Trafodion brings a completely distributed scalable transaction management implementation integrated into HBase. It does not suffer from the scale and performance limitations of other transaction managers on HBase.
This presentation reviews the elegant architecture and how this architecture is leveraged to provide full ACID SQL transactional capabilities across multiple rows, tables, statements, and region servers. It discusses the life of a transaction from BEGIN WORK, to updates, to ABORT WORK, to COMMIT WORK, and then discusses recovery and high availability capabilities provided. An accompanying white paper goes into depth explaining this animated presentation in more detail.
Given the increasing interest for transaction managers on Hadoop, or to provide transactional capabilities for NoSQL users when needed, the Trafodion community can certainly open up this Distributed Transaction Management support to be leveraged by implementations other than Trafodion.
Distributed database system is collection of loosely coupled sites that are independeant of each other.
Distributed transaction model
Concurrency control
2 phase commit protocol
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
Distributed database system is collection of loosely coupled sites that are independeant of each other.
Distributed transaction model
Concurrency control
2 phase commit protocol
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
In transaction processing, databases, and computer networking, the two-phase commit protocol (2PC) is a type of atomic commitment protocol (ACP). ... The protocol achieves its goal even in many cases of temporary system failure (involving either process, network node, communication, etc. failures), and is thus widely used.
Optimistic concurrency control in Distributed Systemsmridul mishra
What is Optimistic concurrency control, how and why it is applied to distributed systems, the Kung Robinson algorithm overview and the advantages-disadvantages have been covered
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
The concurrency control service is the DBE service that is responsible for consistency of the database. In a nutshell, it controls the operations of multiple, concurrent transactions in such a way that the database stays consistent even when these transactions conflict with each other.
In transaction processing, databases, and computer networking, the two-phase commit protocol (2PC) is a type of atomic commitment protocol (ACP). ... The protocol achieves its goal even in many cases of temporary system failure (involving either process, network node, communication, etc. failures), and is thus widely used.
Optimistic concurrency control in Distributed Systemsmridul mishra
What is Optimistic concurrency control, how and why it is applied to distributed systems, the Kung Robinson algorithm overview and the advantages-disadvantages have been covered
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
The concurrency control service is the DBE service that is responsible for consistency of the database. In a nutshell, it controls the operations of multiple, concurrent transactions in such a way that the database stays consistent even when these transactions conflict with each other.
How can you apply affiliate marketing strategies for marketing SMBs in a connected, hyper-local environment? The power of building an online reputation with daily deals can help your SMB stand out.
Experience level: Intermediate
Target audience: Merchant/Advertiser
Niche/vertical: Hyper-Local Marketing
Gene Mikhov, CEO, XVIO (Twitter @genemikhov)
In this presentation, Rishabh introduces IoT and associated trends. In his own words, "My interest is to work in the development of smart systems using a combination of sensors, artificial intelligence and robotics".
JIRA Core je designována pro podporu business týmů během všech fází dodávky.
Kompletně zahrnuje efektivní řízení a sledování úkolů a požadavků v projektu (task and project management).
ACID Transactions in Apache Phoenix with Apache Tephra™ (incubating), by Poor...Cask Data
TITLE: ACID Transactions in Apache Phoenix with Apache Tephra™ (incubating)
SPEAKER: Poorna Chandra, Cask Data
DATE: May 25, 2016
LOCATION: PhoenixCon, San Francisco CA
http://www.meetup.com/SF-Bay-Area-Apache-Phoenix-Meetup/events/230545182/
TALK ABSTRACT:
This talk is about how Apache Phoenix added support for ACID transactions using Apache Tephra™ (incubating), an open source transaction engine on top of Apache HBase. To start off with, the talk will examine the need for Phoenix data operations to be transactional. The talk will then discuss how Tephra implements transactional semantics using Optimistic Concurrency Control by giving an overview of the transactional model of Tephra along with the high level architecture. The talk will then describe the details of Phoenix integration with Tephra, and present performance some benchmark results for Phoenix operations with transactions. The talk will conclude with discussion on some challenges with scaling Tephra and potential solutions.
SAP Change Control Management & TR import automation toolJustAcademy
TRRevtool is SAP Change Control Management tool and Release Automation tool which is built on SAP
NetWeaver 7.52 SP4. It can be deployed on SAP Solution Manager or Individual server which can
control TR movement across SAP Landscape.
• It automates Transport Request movement across SAP landscape.
• It expedites SAP Changes from Dev to Test and PRD environment. It can manage multiple TR release
and import process in multiple SAP instances in different SAP landscape in organization.
• It provides Insights of conflicts of SAP Transport Requests. It also helps to identify any missing objects
and dependencies.
• TRRevtool has been integrated with industry leader in ITSM areas such as Service Now.
An autonomous transaction has its own COMMIT and ROLLBACK scope to ensure that its outcome does not effect the caller’s uncommitted changes. Additionally, the COMMITs and ROLLBACK in the calling transaction should not effect the changes that were finalized on the completion of autonomous transaction itself.
This presentation gives an overview of the Apache Tephra project. It explains Tephra in terms of Pheonix, HBase and HDFS. It examines the project architecture and configuration.
Links for further information and connecting
http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
https://nz.linkedin.com/pub/mike-frampton/20/630/385
https://open-source-systems.blogspot.com/
With the advent of new open source platforms around Hadoop, NoSQL databases & in-memory databases, the data management stack in the enterprise is undergoing complete re-platforming. Batch and stream processing are two distinct data processing paradigms that need to be supported over this new stack. In this session I will talk about the importance of having a unified batch and stream processing engine and share my learning around -
Sample use cases to that bring out the need to have a unified stream & batch processing engine
Important features needed in the unified platform to tackle the above use cases.
Transaction processing is very important and also necessary to maintain data integrity in both your application and database.
The transaction design patterns that are described in the next are :
Client Owner Transaction Design Pattern
Domain Service Owner Transaction Design Pattern
Server Delegate Owner Transaction Design Pattern
Transaction management in Java does not have to be complicated using the transaction design patterns described in this chapter makes transaction processing easy to understand, implement, and maintain.
End of the Myth: Ultra-Scalable Transactional Management by Ricardo Jiménez-P...Big Data Spain
The talk will focus on explaining why operational databases do not scale due to limitations in legacy transactional management.
https://www.bigdataspain.org/2017/talk/end-of-the-myth-ultra-scalable-transactional-management
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Flink Forward Berlin 2018: Viktor Klang - Keynote "The convergence of stream ...Flink Forward
Two of the main software architectural trends in software development this decade has been the move to streaming data processing, and the move to microservice architecture.
Both of these architectures are driven by the needs of managing and mining knowledge from ever increasing volumes of data in a close to real-time fashion—all while being reactive: responsive under failure, and responsive under load. I'm here to tell you that these two trends are converging, and a fusion of the two is both logical and inevitable. In this session we will talk about what a fused approach to stream processing and microservices could look like, what opportunities exist—what software development for business software can look like in the following decade.
Webinar Slides: MySQL HA/DR/Geo-Scale - High Noon #2: Galera ClusterContinuent
Galera Cluster vs. Continuent Tungsten Clusters
Building a Geo-Scale, Multi-Region and Highly Available MySQL Cloud Back-End
This second installment of our High Noon series of on-demand webinars is focused on Galera Cluster (including MariaDB Cluster & Percona XtraDB Cluster). It looks at some of the key characteristics of Galera Cluster and how it fares as a MySQL HA / DR / Geo-Scale solution, especially when compared to Continuent Tungsten Clustering.
Watch this webinar to learn how to do better MySQL HA / DR / Geo-Scale.
AGENDA
- Goals for the High Noon Webinar Series
- High Noon Series: Tungsten Clustering vs Others
- Galera Cluster (aka MariaDB Cluster & Percona XtraDB Cluster)
- Key Characteristics
- Certification-based Replication
- Galera Multi-Site Requirements
- Limitations Using Galera Cluster
- How to do better MySQL HA / DR / Geo-Scale?
- Galera Cluster vs Tungsten Clustering
- About Continuent & Its Solutions
PRESENTER
Matthew Lang - Customer Success Director – Americas, Continuent - has over 25 years of experience in database administration, database programming, and system architecture, including the creation of a database replication product that is still in use today. He has designed highly available, scaleable systems that have allowed startups to quickly become enterprise organizations, utilizing a variety of technologies including open source projects, virtualization and cloud.
In search of database nirvana - The challenges of delivering Hybrid Transacti...Rohit Jain
Companies are looking for a single database engine that can address all their varied needs—from transactional to analytical workloads, against structured, semi-structured, and unstructured data, leveraging graph, document, text search, column, key value, wide column, and relational data stores; on a single platform without the latency of data transformation and replication. They are looking for the ultimate database nirvana.
The term hybrid transactional/analytical processing (HTAP), coined by Gartner, perhaps comes closest to describing this concept. 451 Research uses the terms convergence or converged data platform. The terms multi-model or unified are also used. But can such a nirvana be achieved? Some database vendors claim to have already achieved this nirvana. In this talk we will discuss the following challenges on the path to this nirvana, for you to assess how accurate these claims are:
· What is needed for a single query engine to support all workloads?
· What does it take for that single query engine to support multiple storage engines, each serving a different need?
· Can a single query engine support all data models?
· Can it provide enterprise-caliber capabilities?
Attendees looking to assess query and storage engines would benefit from understanding what the key considerations are when picking an engine to run their targeted workloads. Also, developers working on such engines can better understand capabilities they need to provide in order to run workloads that span the HTAP spectrum.
Overview of Apache Trafodion (incubating), Enterprise Class Transactional SQL-on-Hadoop DBMS, with operational use cases, what it takes to be a world class RDBMS, some performance information, and the new company Esgyn which will leverage Apache Trafodion for operational solutions.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.