The document provides an overview of transaction management concepts in database systems. It discusses the ACID properties that transactions must satisfy, including atomicity, consistency, isolation, and durability. Transaction states like active, committed, and aborted are defined. Various concurrency control techniques are covered, such as locking-based protocols like two-phase locking and timestamp-based protocols. Serializability is introduced as a way to ensure isolation among concurrently executing transactions. Implementation techniques for atomicity and durability using shadow databases are also summarized.
This document discusses transactions and concurrency control in databases. It covers the ACID properties that transactions must satisfy - atomicity, consistency, isolation, and durability. It describes transaction states like active, committed, aborted. It discusses how allowing concurrent executions can improve performance but how concurrency control is needed to prevent inconsistent outcomes. It covers different types of schedules for concurrent transactions and the concepts of conflict serializability and view serializability.
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.
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 transactions and concurrency control in databases. It covers the ACID properties that transactions must satisfy - atomicity, consistency, isolation, and durability. It describes transaction states like active, committed, aborted. It discusses how allowing concurrent executions can improve performance but how concurrency control is needed to prevent inconsistent outcomes. It covers different types of schedules for concurrent transactions and the concepts of conflict serializability and view serializability.
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.
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.
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.
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.
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 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 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.
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.
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 various types of transaction schedules:
- A schedule specifies the order transactions execute their instructions. Schedules can be serial or non-serial.
- Conflict serializability requires a schedule to be equivalent to a serial schedule after swapping non-conflicting operations.
- View serializability is less restrictive; a schedule is view-serializable if transactions see the same data reads.
- Recoverable schedules ensure reads see committed writes to avoid inconsistencies if transactions abort. Cascadeless schedules prevent cascading rollbacks.
Serializability is a concept that helps check if schedules are serializable. A serializable schedule always leaves the database in a consistent state. Non-serial schedules may cause inconsistencies, so serializability checks if they can be converted to an equivalent serial schedule to maintain consistency. Different types of serializability include view serializability and conflict serializability. View serializability requires schedules be view equivalent to a serial schedule with matching initial reads, final writes, and update reads. Conflict serializability converts a schedule by swapping non-conflicting operations, where two operations conflict if they are in different transactions, access the same data item, and one is a write.
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.
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.
Transactions must maintain ACID properties to ensure database consistency and integrity. A transaction is a logical unit of work, such as withdrawing money from an account. Serializability determines if concurrent transactions maintain data consistency. Conflict serializability checks for cycles in a precedence graph of transactions operating on the same data items. View serializability requires transactions have the same write-read sequences as a serial schedule. Recoverable and cascadeless schedules commit independent transactions first to maintain recoverability if failures occur.
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.
Transaction processing systems handle large databases and hundreds of concurrent users executing transactions. A transaction is a logical unit of database processing that includes database access operations like insertions, deletions, modifications, or retrievals. Transactions must be atomic, consistent, isolated, and durable (ACID properties). Concurrency control techniques like locking and timestamps are used to coordinate concurrent transactions and ensure serializability and isolation. The two-phase locking protocol enforces serializability by acquiring all locks in the growing phase before releasing any locks in the shrinking phase.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
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.
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 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 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.
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.
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 various types of transaction schedules:
- A schedule specifies the order transactions execute their instructions. Schedules can be serial or non-serial.
- Conflict serializability requires a schedule to be equivalent to a serial schedule after swapping non-conflicting operations.
- View serializability is less restrictive; a schedule is view-serializable if transactions see the same data reads.
- Recoverable schedules ensure reads see committed writes to avoid inconsistencies if transactions abort. Cascadeless schedules prevent cascading rollbacks.
Serializability is a concept that helps check if schedules are serializable. A serializable schedule always leaves the database in a consistent state. Non-serial schedules may cause inconsistencies, so serializability checks if they can be converted to an equivalent serial schedule to maintain consistency. Different types of serializability include view serializability and conflict serializability. View serializability requires schedules be view equivalent to a serial schedule with matching initial reads, final writes, and update reads. Conflict serializability converts a schedule by swapping non-conflicting operations, where two operations conflict if they are in different transactions, access the same data item, and one is a write.
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.
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.
Transactions must maintain ACID properties to ensure database consistency and integrity. A transaction is a logical unit of work, such as withdrawing money from an account. Serializability determines if concurrent transactions maintain data consistency. Conflict serializability checks for cycles in a precedence graph of transactions operating on the same data items. View serializability requires transactions have the same write-read sequences as a serial schedule. Recoverable and cascadeless schedules commit independent transactions first to maintain recoverability if failures occur.
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.
Transaction processing systems handle large databases and hundreds of concurrent users executing transactions. A transaction is a logical unit of database processing that includes database access operations like insertions, deletions, modifications, or retrievals. Transactions must be atomic, consistent, isolated, and durable (ACID properties). Concurrency control techniques like locking and timestamps are used to coordinate concurrent transactions and ensure serializability and isolation. The two-phase locking protocol enforces serializability by acquiring all locks in the growing phase before releasing any locks in the shrinking phase.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Large Language Model (LLM) and it’s Geospatial Applications
unit06-dbms-new.ppt
1. DATABASE MANAGEMENT SYSTEMS
II B. Tech CSE II Semester
UNIT-V PPT SLIDES
Text Books: (1) DBMS by Raghu
Ramakrishnan
(2) DBMS by Sudarshan and Korth
2. INDEX
S.NO Module as per
Session planner
UNIT-6 PPT SLIDES
Lecture No PPT
Slide NO
--------------------------------------------------------------------------------------------------------------------------------
1. Transaction concept & State L1
2. Implementation of atomicity and durability L2
3. Serializability L3
4. Recoverability L4
5. Implementation of isolation L5
6. Lock based protocols L6
7. Lock based protocols L7
8. Timestamp based protocols L8
9. Validation based protocol L9
L1- 1 to L1- 7
L2- 1 to L2-8
L3- 1 to L3-8
L4- 1 to L4-8
L5- 1 to L5- 6
L6- 1 to L6-5
L7- 1 to L7- 10
L8- 1 to L8-6
L9- 1 to L9-9
3. Transaction Concept
Slide No.L1-1
•
•
•
A transaction is a unit of program execution that accesses and
possibly updates various data items.
g.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)
Two main issues to deal with:
–Failures of various kinds, such as hardware failures and system
crashes
–Concurrent execution of multiple transactions
4. Example of Fund Transfer
Slide No.L1-2
•
•
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)
Atomicity requirement
–
– if the transaction fails after step 3 and before step 6, money will be “lost”
leading to an inconsistent database state
• Failure could be due to software or hardware
the system should ensure that updates of a partially executed transaction are
not reflected in the database
• 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 even if there
are software or hardware failures.
5. Example of Fund Transfer(Cont.)
Slide No.L1-3
•
•
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 in above example:
– the sum of A and B is unchanged by the execution
of the transaction
In general, consistency requirements include
• Explicitly specified integrity constraints such as primary keys
and foreign keys
• Implicit integrity constraints
– e.g. sum of balances of all accounts, minus sum of loan
amounts must equal value of cash-in-hand
– A transaction must see a consistent database.
– During transaction execution the database may be temporarily
inconsistent.
– When the transaction completes successfully the database must be
consistent
• Erroneous transaction logic can lead to inconsistency
6. Example of Fund Transfer(Cont.)
Slide No.L1-4
• Isolation requirement — if between steps 3 and 6, another transaction
T2 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).
T2
T1
1. read(A)
2. A := A – 50
3. write(A)
read(A), read(B), print(A+B)
•
4. read(B)
5. B := B +50
6. write(B
Isolation 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 later.
7. ACID Properties
Slide No.L1-5
•
•
• 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.
A transaction is a unit of program execution that accesses and possibly
updates various data items.To preserve the integrity of data the database
system must ensure:
8. Transaction State
Slide No.L1-6
•
•
•
•
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
• can be done only if no internal logical error
– kill the transaction
Committed – after successful completion.
10. Schedules
Slide No.L2-4
• Schedule –
A series of operation from one transaction to another transaction is
known as schedule. It is used to preserve the order of the operation
in each of the individual transaction.
11. Serial Schedule
The serial schedule is a type of schedule where one transaction is
executed completely before starting another transaction.
For example: Suppose there are two transactions T1 and T2 which
have some operations. If it has no interleaving of operations, then
there are the following two possible outcomes:
Execute all the operations of T1 which was followed by all the
operations of T2.
Execute all the operations of T1 which was followed by all the
operations of T2.
12. Serial Schedule
•
•
Let T1 transfer $50 from A to B, and T2 transfer 10% of the
balance from A to B.
A serial schedule in which T1 is followed by T2 :
Slide No.L2-5
13. Schedule 2
• A serial schedule where T2 is followed by T1
Slide No.L2-6
14. Non-serial Schedule
•If interleaving of operations is allowed, then there will be non-
serial schedule.
•It contains many possible orders in which the system can execute
the individual operations of the transactions.(Ex:schedule-3)
Serializable schedule:
The serializability of schedules is used to find non-serial schedules
that allow the transaction to execute concurrently without
interfering with one another.
A non-serial schedule will be serializable if its result is equal to the
result of its transactions executed serially. .(Ex:schedule-4)
15. Schedule 3
• Let T1 and T2 be the transactions defined previously. The
following schedule is not a serial schedule, but it is equivalent
to Schedule 1.
In Schedules 1, 2 and 3, the sum A + B is preserved.
Slide No.L2-7
17. Serializability
•Serializability is a concurrency scheme where the concurrent
transaction is equivalent to one that executes the transactions
serially.
•A schedule is a list of transactions.
•Serial schedule defines each transaction is executed consecutively
without any interference from other transactions.
•Different forms of schedule equivalence give rise to the
notions of:
1. conflict serializability
2. view serializability
18. Conflicting Instructions
Slide No.L3-2
• Instructions li and lj of transactions Ti and Tj respectively,
conflict if and only if there exists some item Q accessed by
both li and lj, and at least one of these instructions wrote Q.
1.li = read(Q), lj = read(Q). li and lj don’t conflict.
2.li = read(Q), lj = write(Q). They conflict.
3.li = write(Q), lj = read(Q). They conflict
4.li = write(Q), lj = write(Q). They conflict
• Intuitively, a conflict between li and lj forces a (logical)
temporal order between them.
– If li and lj 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.
19. Conflict Serializability
Slide No.L3-3
•
•
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.(ex1:no conflit)
20. Conflict Serializability (Cont.)
• Schedule 3 can be transformed into Schedule 6, a serial
schedule where T2 follows T1, by series of swaps of non-
conflicting instructions.
– Therefore Schedule 3 is conflict serializable.
Schedule 3
Slide No.L3-4
Schedule 6
21. Conflict Serializability (Cont.)
• Example of a schedule that is not conflict serializable:
• We are unable to swap instructions in the above schedule to
obtain either the serial schedule < T3, T4 >, or the serial
schedule < T4, T3 >.
Slide No.L3-5
22. View Serializability
Slide No.L3-6
• Let S and S´ be two schedules with the same set of transactions.
S and S´ are view equivalent if the following three
conditions are met, for each data item Q,
1.If in schedule S, transaction Ti reads the initial value of Q, then in
schedule S’ also transaction Ti must read the initial value of Q.
2.If in schedule S transaction Ti executes read(Q), and that value
was produced by transaction Tj (if any), then in schedule S’ also
transaction Ti must read the value of Q
that was produced by the same write(Q) operation of transaction Tj
.
3.The transaction (if any) that performs the final write(Q)
operation in schedule S must also perform the final write(Q)
operation in schedule S’.
As can be seen, view equivalence is also based purely on reads
and writes alone.
23. View Serializability (Cont.)
•
•
•
A schedule S is view serializable if it is view equivalent to a
serial schedule.
Every conflict serializable schedule is also view serializable.
Below is a schedule which is view-serializable but not
conflict serializable.
What serial schedule is above equivalent to?
•
• Every view serializable schedule that is not conflict
serializable has blind writes.
Slide No.L3-7
24. Implementation of Atomicity and Durability
•
•
The recovery-management component of a database system
implements the support for atomicity and durability.
the shadow-database scheme:
– all updates are made on a shadow copy of the database
Slide No.L2-1
25. Implementation of Atomicity and Durability (Cont.)
Slide No.L2-2
• A database pointer will be always pointing to the consistent copy of the
database, and copy of the database is used by transactions to update.
• Once all the transactions are complete, the DB pointer is modified to
point to new copy of DB, and old copy is deleted.
• If there is any failure during the transaction, the pointer will be still
pointing to old copy of database, and shadow database will be deleted.
• If the transactions are complete then the pointer is changed to point to
shadow DB, and old DB is deleted.
– Shadow paging is a copy-on-write technique for avoiding in-place
updates of pages. Instead, when a page is to be modified, a shadow
page is allocated.
– Advantages:
No Undo / No Redo algorithm.
• Recovery is faster.
•
26. Testing 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 1 x
y
Slide No.L5-3
27. Concurrent Executions
Slide No.L2-3
• Multiple transactions are allowed to run concurrently in the
system. Advantages are:
– increased processor and disk utilization, leading to
better transaction throughput
• E.g. 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
28. Lock-Based Protocols
Slide No.L6-1
•
•
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.
29. 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.
Slide No.L6-2
30. The Two-Phase Locking Protocol
Slide No.L7-1
•
•
•
•
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).
31. The Two-Phase Locking Protocol (Cont.)
Slide No.L7-2
•
•
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.
32. Timestamp-Based Protocols
Slide No. L8-1
•
•
• 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.
33. Timestamp-Based Protocols (Cont.)
Slide No. L8-2
•
•
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.
1. If TS(Ti) W-timestamp(Q), then the read operation is
executed, and R-timestamp(Q) is set to max(R-
timestamp(Q), TS(Ti)).
34. Timestamp-Based Protocols (Cont.)
Slide No. L8-3
• Suppose that transaction Ti issues write(Q).
1. 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.
1. 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.
1. Otherwise, the write operation is executed, and W-
timestamp(Q) is set to TS(Ti).
35. Example Use of the Protocol
Slide No. L8-4
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)
read(X)
abort
write(Y)
write(Z)
write(Z)
abort
read(X)
read(Z)
write(Y)
write(Z)
36. Multiple Granularity
Slide No. L9-5
•
•
•
•
Allow data items to be of various sizes and define a hierarchy of
data granularities, where the small granularities are nested within
larger ones
Can be represented graphically as a tree (but don't confuse with
tree-locking protocol)
When a transaction locks a node in the tree explicitly, it
implicitly locks all the node's descendents in the same mode.
Granularity of locking (level in tree where locking is done):
–fine granularity (lower in tree): high concurrency, high locking
overhead
–coarse granularity (higher in tree): low locking overhead, low
concurrency
37. Slide No. L9-6
Example of Granularity Hierarchy
The levels, starting from the coarsest (top) level are
– database
– area
– file
– record