So we're running Apache ZooKeeper. Now What? By Camille Fournier Hakka Labs
The ZooKeeper framework was originally built at Yahoo! to make it easy for the company’s applications to access configuration information in a robust and easy-to-understand way, but it has since grown to offer a lot of features that help coordinate work across distributed clusters. Apache Zookeeper became a de-facto standard for coordination service and used by Storm, Hadoop, HBase, ElasticSearch and other distributed computing frameworks.
Introduction to ZooKeeper - TriHUG May 22, 2012mumrah
Presentation given at TriHUG (Triangle Hadoop User Group) on May 22, 2012. Gives a basic overview of Apache ZooKeeper as well as some common use cases, 3rd party libraries, and "gotchas"
Demo code available at https://github.com/mumrah/trihug-zookeeper-demo
[Download the slide to get the entire talk in the form of presentation note embedded in the ppt] Apache ZooKeeper is the chosen leader in distributed coordination. In this talk, I have explored the atomic elements of Apache ZooKeeper, how it fits everything together and some of its popular use cases. For ZooKeeper simplicity is the key and as a consumer of the API, our imagination enables us to push the limits of the ZooKeeper world.
This is Apache ZooKeeper session.
ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.
By the end of this presentation you should be fairly clear about Apache ZooKeeper.
To watch the video or know more about the course, please visit
http://www.knowbigdata.com/page/big-data-and-hadoop-online-instructor-led-training
So we're running Apache ZooKeeper. Now What? By Camille Fournier Hakka Labs
The ZooKeeper framework was originally built at Yahoo! to make it easy for the company’s applications to access configuration information in a robust and easy-to-understand way, but it has since grown to offer a lot of features that help coordinate work across distributed clusters. Apache Zookeeper became a de-facto standard for coordination service and used by Storm, Hadoop, HBase, ElasticSearch and other distributed computing frameworks.
Introduction to ZooKeeper - TriHUG May 22, 2012mumrah
Presentation given at TriHUG (Triangle Hadoop User Group) on May 22, 2012. Gives a basic overview of Apache ZooKeeper as well as some common use cases, 3rd party libraries, and "gotchas"
Demo code available at https://github.com/mumrah/trihug-zookeeper-demo
[Download the slide to get the entire talk in the form of presentation note embedded in the ppt] Apache ZooKeeper is the chosen leader in distributed coordination. In this talk, I have explored the atomic elements of Apache ZooKeeper, how it fits everything together and some of its popular use cases. For ZooKeeper simplicity is the key and as a consumer of the API, our imagination enables us to push the limits of the ZooKeeper world.
This is Apache ZooKeeper session.
ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.
By the end of this presentation you should be fairly clear about Apache ZooKeeper.
To watch the video or know more about the course, please visit
http://www.knowbigdata.com/page/big-data-and-hadoop-online-instructor-led-training
Distributed system coordination by zookeeper and introduction to kazoo python...Jimmy Lai
Zookeeper is a coordination tool to let people build distributed systems easier. In this slides, the author summarizes the usage of zookeeper and provides Kazoo Python library as example.
Introduction to Apache ZooKeeper | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kvXlPd
This CloudxLab Introduction to Apache ZooKeeper tutorial helps you to understand ZooKeeper in detail. Below are the topics covered in this tutorial:
1) Data Model
2) Znode Types
3) Persistent Znode
4) Sequential Znode
5) Architecture
6) Election & Majority Demo
7) Why Do We Need Majority?
8) Guarantees - Sequential consistency, Atomicity, Single system image, Durability, Timeliness
9) ZooKeeper APIs
10) Watches & Triggers
11) ACLs - Access Control Lists
12) Usecases
13) When Not to Use ZooKeeper
In the big data world, our data stores communicate over an asynchronous, unreliable network to provide a facade of consistency. However, to really understand the guarantees of these systems, we must understand the realities of networks and test our data stores against them.
Jepsen is a tool which simulates network partitions in data stores and helps us understand the guarantees of our systems and its failure modes. In this talk, I will help you understand why you should care about network partitions and how can we test datastores against partitions using Jepsen. I will explain what Jepsen is and how it works and the kind of tests it lets you create. We will try to understand the subtleties of distributed consensus, the CAP theorem and demonstrate how different data stores such as MongoDB, Cassandra, Elastic and Solr behave under network partitions. Finally, I will describe the results of the tests I wrote using Jepsen for Apache Solr and discuss the kinds of rare failures which were found by this excellent tool.
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkitthelabdude
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will demonstrate how to provision, configure, and manage a SolrCloud cluster in Amazon EC2, using a Fabric/boto based solution for automating SolrCloud operations. Attendees will come away with a solid understanding of how to operate a large-scale Solr cluster, as well as tools to help them do it. Tim will also demonstrate these tools live during his presentation. Covered technologies, include: Apache Solr, Apache ZooKeeper, Linux, Python, Fabric, boto, Apache Kafka, Apache JMeter.
This talk covers why Apache Zookeeper is a good fit for coordinating processes in a distributed environment, prior Python attempts at a client and the current state of the art Python client library, how unifying development efforts to merge several Python client libraries has paid off, features available to Python processes, and how to gracefully handle failures in a set of distributed processes.
Container monitoring for resource and application metrics with cAdvisor. Shipping monitoring information with the container so it is monitored irrespective of the host it runs on.
Intro to monitoring in distributed systems, cAdvisor, heapster, kubedash, kubernetes
How to make a simple cheap high availability self-healing solr clusterlucenerevolution
Presented by Stephane Gamard, Chief Technology Officer, Searchbox
In this presentation we aim to show how to make a high availability Solr cloud with 4.1 using only Solr and a few bash scripts. The goal is to present an infrastructure which is self healing using only cheap instances based on ephemeral storage. We will start by providing a comprehensive overview of the relation between collections, Solr cores, shardes, and cluster nodes. We continue by an introduction to Solr 4.x clustering using zookeeper with a particular emphasis on cluster state status/monitoring and solr collection configuration. The core of our presentation will be demonstrated using a live cluster.
We will show how to use cron and bash to monitor the state of the cluster and the state of its nodes. We will then show how we can extend our monitoring to auto generate new nodes, attach them to the cluster, and assign them shardes (selecting between missing shardes or replication for HA). We will show that using a high replication factor it is possible to use ephemeral storage for shards without the risk of data loss, greatly reducing the cost and management of the architecture. Future work discussions, which might be engaged using an open source effort, include monitoring activity of individual nodes as to scale the cluster according to traffic and usage.
.NET developers have a lot of options when it comes to databases these days. Apache Cassandra is a scalable, fault-tolerant database that has already found its way into more than 25% of the Fortune 100 and continues to grow in popularity. But what makes it different from the myriad of other options available? In this talk, we’ll take a deep dive into Cassandra and learn about:
- Cassandra’s internals and how it works
- CQL (the SQL-like query language for Cassandra)
- Data Modeling like a pro
- Tools available for developers
- Writing .NET code that talks to Cassandra
If there’s time and interest, we’ll finish up with how some companies are already using Cassandra to power services you probably interact with in your daily life. You’ll leave with all the tools you need to start build highly available .NET applications and services on top of Cassandra.
Docker is all the rage these days. While one doesn't hear much about Solr on Docker, we're here to tell you not only that it can be done, but also share how it's done.
We'll quickly go over the basic Docker ideas - containers are lighter than VMs, they solve "but it worked on my laptop" issues - so we can dive into the specifics of running Solr on Docker.
We'll do a live demo showing you how to run Solr master - slave as well as SolrCloud using containers, how to manage CPU assignments, constraint memory and use Docker data volumes when running Solr in containers. We will also show you how to create your own containers with custom configurations.
Finally, we'll address one of the core Solr questions - which deployment type should I use? We will demonstrate performance differences between the following deployment types:
- Single Solr instance running on a bare metal machine
- Multiple Solr instances running on a single bare metal machine
- Solr running in containers
- Solr running on virtual machine
- Solr running on virtual machine using unikernel
For each deployment type we'll address how it impacts performance, operational flexibility and all other key pros and cons you ought to keep in mind.
Slides for presentation on ZooKeeper I gave at Near Infinity (www.nearinfinity.com) 2012 spring conference.
The associated sample code is on GitHub at https://github.com/sleberknight/zookeeper-samples
Distributed system coordination by zookeeper and introduction to kazoo python...Jimmy Lai
Zookeeper is a coordination tool to let people build distributed systems easier. In this slides, the author summarizes the usage of zookeeper and provides Kazoo Python library as example.
Introduction to Apache ZooKeeper | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kvXlPd
This CloudxLab Introduction to Apache ZooKeeper tutorial helps you to understand ZooKeeper in detail. Below are the topics covered in this tutorial:
1) Data Model
2) Znode Types
3) Persistent Znode
4) Sequential Znode
5) Architecture
6) Election & Majority Demo
7) Why Do We Need Majority?
8) Guarantees - Sequential consistency, Atomicity, Single system image, Durability, Timeliness
9) ZooKeeper APIs
10) Watches & Triggers
11) ACLs - Access Control Lists
12) Usecases
13) When Not to Use ZooKeeper
In the big data world, our data stores communicate over an asynchronous, unreliable network to provide a facade of consistency. However, to really understand the guarantees of these systems, we must understand the realities of networks and test our data stores against them.
Jepsen is a tool which simulates network partitions in data stores and helps us understand the guarantees of our systems and its failure modes. In this talk, I will help you understand why you should care about network partitions and how can we test datastores against partitions using Jepsen. I will explain what Jepsen is and how it works and the kind of tests it lets you create. We will try to understand the subtleties of distributed consensus, the CAP theorem and demonstrate how different data stores such as MongoDB, Cassandra, Elastic and Solr behave under network partitions. Finally, I will describe the results of the tests I wrote using Jepsen for Apache Solr and discuss the kinds of rare failures which were found by this excellent tool.
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkitthelabdude
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will demonstrate how to provision, configure, and manage a SolrCloud cluster in Amazon EC2, using a Fabric/boto based solution for automating SolrCloud operations. Attendees will come away with a solid understanding of how to operate a large-scale Solr cluster, as well as tools to help them do it. Tim will also demonstrate these tools live during his presentation. Covered technologies, include: Apache Solr, Apache ZooKeeper, Linux, Python, Fabric, boto, Apache Kafka, Apache JMeter.
This talk covers why Apache Zookeeper is a good fit for coordinating processes in a distributed environment, prior Python attempts at a client and the current state of the art Python client library, how unifying development efforts to merge several Python client libraries has paid off, features available to Python processes, and how to gracefully handle failures in a set of distributed processes.
Container monitoring for resource and application metrics with cAdvisor. Shipping monitoring information with the container so it is monitored irrespective of the host it runs on.
Intro to monitoring in distributed systems, cAdvisor, heapster, kubedash, kubernetes
How to make a simple cheap high availability self-healing solr clusterlucenerevolution
Presented by Stephane Gamard, Chief Technology Officer, Searchbox
In this presentation we aim to show how to make a high availability Solr cloud with 4.1 using only Solr and a few bash scripts. The goal is to present an infrastructure which is self healing using only cheap instances based on ephemeral storage. We will start by providing a comprehensive overview of the relation between collections, Solr cores, shardes, and cluster nodes. We continue by an introduction to Solr 4.x clustering using zookeeper with a particular emphasis on cluster state status/monitoring and solr collection configuration. The core of our presentation will be demonstrated using a live cluster.
We will show how to use cron and bash to monitor the state of the cluster and the state of its nodes. We will then show how we can extend our monitoring to auto generate new nodes, attach them to the cluster, and assign them shardes (selecting between missing shardes or replication for HA). We will show that using a high replication factor it is possible to use ephemeral storage for shards without the risk of data loss, greatly reducing the cost and management of the architecture. Future work discussions, which might be engaged using an open source effort, include monitoring activity of individual nodes as to scale the cluster according to traffic and usage.
.NET developers have a lot of options when it comes to databases these days. Apache Cassandra is a scalable, fault-tolerant database that has already found its way into more than 25% of the Fortune 100 and continues to grow in popularity. But what makes it different from the myriad of other options available? In this talk, we’ll take a deep dive into Cassandra and learn about:
- Cassandra’s internals and how it works
- CQL (the SQL-like query language for Cassandra)
- Data Modeling like a pro
- Tools available for developers
- Writing .NET code that talks to Cassandra
If there’s time and interest, we’ll finish up with how some companies are already using Cassandra to power services you probably interact with in your daily life. You’ll leave with all the tools you need to start build highly available .NET applications and services on top of Cassandra.
Docker is all the rage these days. While one doesn't hear much about Solr on Docker, we're here to tell you not only that it can be done, but also share how it's done.
We'll quickly go over the basic Docker ideas - containers are lighter than VMs, they solve "but it worked on my laptop" issues - so we can dive into the specifics of running Solr on Docker.
We'll do a live demo showing you how to run Solr master - slave as well as SolrCloud using containers, how to manage CPU assignments, constraint memory and use Docker data volumes when running Solr in containers. We will also show you how to create your own containers with custom configurations.
Finally, we'll address one of the core Solr questions - which deployment type should I use? We will demonstrate performance differences between the following deployment types:
- Single Solr instance running on a bare metal machine
- Multiple Solr instances running on a single bare metal machine
- Solr running in containers
- Solr running on virtual machine
- Solr running on virtual machine using unikernel
For each deployment type we'll address how it impacts performance, operational flexibility and all other key pros and cons you ought to keep in mind.
Slides for presentation on ZooKeeper I gave at Near Infinity (www.nearinfinity.com) 2012 spring conference.
The associated sample code is on GitHub at https://github.com/sleberknight/zookeeper-samples
Introduction to Apache Flink - Fast and reliable big data processingTill Rohrmann
This presentation introduces Apache Flink, a massively parallel data processing engine which currently undergoes the incubation process at the Apache Software Foundation. Flink's programming primitives are presented and it is shown how easily a distributed PageRank algorithm can be implemented with Flink. Intriguing features such as dedicated memory management, Hadoop compatibility, streaming and automatic optimisation make it an unique system in the world of Big Data processing.
Keeping Spark on Track: Productionizing Spark for ETLDatabricks
ETL is the first phase when building a big data processing platform. Data is available from various sources and formats, and transforming the data into a compact binary format (Parquet, ORC, etc.) allows Apache Spark to process it in the most efficient manner. This talk will discuss common issues and best practices for speeding up your ETL workflows, handling dirty data, and debugging tips for identifying errors.
Speakers: Kyle Pistor & Miklos Christine
This talk was originally presented at Spark Summit East 2017.
Introduction to automation in the cloud, why it's needed, what are the tools or ways of working, the processes, the best practises with some examples and takeaways.
Leveraging Hadoop in your PostgreSQL EnvironmentJim Mlodgenski
This talk will begin with a discussion of the strengths of PostgreSQL and Hadoop. We will then lead into a high level overview of Hadoop and its community of projects like Hive, Flume and Sqoop. Finally, we will dig down into various use cases detailing how you can leverage Hadoop technologies for your PostgreSQL databases today. The use cases will range from using HDFS for simple database backups to using PostgreSQL and Foreign Data Wrappers to do low latency analytics on your Big Data.
Copper: A high performance workflow enginedmoebius
COPPER (COmmon Persistable Process Excecution Runtime) is an open-source high performance workflow engine, that persists the workflow instances (process) state into a database. So there is no limit to the runtime of a process. It can run for weeks, month or years. In addition, this strategy leads to crash safety.
A workflow can describe business processes for example, however any kind of use case is supported. The "modelling" language is Java, that has several advantages:
* with COPPER any Java developer is able to design workflows
* all Java developers like to use Java
* many Java libs can be integrated within COPPER
* many Java tools, like IDEs, can be used
* with COPPER your productivity will be increased when using a workflow engine
* using Java solutions will protect your investment
* COPPER is OpenSource under Apache Licence 2.0
Please visit copper-engine.org for details.
6° Sessione - Ambiti applicativi nella ricerca di tecnologie statistiche avan...Jürgen Ambrosi
In questa sessione vedremo, con il solito approccio pratico di demo hands on, come utilizzare il linguaggio R per effettuare analisi a valore aggiunto,
Toccheremo con mano le performance di parallelizzazione degli algoritmi, aspetto fondamentale per aiutare il ricercatore nel raggiungimento dei suoi obbiettivi.
In questa sessione avremo la partecipazione di Lorenzo Casucci, Data Platform Solution Architect di Microsoft.
Dataservices: Processing (Big) Data the Microservice WayQAware GmbH
Apache Big Data 2017, Miami (Florida/USA): Talk by Josef Adersberger (@adersberger, CTO at QAware)
Abstract:
We see a big data processing pattern emerging using the Microservice approach to build an integrated, flexible, and distributed system of data processing tasks. We call this the Dataservice pattern. In this presentation we'll introduce into Dataservices: their basic concepts, the technology typically in use (like Kubernetes, Kafka, Cassandra and Spring) and some architectures from real-life.
Building scalable, highly-available applications that perform well is not an easy task. These features cannot be simply “bolted” onto an existing application – they have to be architected into it. Unfortunately, the things we need to do to achieve them are often in conflict with each other, and finding the right balance is crucial. In this session we will discuss why scaling web applications is difficult and will look at some of solutions we have come up with in the past to deal with the issues involved. We will then look at how in-memory data grids can make our jobs easier by providing a solid architectural foundation to build our applications on top of. If you are new to in-memory data grids, you are guaranteed to leave the presentation eager to learn more. However, even if you are already using one you will likely walk out with a few ideas on how to improve performance and scalability of your applications.
Talk about add proxy user in Spark Task execution time given in Spark Summit East 2017 by Jorge López-Malla and Abel Ricon
full video:
https://www.youtube.com/watch?v=VaU1xC0Rixo&feature=youtu.be
My Hadoop Ecosystem presentation at the 2011 BreizhCamp.
See the talk video (in french):
http://mediaserver.univ-rennes1.fr/videos/?video=MEDIA110628093346744
Similar to Distributed Applications with Apache Zookeeper (20)
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
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performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
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2. What is Zookeeper?
“ZooKeeper is a centralized service for
maintaining configuration information,
naming, providing distributed
synchronization, and providing group
services.”
Zookeeper Wiki
3. ZooKeeper: A Coordination Service for Distributed Applications
Coordination & synchronization for
distributed processes
Logical namespacing implemented by a
hierarchy (tree) of znodes
Replicated in-memory over multiple hosts
for reliability, availability, and performance
Simple API of CRUD & basic tree operations
for client integration
4. Zookeeper: Reliability & Consistency
Distributed ensemble with automatic leader
election through quorum
Replicated in-memory on every instance with
snapshot writes to disk
Client TCP connection maintained to any
node with failover support
Guaranteed atomicity & sequential
consistency
5. Zookeeper: Watches & Ephemeral nodes
Underlying znodes have a data structure consisting of version numbers (cversion, aversion) &
timestamps
Watches
● Client-initiated subscriptions to znodes
● Changes to a watched znode trigger notification to subscribed clients
Ephemeral Nodes
● Backed by a client session and deleted when client session ends
● Cannot have children
6. Zookeeper: But… why?
“Because of the difficulty of implementing
these kinds of services, applications initially
usually skimp on them, which make them
brittle in the presence of change and
difficult to manage. Even when done
correctly, different implementations of
these services lead to management
complexity when the applications are
deployed.”
Zookeeper Wiki
7. Zookeeper: Advantages for Backing a Server Cluster
Server workers can become cluster-aware
So much out-of-the-box that would be duplicated with a custom solution
Extremely fast reads (10:1 performance against writes)
Small footprint - An ensemble of only 5-7 zk instances can serve the
coordination needs of several large production applications
Centralized event broadcasting & failure detection (heartbeat)
8. Zookeeper: Common Use Cases
● Configuration Management
● Service Discovery
● Distributed Cloud-Based File Systems
● Internal DNS Management
● Master (Leader) Election and Voting
● Messaging Queue
● Event Broadcasting & Notification
10. ZK Use Case Example #1 - Pinterest
Pinterest stores their entire follower model inside sharded Redis instances (
~9000 Redis shards, multiple instances per core)
Shard configuration is stored and managed by Zookeeper
Client lookups and watches for shard location & subsequent data retrieval
Master-slave failover triggers updates to znode representation (slave address replaces master)
Vertical splitting of data broadcasted to watching clients
16. Exhibitor: A ZK Monitoring & Administration Tool from Netflix
Centralization & externalization of zk ensemble configuration* (S3/remote FS)
Web UI & REST API for ease of management
Instance monitoring with automatic configuration updates
Rolling ensemble changes while maintaining quorum
Miscellaneous administration tasks (backup/restore, log & snapshot cleanup)
* Configuration management for a configuration manager.... so meta!
19. Zookeeper Atomic Broadcast (ZAB) Algorithm
● Protocol for managing atomic updates to replicas
● Responsible for:
o Agreeing on an ensemble leader
o Synchronizing replicas
o Managing transactions and broadcasts
o Recovery of state
● ZXIDs & transactional ordering
● Guarantees:
o Local & global primary order
o Primary integrity
Wears a lot of hats. Can serve multiple purposes, all related to coordination of a distributed system. More concisely, Zookeeper is a coordination service for distributed applications.
A distributed system is a software system in which components located on networked computers communicate and coordinate their actions by passing messages. The components interact with each other in order to achieve a common goal.
Wikipedia
Digging a bit deeper:
Simply put, it backs the nodes of your distributed system through a tree structure of znodes (more in upcoming slides)
Central nervous system for your distributed application
Centralized and replicated across an odd number of hosts to make up an ensemble
Data is kept in memory and is backed up to a log for reliability. By using memory ZooKeeper is very fast and can handle the high loads typically seen in chatty coordination protocols across huge numbers of processes.
Prefers read-heavy based applications
Clients connect to any zk node in the ensemble and maintain that connection
API of create, read, update, delete (but also watches, more in later slides)
Replicated hosts make up an “ensemble”
Writes are bubbled up to one elected leader (which is why there should be an odd # of instances). A quorum must confirm the update.
Updates are submitted concurrently by clients and committed by FIFO order.
After update, incremental state changes are broadcast to replicas using Zookeeper Atomic Broadcast (ZAB).
Each state change is incremental with respect to the previous state, so there is an implicit dependence on the order of the state changes.
This guarantees:
Sequential Consistency - Updates from a client will be applied in the order that they were sent.
Atomicity - Updates either succeed or fail. No partial results.
Znodes maintain a stat structure that includes version numbers for data changes, acl changes. The stat structure also has timestamps. The version number, together with the timestamp allow ZooKeeper to validate the cache and to coordinate updates. Each time a znode's data changes, the version number increases.
Watches
Clients can set watches on znodes. Changes to that znode trigger the watch and then clear the watch. When a watch triggers, ZooKeeper sends the client a notification.
Ephemeral Nodes
ZooKeeper also has the notion of ephemeral nodes. These znodes exists as long as the session that created the znode is active. When the session ends the znode is deleted. Because of this behavior ephemeral znodes are not allowed to have children.
Time in ZooKeeper
ZooKeeper tracks time multiple ways:
Zxid
Every change to the ZooKeeper state receives a stamp in the form of a zxid (ZooKeeper Transaction Id). This exposes the total ordering of all changes to ZooKeeper. Each change will have a unique zxid and if zxid1 is smaller than zxid2 then zxid1 happened before zxid2.
Version numbers
Every change to a a node will cause an increase to one of the version numbers of that node. The three version numbers are version (number of changes to the data of a znode), cversion (number of changes to the children of a znode), and aversion (number of changes to the ACL of a znode).
Ticks
When using multi-server ZooKeeper, servers use ticks to define timing of events such as status uploads, session timeouts, connection timeouts between peers, etc. The tick time is only indirectly exposed through the minimum session timeout (2 times the tick time); if a client requests a session timeout less than the minimum session timeout, the server will tell the client that the session timeout is actually the minimum session timeout.
Real time
ZooKeeper doesn't use real time, or clock time, at all except to put timestamps into the stat structure on znode creation and znode modification.
A critical component of ZooKeeper is Zab, the ZooKeeper
Atomic Broadcast algorithm, which is the protocol that manages atomic updates to
the replicas. It is responsible for agreeing on a leader in the ensemble, synchronizing
the replicas, managing update transactions to be broadcast, as well as recovering from
a crashed state to a valid state.
Why not simply use a database?
Because of the guarantees
Fast especially in read-heavy workload (10:1 performance against writes)
More performance data in backup slides
Service Discovery - watch thru heartbeat
Let ELECTION be a path of choice of the application. To volunteer to be a leader:
Create znode z with path "ELECTION/guid-n_" with both SEQUENCE and EPHEMERAL flags;
Let C be the children of "ELECTION", and i be the sequence number of z;
Watch for changes on "ELECTION/guid-n_j", where j is the largest sequence number such that j < i and n_j is a znode in C;
Upon receiving a notification of znode deletion:
Let C be the new set of children of ELECTION;
If z is the smallest node in C, then execute leader procedure;
Otherwise, watch for changes on "ELECTION/guid-n_j", where j is the largest sequence number such that j < i and n_j is a znode in C;
http://engineering.pinterest.com/post/55272557617/building-a-follower-model-from-scratch
When a Redis machine reaches either the memory or CPU thresholds, we split it either horizontally orvertically. Vertical sharding a Redis machine is simply cutting the number of running Redis instances on the machine by half. We bring up a new master as a slave of the existing master and once the slaving is complete, we make it the new master for half of the Redis instances leaving the old master as the master for the other half.
The entire user id space is split into 8192 virtual shards. We place one virtual shard per Redis DB, and run multiple Redis instances (ranging from 8 to 32) on each machine depending on the memory and CPU consumption of the shards on those instances. Similarly, we run multiple Redis DBs per Redis instance.
Once a part of Hadoop
Currently maintained by Yahoo! and the Apache Software Foundation
HBase is the Hadoop database, a distributed, scalable, big data store.
HBase supports hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware.
Currently, hbase clients find the cluster to connect to by asking zookeeper. The only configuration a client needs is the zk quorum to connect to. Masters and hbase slave nodes (regionservers) all register themselves with zk. If their znode evaporates, the master or regionserver is consided lost and repair begins.
HBase currently will default to manage the zookeeper cluster. It does this in an attempt at not burdening users with yet another technology to figure; things are bad enough for the hbase noob what with hbase, hdfs, and mapreduce. Part of hbase's management of zk includes being able to see zk configuration in the hbase configuration files. Anything that has the hbase.zookeeper prefix will have its suffix mapped to the corresponding zoo.cfg setting (HBase parses its config. and feeds the relevant zk configurations to zk on start).
Using Java as it’s very readable and shows type information. The same can easily be achieved with Node.js, Perl, or your language of choice.
Create - First we create a persistent node to act as a group (parent) for child nodes.
Join - Second we create and join ephemeral child nodes to that parent node.
Each example creates with “unsafe acl”, so anyone who interacts with ZK can perform an update to that node. An alternative is CREATOR_ALL_ACL where only the creator can control the node.
List
Think of use cases (active sessions, active system workers, etc)
Delete
-1 deletes unconditionally. Can also put a version in here to delete, so it will not delete a node that does not match the version specified.
Will broadcast to any clients watching that node.
Because of the simplicity of operations on paths, each must catch errors if that node does not exist.