Celery introduction
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Celery is a distributed task queue system that allows putting tasks in a task queue to be processed asynchronously by worker processes or threads. It supports common features like events, workflows, rate limits, retries, and routing. Celery can use many different brokers like RabbitMQ, Redis, MongoDB, and SQL databases to store tasks and results. It is extensible and works well for asynchronous tasks like emails, image processing, data imports, and API calls.
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This summary provides an overview of the key points from the document in 3 sentences:
The document outlines the agenda for Season 3 Episode 1 of the Netflix OSS podcast, which includes lightning talks on 8 new projects including Atlas, Prana, Raigad, Genie 2, Inviso, Dynomite, Nicobar, and MSL. Representatives from Netflix, IBM Watson, Nike Digital, and Pivotal then each provide a 3-5 minute presentation on their featured project. The presentations describe the motivation, features and benefits of each project for observability, integration with the Netflix ecosystem, automation of Elasticsearch deployments, job scheduling, dynamic scripting for Java, message security, and developing microservices
Function Mesh for Apache Pulsar, the Way for Simple Streaming Solutions
Pulsar Function is a succinct computing abstraction Apache Pulsar provides to express simple ETL and streaming tasks. The simplicity comes in two folds: Simple Interface and Simple Deployment. As it has been adopted, we realized that the ability to run natively on cloud and integrate multiple functions into one integrity are key to user success. We developed this new feature -- Function Mesh -- to support these new requirements.
This talk aims to provide a thorough walkthrough of this new Function Mesh Feature, including its design, implementation, use cases, and examples, to help people seeking simple streaming solutions understand this newly created powerful tool in Apache Pulsar.
Apache Airflow in Production
We will introduce Airflow, an Apache Project for scheduling and workflow orchestration. We will discuss use cases, applicability and how best to use Airflow, mainly in the context of building data engineering pipelines. We have been running Airflow in production for about 2 years, we will also go over some learnings, best practices and some tools we have built around it.
Speakers: Robert Sanders, Shekhar Vemuri
Activity feeds (and more) at mate1
The document provides an overview of Mate1, an online dating company, and their transition to using Kafka and Cassandra for their activity feed and data storage. Some key points:
- Mate1 started in 2003 and has grown to around 40 employees, with a 13 person engineering team. They own their own hardware.
- They previously used a "fan-out on read" approach where activity data was stored across different databases and querying them to generate feeds was slow.
- They transitioned to a "fan-out on write" approach using Kafka to queue activity events and Cassandra to store aggregated user feeds and counters. This improved performance and scalability.
- Events are published to Kafka topics and
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
In this presentation, Paul introduces InfluxDB, a distributed time series database that he open sourced based on the backend infrastructure at Errplane. He talks about why you'd want a database specifically for time series and he covers the API and some of the key features of InfluxDB, including:
• Stores metrics (like Graphite) and events (like page views, exceptions, deploys)
• No external dependencies (self contained binary)
• Fast. Handles many thousands of writes per second on a single node
• HTTP API for reading and writing data
• SQL-like query language
• Distributed to scale out to many machines
• Built in aggregate and statistics functions
• Built in downsampling
Machine learning and big data @ uber a tale of two systems
This document discusses machine learning and big data at Uber. It describes Uber's mission to provide reliable transportation everywhere. It then discusses Uber's use of Kafka for data infrastructure and Hadoop/Spark for batch processing and machine learning. The document outlines pain points with the current system and proposes addressing them by building machine learning capabilities directly into Presto, an interactive SQL query engine, to improve efficiency and allow machine learning and data analytics to leverage shared resources. Key benefits of this approach are reducing complexity for data scientists and improving performance of machine learning pipelines.
Automation tools: making things go... (March 2019)
Developed for DANS-KNAW. This presentation covers some of the fundamentals of the automation-tools. Helper scripts for automation of transfers in Archivematica. Designed to complement the API slide-deck, the two resources can probably be consumed in any order. Knowing the API will help you understand the automation-tools, but knowing the automation-tools may help you understand what you want to create using the API.
API slide-deck here: https://www.slideshare.net/Archivematica/introduction-to-the-archivematica-api-september-2018-122548752
The Accidental DBA
This document provides a summary of a presentation on becoming an accidental PostgreSQL database administrator (DBA). It covers topics like installation, configuration, connections, backups, monitoring, slow queries, and getting help. The presentation aims to help those suddenly tasked with DBA responsibilities to not panic and provides practical advice on managing a PostgreSQL database.
Journey through high performance django application
Speaker: Gowtham
Host: Bangalore Django User Group
Venue: Synup, Bangalore
Event Date: Jan 5, 2019
Serverless Event Streaming with Pulsar Functions
The last few years have seen the emergence of Serverless as a paradigm for event streaming. Its very simple programming model has attracted developers in droves. At the same time, its ability to elastically scale has simplified operations significantly. Combined together with the ubiquity of their presence across all cloud providers, serverless today has become the leading choice to do event processing at scale for a lot of companies.
In this talk, Sijie Guo from StreamNative will explore how the serverless paradigm is applied to event streaming in Apache Pulsar, a next-generation event streaming system. Pulsar provides native support for serverless functions where the events are processed as soon as they arrive in a streaming manner and that provides flexible deployment options (thread, process, container). He will describe how these serverless functions make data engineering easier and share the real world usage of Pulsar Functions.
Similar to Celery introduction (20)
Simplify Pulsar Functions Development with SQL - Pulsar Summit SF 2022
Simplify Pulsar Functions Development with SQL - Pulsar Summit SF 2022
Function Mesh for Apache Pulsar, the Way for Simple Streaming Solutions
Function Mesh for Apache Pulsar, the Way for Simple Streaming Solutions
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
Machine learning and big data @ uber a tale of two systems
Machine learning and big data @ uber a tale of two systems
Automation tools: making things go... (March 2019)
Automation tools: making things go... (March 2019)
Journey through high performance django application
Journey through high performance django application
Recently uploaded
Enums On Steroids - let's look at sealed classes !
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SMS API Integration in Saudi Arabia| Best SMS API Service
Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
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UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
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https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Using Xen Hypervisor for Functional Safety
An update on making Xen hypervisor functionally safe and enhancing its usage in automotive and industrial use cases
在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
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本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
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原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国纽约州立大学奥尔巴尼分校毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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此贴长年有效【贴心专线/微-信: 95270640】敬请保留此联系方式以备用！如有不在线请给我们留言！我们将在第一时间给您回复!上散发着一抹抹的光晕而这每处自然形成的细节融合在一起浑然天成的美实在令人心生愉悦小道的周边无秩序的生长着几株艳丽的野花红的粉的紫的虽混乱无章却给这幅美景更增添一份性感夹杂着一份纯洁的妖娆毫无违和感实在给人带来一份悠然幸福的心情如果说现在的审美已经断然拒绝了无声的话那么在树林间飞掠而过的小鸟叽叽咋咋的叫声是否就是这最后的点睛之笔悠然走在林间的小路上宁静与清香一丝丝的盛夏气息吸入身体昔日生活里的繁忙多
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Odoo ERP Vs. Traditional ERP Systems – A Comparative Analysis
When it is all about ERP solutions, companies typically meet their needs with common ERP solutions like SAP, Oracle, and Microsoft Dynamics. These big players have demonstrated that ERP systems can be either simple or highly comprehensive. This remains true today, but there are new factors to consider, including a promising new contender in the market that’s Odoo. This blog compares Odoo ERP with traditional ERP systems and explains why many companies now see Odoo ERP as the best choice.
What are ERP Systems?
An ERP, or Enterprise Resource Planning, system provides your company with valuable information to help you make better decisions and boost your ROI. You should choose an ERP system based on your company’s specific needs. For instance, if you run a manufacturing or retail business, you will need an ERP system that efficiently manages inventory. A consulting firm, on the other hand, would benefit from an ERP system that enhances daily operations. Similarly, eCommerce stores would select an ERP system tailored to their needs.
Because different businesses have different requirements, ERP system functionalities can vary. Among the various ERP systems available, Odoo ERP is considered one of the best in the ERp market with more than 12 million global users today.
Odoo is an open-source ERP system initially designed for small to medium-sized businesses but now suitable for a wide range of companies. Odoo offers a scalable and configurable point-of-sale management solution and allows you to create customised modules for specific industries. Odoo is gaining more popularity because it is built in a way that allows easy customisation, has a user-friendly interface, and is affordable. Here, you will cover the main differences and get to know why Odoo is gaining attention despite the many other ERP systems available in the market.
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Celery introduction
- 1. Celery Distributed task queue system
- 2. What is celery ● it’s a distributed task queue system ○ abstractions to put tasks in the task queue ○ abstractions to manage task results ○ services to run tasks from the task queue ○ monitoring tools ● can use almost any database or message queue ● works best with a task queue but can use other non-traditional transports like a sql db or redis
- 3. Possible uses ● asynchronous processing ○ emails ○ image processing ○ transcoding ○ batch import / processing ○ that slow api you need to use ● scrapping ● processing large amount of data
- 4. What is celery task queue (“broker”) result backend worker 1 worker 2 worker 1 worker 2 machine 1 machine 2 webserver resize picture
- 5. Usage
- 6. Usage ……
- 8. Notable features ● events ● workflow ● rate limits ● time limits ● retry ● acks (reliable execution) ● routing ● task schedulers (like cron) ● various worker pools: process (fork), threaded, eventlet, gevent ● extensible
- 9. Brokers & backends overview ● rabbitmq ● redis ● mongodb ● sql (sqlalchemy or django) ● beanstalk
- 10. The canvas Workflow primitives: ● group ○ apply tasks in parallel ● chain ○ apply tasks in sequence ● chord ○ apply task after group or task completes ● map, starmap, chunks ○ abstractions over group
- 11. Rate limits ● can use “/h”, “/m”
- 12. Revokes & time limits ● ●
- 13. Similar projects & alternatives ● Kuyruk ● huey ● pyres ● rq