This document summarizes transactions in Apache Geode, including:
- The semantics of repeatable read and optimistic concurrency control.
- The transaction API for basic, suspend/resume, and single entry operations.
- The implementation of using ThreadLocal to isolate transactions and conflict detection at commit.
- Handling transactions with replicated and partitioned regions, including failure scenarios.
- Support for client-initiated transactions, data colocation, and interaction with other Geode features.
- Types of exceptions and how to handle failures.
This document provides an overview of jBPM5, an open source business process management platform. It discusses key features such as supporting the full BPM lifecycle for both developers and business users, advanced and adaptive process capabilities, and being a lightweight, native BPMN 2.0 engine. The core jBPM engine uses a workflow-based approach with pluggable persistence and transactions. The platform also provides domain-specific processes, a human task service, and extra features including testing, Spring integration, and migration tools.
Apache Apex Fault Tolerance and Processing SemanticsApache Apex
Components of an Apex application running on YARN, how they are made fault tolerant, how checkpointing works, recovery from failures, incremental recovery, processing guarantees.
Transaction processing monitors (TP monitors) provide core support for distributed transaction processing. They act as middleware, routing client messages to application servers and providing infrastructure for building complex transaction systems. TP monitor architectures include the process-per-client model, single-server model, many-server single-router model, and many-server many-router model. TP monitors ensure the ACID properties of atomicity, consistency, isolation, and durability through features like persistent queuing, logging, recovery, and concurrency control. They have proven resilient over time and will continue impacting emerging middleware technologies.
Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.
This document introduces TaskFlow, an OpenStack library for managing task execution. It describes TaskFlow as a lightweight framework that makes task execution reliable, consistent and resumable through the use of tasks, flows, engines and persistence. Key concepts are explained such as tasks, flows, engines, persistence and jobs. Current capabilities are outlined along with examples and planned integration points. The document encourages involvement in TaskFlow's development.
This document discusses key factors of real-time distributed database systems. It defines hard and soft real-time systems and explains how concurrency control is more challenging in a distributed real-time environment. Both pessimistic and optimistic concurrency control approaches are covered. Replication strategies are also discussed, including full replication with eager vs lazy updating, and primary vs update-anywhere models. Partial replication is presented as an alternative to reduce overhead. The conclusion emphasizes that replication strategies must adapt to real-time constraints.
Apache Apex (incubating) is a next generation native Hadoop big data platform. This talk will cover details about how it can be used as a powerful and versatile platform for big data.
Presented by Pramod Immaneni at Data Riders Meetup hosted by Nexient on Apr 5th, 2016
The document discusses concurrency control in databases. It describes transactions, their ACID properties (atomicity, consistency, independence, durability), and problems that can occur with concurrent transactions like lost updates, integrity constraint violations, and inconsistent retrievals. It also covers serialization, serial schedules, serializable schedules, and concurrency control techniques like locking, timestamps, and optimistic methods.
This document provides an overview and deep dive into jBPM6, an open source business process management suite. It highlights key characteristics including being lightweight, standards-based, supporting the full life cycle from design to runtime. It then demonstrates the jBPM Execution Server, runtime management strategies, remote APIs including REST and JMS, and service tasks. Repository and deployment are discussed as well as getting started resources.
Transaction Processing Monitors represent an early type of middleware that is still widely used for performing distributed transactions involving multiple databases.
Usually TPMs employ the two phase commit protocol that ensures ACID properties (Atomicity, Consistency, Isolation, Durability) as in relational databases.
Transaction processing monitors (TPM) ensure transactions process completely or are rolled back if errors occur. TPMs work in multi-tier architectures by forwarding transactions between servers running on different platforms. TPMs use ACID properties to provide atomicity, consistency, isolation, and durability of transactions. Examples of TPMs include IBM's CICS and BEA's TUXEDO. TPMs are critical for ensuring transactions are processed reliably in multi-step distributed systems.
This document provides an overview of what's new in jBPM6, including improvements to the user interface, authoring, deployment, execution and management of business processes. Key features include a new UberFire workspace for authoring processes and rules, Git-based version control for deployment, and runtime management of processes using services like the task service. It also outlines the jBPM roadmap with plans to enhance the web UI, add connectors, support dynamic/case-based processes, cloud deployment, and collaboration features.
Apache Apex is a stream processing framework that provides high performance, scalability, and fault tolerance. It uses YARN for resource management, can achieve single digit millisecond latency, and automatically recovers from failures without data loss through checkpointing. Apex applications are modeled as directed acyclic graphs of operators and can be partitioned for scalability. It has a large community of committers and is in the process of becoming a top-level Apache project.
This document discusses Linux containers and PostgreSQL in Docker containers. It begins with an overview of containers, their advantages and disadvantages compared to virtual machines. It then discusses different implementations of containers like LXC and systemd-nspawn. A large portion of the document is dedicated to Docker containers - how to install, use images and volumes, and common commands. It concludes with best practices for running PostgreSQL in Docker containers, including mounting data volumes, linking containers, checking stats and processes.
Boitumelo Lesabane's curriculum vitae summarizes her educational and professional background. She has a National Diploma in Business Management and is currently studying for a BCom in Supply Chain and Operational Management at Unisa. Her work experience includes administrative roles in procurement for the Gauteng Department of Education since 2008. She has strengths in communication, administration, and working well under pressure.
ODPi is an open source initiative to advance Apache Hadoop and big data technologies for enterprise use. It aims to drive interoperability through an open governance model and a shared core specification. The ODPi Core initially includes HDFS, YARN, MapReduce, and Ambari and provides specifications, reference implementations, and tests for compatibility and certification. A diverse set of members from the big data ecosystem contribute to and govern ODPi projects.
Motivation and goals for off-heap storage
Off-heap features and usage
Implementation overview
Preliminary benchmarks: off-heap vs. heap
Tips and best practices
The document summarizes the journey of HAWQ and MADlib from being proprietary Pivotal technologies to becoming Apache open source projects. It provides an overview of HAWQ, including its key features like SQL compliance, performance advantages over other SQL-on-Hadoop systems, and flexible deployment options. It also summarizes MADlib, describing its machine learning functions and advantages of scalable in-database machine learning. Both projects are now available on open source platforms like Hadoop and aim to advance SQL and machine learning on big data through open collaboration.
#GeodeSummit - Integration & Future Direction for Spring Cloud Data Flow & GeodePivotalOpenSourceHub
In this session we review the design of the current state of support for Apache Geode by Spring Cloud Data Flow, and explore additional use cases and future direction that Spring Cloud Data Flow and Apache Geode might evolve.
Apache Apex and Apache Geode are two of the most promising incubating open source projects. Combined, they promise to fill gaps of existing big data analytics platforms. Apache Apex is an enterprise grade native YARN big data-in-motion platform that unifies stream and batch processing. Apex is highly scalable, performant, fault tolerant, and strong in operability. Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing. We will also look at some use cases where how these two projects can be used together to form distributed, fault tolerant, reliable in memory data processing layer.
#GeodeSummit: Architecting Data-Driven, Smarter Cloud Native Apps with Real-T...PivotalOpenSourceHub
This talk introduces an open-source solution that integrates cloud native apps running on Cloud Foundry with an open-source hybrid transactions + analytics real-time solution. The architecture is based on the fastest scalable, highly available and fully consistent In-Memory Data Grid (Apache Geode / GemFire), natively integrated to the first open-source massive parallel data warehouse (Greenplum Database) in a hybrid transactional and analytical architecture that is extremely fast, horizontally scalable, highly resilient and open source. This session also features a live demo running on Cloud Foundry, showing a real case of real-time closed-loop analytics and machine learning using the featured solution.
#GeodeSummit - Large Scale Fraud Detection using GemFire Integrated with Gree...PivotalOpenSourceHub
In this session we explore a case study of a large-scale government fraud detection program that prevents billions of dollars in fraudulent payments each year leveraging the beta release of the GemFire+Greenplum Connector, which is planned for release in GemFire 9. Topics will include an overview of the system architecture and a review of the new GemFire+Greenplum Connector features that simplify use cases requiring a blend of massively parallel database capabilities and accelerated in-memory data processing.
CV of Rosanna Ester Navarro Acosta Feb 2016Rosanna Acosta
Rosanna Navarro Acosta has over 20 years of experience in quality engineering and assurance roles. She is fluent in English and Filipino with intermediate skills in Spanish and Cantonese. She holds a Bachelor's degree in Electronics and Communications Engineering and has worked for several companies in Hong Kong and the Philippines performing quality control inspections, reliability testing, and ensuring products meet various industry standards. She has experience across many product categories including appliances, audio/video equipment, toys, and power tools.
Building Apps with Distributed In-Memory Computing Using Apache GeodePivotalOpenSourceHub
Slides from the Meetup Monday March 7, 2016 just before the beginning of #GeodeSummit, where we cover an introduction of the technology and community that is Apache Geode, the in-memory data grid.
#GeodeSummit - Modern manufacturing powered by Spring XD and GeodePivotalOpenSourceHub
This document summarizes a presentation about how TEKsystems Global Services helps modern manufacturing industries address challenges through big data solutions. It outlines TEKsystems' services and capabilities, as well as real-world applications for manufacturing, financial services, and life sciences. The presentation describes reference architectures and customer success stories in marine seismic data and gaming industries. It positions TEKsystems as having expertise, proven track records, and packaged offerings to provide big data solutions from pilot to production.
In this session we review the design of the newly released off heap storage feature in Apache Geode, and discuss use cases and potential direction for additional capabilities of this feature.
#GeodeSummit - Where Does Geode Fit in Modern System ArchitecturesPivotalOpenSourceHub
The document discusses how Apache Geode fits into modern system architectures using the Command Query Responsibility Segregation (CQRS) pattern. CQRS separates reads and writes so that each can be optimized independently. Geode is well-suited as the read store in a CQRS system due to its ability to efficiently handle queries and cache data through regions. The document provides references on CQRS and related patterns to help understand how they can be applied with Geode.
#GeodeSummit: Easy Ways to Become a Contributor to Apache GeodePivotalOpenSourceHub
The document provides steps for becoming a contributor to the Apache Geode project, beginning with joining online conversations about the project, then test-driving it by building and running examples, and finally improving the project by reporting findings, fixing bugs, or adding new features through submitting code. The key steps are to join mailing lists or chat forums to participate in discussions, quickly get started with the project by building and testing examples in 5 minutes, and then test release candidates and report any issues found on the project's issue tracker or documentation pages. Contributions to the codebase are also welcomed by forking the GitHub repository and submitting pull requests with bug fixes or new features.
#GeodeSummit Keynote: Creating the Future of Big Data Through 'The Apache Way"PivotalOpenSourceHub
Keynote at Geode Summit 2016 by Dr. Justin Erenkrantz, Bloolmberg LP. Creating the Future of Big Data Through "The Apache Way" and why this matters to the community
This document discusses transaction processing in databases. It covers topics like ACID properties, transaction states, concurrency control techniques like locking, and problems that can occur in transaction processing like dirty reads, lost updates, and phantoms reads. Transaction processing aims to ensure transactions are atomic, consistent, isolated, and durable despite concurrent execution through techniques like locking, logging, and multi-version concurrency control.
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
GE IOT Predix Time Series & Data Ingestion Service using Apache Apex (Hadoop)Apache Apex
This presentation will introduce usage of Apache Apex for Time Series & Data Ingestion Service by General Electric Internet of things Predix platform. Apache Apex is a native Hadoop data in motion platform that is being used by customers for both streaming as well as batch processing. Common use cases include ingestion into Hadoop, streaming analytics, ETL, database off-loads, alerts and monitoring, machine model scoring, etc.
Abstract: Predix is an General Electric platform for Internet of Things. It helps users develop applications that connect industrial machines with people through data and analytics for better business outcomes. Predix offers a catalog of services that provide core capabilities required by industrial internet applications. We will deep dive into Predix Time Series and Data Ingestion services leveraging fast, scalable, highly performant, and fault tolerant capabilities of Apache Apex.
Speakers:
- Venkatesh Sivasubramanian, Sr Staff Software Engineer, GE Predix & Committer of Apache Apex
- Pramod Immaneni, PPMC member of Apache Apex, and DataTorrent Architect
This document provides an overview of Kafka Streams, a stream processing library built on Apache Kafka. It discusses how Kafka Streams addresses limitations of traditional batch-oriented ETL processes by enabling low-latency, continuous stream processing of real-time data across diverse sources. Kafka Streams applications are fault-tolerant distributed applications that leverage Kafka's replication and partitioning. They define processing topologies with stream processors connected by streams. State is stored in fault-tolerant state stores backed by change logs.
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.
Choosing the right high availability strategyMariaDB plc
This document discusses different high availability strategies for MariaDB databases. It covers asynchronous and semi-synchronous replication, which provide redundancy and failover capabilities but can have data loss risks. Synchronous replication with Galera Cluster is also described, which guarantees no data loss but has higher latency. Other topics include terminology, data redundancy approaches, and how features can be combined for resilient configurations.
Stream processing involves processing unbounded streams of data in near real-time to produce derived data outputs. Samza is a distributed stream processing framework that allows processing of streams at large scale. At LinkedIn, Samza is used to process over 1 trillion events per day across many jobs and clusters for applications like tracking, analytics, and data standardization. Upcoming Samza features include improvements to local state storage, dynamic configuration, easier deployment of standalone jobs, and a high-level query language.
As fast as a grid, as safe as a databasegojkoadzic
From the Gaming Scalability event, June 2009 in London (http://gamingscalability.org).
In this talk, Matthew Fowler from NT/e looks at the persistence issues on computing clouds. He discusses architectural principles and problems that cloud persistence presents to application developers and presents a possible solution, focusing on the key ideas, the tooling and the deployment options.
Matthew Fowler runs the Java business unit of New Technology/enterprise. Matthew received a BSc in Computer Science from MIT. He has developed and marketed products in many areas of software - LANs, WANs, software tools, language processors and generation of enterprise applications. His current interests are system generation and grid/cloud applications.
Transaction in HBase, by Andreas Neumann, CaskCask Data
Title: Transactions in HBase
Speaker: Andreas Neumann, Cask
ApacheCon Big Data, Miami, FL
May 18, 2017
Abstract:
In the age of NoSQL, big data storage engines such as HBase have given up ACID semantics of traditional relational databases, in exchange for high scalability and availability. However, it turns out that in practice, many applications require consistency guarantees to protect data from concurrent modification in a massively parallel environment. In the past few years, several transaction engines have been proposed as add-ons to HBase: Three different engines, namely Omid, Tephra, and Trafodion were open-sourced within the Apache ecosystem alone. In this talk, Andreas Neumann will introduce and compare the different approaches from various perspectives including scalability, efficiency, operability and portability, and make recommendations pertaining to different use cases.
Speaker Bio:
Andreas Neumann develops big data software at Cask, and has formerly done so at places that are known for massive scale. He was the chief architect for Hadoop at Yahoo! and also for the foundational content management system that Yahoo! built on Hadoop. Previously he was a research engineer at Yahoo! and a search architect at IBM. Andreas holds a doctoral degree in computer science for his work on querying XML documents.
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.
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.
Choosing the right high availability strategyMariaDB plc
- MariaDB provides several high availability options including asynchronous replication, semi-synchronous replication, Galera synchronous replication, and MaxScale for load balancing and failover.
- Asynchronous replication allows for read scaling but carries a risk of data loss during failover. Semi-synchronous replication reduces this risk by ensuring data is written to at least one slave before confirming to the client.
- Galera synchronous multi-master replication ensures all nodes remain in sync with no data loss but can impact performance. MaxScale helps manage replication topology and perform automated failovers.
The document discusses key concepts related to application servers, including:
1) Request controllers coordinate requests between clients and transaction servers, while transaction servers access resources but do not directly communicate with clients.
2) Remote procedure calls allow programs to call remote procedures like local ones, hiding communication errors and automating parameter passing.
3) Transactional RPC extends RPC to support transactions by passing transaction contexts and enlisting callees in transactions.
4) A transaction manager supports transactions through operations like start, commit, and abort, and implements two-phase commit across resources.
Dynamic routing in microservice oriented architectureDaniel Leon
When splitting an application into different micro-services and each application access URL is dynamically generated, the hell gets loose. If you are tired of manually setting a route in your ngnix, come see linkerd in action.
3450 - Writing and optimising applications for performance in a hybrid messag...Timothy McCormick
Messaging architectures in any environment, from local standalone deployments through to public clouds, must provide the highest reliability yet maximize their performance. This session gives you an insight into IBM MQ and how applications can be made to perform to their absolute best while maintaining the data integrity that IBM MQ is renowned for. We'll see how this can be achieved through a combination of good application design, system tuning and architectural patterns.
Retaining Goodput with Query Rate LimitingScyllaDB
ScyllaDB uses a shared-nothing architecture where data is split into partitions across nodes and shards. The "hot partition problem" can occur when a partition becomes overloaded, impacting other nearby partitions. To address this, ScyllaDB implements per-partition rate limiting which counts operations and rejects some to keep the rate under a defined limit. Exceptions were initially making rejections expensive, but this was addressed by avoiding exceptions or implementing missing exception inspection capabilities. In benchmarks, rate limiting restored goodput and provided more stable performance under timeouts.
An adaptive and eventually self healing framework for geo-distributed real-ti...Angad Singh
This document discusses an adaptive and self-healing framework for real-time data ingestion across geographically distributed data centers. It describes the problem domain of ingesting 15 billion events per day across multiple schemas and data types from various sources. The proposed architecture includes an ingestion layer using technologies like Storm, Kafka and HDFS to ingest, transform and replicate streaming and batch data. It also includes a serving layer using Aerospike to provide low-latency aggregated user views. Issues encountered with technologies like Storm and Kafka are discussed, as well as features still under development.
Geek Sync | How to Detect, Analyze, and Minimize SQL Server Blocking and LockingIDERA Software
You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/sbap50AJw6b
Learn the good, the bad, and the ugly of blocking. Join IDERA and Hilary Cotter for this Geek Sync to understand how to monitor locking and blocking.
The goals of this session are:
-To discover what is the difference between locking, blocking and deadlocking
-To understand how to minimize blocking during OLTP operations, batch processing, bulk inserts, and large scale deletes
-To study how to use the appropriate isolation levels to reduce/increase blocking and partitioning and blocking
Attend this webinar to detect, analyze, and minimize SQL Server blocking and locking. The session will also discuss what Snapshot and Read Committed Snapshot Isolation levels are all about and when you should use them. Finally, we will go over In-Memory Tables and learn how to monitor and troubleshoot blocking processes.
Speaker: Hilary Cotter is a 20 year IT veteran who has answered over 20,000 questions on the forums. Some of them correctly. He specializes in HA technologies, especially replication, performance tuning, full-text search, and SQL Server Service Broker. Hilary is also an author, or contributor on a number of books on SQL Server.
Compensating Transactions: When ACID is too muchJBUG London
The talk was presented by Dr Paul Robinson (Red Hat) at the London JBoss User Group event on the 25th of September 2013.
Video available soon
ACID transactions are a useful tool for application developers and can provide very strong guarantees, even in the presence of failures. However, ACID transactions are not always appropriate for every situation.
ACID transactions are achieved by holding locks on resources and require the ability to rollback changes. In some situations, the blocking nature of the protocol can be too limiting to performance, especially if the transaction is distributed. Also, some actions can’t simply be rolled back; for example, sending an email.
A common strategy for applications that can’t use ACID, is to throw out transactions altogether. However, with this approach you are missing out on many of the benefits that transactions can provide. There are alternative transaction models that relax some of the ACID properties, while still retaining many of the strong guarantees essential for building robust enterprise applications. These should be considered before deciding not to use transactions at all.
In this talk, I’ll present one such alternative to ACID transactions: compensation-based transactions. I’ll provide an overview of ACID and it’s limitations and describe some use-cases where ACID is not appropriate. I’ll then present our new API for compensation-based transactions and demonstrate how it can be used to address these problems. Finally, I’ll present our future plans to improve and expand our support for compensation-based transactions.
Similar to Geode Transactions by Swapnil Bawaskar (20)
Here are the slides for Greenplum Chat #8. You can view the replay here: https://www.youtube.com/watch?v=FKFiyJDgdQk
The increased frequency and sophistication of high-profile data breaches and malicious hacking is putting organizations at continued risk of data theft and significant business disruption. Complicating this scenario is the unbounded growth of Big Data and petabyte-scale data storage, new open source database and distribution schemes, and the continued adoption of cloud services by enterprises.
Pivotal Greenplum customers often look for additional encryption of data-at-rest and data-in-motion. The massively parallel processing (MPP) architecture of Pivotal Greenplum provides an architecture that is unlike traditional OLAP on RDBMS for data warehousing, and encryption capabilities must address the scale-out architecture.
The Zettaset Big Data Encryption Suite has been designed for optimal performance and scalability in distributed Big Data systems like Greenplum Database and Apache HAWQ.
Here is a replay of our recent Greenplum Chat with Zettaset:
00:59 What is Greenplum’s approach for encryption and why Zettaset?
02:17 Results of field testing Zettaset with Greenplum
03:50 Introduction to Zettaset, the security company
05:36 Overview of Zettaset and their solutions
14:51 Different layers for encrypting data at rest
16:50 Encryption key management for big data
20:51 Zettaset BD Encrypt for data at rest and data in motion
22:19 How to mitigate encryption overhead with an MPP scale-out system
24:12 How to deploy BD Encrypt
25:50 Deep dive on data at rest encryption
30:44 Deep dive on data in motion encryption
36:72 Q: How does Zettaset deal with encrypting Greenplums multiple interfaces?
38:08 Q: Can I encrypt data for a particular column?
40:26 How Zettaset fits into a security strategy
41:21 Q: What is the performance impact on queries by encrypting the entire database?
43:28 How Zettaset helps Greenplum meet IT compliance requirements
45:12 Q: How authentication for keys is obtained
48:50 Q: How can Greenplum users try out Zettaset?
50:53 Q: What is a ‘Zettaset Security Coach’?
How to use the WAN Gateway feature of Apache Geode to implement multi-site and active-active failover, disaster recovery, and global scale applications.
#GeodeSummit: Combining Stream Processing and In-Memory Data Grids for Near-R...PivotalOpenSourceHub
This document discusses combining stream processing and in-memory data grids for near-real-time aggregation and notifications. It describes storing immutable event data and filtering and aggregating events in real-time based on requested perspectives. Perspectives can be requested at any time for historical or real-time event data. The solution aims to be scalable, resilient, and low latency using Apache Storm for stream processing, Apache Geode for the event log and storage, and deployment patterns to collocate them for better performance.
This document discusses implementing a Redis adaptor using Apache Geode. It provides an overview of Redis data structures and commands, describes how Geode partitioned regions and indexes can be used to store and access Redis data, outlines advantages like scalability and high availability, and presents a roadmap for further development including supporting additional commands and performance optimization.
In this session we review the design of the current capabilities of the Spring Data GemFire API that supports Geode, and explore additional use cases and future direction that the Spring API and underlying Geode support might evolve.
#GeodeSummit - Using Geode as Operational Data Services for Real Time Mobile ...PivotalOpenSourceHub
One of the largest retailers in North America are considering Apache Geode for their new mobile loyalty application, to support their digital transformation effort. They would use Geode to provide operational data services for their mobile cloud service. This retailer needs to replace sluggish response times with sub-second response which will improved conversion rates. They also want to able to close the loop between data science findings and app experience. This way the right customer interaction is suggested when it is needed such as when customers are looking at their mobile app while walking in the store, or sending notifications at the individuals most likely shopping times. The final benefits of using Geode will include faster development cycles, increased customer loyalty, and higher revenue.
#GeodeSummit: Democratizing Fast Analytics with Ampool (Powered by Apache Geode)PivotalOpenSourceHub
Today, if events change the decision model, we wait until the next batch model build for new insights. By extending fast “time-to-decisions” into the world of Big Data Analytics to get fast “time-to-insights”, apps will get what used to be batch insights in near real time. The technology enabling this includes smart in-memory data storage, new storage class memory, and products designed to do one or more parts of an analysis pipeline very well. In this talk we describe how Ampool is building on Apache Geode to allow Big Data analysis solutions to work together with a scalable smart storage class memory layer to allow fast and complex end-to-end pipelines to be built -- closing the loop and providing dramatically lower time to critical insights.
How Southwest Airlines Uses Geode
Distributed systems and fast data require new software patterns and implementation skills. Learn how Southwest Airlines uses Apache Geode, organizes team responsibilities, and approaches design tradeoffs. Drawing inspiration from real whiteboard conversations, we’ll explore: common development pitfalls, environment capacity planning, streaming data patterns like consumer checkpointing, support roles, and production lessons learned.
Every day, Apache Geode improves how Southwest Airlines schedules nearly 4,000 flights and serves over 500,000 passengers. It’s an essential component of Southwest’s ability to reduce flight delays and support future growth.
#GeodeSummit - Wall St. Derivative Risk Solutions Using GeodePivotalOpenSourceHub
In this talk, Andre Langevin discusses how Geode forms the core of many Wall Street derivative risk solutions. By externalizing risk from trading systems, Geode-based solutions provide cross-product risk management at speeds suitable for automated hedging, while simultaneously eliminating the back office costs associated with traditional trading system based solutions.
GPORCA is newly open source advanced query optimizer that is a subproject of Greenplum Database open source project. GPORCA is the query optimizer used in commercial distributions of both Greenplum and HAWQ. In these distributions GPORCA has achieved 1000x performance improvement across TPC-DS queries by focusing on three distinct areas: Dynamic Partition Elimination, SubQuery Unnesting, and Common Table Expression.
Now that GPORCA is open source, we are looking for collaborators to help us realize the ultimate dream for GPORCA - to work with any database.
The new breed of data management systems in Big Data have to process so much data that optimization mistakes are magnified in traditional optimizers. Furthermore, coding and manual optimization of complex queries has proven to be hard.
In this session, Venkatesh will discuss:
- Overview of GPORCA
- How to add GPORCA to HAWQ with a build option
- How GPORCA could be made to work with any database
- Future vision for GPORCA and more immediate plans
- How to work with GPORCA, and how to contribute to GPORCA
Pivoting Spring XD to Spring Cloud Data Flow with Sabby AnandanPivotalOpenSourceHub
Pivoting Spring XD to Spring Cloud Data Flow: A microservice based architecture for stream processing
Microservice based architectures are not just for distributed web applications! They are also a powerful approach for creating distributed stream processing applications. Spring Cloud Data Flow enables you to create and orchestrate standalone executable applications that communicate over messaging middleware such as Kafka and RabbitMQ that when run together, form a distributed stream processing application. This allows you to scale, version and operationalize stream processing applications following microservice based patterns and practices on a variety of runtime platforms such as Cloud Foundry, Apache YARN and others.
About Sabby Anandan
Sabby Anandan is a Product Manager at Pivotal. Sabby is focused on building products that eliminate the barriers between application development, cloud, and big data.
Zeppelin Interpreters
PSQL (to became JDBC in 0.6.x)
Geode
SpringXD
Apache Ambari
Zeppelin Service
Geode, HAWQ and Spring XD services
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The document discusses Greenplum Database, an open source massively parallel processing (MPP) relational database system for big data. It provides an overview of Greenplum's architecture, including its master-segment structure and distributed transaction management. It also covers topics like defining data storage, distributions, partitioning, and analytics capabilities. Examples of Greenplum deployments are listed across various industries. Recent accomplishments and roadmap items are also summarized.
MADlib Architecture and Functional Demo on How to Use MADlib/PivotalRPivotalOpenSourceHub
This document discusses the MADlib architecture for performing scalable machine learning and analytics on large datasets using massively parallel processing. It describes how MADlib implements algorithms like linear regression across distributed database segments to solve challenges like multiplying data across nodes. It also discusses how MADlib uses a convex optimization framework to iteratively solve machine learning problems and the use of streaming algorithms to compute analytics in a single data scan. Finally, it outlines how the MADlib architecture provides scalable machine learning capabilities to data scientists through interfaces like PivotalR.
The document discusses how to build predictive models from noisy sensor data collected during oil and gas drilling operations. It notes that sensor data can be noisy, requiring data cleansing techniques to derive meaningful signals. It also discusses extracting relevant features from the cleansed sensor data and using those features to build predictive models, with the goal of predicting drilling failures and improving operations.
Overview of Statistical software such as ODK, surveyCTO,and CSPro
2. Software installation(for computer, and tablet or mobile devices)
3. Create a data entry application
4. Create the data dictionary
5. Create the data entry forms
6. Enter data
7. Add Edits to the Data Entry Application
8. CAPI questions and texts
Data analytics is a powerful tool that can transform business decision-making across industries. Contact District 11 Solutions, which specializes in data analytics, to make informed decisions and achieve your business goals.
Introduction to Data Science
1.1 What is Data Science, importance of data science,
1.2 Big data and data Science, the current Scenario,
1.3 Industry Perspective Types of Data: Structured vs. Unstructured Data,
1.4 Quantitative vs. Categorical Data,
1.5 Big Data vs. Little Data, Data science process
1.6 Role of Data Scientist
Annex K RBF's The World Game pdf documentSteven McGee
Signals & Telemetry Annex K for RBF's The World Game / Trade Federations / USPTO 13/573,002 Heart Beacon Cycle Time - Space Time Chain meters, metrics, standards. Adaptive Procedural template framework structured data derived from DoD / NATO's system of systems engineering tech framework
Combined supervised and unsupervised neural networks for pulse shape discrimi...Samuel Jackson
Our methodology for pulse shape discrimination is split into two steps. Firstly, we learn a model to discriminate between pulses using "clean" low-rate examples by removing pile-up & saturated events. In addition to traditional tail sum discrimination, we investigate three different choices for discrimination between γ-pulses, fast, thermal neutrons. We consider clustering the pulses directly using Gaussian Mixture Modelling (GMM), using variational autoencoders to learn a representation of the pulses and then clustering the learned representation (VAE+GMM) and using density ratio estimation to discriminate between a mixed (γ + neutron) and pure (γ only) sources using a multi-layer perceptron (MLP) as a supervised learning problem.
Secondly, we aim to classify and recover pile-up events in the < 150 ns regime by training a single unified multi-label MLP. To frame the problem as a multi-label supervised learning method, we first simulate pile-up events with known components. Then, using the simulated data and combining it with single event data, we train a final multi-label MLP to output a binary code indicating both how many and which type of events are present within an event window.
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...rightmanforbloodline
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
How AI is Revolutionizing Data Collection.pdfPromptCloud
Artificial Intelligence (AI) is transforming the landscape of data collection, making it more efficient, accurate, and insightful than ever before. With AI, businesses can automate the extraction of vast amounts of data from diverse sources, analyze patterns in real-time, and gain deeper insights with minimal human intervention. This revolution in data collection enables companies to make faster, data-driven decisions, enhance their competitive edge, and unlock new opportunities for growth.
AI-powered tools can handle complex and dynamic web content, adapt to changes in website structures, and even understand the context of data through natural language processing. This means that data collection is not only faster but also more precise, reducing the time and effort required for manual data extraction. Furthermore, AI can process unstructured data, such as social media posts and customer reviews, providing valuable insights into customer sentiment and market trends.
Embrace the future of data collection with AI and stay ahead of the curve. Learn more about how PromptCloud’s AI-driven web scraping solutions can transform your data strategy. https://www.promptcloud.com/contact/
Harnessing Wild and Untamed (Publicly Available) Data for the Cost efficient ...weiwchu
We recently discovered that models trained with large-scale speech datasets sourced from the web could achieve superior accuracy and potentially lower cost than traditionally human-labeled or simulated speech datasets. We developed a customizable AI-driven data labeling system. It infers word-level transcriptions with confidence scores, enabling supervised ASR training. It also robustly generates phone-level timestamps even in the presence of transcription or recognition errors, facilitating the training of TTS models. Moreover, It automatically assigns labels such as scenario, accent, language, and topic tags to the data, enabling the selection of task-specific data for training a model tailored to that particular task. We assessed the effectiveness of the datasets by fine-tuning open-source large speech models such as Whisper and SeamlessM4T and analyzing the resulting metrics. In addition to openly-available data, our data handling system can also be tailored to provide reliable labels for proprietary data from certain vertical domains. This customization enables supervised training of domain-specific models without the need for human labelers, eliminating data breach risks and significantly reducing data labeling cost.
DESIGN AND DEVELOPMENT OF AUTO OXYGEN CONCENTRATOR WITH SOS ALERT FOR HIKING ...JeevanKp7
Long-term oxygen therapy (LTOT) and novel techniques of evaluating treatment efficacy have enhanced the quality of life and decreased healthcare expenses for COPD patients.
The cost of a pulmonary blood gas test is comparable to the cost of two days of oxygen therapy and the cost of a hospital stay is equivalent to the cost of one month of oxygen therapy, long-term oxygen therapy (LTOT) is a cost-effective technique of treating this disease.
A small number of clinical investigations on LTOT have shown that it improves the quality of life of COPD patients by reducing the loss of their respiratory capacity. A study of 8487 Danish patients found that LTOT for 1524 hours per day extended life expectancy from 1.07 to 1.40 years.
3. Semantics
• Repeatable Read
– Thread sees own changes
– Other threads do not until commit is called
• Optimistic
– No Entry level locks, Readers not blocked
– Conflict Detection
• NOT Persistent (yet)
– Not a problem if you have at-least one member up
3
4. API
BASIC
CacheTransactionManager mgr = cache.getCacheTransactionManager();
mgr.begin();
region1.put("K1", "V1");
region1.put("K2", "V2");
region2.put("K2", "V2");
mgr.commit();
SUSPEND/RESUME
TransactionId txId = mgr.suspend();
… other non-transactional work
mgr.resume(txId);
mgr.tryResume(…);
SINGLE ENTRY
region.putIfAbsent(K, V);
region.replace(K, V, V);
region.remove(K, V);
OTHER
mgr.addListener(new MyTransactionListener());
mgr.setWriter(new TransactionWriter());
4
5. Implementation
• Isolation through ThreadLocal
– Copy existing
reference in
ThreadLocal
– Perform conflict
check under lock
at commit time
Region
Thread
TXState
Entry
Reference
5
7. Notes
• copy-on-read must be set to true
• D-lock grantor single point of contention
• Non-transactional threads may see
intermediate state (non atomic)
– Reads should be done within a transaction
– Set system property Dgemfire.detectReadConflicts=true
• Prone to ABA problem
• Faster than doing individual operations
7
8. Failure Scenarios
• Replica Fails
– No problem, It will do a GII from other members
• Coordinator Fails
– Replicas gossip to arrive at the outcome of the
transaction
• If no member has “Apply Commit” message, some
members missing commit set, Abort transaction
• If at-least one member has “Apply Commit” message,
all members have commit set, apply transaction
8
12. Partitioned Region
• TX State on member with Primary copy (TX HOST)
• Only one TX Host per transaction
– First operation in TX establishes the host
– All subsequent operations (even for Replicate Regions)
sent to the same host
– Throws TransactionDataNotColocatedException if TX Host is
not primary
• D-lock service is striped
– TX Locks are local, no messaging
– No single point of contention in the system
12
13. Data Colocation
• Inspiration
“For scalability, applications should manipulate single
collection of data that lives on one JVM”
- Pat Helland (Life Beyond Distributed Transactions)
- Custom Partitioning
- Within one Partitioned Region
- E.g. All trades in January
- Data Colocation
- Between Two or more Partitioned Regions
- All Orders of a Customer
13
14. Failure Scenarios
• Failures before Commit
– TX Host Crashes (TransactionDataNodeHasDeparted)
– On Re-balance (TransactionDataRebalanced)
• Entire transaction should be re-tried
• Failures after Commit
– TX Host Crashes
» Replicas would have applied all or none changes
» Consistent but Outcome unknown TransactionInDoubt
– Replica Crashes, Succeeds
14
15. • All operations sent to the server
• If necessary, server delegates to primary
• HA supported when delegate fails
Client
Delegate TX Host
TX State
Client Initiated
15
16. Feature Interaction
• Eviction/Expiration
– Entry is reference counted
– Entry kept around if reference count > 0
• OQL
– Does not honor Repeatable Read
• Persistence
– gemfire.ALLOW_PERSISTENT_TRANSACTIONS=true
– No delineation on Disk
– Works as long as one replica survives
• Functions
– Can begin, commit, suspend/resume transactions
16
17. Handling Failure
• Types of Exception
– CommitConflictException
– TransactionDataNodeHasDepartedException
– TransactionDataNotColocatedException
– TransactionDataRebalancedException
– TransactionInDoubtException
• Catch TransactionException and retry in loop
17
18. JTA
• Enlists as a Synchronization with external JTA
Managers
– Last to prepare, first to Commit
• Has a JCA adapter for Last Resource Commit
with Weblogic.
• Has an implementation of JTA Manager
– not production grade
18
19. Road Map
• Distributed Transactions (GEODE-16)
• XA Data Source
• Persistent Transactions
• OQL/Query Engine support
19
Isolation level supported by Geode is Repeatable Read.
Built for performance, so reads do not block, this causes interesting behavior at commit time, which we will touch upon later.
Since there is no lock, check to see if the entry has changed underneath us at commit time, abort tx if it has.
In terms of durability, not on disk, but no problem