The document discusses transaction management and concurrency control in database systems. It covers topics such as transactions and their properties, concurrency control methods like locking, time stamping and optimistic control, and database recovery management. The goal of these techniques is to coordinate simultaneous transaction execution while maintaining data consistency and integrity in multi-user database environments.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
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
ACID properties
Atomicity, Consistency, Isolation, Durability
Transactions should possess several properties, often called the ACID properties; they should be enforced by the concurrency control and recovery methods of the DBMS.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
Unified Modeling Language (UML) is a modeling language, used for design. Designed based on OMG Standard, Object this helps to express and design documents, software. This is particularly useful for OO design. Here is a brief tutorial that talks about UML usage.
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
ACID properties
Atomicity, Consistency, Isolation, Durability
Transactions should possess several properties, often called the ACID properties; they should be enforced by the concurrency control and recovery methods of the DBMS.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
Unified Modeling Language (UML) is a modeling language, used for design. Designed based on OMG Standard, Object this helps to express and design documents, software. This is particularly useful for OO design. Here is a brief tutorial that talks about UML usage.
Transaction concept, ACID property, Objectives of transaction management, Types of transactions, Objectives of Distributed Concurrency Control, Concurrency Control anomalies, Methods of concurrency control, Serializability and recoverability, Distributed Serializability, Enhanced lock based and timestamp based protocols, Multiple granularity, Multi version schemes, Optimistic Concurrency Control techniques
Chapter-10 Transaction Processing and Error RecoveryKunal Anand
This chapter discusses the concept of concurrency in database systems. We talk about different concurrency control techniques along with error recovery.
we will discuss important topics related to multi-master setups:
* Practical considerations when using Galera in a multi-master setup
* Evaluating the characteristics of your database workload
* Preparing your application for multi-master
* Detecting and dealing with transaction conflicts
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
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Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Database Systems, 8th
Edition 2
Objectives
• In this chapter, you will learn:
– About database transactions and their properties
– What concurrency control is and what role it
plays in maintaining the database’s integrity
– What locking methods are and how they work
3. Database Systems, 8th
Edition 3
Objectives (continued)
• In this chapter, you will learn: (continued)
– How stamping methods are used for
concurrency control
– How optimistic methods are used for
concurrency control
– How database recovery management is used to
maintain database integrity
4. Database Systems, 8th
Edition 4
What is a Transaction?
• Logical unit of work that must be either entirely
completed or aborted
• Successful transaction changes database from
one consistent state to another
– One in which all data integrity constraints are
satisfied
• Most real-world database transactions are
formed by two or more database requests
– Equivalent of a single SQL statement in an
application program or transaction
6. Database Systems, 8th
Edition 6
Evaluating Transaction Results
• Not all transactions update database
• SQL code represents a transaction because
database was accessed
• Improper or incomplete transactions can have
devastating effect on database integrity
– Some DBMSs provide means by which user can
define enforceable constraints
– Other integrity rules are enforced automatically
by the DBMS
8. Database Systems, 8th
Edition 8
Transaction Properties
• Atomicity
– All operations of a transaction must be
completed
• Consistency
– Permanence of database’s consistent state
• Isolation
– Data used during transaction cannot be used by
second transaction until the first is completed
9. Database Systems, 8th
Edition 9
Transaction Properties (continued)
• Durability
– Once transactions are committed, they cannot
be undone
• Serializability
– Concurrent execution of several transactions
yields consistent results
• Multiuser databases subject to multiple
concurrent transactions
10. Database Systems, 8th
Edition 10
Transaction Management with SQL
• ANSI has defined standards that govern SQL
database transactions
• Transaction support is provided by two SQL
statements: COMMIT and ROLLBACK
• Transaction sequence must continue until:
– COMMIT statement is reached
– ROLLBACK statement is reached
– End of program is reached
– Program is abnormally terminated
11. Database Systems, 8th
Edition 11
The Transaction Log
• Transaction log stores:
– A record for the beginning of transaction
– For each transaction component:
• Type of operation being performed (update,
delete, insert)
• Names of objects affected by transaction
• “Before” and “after” values for updated fields
• Pointers to previous and next transaction log
entries for the same transaction
– Ending (COMMIT) of the transaction
13. Database Systems, 8th
Edition 13
Concurrency Control
• Coordination of simultaneous transaction
execution in a multiprocessing database
• Objective is to ensure serializability of
transactions in a multiuser environment
14. Database Systems, 8th
Edition 14
Lost Updates
• Lost update problem:
– Two concurrent transactions update same data
element
– One of the updates is lost
• Overwritten by the other transaction
16. Database Systems, 8th
Edition 16
Uncommitted Data
• Uncommitted data phenomenon:
– Two transactions executed concurrently
– First transaction rolled back after second already
accessed uncommitted data
18. Database Systems, 8th
Edition 18
Inconsistent Retrievals
• Inconsistent retrievals:
– First transaction accesses data
– Second transaction alters the data
– First transaction accesses the data again
• Transaction might read some data before they
are changed and other data after changed
• Yields inconsistent results
21. Database Systems, 8th
Edition 21
The Scheduler
• Special DBMS program
– Purpose is to establish order of operations within
which concurrent transactions are executed
• Interleaves execution of database operations:
– Ensures serializability
– Ensures isolation
• Serializable schedule
– Interleaved execution of transactions yields
same results as serial execution
22. Database Systems, 8th
Edition 22
Concurrency Control
with Locking Methods
• Lock
– Guarantees exclusive use of a data item to a
current transaction
– Required to prevent another transaction from
reading inconsistent data
• Lock manager
– Responsible for assigning and policing the locks
used by transactions
23. Database Systems, 8th
Edition 23
Lock Granularity
• Indicates level of lock use
• Locking can take place at following levels:
– Database
– Table
– Page
– Row
– Field (attribute)
25. Database Systems, 8th
Edition 25
Lock Granularity (continued)
• Row-level lock
– Allows concurrent transactions to access
different rows of same table
• Even if rows are located on same page
• Field-level lock
– Allows concurrent transactions to access same
row
• Requires use of different fields (attributes) within
the row
30. Database Systems, 8th
Edition 30
Lock Types
• Binary lock
– Two states: locked (1) or unlocked (0)
• Exclusive lock
– Access is specifically reserved for transaction that
locked object
– Must be used when potential for conflict exists
• Shared lock
– Concurrent transactions are granted read access on
basis of a common lock
32. Database Systems, 8th
Edition 32
Two-Phase Locking
to Ensure Serializability
• Defines how transactions acquire and
relinquish locks
• Guarantees serializability, but does not prevent
deadlocks
– Growing phase
• Transaction acquires all required locks without
unlocking any data
– Shrinking phase
• Transaction releases all locks and cannot obtain
any new lock
33. Database Systems, 8th
Edition 33
Two-Phase Locking
to Ensure Serializability (continued)
• Governed by the following rules:
– Two transactions cannot have conflicting locks
– No unlock operation can precede a lock
operation in the same transaction
– No data are affected until all locks are obtained
35. Database Systems, 8th
Edition 35
Deadlocks
• Condition that occurs when two transactions
wait for each other to unlock data
• Possible only if one of the transactions wants
to obtain an exclusive lock on a data item
– No deadlock condition can exist among
shared locks
36. Database Systems, 8th
Edition 36
Deadlocks (continued)
• Three techniques to control deadlock:
– Prevention
– Detection
– Avoidance
• Choice of deadlock control method depends
on database environment
– Low probability of deadlock, detection
recommended
– High probability, prevention recommended
38. Database Systems, 8th
Edition 38
Concurrency Control
with Time Stamping Methods
• Assigns global unique time stamp to each
transaction
• Produces explicit order in which transactions
are submitted to DBMS
• Uniqueness
– Ensures that no equal time stamp values can
exist
• Monotonicity
– Ensures that time stamp values always increase
39. Database Systems, 8th
Edition 39
Wait/Die and Wound/Wait Schemes
• Wait/die
– Older transaction waits and younger is rolled
back and rescheduled
• Wound/wait
– Older transaction rolls back younger transaction
and reschedules it
41. Database Systems, 8th
Edition 41
Concurrency Control
with Optimistic Methods
• Optimistic approach
– Based on assumption that majority of database
operations do not conflict
– Does not require locking or time stamping
techniques
– Transaction is executed without restrictions until
it is committed
– Phases: read, validation, and write
42. Database Systems, 8th
Edition 42
Database Recovery Management
• Restores database to previous consistent state
• Based on atomic transaction property
– All portions of transaction treated as single
logical unit of work
– All operations applied and completed to produce
consistent database
• If transaction operation cannot be completed
– Transaction aborted
– Changes to database rolled back
43. Database Systems, 8th
Edition 43
Transaction Recovery
• Write-ahead-log protocol: ensures transaction
logs are written before data is updated
• Redundant transaction logs: ensure physical
disk failure will not impair ability to recover
• Buffers: temporary storage areas in primary
memory
• Checkpoints: operations in which DBMS writes
all its updated buffers to disk
44. Database Systems, 8th
Edition 44
Transaction Recovery (continued)
• Deferred-write technique
– Only transaction log is updated
• Recovery process:
– Identify last checkpoint
– If transaction committed before checkpoint
• Do nothing
– If transaction committed after checkpoint
• Use transaction log to redo the transaction
– If transaction had ROLLBACK operation
• Do nothing
45. Database Systems, 8th
Edition 45
Transaction Recovery (continued)
• Write-through technique
– Database is immediately updated by transaction
operations during transaction’s execution
• Recovery process
– Identify last checkpoint
– If transaction was committed before checkpoint
• Do nothing
– If transaction committed after last checkpoint
• DBMS redoes the transaction using “after” values
– If transaction had ROLLBACK or was left active
• Do nothing because no updates were made
47. Database Systems, 8th
Edition 47
Summary
• Transaction: sequence of database operations
that access database
– Logical unit of work
• No portion of transaction can exist by itself
– Five main properties: atomicity, consistency,
isolation, durability, and serializability
• COMMIT saves changes to disk
• ROLLBACK restores previous database state
• SQL transactions are formed by several SQL
statements or database requests
48. Database Systems, 8th
Edition 48
Summary (continued)
• Transaction log keeps track of all transactions
that modify database
• Concurrency control coordinates simultaneous
execution of transactions
• Scheduler establishes order in which
concurrent transaction operations are executed
• Lock guarantees unique access to a data item
by transaction
• Two types of locks: binary locks and
shared/exclusive locks
49. Database Systems, 8th
Edition 49
Summary (continued)
• Serializability of schedules is guaranteed
through the use of two-phase locking
• Deadlock: when two or more transactions wait
indefinitely for each other to release lock
• Three deadlock control techniques: prevention,
detection, and avoidance
• Time stamping methods assign unique time
stamp to each transaction
– Schedules execution of conflicting transactions
in time stamp order
50. Database Systems, 8th
Edition 50
Summary (continued)
• Optimistic methods assume the majority of
database transactions do not conflict
– Transactions are executed concurrently, using
private copies of the data
• Database recovery restores database from
given state to previous consistent state