This document discusses transactions and concurrency control in database systems. It covers key concepts like atomicity, consistency, isolation, and durability (ACID) properties of transactions. It also discusses serialization, schedules, conflict serializability, and various concurrency control protocols like locking and timestamp ordering to achieve isolation while allowing concurrent execution of transactions.
The document discusses database management systems and transaction concepts. It provides examples to illustrate transaction properties like atomicity, consistency, isolation, and durability. It defines transaction states, discusses implementation of atomicity and durability using shadow databases. It also covers topics like serializability, recoverability, concurrency control protocols, and different levels of consistency.
Transaction management provides concurrency control and recovery in databases. Concurrency control ensures transactions execute correctly and reliably despite concurrent access through techniques like locking. Recovery ensures the database remains fault tolerant by undoing aborted transactions and redoing committed ones using write-ahead logging to survive failures. The ARIES protocol analyzes the log, redoes dirty writes, and undoes uncommitted transactions to recover the consistent database state after a crash.
Introduction to database-Transaction Concurrency and RecoveryAjit Nayak
Three key points about transactions from the document:
1. A transaction is a unit of program execution that accesses and possibly updates data items in a database. It must have ACID properties - Atomicity, Consistency, Isolation, and Durability - to preserve data integrity.
2. Concurrency control schemes allow multiple transactions to run concurrently for better performance, but they must enforce transaction isolation to prevent inconsistent outcomes.
3. A concurrent schedule is considered serializable, and thus preserves isolation, if it is equivalent to some serial schedule where transactions execute one after another. This can be determined using precedence graphs.
The document discusses transaction management in database systems. It defines a transaction as a unit of program execution that accesses and updates data items. For transactions to preserve data integrity, the database system must ensure atomicity, consistency, isolation, and durability (ACID properties). Concurrency control schemes are mechanisms that achieve isolation by controlling interactions between concurrent transactions to prevent inconsistent database states. A schedule specifies the order of transaction instructions. For a schedule to be serializable, it must be equivalent to a serial schedule where transactions execute one after another.
This chapter discusses transactions in database systems. It defines transactions as units of program execution that access and update data. For transactions to preserve data integrity, they must have ACID properties - Atomicity, Consistency, Isolation, and Durability. The chapter covers transaction concepts, states, concurrent execution, serializability, and recovery implementation techniques. It discusses how concurrency control ensures transactions execute correctly when run concurrently.
This document discusses concurrency control in distributed transactions. It describes three approaches: locking, timestamp ordering, and optimistic concurrency control. Locking involves servers locally holding locks on objects and deadlocks can occur if transactions are interleaved incorrectly across servers. Timestamp ordering assigns unique timestamps to transactions to determine a consistent ordering. Optimistic concurrency control validates transactions have not conflicted before committing updates, aborting conflicting transactions.
The document discusses transaction management and concurrency control in database systems. It defines a transaction as a sequence of reads and writes to the database by a user program. Transactions must satisfy ACID properties like atomicity, consistency, isolation and durability. Concurrency control techniques like locking and logging are used to execute transactions concurrently while maintaining isolation and serializability. The document provides examples of scheduling transactions and discusses anomalies that can occur without proper concurrency control.
This document discusses transaction management and concurrency control. It defines a transaction as a logical unit of work that must be completed or aborted with no intermediate states. It describes the ACID properties of atomicity, consistency, isolation, and durability that transactions should have. It also discusses concurrency control techniques like locking and time stamping to ensure transactions execute serially for consistency despite concurrent access.
The document discusses database management systems and transaction concepts. It provides examples to illustrate transaction properties like atomicity, consistency, isolation, and durability. It defines transaction states, discusses implementation of atomicity and durability using shadow databases. It also covers topics like serializability, recoverability, concurrency control protocols, and different levels of consistency.
Transaction management provides concurrency control and recovery in databases. Concurrency control ensures transactions execute correctly and reliably despite concurrent access through techniques like locking. Recovery ensures the database remains fault tolerant by undoing aborted transactions and redoing committed ones using write-ahead logging to survive failures. The ARIES protocol analyzes the log, redoes dirty writes, and undoes uncommitted transactions to recover the consistent database state after a crash.
Introduction to database-Transaction Concurrency and RecoveryAjit Nayak
Three key points about transactions from the document:
1. A transaction is a unit of program execution that accesses and possibly updates data items in a database. It must have ACID properties - Atomicity, Consistency, Isolation, and Durability - to preserve data integrity.
2. Concurrency control schemes allow multiple transactions to run concurrently for better performance, but they must enforce transaction isolation to prevent inconsistent outcomes.
3. A concurrent schedule is considered serializable, and thus preserves isolation, if it is equivalent to some serial schedule where transactions execute one after another. This can be determined using precedence graphs.
The document discusses transaction management in database systems. It defines a transaction as a unit of program execution that accesses and updates data items. For transactions to preserve data integrity, the database system must ensure atomicity, consistency, isolation, and durability (ACID properties). Concurrency control schemes are mechanisms that achieve isolation by controlling interactions between concurrent transactions to prevent inconsistent database states. A schedule specifies the order of transaction instructions. For a schedule to be serializable, it must be equivalent to a serial schedule where transactions execute one after another.
This chapter discusses transactions in database systems. It defines transactions as units of program execution that access and update data. For transactions to preserve data integrity, they must have ACID properties - Atomicity, Consistency, Isolation, and Durability. The chapter covers transaction concepts, states, concurrent execution, serializability, and recovery implementation techniques. It discusses how concurrency control ensures transactions execute correctly when run concurrently.
This document discusses concurrency control in distributed transactions. It describes three approaches: locking, timestamp ordering, and optimistic concurrency control. Locking involves servers locally holding locks on objects and deadlocks can occur if transactions are interleaved incorrectly across servers. Timestamp ordering assigns unique timestamps to transactions to determine a consistent ordering. Optimistic concurrency control validates transactions have not conflicted before committing updates, aborting conflicting transactions.
The document discusses transaction management and concurrency control in database systems. It defines a transaction as a sequence of reads and writes to the database by a user program. Transactions must satisfy ACID properties like atomicity, consistency, isolation and durability. Concurrency control techniques like locking and logging are used to execute transactions concurrently while maintaining isolation and serializability. The document provides examples of scheduling transactions and discusses anomalies that can occur without proper concurrency control.
This document discusses transaction management and concurrency control. It defines a transaction as a logical unit of work that must be completed or aborted with no intermediate states. It describes the ACID properties of atomicity, consistency, isolation, and durability that transactions should have. It also discusses concurrency control techniques like locking and time stamping to ensure transactions execute serially for consistency despite concurrent access.
This document provides an overview of database transactions. It defines transactions and discusses their key concepts and states. It describes ACID properties including atomicity, consistency, isolation, and durability. It discusses implementation of atomicity and durability using shadow databases. It also covers concurrent execution, serializability, recoverability, and how transactions are defined in SQL.
The document discusses concurrency control in database management systems. It defines key terms like transaction, atomicity, consistency, isolation, and durability. Transactions must have ACID properties - Atomicity, Consistency, Isolation, and Durability. Atomicity means all operations of a transaction are completed or none are. Consistency means the database remains consistent before and after a transaction. Isolation means transactions appear to execute serially despite concurrent execution. Durability means committed transactions persist even after failures.
Transactions and Concurrency Control in distributed systems. Transaction properties, classification, and transaction implementation. Flat, Nested, and Distributed transactions. Inconsistent Retrievals, Lost Update, Dirty Read, and Premature Writes Problem
The document discusses concurrency control techniques for databases, including lock-based protocols, timestamp-based protocols, and validation-based protocols. It focuses on lock-based protocols, describing how locks work, the two-phase locking protocol, deadlocks, and ways to handle them such as deadlock prevention and detection. It also discusses topics like multiple granularity locking, intention locks, and graph-based protocols.
The document discusses transaction management in database systems. It covers the ACID properties that transactions must satisfy - atomicity, consistency, isolation, and durability. It also discusses concurrency control techniques used to allow concurrent execution of transactions while preventing anomalies, including strict two-phase locking and lock-based concurrency control. Serializability is introduced as a way to ensure concurrent schedules have the same effect as serial schedules.
A transaction is a logical unit of work that transforms the database from one consistent state to another. It has four key properties: atomicity, consistency, isolation, and durability (ACID). Concurrency control algorithms like locking and timestamping are used by the database scheduler to ensure transactions execute reliably in a concurrent environment and serialize properly. Locking involves acquiring locks on data to prevent inconsistent reads or writes. Problems can arise from deadlocks when transactions wait for each other's locks.
The document summarizes topics discussed in a database management systems lecture, including concurrency control techniques like intention locks, index locking, optimistic concurrency control using validation, and timestamp ordering algorithms. It also discusses multi-version concurrency control and challenges with commit in distributed databases using two phase commit and the Paxos algorithm. The lecture covers lock-based and optimistic approaches to concurrency control and managing concurrent transactions in a database system.
Concurrency control techniques ensure transactions are executed atomically and isolated. There are two main types - lock-based and timestamp-based protocols. Lock-based protocols use locks to control access to data during read/write operations. Timestamp protocols order transactions based on their timestamp to ensure serializability. Distributed transactions involve statements that access data on multiple database nodes. Replication improves availability by storing copies of data at different sites.
The document discusses various database recovery techniques including log-based recovery, shadow paging recovery, and recovery with concurrent transactions. Log-based recovery uses a log to record transactions and supports either deferred or immediate database modification. Shadow paging maintains a shadow page table to allow recovery to a previous state. Checkpointing improves recovery performance. Recovery for concurrent transactions uses undo and redo lists constructed during the recovery process.
Distributed database system is collection of loosely coupled sites that are independeant of each other.
Distributed transaction model
Concurrency control
2 phase commit protocol
The document discusses concurrent transactions in database systems. It defines a transaction as a unit of program execution that accesses and updates database data. Concurrent transaction execution improves system throughput and resource utilization while reducing wait times. However, concurrency control schemes like locking protocols and timestamp ordering are needed to ensure transactions execute serially to preserve data consistency and serializability. The document provides an overview of transaction concepts, states, concurrency control techniques, and how SQL handles transactions.
The document discusses transaction concepts in database systems. It defines transactions as units of program execution that access and update database items. Transactions must satisfy the ACID properties of atomicity, consistency, isolation, and durability. Concurrent transaction execution allows for increased throughput but requires mechanisms to ensure serializability and recoverability. The document describes transaction states, schedule serializability testing using precedence graphs, and the goal of concurrency control protocols to enforce serializability without examining schedules after execution.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
Concurrency control ensures that operations from concurrent processes generate correct results while maximizing performance. It maintains consistency when components interacting concurrently could violate one another's consistency rules. In databases, concurrency control guarantees transactions are serializable and follow the ACID properties of atomicity, consistency, isolation, and durability. Mechanisms include optimistic, pessimistic, and semi-optimistic approaches, with the goal of generating serializable schedules for concurrency and recoverability.
The document provides an introduction to transaction processing concepts including:
- Defining transactions and their basic operations like read and write
- Desirable ACID properties of transactions including atomicity, consistency, isolation, and durability
- Characterizing transaction schedules based on recoverability and serializability
- Explaining concurrency control and recovery techniques needed to ensure schedules meet these properties
The document discusses database management systems and recovery techniques. It contains 8 sections covering topics like log-based recovery, deferred and immediate database modification, checkpoints, recovery with concurrent transactions, log record buffering, and database buffering. The sections include slides with explanations of key concepts and examples to illustrate recovery procedures and algorithms.
The document discusses transaction concepts in database systems. It defines a transaction as a unit of program execution that accesses and updates data. Transactions must satisfy the ACID properties: Atomicity, Consistency, Isolation, and Durability. Concurrency control schemes allow concurrent execution of transactions while maintaining isolation. A schedule specifies the order of transaction operations. A schedule is serializable if it is equivalent to a serial schedule where transactions execute one after another. Conflict serializability and view serializability are approaches to determine if a schedule is serializable.
Transactions are units of program execution that access and update database items. A transaction must preserve database consistency. Concurrent transactions are allowed for increased throughput but can result in inconsistent views. Serializability ensures transactions appear to execute serially in some order. Conflict serializability compares transaction instruction orderings while view serializability compares transaction views. Concurrency control protocols enforce serializability without examining schedules after execution.
This document provides an overview of database transactions. It defines transactions and discusses their key concepts and states. It describes ACID properties including atomicity, consistency, isolation, and durability. It discusses implementation of atomicity and durability using shadow databases. It also covers concurrent execution, serializability, recoverability, and how transactions are defined in SQL.
The document discusses concurrency control in database management systems. It defines key terms like transaction, atomicity, consistency, isolation, and durability. Transactions must have ACID properties - Atomicity, Consistency, Isolation, and Durability. Atomicity means all operations of a transaction are completed or none are. Consistency means the database remains consistent before and after a transaction. Isolation means transactions appear to execute serially despite concurrent execution. Durability means committed transactions persist even after failures.
Transactions and Concurrency Control in distributed systems. Transaction properties, classification, and transaction implementation. Flat, Nested, and Distributed transactions. Inconsistent Retrievals, Lost Update, Dirty Read, and Premature Writes Problem
The document discusses concurrency control techniques for databases, including lock-based protocols, timestamp-based protocols, and validation-based protocols. It focuses on lock-based protocols, describing how locks work, the two-phase locking protocol, deadlocks, and ways to handle them such as deadlock prevention and detection. It also discusses topics like multiple granularity locking, intention locks, and graph-based protocols.
The document discusses transaction management in database systems. It covers the ACID properties that transactions must satisfy - atomicity, consistency, isolation, and durability. It also discusses concurrency control techniques used to allow concurrent execution of transactions while preventing anomalies, including strict two-phase locking and lock-based concurrency control. Serializability is introduced as a way to ensure concurrent schedules have the same effect as serial schedules.
A transaction is a logical unit of work that transforms the database from one consistent state to another. It has four key properties: atomicity, consistency, isolation, and durability (ACID). Concurrency control algorithms like locking and timestamping are used by the database scheduler to ensure transactions execute reliably in a concurrent environment and serialize properly. Locking involves acquiring locks on data to prevent inconsistent reads or writes. Problems can arise from deadlocks when transactions wait for each other's locks.
The document summarizes topics discussed in a database management systems lecture, including concurrency control techniques like intention locks, index locking, optimistic concurrency control using validation, and timestamp ordering algorithms. It also discusses multi-version concurrency control and challenges with commit in distributed databases using two phase commit and the Paxos algorithm. The lecture covers lock-based and optimistic approaches to concurrency control and managing concurrent transactions in a database system.
Concurrency control techniques ensure transactions are executed atomically and isolated. There are two main types - lock-based and timestamp-based protocols. Lock-based protocols use locks to control access to data during read/write operations. Timestamp protocols order transactions based on their timestamp to ensure serializability. Distributed transactions involve statements that access data on multiple database nodes. Replication improves availability by storing copies of data at different sites.
The document discusses various database recovery techniques including log-based recovery, shadow paging recovery, and recovery with concurrent transactions. Log-based recovery uses a log to record transactions and supports either deferred or immediate database modification. Shadow paging maintains a shadow page table to allow recovery to a previous state. Checkpointing improves recovery performance. Recovery for concurrent transactions uses undo and redo lists constructed during the recovery process.
Distributed database system is collection of loosely coupled sites that are independeant of each other.
Distributed transaction model
Concurrency control
2 phase commit protocol
The document discusses concurrent transactions in database systems. It defines a transaction as a unit of program execution that accesses and updates database data. Concurrent transaction execution improves system throughput and resource utilization while reducing wait times. However, concurrency control schemes like locking protocols and timestamp ordering are needed to ensure transactions execute serially to preserve data consistency and serializability. The document provides an overview of transaction concepts, states, concurrency control techniques, and how SQL handles transactions.
The document discusses transaction concepts in database systems. It defines transactions as units of program execution that access and update database items. Transactions must satisfy the ACID properties of atomicity, consistency, isolation, and durability. Concurrent transaction execution allows for increased throughput but requires mechanisms to ensure serializability and recoverability. The document describes transaction states, schedule serializability testing using precedence graphs, and the goal of concurrency control protocols to enforce serializability without examining schedules after execution.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
Concurrency control ensures that operations from concurrent processes generate correct results while maximizing performance. It maintains consistency when components interacting concurrently could violate one another's consistency rules. In databases, concurrency control guarantees transactions are serializable and follow the ACID properties of atomicity, consistency, isolation, and durability. Mechanisms include optimistic, pessimistic, and semi-optimistic approaches, with the goal of generating serializable schedules for concurrency and recoverability.
The document provides an introduction to transaction processing concepts including:
- Defining transactions and their basic operations like read and write
- Desirable ACID properties of transactions including atomicity, consistency, isolation, and durability
- Characterizing transaction schedules based on recoverability and serializability
- Explaining concurrency control and recovery techniques needed to ensure schedules meet these properties
The document discusses database management systems and recovery techniques. It contains 8 sections covering topics like log-based recovery, deferred and immediate database modification, checkpoints, recovery with concurrent transactions, log record buffering, and database buffering. The sections include slides with explanations of key concepts and examples to illustrate recovery procedures and algorithms.
The document discusses transaction concepts in database systems. It defines a transaction as a unit of program execution that accesses and updates data. Transactions must satisfy the ACID properties: Atomicity, Consistency, Isolation, and Durability. Concurrency control schemes allow concurrent execution of transactions while maintaining isolation. A schedule specifies the order of transaction operations. A schedule is serializable if it is equivalent to a serial schedule where transactions execute one after another. Conflict serializability and view serializability are approaches to determine if a schedule is serializable.
Transactions are units of program execution that access and update database items. A transaction must preserve database consistency. Concurrent transactions are allowed for increased throughput but can result in inconsistent views. Serializability ensures transactions appear to execute serially in some order. Conflict serializability compares transaction instruction orderings while view serializability compares transaction views. Concurrency control protocols enforce serializability without examining schedules after execution.
Transactions are units of program execution that access and update database items. A transaction must ensure database consistency. Concurrent transactions are allowed for increased throughput but can violate consistency if not isolated. Isolation is achieved through conflict and view serializability, where schedules are equivalent to a serial order. Concurrency control protocols enforce serializability without examining schedules after execution.
Transactions are units of program execution that access and update database items. A transaction must preserve database consistency. Concurrent transactions are allowed for increased throughput but can result in inconsistent views. Serializability ensures transactions appear to execute serially in some order. Conflict serializability compares transaction instruction orderings while view serializability compares transaction views. Concurrency control protocols enforce serializability without examining schedules after execution.
This document discusses transaction management in databases. It defines a transaction as a unit of program execution that accesses and updates data items. Transactions must satisfy the ACID properties of atomicity, consistency, isolation, and durability to maintain data integrity. Atomicity ensures that transactions are fully completed or rolled back. Consistency means transactions preserve the consistency constraints of the database. Isolation ensures transactions execute independently without interfering with each other. Durability means transaction changes persist even after failures. The document discusses various concurrency control techniques like serializability to coordinate concurrent transaction execution while preserving isolation.
This document summarizes key concepts related to database transactions from Chapter 15 of the textbook "Database System Concepts". It discusses transaction concepts, properties of atomicity, consistency, isolation, and durability (ACID), transaction states, implementation of atomicity and durability, concurrent executions, serializability, recoverability, implementation of isolation, transaction definition in SQL, and testing for serializability.
Concurrent execution of database transactions in a multi-user system allows multiple users to access the same database simultaneously. Concurrency control is needed to prevent inconsistencies that can arise from transactions interacting and interfering with each other. It works by locking data that is being accessed by a transaction until that transaction completes, preventing other transactions from accessing and potentially changing that data mid-transaction. Serializability is a common correctness criterion used in database concurrency control that requires the concurrent execution of transactions to have the same effect as executing the transactions sequentially in some order.
1) The document discusses transaction processing concepts including transactions, concurrency control problems, and ACID properties.
2) Transactions must be atomic, consistent, isolated, and durable (ACID) to maintain database integrity. Concurrency without proper control can cause issues like lost updates, dirty reads, and incorrect summaries.
3) Several concurrency control techniques ensure transactions operate correctly when run concurrently through schedule serialization and avoiding conflicts.
The document discusses transactions in database management systems and the ACID properties that transactions must satisfy. It describes the four ACID properties - atomicity, consistency, isolation, and durability. Atomicity ensures that transactions are treated as an atomic unit and either fully occur or not at all. Consistency requires that transactions alone preserve the consistency of the database. Isolation ensures that concurrently executing transactions are isolated from each other. Durability means the effects of committed transactions persist even if the system crashes. The document also discusses transaction schedules, concurrency control, and anomalies that can occur with concurrent transaction execution.
UNIT 2- TRANSACTION CONCEPTS AND CONCURRENCY CONCEPTS (1).pdfKavitaShinde26
This document discusses transaction concepts and concurrency control. It defines a transaction as a collection of operations that performs a logical function in a database. The four properties of transactions are outlined as atomicity, consistency, isolation, and durability. Transaction states such as active, committed, and aborted are defined. Concurrency control techniques including locking, timestamps, and deadlock handling are explained. Deadlock avoidance algorithms like wait-die and wound-wait are summarized.
Distributed Database Design and Relational Query LanguageAAKANKSHA JAIN
1) The document discusses topics related to distributed database design and relational query languages including transaction management, serializability, blocking, deadlocks, and query optimization.
2) A transaction begins with the first SQL statement and ends when committed or rolled back. It has ACID properties - atomicity, consistency, isolation, and durability.
3) Serializability ensures transactions are processed in a consistent order. Conflict serializability allows swapping non-conflicting operations while view serializability requires equivalent initial reads, write-read sequences, and final writers.
Transactions allow multiple users to access and update shared data concurrently in a database. They have four main properties: atomicity, consistency, isolation, and durability (ACID). Concurrency control schemes ensure transactions are isolated from each other to preserve consistency. A schedule is serializable if its outcome is equivalent to running transactions sequentially. Conflict serializability checks for conflicts between transactions' instructions and views the schedule as equivalent to a serial schedule after swapping non-conflicting instructions. Precedence graphs can test for conflict serializability by checking for cycles.
The document discusses transactions management and concurrency control in databases. It defines transactions as logical operations like bank transactions or airline reservations that consist of sets of read and write operations. Transactions must have ACID properties - atomicity, consistency, isolation, and durability. Concurrency control techniques like lock-based and timestamp-based protocols are used to coordinate concurrent execution of transactions and prevent conflicts. Schedules can be serial or non-serial, with non-serial schedules further classified as serializable or non-serializable. Recoverable and cascading schedules are discussed.
This document provides an overview of transactions in database systems. It discusses key concepts like atomicity, consistency, isolation, and durability (ACID) that transactions must satisfy. Transactions can execute concurrently for increased performance but the database must enforce serializability to maintain consistency. The document defines transactions, schedules, and conflicting operations. It introduces the concepts of conflict serializability and view serializability to determine when concurrent schedules are equivalent to serial schedules.
This document provides an overview of transaction processing and recovery in database management systems. It discusses topics like transaction processing, concurrency control techniques including locking and timestamping protocols, recovery from transaction failures using log-based recovery, and checkpoints. The key aspects covered are the ACID properties of transactions, serialization testing using precedence graphs, recoverable schedules, and concurrency control methods like locking, timestamp ordering, and validation-based protocols.
TRANSACTION MANAGEMENT AND TIME STAMP PROTOCOLS AND BACKUP RECOVERYRohit Kumar
The document discusses transactions and concurrency control in database systems. It defines transactions as logical units of work that ensure data integrity during concurrent operations. It describes four key properties of transactions - atomicity, consistency, isolation, and durability (ACID) - and explains how they maintain data correctness. The document also discusses serialization, schedules, locking protocols like two-phase locking, and isolation levels to coordinate concurrent transactions and avoid anomalies like dirty reads.
Dokumen tersebut membahas tentang metode pengiriman form (POST dan GET) serta penggunaan session dalam PHP. Metode POST menyembunyikan variabel yang dikirim di alamat web, sedangkan metode GET menampilkan variabelnya. Session digunakan untuk menyimpan sementara variabel antar halaman dengan mendaftarkan, mengisi, dan menampilkan variabel session. Contoh koding mendemonstrasikan penggunaan form dengan metode POST, penyimpanan variabel ke session, dan penampil
Dokumen ini membahas penggunaan beberapa tag HTML penting untuk format tampilan dokumen seperti heading, paragraph, line break, dan daftar termasuk ordered list, unordered list, dan menu list.
The PHP script connects to a database to log website visitor statistics including the visitor's IP address, date, number of page hits, and time online. It checks if the IP address already exists for the current date, and if not, inserts a new entry, otherwise it updates the existing entry by incrementing the hits count and setting the online time. Various metrics are then calculated from the database like current visitors, total visitors, hits for the day, total hits, and current online users. These statistics are output in an HTML table.
Web/HTML Editor digunakan untuk membuat halaman web statis dan dinamis secara visual atau menggunakan teks editor. Editor web profesional menyediakan fitur yang mempercepat pembuatan halaman seperti GUI, otomatisasi kode, dan sambungan basis data. Browser menerjemahkan kode HTML menjadi tampilan yang diinginkan. Microsoft Internet Explorer, Firefox, dan Safari adalah contoh browser web. Ada dua model pembuatan halaman web statis yaitu secara lokal dan di server. Str
CSS digunakan untuk mengubah tampilan halaman website seperti warna dan format dengan mudah. CSS memungkinkan pengguna untuk mempercantik tampilan teks, tombol, tabel dan elemen lainnya. CSS dapat ditempatkan langsung di tag HTML, di dalam file HTML, atau di file CSS terpisah yang dapat digunakan untuk semua halaman website. Kelas CSS memungkinkan pengguna untuk menerapkan gaya yang sama pada elemen-elemen yang berbeda.
Dokumen ini membahas konsep dasar penggunaan basis data pada sistem berbasis web. Terdapat penjelasan tentang koneksi database, mengeksekusi query, dan fungsi-fungsi PHP untuk MySQL. Juga dijelaskan cara membuat database, tabel, dan file-file pendukung seperti config, connection, dan SQL. Selanjutnya dijelaskan cara menampilkan, menambahkan, mengubah, dan menghapus data kota pada tabel melalui beberapa file seperti form input, tampil, edit
This document discusses PHP control structures including if/else statements, switch statements, and looping structures like while, do-while and for loops.
If/else statements allow for conditional execution of code based on simple or compound expressions. Switch statements allow checking a variable against multiple case values.
While and do-while loops check a condition at the start or end of each loop iteration. For loops allow iterating with a counter variable through initialization, condition checking, and increment/decrement each loop.
HTML dikembangkan oleh Tim Berners-Lee di CERN dan dipopulerkan oleh browser Mosaic pada tahun 1990-an. HTML menggunakan tag yang diletakkan di antara tanda kurung siku untuk menandai teks dan elemen lainnya. Struktur dasar file HTML terdiri atas bagian Header dan Body.
The document discusses visualizing an HTML table containing poll results using Highcharts. It includes instructions to include necessary JavaScript libraries, initialize a chart on page load by passing the table and chart options to a Highcharts visualization function, and output the poll response counts from a database into the table. This will generate an interactive column chart of the poll results from the data in the HTML table.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
2. 2
Transaction ConceptTransaction Concept
A transaction is a unit of program execution that
accesses and possibly updates various data items.
A transaction must see a consistent database.
During transaction execution the database may be
inconsistent.
When the transaction is committed, the database must
be consistent.
Two main issues to deal with:
Failures of various kinds, such as hardware failures and
system crashes
Concurrent execution of multiple transactions
3. 3
ACID PropertiesACID Properties
Atomicity. Either all operations of the transaction are
properly reflected in the database or none are.
Consistency. Execution of a transaction in isolation
preserves the consistency of the database.
Isolation. Although multiple transactions may execute
concurrently, each transaction must be unaware of other
concurrently executing transactions. Intermediate
transaction results must be hidden from other concurrently
executed transactions.
That is, for every pair of transactions Ti and Tj, it appears to Ti
that either Tj, finished execution before Ti started, or Tj started
execution after Ti finished.
Durability. After a transaction completes successfully,
the changes it has made to the database persist, even if
there are system failures.
To preserve integrity of data, the database system must ensure:
4. 4
Example of Fund TransferExample of Fund Transfer
Transaction to transfer $50 from account A to account B:
1. read(A)
2. A := A – 50
3. write(A)
4. read(B)
5. B := B + 50
6. write(B)
Consistency requirement – the sum of A and B is unchanged
by the execution of the transaction.
Atomicity requirement — if the transaction fails after step 3 and
before step 6, the system should ensure that its updates are
not reflected in the database, else an inconsistency will result.
5. 5
Example of Fund Transfer (Cont.)Example of Fund Transfer (Cont.)
Durability requirement — once the user has been notified
that the transaction has completed (i.e., the transfer of the
$50 has taken place), the updates to the database by the
transaction must persist despite failures.
Isolation requirement — if between steps 3 and 6, another
transaction is allowed to access the partially updated
database, it will see an inconsistent database
(the sum A + B will be less than it should be).
Can be ensured trivially by running transactions serially,
that is one after the other. However, executing multiple
transactions concurrently has significant benefits, as we
will see.
6. 6
Transaction StateTransaction State
Active, the initial state; the transaction stays in this state
while it is executing
Partially committed, after the final statement has been
executed.
Failed, after the discovery that normal execution can no
longer proceed.
Aborted, after the transaction has been rolled back and the
database restored to its state prior to the start of the
transaction. Two options after it has been aborted:
restart the transaction – only if no internal logical error
kill the transaction
Committed, after successful completion.
8. 8
Implementation of Atomicity andImplementation of Atomicity and
DurabilityDurability
The recovery-management component of a database
system implements the support for atomicity and
durability.
The shadow-database scheme:
assume that only one transaction is active at a time.
a pointer called db_pointer always points to the current
consistent copy of the database.
all updates are made on a shadow copy of the database, and
db_pointer is made to point to the updated shadow copy
only after the transaction reaches partial commit and all
updated pages have been flushed to disk.
in case transaction fails, old consistent copy pointed to by
db_pointer can be used, and the shadow copy can be
deleted.
9. 9
Implementation of Atomicity and DurabilityImplementation of Atomicity and Durability
Assumes disks to not fail
Useful for text editors, but extremely inefficient for large
databases: executing a single transaction requires copying
the entire database.
The shadow-database scheme:
10. 10
Concurrent ExecutionsConcurrent Executions
Multiple transactions are allowed to run concurrently in the
system. Advantages are:
increased processor and disk utilization, leading to
better transaction throughput: one transaction can be using the
CPU while another is reading from or writing to the disk
reduced average response time for transactions: short
transactions need not wait behind long ones.
Concurrency control schemes – mechanisms to achieve
isolation, i.e., to control the interaction among the
concurrent transactions in order to prevent them from
destroying the consistency of the database
11. 11
SchedulesSchedules
Schedules – sequences that indicate the chronological order in
which instructions of concurrent transactions are executed
a schedule for a set of transactions must consist of all instructions of
those transactions
must preserve the order in which the instructions appear in each
individual transaction.
12. 12
Example SchedulesExample Schedules
Let T1 transfer $50 from A to B, and T2 transfer 10% of
the balance from A to B. The following is a serial
schedule (Schedule 1 in the text), in which T1 is
followed by T2.
13. 13
Example ScheduleExample Schedule
Let T1 and T2 be the transactions defined previously. The
following schedule (Schedule 3 in the text) is not a serial
schedule, but it is equivalent to Schedule 1.
In both Schedule 1 and 3, the sum A + B is preserved.
14. 14
Example Schedules (Cont.)Example Schedules (Cont.)
The following concurrent schedule (Schedule 4 in the
text) does not preserve the value of the the sum A + B.
15. 15
SerializabilitySerializability
Basic Assumption – Each transaction preserves database
consistency.
Thus serial execution of a set of transactions preserves
database consistency.
A (possibly concurrent) schedule is serializable if it is
equivalent to a serial schedule. Different forms of schedule
equivalence give rise to the notion of conflict serializability
We ignore operations other than read and write instructions,
and we assume that transactions may perform arbitrary
computations on data in local buffers in between reads and
writes. Our simplified schedules consist of only read and
write instructions.
16. 16
Conflict SerializabilityConflict Serializability
Operations oi and oj of transactions Ti and Tj respectively are
conflicting if and only if there exists some item x accessed by
both oi and oj, and at least one of these operations is write(x).
1. oi = read(x), oj = read(x). oi and oj don’t conflict.
2. oi = read(x), oj = write(x). They conflict.
3. oi = write(x), oj = read(x). They conflict
4. oi = write(x), oj = write(x). They conflict
Intuitively, a conflict between oi and oj forces a (logical) temporal
order between them. If oi and oj are consecutive in a schedule
and they do not conflict, their results would remain the same
even if they had been interchanged in the schedule.
17. 17
Conflict Serializability (Cont.)Conflict Serializability (Cont.)
If a schedule S can be transformed into a schedule S´ by a
series of swaps of non-conflicting instructions, we say that
S and S´ are conflict equivalent.
We say that a schedule S is conflict serializable if it is
conflict equivalent to a serial schedule
Example of a schedule that is not conflict serializable:
T1 T2
read(x)
write(x)
write(x)
We are unable to swap instructions in the above schedule
to obtain either the serial schedule < T1, T2 >, or the serial
schedule < T2, T1 >.
18. 18
Conflict Serializability (Cont.)Conflict Serializability (Cont.)
Schedule below can be transformed into a serial schedule
where T2 follows T1, by series of swaps of non-conflicting
instructions. Therefore Schedule below is conflict
serializable.
19. 19
RecoverabilityRecoverability
Recoverable schedule — if a transaction Tj reads a data
items previously written by a transaction Ti , the commit operation
of Ti appears before the commit operation of Tj.
The following schedule is not recoverable if T9 commits
immediately after the read
If T8 should abort, T9 would have read (and possibly shown to the
user) an inconsistent database state. Hence database must
ensure that schedules are recoverable.
Need to address the effect of transaction failures on concurrently
running transactions.
20. 20
Recoverability (Cont.)Recoverability (Cont.)
Cascading rollback – a single transaction failure leads
to a series of transaction rollbacks. Consider the following
schedule where none of the transactions has yet
committed (so the schedule is recoverable)
If T10 fails, T11 and T12 must also be rolled back.
Can lead to the undoing of a significant amount of work
21. 21
Recoverability (Cont.)Recoverability (Cont.)
Cascadeless schedules — cascading rollbacks cannot
occur; for each pair of transactions Ti and Tj such that Tj reads a
data item previously written by Ti, the commit operation of Ti
appears before the read operation of Tj.
Every cascadeless schedule is also recoverable
It is desirable to restrict the schedules to those that are
cascadeless
22. 22
Recoverability (Cont.)Recoverability (Cont.)
Strict schedules — Dirty write and reads cannot occur; for
each pair of transactions Ti and Tj such that Tj reads or writes a
data item previously written by Ti, the commit operation of Ti
appears before the read or write operation of Tj.
Every strict schedule is also cascadeless
It is desirable to further restrict the schedules to those that are
strict.
Rigorous schedules — For each pair of transactions Ti and Tj
conflicting operations of Ti and Ti are separated by a commit
operation.
Every rigorous schedule is strict.
It is most desirable to to consider only rigorous schedules
23. 23
Implementation of IsolationImplementation of Isolation
Schedules must be conflict serializable, and recoverable, for
the sake of database consistency, and preferably rigorous.
A policy in which only one transaction can execute at a time
generates serial schedules, but provides a poor degree of
concurrency..
Concurrency-control schemes tradeoff between the amount
of concurrency they allow and the amount of overhead that
they incur.
Some schemes allow only conflict-serializable schedules to
be generated, while others allow view-serializable
schedules that are not conflict-serializable.
24. 24
Transaction Definition in SQLTransaction Definition in SQL
Data manipulation language must include a construct for
specifying the set of actions that comprise a transaction.
In SQL, a transaction begins implicitly.
A transaction in SQL ends by:
Commit work commits current transaction and begins a new
one.
Rollback work causes current transaction to abort.
25. 25
Levels of Consistency in SQL-92Levels of Consistency in SQL-92
Serializable — default
Repeatable read — only committed records to be read,
repeated reads of same record must return same value.
However, aschedulemay not be serializable – it may find some
records inserted by a transaction but not find others.
Read committed — only committed records can be read,
but successive reads of record may return different (but
committed) values.
Read uncommitted — even uncommitted records may be
read.
Lower degrees of consistency useful for gathering approximate
information about the database, e.g., statistics for query optimizer.
26. 26
Testing for SerializabilityTesting for Serializability
Consider some schedule of a set of transactions T1,
T2, ..., Tn
Precedence graph — a direct graph where the
vertices are the transactions (names).
We draw an arc from Ti to Tj if the two transaction
conflict, and Ti accessed the data item on which the
conflict arose earlier.
We may label the arc by the item that was accessed.
Example x
y
29. 29
Test for Conflict SerializabilityTest for Conflict Serializability
A schedule is conflict serializable if and only if its precedence
graph is acyclic.
Cycle-detection algorithms exist which take order n2
time, where
n is the number of vertices in the graph. (Better algorithms take
order n + e where e is the number of edges.)
If precedence graph is acyclic, the serializability order can be
obtained by a topological sorting of the graph. This is a linear
order consistent with the partial order of the graph.
For example, a serializability order for Schedule A would be
T5 → T1 → T3 → T2 → T4 .
31. 31
Concurrency Control vs. SerializabilityConcurrency Control vs. Serializability
TestsTests
Testing a schedule for serializability after it has executed is a
little too late!
Goal – to develop concurrency control protocols that will assure
serializability. They will generally not examine the precedence
graph as it is being created; instead a protocol will impose a
discipline that avoids nonseralizable schedules.
Tests for serializability help understand why a concurrency
control protocol is correct.
32. 32
Concurrency ControlConcurrency Control
Lock-Based Protocols
Timestamp-Based Protocols
Validation-Based Protocols
Multiple Granularity
Deadlock Handling
Insert and Delete Operations
Concurrency in Index Structures
33. 33
Lock-Based ProtocolsLock-Based Protocols
A lock is a mechanism to control concurrent access to a data item
Data items can be locked in two modes :
1. exclusive (X) mode. Data item can be both read as well as
written. X-lock is requested using lock-X instruction.
2. shared (S) mode. Data item can only be read. S-lock is
requested using lock-S instruction.
Lock requests are made to concurrency-control manager.
Transaction can proceed only after request is granted.
34. 34
Lock-Based Protocols (Cont.)Lock-Based Protocols (Cont.)
Lock-compatibility matrix
A transaction may be granted a lock on an item if the requested
lock is compatible with locks already held on the item by other
transactions
Any number of transactions can hold shared locks on an item,
but if any transaction holds an exclusive on the item no other
transaction may hold any lock on the item.
If a lock cannot be granted, the requesting transaction is made to
wait till all incompatible locks held by other transactions have
been released. The lock is then granted.
35. 35
Lock-Based Protocols (Cont.)Lock-Based Protocols (Cont.)
Example of a transaction performing locking:
T2: lock-S(A);
read (A);
unlock(A);
lock-S(B);
read (B);
unlock(B);
display(A+B)
Locking as above is not sufficient to guarantee serializability — if A and B
get updated in-between the read of A and B, the displayed sum would be
wrong.
A locking protocol is a set of rules followed by all transactions while
requesting and releasing locks. Locking protocols restrict the set of
possible schedules.
36. 36
Pitfalls of Lock-Based ProtocolsPitfalls of Lock-Based Protocols
Consider the partial schedule
Neither T3 nor T4 can make progress — executing lock-S(B) causes T4
to wait for T3 to release its lock on B, while executing lock-X(A) causes
T3 to wait for T4 to release its lock on A.
Such a situation is called a deadlock.
To handle a deadlock one of T3 or T4 must be rolled back
and its locks released.
37. 37
Pitfalls of Lock-Based Protocols
(Cont.)(Cont.)
The potential for deadlock exists in most locking protocols.
Deadlocks are a necessary evil.
Starvation is also possible if concurrency control manager is
badly designed. For example:
A transaction may be waiting for an X-lock on an item, while a
sequence of other transactions request and are granted an S-lock
on the same item.
The same transaction is repeatedly rolled back due to deadlocks.
Concurrency control manager can be designed to prevent
starvation.
38. 38
The Two-Phase Locking ProtocolThe Two-Phase Locking Protocol
This is a protocol which ensures conflict-serializable schedules.
Phase 1: Growing Phase
transaction may obtain locks
transaction may not release locks
Phase 2: Shrinking Phase
transaction may release locks
transaction may not obtain locks
The protocol assures serializability. It can be proved that the
transactions can be serialized in the order of their lock points
(i.e. the point where a transaction acquired its final lock).
39. 39
The Two-Phase Locking Protocol
(Cont.)(Cont.)
Two-phase locking does not ensure freedom from deadlocks
Cascading roll-back is possible under two-phase locking. To
avoid this, follow a modified protocol called strict two-phase
locking. Here a transaction must hold all its exclusive locks till it
commits/aborts.
Rigorous two-phase locking is even stricter: here all locks
are held till commit/abort. In this protocol transactions can be
serialized in the order in which they commit.
40. 40
(Cont.)(Cont.)
There can be conflict serializable schedules that cannot be
obtained if two-phase locking is used.
However, in the absence of extra information (e.g., ordering of
access to data), two-phase locking is needed for conflict
serializability in the following sense:
Given a transaction Ti that does not follow two-phase locking, we
can find a transaction Tj that uses two-phase locking, and a
schedule for Ti and Tj that is not conflict serializable.
41. 41
Lock ConversionsLock Conversions
Two-phase locking with lock conversions:
– First Phase:
can acquire a lock-S on item
can acquire a lock-X on item
can convert a lock-S to a lock-X (upgrade)
– Second Phase:
can release a lock-S
can release a lock-X
can convert a lock-X to a lock-S (downgrade)
This protocol assures serializability. But still relies on the
programmer to insert the various locking instructions.
42. 42
Automatic Acquisition of LocksAutomatic Acquisition of Locks
A transaction Ti issues the standard read/write instruction,
without explicit locking calls.
The operation read(D) is processed as:
if Ti has a lock on D
then
read(D)
else
begin
if necessary wait until no other
transaction has a lock-X on D
grant Ti a lock-S on D;
read(D)
end
43. 43
Automatic Acquisition of LocksAutomatic Acquisition of Locks
(Cont.)(Cont.)
write(D) is processed as:
if Ti has a lock-X on D
then
write(D)
else
begin
if necessary wait until no other trans. has any lock on D,
if Ti has a lock-S on D
then
upgrade lock on D to lock-X
else
grant Ti a lock-X on D
write(D)
end;
All locks are released after commit or abort
44. 44
Implementation of LockingImplementation of Locking
A Lock manager can be implemented as a separate process
to which transactions send lock and unlock requests
The lock manager replies to a lock request by sending a lock
grant messages (or a message asking the transaction to roll
back, in case of a deadlock)
The requesting transaction waits until its request is answered
The lock manager maintains a data structure called a lock
table to record granted locks and pending requests
The lock table is usually implemented as an in-memory hash
table indexed on the name of the data item being locked
45. 45
Lock TableLock TableBlack rectangles indicate granted
locks, white ones indicate waiting
requests
Lock table also records the type of
lock granted or requested
New request is added to the end of
the queue of requests for the data
item, and granted if it is compatible
with all earlier locks
Unlock requests result in the
request being deleted, and later
requests are checked to see if they
can now be granted
If transaction aborts, all waiting or
granted requests of the transaction
are deleted
lock manager may keep a list of
locks held by each transaction, to
implement this efficiently
46. 46
Graph-Based ProtocolsGraph-Based Protocols
Graph-based protocols are an alternative to two-phase locking
Impose a partial ordering → on the set D = {d1, d2 ,..., dh} of all
data items.
If di → dj then any transaction accessing both di and dj must access
di before accessing dj.
Implies that the set D may now be viewed as a directed acyclic
graph, called a database graph.
The tree-protocol is a simple kind of graph protocol.
47. 47
Tree ProtocolTree Protocol
Only exclusive locks are allowed.
The first lock by Ti may be on any data item. Subsequently, a
data Q can be locked by Ti only if the parent of Q is currently
locked by Ti.
Data items may be unlocked at any time.
48. 48
Graph-Based Protocols (Cont.)Graph-Based Protocols (Cont.)
The tree protocol ensures conflict serializability as well as
freedom from deadlock.
Unlocking may occur earlier in the tree-locking protocol than in
the two-phase locking protocol.
shorter waiting times, and increase in concurrency
protocol is deadlock-free, no rollbacks are required
the abort of a transaction can still lead to cascading rollbacks.
(this correction has to be made in the book also.)
However, in the tree-locking protocol, a transaction may have to
lock data items that it does not access.
increased locking overhead, and additional waiting time
potential decrease in concurrency
Schedules not possible under two-phase locking are possible
under tree protocol, and vice versa.
49. 49
Timestamp-Based ProtocolsTimestamp-Based Protocols
Each transaction is issued a timestamp when it enters the system. If
an old transaction Ti has time-stamp TS(Ti), a new transaction Tj is
assigned time-stamp TS(Tj) such that TS(Ti) <TS(Tj).
The protocol manages concurrent execution such that the time-
stamps determine the serializability order.
In order to assure such behavior, the protocol maintains for each data
Q two timestamp values:
W-timestamp(Q) is the largest time-stamp of any transaction that
executed write(Q) successfully.
R-timestamp(Q) is the largest time-stamp of any transaction that
executed read(Q) successfully.
50. 50
Timestamp-Based Protocols (Cont.)Timestamp-Based Protocols (Cont.)
The timestamp ordering protocol ensures that any conflicting
read and write operations are executed in timestamp order.
Suppose a transaction Ti issues a read(Q)
1. If TS(Ti) ≤ W-timestamp(Q), then Ti needs to read a value of Q
that was already overwritten. Hence, the read operation is
rejected, and Ti is rolled back.
2. If TS(Ti)≥ W-timestamp(Q), then the read operation is
executed, and R-timestamp(Q) is set to the maximum of R-
timestamp(Q) and TS(Ti).
51. 51
Timestamp-Based Protocols (Cont.)Timestamp-Based Protocols (Cont.)
Suppose that transaction Ti issues write(Q).
If TS(Ti) < R-timestamp(Q), then the value of Q that Ti is
producing was needed previously, and the system assumed that
that value would never be produced. Hence, the write operation
is rejected, and Ti is rolled back.
If TS(Ti) < W-timestamp(Q), then Ti is attempting to write an
obsolete value of Q. Hence, this write operation is rejected, and
Ti is rolled back.
Otherwise, the write operation is executed, and W-
timestamp(Q) is set to TS(Ti).
52. 52
Example Use of the ProtocolExample Use of the Protocol
A partial schedule for several data items for transactions with
timestamps 1, 2, 3, 4, 5
T1 T2 T3 T4 T5
read(Y)
read(X)
read(Y)
write(Y)
write(Z)
read(Z)
read(X)
abort
read(X)
write(Z)
abort
write(Y)
write(Z)
53. 53
Correctness of Timestamp-Ordering ProtocolCorrectness of Timestamp-Ordering Protocol
The timestamp-ordering protocol guarantees serializability since
all the arcs in the precedence graph are of the form:
Thus, there will be no cycles in the precedence graph
Timestamp protocol ensures freedom from deadlock as no
transaction ever waits.
But the schedule may not be cascade-free, and may not even be
recoverable.
transaction
with smaller
timestamp
transaction
with larger
timestamp
54. 54
Recoverability and CascadeRecoverability and Cascade
FreedomFreedom
Problem with timestamp-ordering protocol:
Suppose Ti aborts, but Tj has read a data item written by Ti
Then Tj must abort; if Tj had been allowed to commit earlier, the
schedule is not recoverable.
Further, any transaction that has read a data item written by Tj must
abort
This can lead to cascading rollback --- that is, a chain of rollbacks
Solution:
A transaction is structured such that its writes are all performed at
the end of its processing
All writes of a transaction form an atomic action; no transaction may
execute while a transaction is being written
A transaction that aborts is restarted with a new timestamp
55. 55
Thomas’ Write RuleThomas’ Write Rule
Modified version of the timestamp-ordering protocol in which
obsolete write operations may be ignored under certain
circumstances.
When Ti attempts to write data item Q, if TS(Ti) < W-
timestamp(Q), then Ti is attempting to write an obsolete value of
{Q}. Hence, rather than rolling back Ti as the timestamp ordering
protocol would have done, this {write} operation can be ignored.
Otherwise this protocol is the same as the timestamp ordering
protocol.
Thomas' Write Rule allows greater potential concurrency. Unlike
previous protocols, it allows some view-serializable schedules
that are not conflict-serializable.
56. 56
Validation-Based ProtocolValidation-Based Protocol
Execution of transaction Ti is done in three phases.
1. Read and execution phase: Transaction Ti writes only to
temporary local variables
2. Validation phase: Transaction Ti performs a ``validation test''
to determine if local variables can be written without violating
serializability.
3. Write phase: If Ti is validated, the updates are applied to the
database; otherwise, Ti is rolled back.
The three phases of concurrently executing transactions can be
interleaved, but each transaction must go through the three
phases in that order.
Also called as optimistic concurrency control since
transaction executes fully in the hope that all will go well during
validation
57. 57
Validation-Based Protocol (Cont.)Validation-Based Protocol (Cont.)
Each transaction Ti has 3 timestamps
− Start(Ti) : the time when Ti started its execution
− Validation(Ti): the time when Ti entered its validation phase
− Finish(Ti) : the time when Ti finished its write phase
Serializability order is determined by timestamp given at
validation time, to increase concurrency. Thus TS(Ti) is given
the value of Validation(Ti).
This protocol is useful and gives greater degree of concurrency if
probability of conflicts is low. That is because the serializability
order is not pre-decided and relatively less transactions will have
to be rolled back.
58. 58
Validation Test for TransactionValidation Test for Transaction TTjj
If for all Ti with TS (Ti) < TS (Tj) either one of the following
condition holds:
finish(Ti) < start(Tj)
start(Tj) < finish(Ti) < validation(Tj) and the set of data items
written by Ti does not intersect with the set of data items read by Tj.
then validation succeeds and Tj can be committed. Otherwise,
validation fails and Tj is aborted.
Justification: Either first condition is satisfied, and there is no
overlapped execution, or second condition is satisfied and
1. the writes of Tj do not affect reads of Ti since they occur after Ti
has finished its reads.
2. the writes of Ti do not affect reads of Tj since Tj does not read
any item written by Ti.
59. 59
Schedule Produced by ValidationSchedule Produced by Validation
Example of schedule produced using validation
T14 T15
read(B)
read(B)
B:- B-50
read(A)
A:- A+50
read(A)
(validate)
display (A+B)
(validate)
write (B)
write (A)
60. 60
Multiversion SchemesMultiversion Schemes
Multiversion schemes keep old versions of data item to increase
concurrency.
Multiversion Timestamp Ordering
Multiversion Two-Phase Locking
Each successful write results in the creation of a new version of
the data item written.
Use timestamps to label versions.
When a read(Q) operation is issued, select an appropriate
version of Q based on the timestamp of the transaction, and
return the value of the selected version.
reads never have to wait as an appropriate version is returned
immediately.
61. 61
Multiversion Timestamp OrderingMultiversion Timestamp Ordering
Each data item Q has a sequence of versions <Q1, Q2,...., Qm>.
Each version Qk contains three data fields:
Content -- the value of version Qk.
W-timestamp(Qk) -- timestamp of the transaction that created
(wrote) version Qk
R-timestamp(Qk) -- largest timestamp of a transaction that
successfully read version Qk
when a transaction Ti creates a new version Qk of Q, Qk's W-
timestamp and R-timestamp are initialized to TS(Ti).
R-timestamp of Qk is updated whenever a transaction Tj reads Qk,
and TS(Tj) > R-timestamp(Qk).
62. 62
Multiversion Timestamp OrderingMultiversion Timestamp Ordering
(Cont)(Cont)
The multiversion timestamp scheme presented next ensures
serializability.
Suppose that transaction Ti issues a read(Q) or write(Q) operation.
Let Qk denote the version of Q whose write timestamp is the largest
write timestamp less than or equal to TS(Ti).
1. If transaction Ti issues a read(Q), then the value returned is the
content of version Qk.
2. If transaction Ti issues a write(Q), and if TS(Ti) < R-
timestamp(Qk), then transaction Ti is rolled
back. Otherwise, if TS(Ti) = W-timestamp(Qk), the contents of Qk
are overwritten, otherwise a new version of Q is created.
Reads always succeed; a write by Ti is rejected if some other
transaction Tj that (in the serialization order defined by the timestamp
values) should read Ti's write, has already read a version created by
a transaction older than Ti.
63. 63
Multiversion Two-Phase LockingMultiversion Two-Phase Locking
Differentiates between read-only transactions and update
transactions
Update transactions acquire read and write locks, and hold all
locks up to the end of the transaction. That is, update
transactions follow rigorous two-phase locking.
Each successful write results in the creation of a new version of the
data item written.
each version of a data item has a single timestamp whose value is
obtained from a counter ts-counter that is incremented during
commit processing.
Read-only transactions are assigned a timestamp by reading the
current value of ts-counter before they start execution; they
follow the multiversion timestamp-ordering protocol for
performing reads.
64. 64
Multiversion Two-Phase LockingMultiversion Two-Phase Locking
(Cont.)(Cont.)
When an update transaction wants to read a data item, it obtains
a shared lock on it, and reads the latest version.
When it wants to write an item, it obtains X lock on; it then
creates a new version of the item and sets this version's
timestamp to ∞.
When update transaction Ti completes, commit processing
occurs:
Ti sets timestamp on the versions it has created to ts-counter + 1
Ti increments ts-counter by 1
Read-only transactions that start after Ti increments ts-counter
will see the values updated by Ti.
Read-only transactions that start before Ti increments the
ts-counter will see the value before the updates by Ti.
Only serializable schedules are produced.
65. 65
Deadlock HandlingDeadlock Handling
Consider the following two transactions:
T1: write (X) T2: write(Y)
write(Y) write(X)
Schedule with deadlock
T1 T2
lock-X on X
write (X)
lock-X on Y
write (X)
wait for lock-X on X
wait for lock-X on Y
66. 66
Deadlock HandlingDeadlock Handling
System is deadlocked if there is a set of transactions such that
every transaction in the set is waiting for another transaction in
the set.
Deadlock prevention protocols ensure that the system will
never enter into a deadlock state. Some prevention strategies :
Require that each transaction locks all its data items before it begins
execution (predeclaration).
Impose partial ordering of all data items and require that a
transaction can lock data items only in the order specified by the
partial order (graph-based protocol).
67. 67
More Deadlock PreventionMore Deadlock Prevention
StrategiesStrategies
Following schemes use transaction timestamps for the sake of
deadlock prevention alone.
wait-die scheme — non-preemptive
older transaction may wait for younger one to release data item.
Younger transactions never wait for older ones; they are rolled back
instead.
a transaction may die several times before acquiring needed data
item
wound-wait scheme — preemptive
older transaction wounds (forces rollback) of younger transaction
instead of waiting for it. Younger transactions may wait for older
ones.
may be fewer rollbacks than wait-die scheme.
68. 68
Deadlock prevention (Cont.)Deadlock prevention (Cont.)
Both in wait-die and in wound-wait schemes, a rolled back
transactions is restarted with its original timestamp. Older
transactions thus have precedence over newer ones, and
starvation is hence avoided.
Timeout-Based Schemes :
a transaction waits for a lock only for a specified amount of time.
After that, the wait times out and the transaction is rolled back.
thus deadlocks are not possible
simple to implement; but starvation is possible. Also difficult to
determine good value of the timeout interval.
69. 69
Deadlock DetectionDeadlock Detection
Deadlocks can be described as a wait-for graph, which consists
of a pair G = (V,E),
V is a set of vertices (all the transactions in the system)
E is a set of edges; each element is an ordered pair Ti →Tj.
If Ti → Tj is in E, then there is a directed edge from Ti to Tj,
implying that Ti is waiting for Tj to release a data item.
When Ti requests a data item currently being held by Tj, then the
edge Ti Tj is inserted in the wait-for graph. This edge is removed
only when Tj is no longer holding a data item needed by Ti.
The system is in a deadlock state if and only if the wait-for graph
has a cycle. Must invoke a deadlock-detection algorithm
periodically to look for cycles.
71. 71
Deadlock RecoveryDeadlock Recovery
When deadlock is detected :
Some transaction will have to rolled back (made a victim) to break
deadlock. Select that transaction as victim that will incur minimum
cost.
Rollback -- determine how far to roll back transaction
Total rollback: Abort the transaction and then restart it.
More effective to roll back transaction only as far as necessary to
break deadlock.
Starvation happens if same transaction is always chosen as victim.
Include the number of rollbacks in the cost factor to avoid starvation
72. 72
Insert and Delete OperationsInsert and Delete Operations
If two-phase locking is used :
A delete operation may be performed only if the transaction
deleting the tuple has an exclusive lock on the tuple to be deleted.
A transaction that inserts a new tuple into the database is given an
X-mode lock on the tuple
Insertions and deletions can lead to the phantom
phenomenon.
A transaction that scans a relation (e.g., find all accounts in
Perryridge) and a transaction that inserts a tuple in the relation (e.g.,
insert a new account at Perryridge) may conflict in spite of not
accessing any tuple in common.
If only tuple locks are used, non-serializable schedules can result:
the scan transaction may not see the new account, yet may be
serialized before the insert transaction.
73. 73
Insert and Delete OperationsInsert and Delete Operations
(Cont.)(Cont.)
The transaction scanning the relation is reading information that
indicates what tuples the relation contains, while a transaction
inserting a tuple updates the same information.
The information should be locked.
One solution:
Associate a data item with the relation, to represent the information
about what tuples the relation contains.
Transactions scanning the relation acquire a shared lock in the data
item,
Transactions inserting or deleting a tuple acquire an exclusive lock on
the data item. (Note: locks on the data item do not conflict with locks on
individual tuples.)
Above protocol provides very low concurrency for
insertions/deletions.
Index locking protocols provide higher concurrency while
preventing the phantom phenomenon, by requiring locks
on certain index buckets.
74. 74
Index Locking ProtocolIndex Locking Protocol
Every relation must have at least one index. Access to a relation
must be made only through one of the indices on the relation.
A transaction Ti that performs a lookup must lock all the index
buckets that it accesses, in S-mode.
A transaction Ti may not insert a tuple ti into a relation r without
updating all indices to r.
Ti must perform a lookup on every index to find all index buckets
that could have possibly contained a pointer to tuple ti, had it
existed already, and obtain locks in X-mode on all these index
buckets. Ti must also obtain locks in X-mode on all index buckets
that it modifies.
The rules of the two-phase locking protocol must be observed.
75. 75
Weak Levels of ConsistencyWeak Levels of Consistency
Degree-two consistency: differs from two-phase locking in
that S-locks may be released at any time, and locks may be
acquired at any time
X-locks must be held till end of transaction
Serializability is not guaranteed, programmer must ensure that no
erroneous database state will occur
Cursor stability:
For reads, each tuple is locked, read, and lock is immediately
released
X-locks are held till end of transaction
Special case of degree-two consistency
76. 76
Concurrency in Index StructuresConcurrency in Index Structures
Indices are unlike other database items in that their only job is to
help in accessing data.
Index-structures are typically accessed very often, much more
than other database items.
Treating index-structures like other database items leads to low
concurrency. Two-phase locking on an index may result in
transactions executing practically one-at-a-time.
It is acceptable to have nonserializable concurrent access to an
index as long as the accuracy of the index is maintained.
In particular, the exact values read in an internal node of a
B+
-tree are irrelevant so long as we land up in the correct leaf
node.
There are index concurrency protocols where locks on internal
nodes are released early, and not in a two-phase fashion.
77. 77
Concurrency in Index StructuresConcurrency in Index Structures
Example of index concurrency protocol:
Use crabbing instead of two-phase locking on the nodes of the
B+
-tree, as follows. During search/insertion/deletion:
First lock the root node in shared mode.
After locking all required children of a node in shared mode, release
the lock on the node.
During insertion/deletion, upgrade leaf node locks to exclusive
mode.
When splitting or coalescing requires changes to a parent, lock the
parent in exclusive mode.
Above protocol can cause excessive deadlocks. Better protocols
are available;