referenced attribute value is used by referencing attribute in referencing relation, it will delete/update the tuple from referenced relation and referencing relation as well.Removal of AnomaliesAnomalies in DBMS can be removed by applying normalization. Normalization involves organizing data into tables and applying rules to ensure data is stored in a consistent and efficient manner. By reducing data redundancy and ensuring data integrity, normalization helps to eliminate anomalies and improve the overall quality of the databaseAccording to E.F.Codd, who is the inventor of the Relational Database, the goals of Normalization include:It helps in vacating all the repeated data from the database.It helps in removing undesirable deletion, insertion, and update anomalies.It helps in making a proper and useful relationship between tables. Key steps include:First Normal Form (1NF): Ensures each column contains atomic values and removes repeating groups.Second Normal Form (2NF): Eliminates partial dependencies by ensuring all non-key attributes are fully dependent on the primary key.Third Normal Form (3NF): Removes transitive dependencies by ensuring non-key attributes depend only on the primary key.By implementing these normalization steps, the database becomes more structured, reducing the likelihood of insertion, update, and deletion anomalies.Read more about Normal Forms in DBMS.ConclusionEnsuring data integrity requires addressing anomalies such as insertion, update, and deletion problems in the Relational Model. By effectively arranging data, normalization techniques offer a solution that guarantees consistency and dependability in relational databases. 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Student_ID Name Contact College Course Rank 100Hi 6 min read Dependency Preserving Decomposition - DBMSIn a Database Management System (DBMS), dependency-preserving decomposition refers to the process of breaking down a complex database schema into simpler, smaller tables, such that all the functional dependencies of the original schema are still enforceable without needing to perform additional join 7 min read Lossless Decomposition in DBMSThe original relation and relation reconstructed from joining decomposed relations must contain the same number of tuples if the number is increased or decreased then it is Lossy Join decomposition. Lossless join decomposition ensures that never get the situation where spurious tuples are generated 5 min read Lossless Join and Dependency Preserving DecompositionDecomposition of a relation is done when a relation in a relational model is not in appropriate normal form. 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The DBMS records every transaction on stable storage, allowing for easy data recovery when a failure occurs. For each operation performed on the database, a log file is created. Transactions are l 10 min read Timestamp based Concurrency ControlTimestamp-based concurrency control is a method used in database systems to ensure that transactions are executed safely and consistently without conflicts, even when multiple transactions are being processed simultaneously. This approach relies on timestamps to manage and coordinate the execution o 5 min read Dirty Read in SQLPre-Requisite - Types of Schedules, Transaction Isolation Levels in DBMS A Dirty Read in SQL occurs when a transaction reads data that has been modified by another transaction, but not yet committed. 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Serial or one by one execution of schedules has less resource utilization and low throughput. To improve it, two or more transactions are run concurrently. View S 6 min read Recoverability in DBMSRecoverability is a critical feature of database systems that ensures the database can return to a consistent and reliable state after a failure or error. It guarantees that the effects of committed transactions are saved permanently, while uncommitted transactions are rolled back to maintain data i 7 min read Precedence Graph for Testing Conflict Serializability in DBMSA Precedence Graph or Serialization Graph is used commonly to test the Conflict Serializability of a schedule. It is a directed Graph (V, E) consisting of a set of nodes V = {T1, T2, T3..........Tn} and a set of directed edges E = {e1, e2, e3..................em}. The graph contains one node for eac 6 min read Database Recovery Techniques in DBMSDatabase Systems like any other computer system, are subject to failures but the data stored in them must be available as and when required. When a database fails it must possess the facilities for fast recovery. It must also have atomicity i.e. either transactions are completed successfully and com 11 min read Starvation in DBMSStarvation in DBMS is a problem that happens when some processes are unable to get the resources they need because other processes keep getting priority. This can happen in situations like locking or scheduling, where some processes keep getting the resources first, leaving others waiting indefinite 8 min read Deadlock in DBMSIn database management systems (DBMS) a deadlock occurs when two or more transactions are unable to the proceed because each transaction is waiting for the other to the release locks on resources. 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