We will discuss most popular ORM libraries and will perform a brief comparison of them. Also we will see some GORM-specific implementation of query result caching.
What is Amazon OpenSearch Service?
OpenSearch is a distributed, open-source search and analytics package that may be used for real-
time application monitoring, log analysis, and internet search, among other things. With OpenSearch
Dashboards, an integrated visualization tool that makes it easy for users to examine their data,
OpenSearch provides a highly scalable solution for quick access and reaction to massive amounts of
data. The Apache Lucene search library, as well as OpenSearch, Elasticsearch, and Apache Solr,
support it. Elasticsearch 7.10.2 and Kibana 7.10.2 were used to create OpenSearch and OpenSearch
Dashboards. The Apache License Version 2.0 applies to all software in the OpenSearch project (ALv2).
This slides are for a brief seminar that I give in a Ph.D. exam "Perspective in Parallel Computing" (held by prof. Marco Danelutto) at University of Pisa (Italy).
They are a rapid introduction to Apache Storm and how it relates to classical algorithmic skeleton parallel frameworks
What is Amazon OpenSearch Service?
OpenSearch is a distributed, open-source search and analytics package that may be used for real-
time application monitoring, log analysis, and internet search, among other things. With OpenSearch
Dashboards, an integrated visualization tool that makes it easy for users to examine their data,
OpenSearch provides a highly scalable solution for quick access and reaction to massive amounts of
data. The Apache Lucene search library, as well as OpenSearch, Elasticsearch, and Apache Solr,
support it. Elasticsearch 7.10.2 and Kibana 7.10.2 were used to create OpenSearch and OpenSearch
Dashboards. The Apache License Version 2.0 applies to all software in the OpenSearch project (ALv2).
This slides are for a brief seminar that I give in a Ph.D. exam "Perspective in Parallel Computing" (held by prof. Marco Danelutto) at University of Pisa (Italy).
They are a rapid introduction to Apache Storm and how it relates to classical algorithmic skeleton parallel frameworks
Ekoparty 2017 - The Bug Hunter's Methodologybugcrowd
Goals of this Presentation:
- Outline and provide an actionable methodology for effectively and efficiently testing for, and finding security vulnerabilities in web applications
- Cover common vulnerability classes/types/categories from a high level
- Provide useful tools and processes that you can take right out into the world to immediately improve your own bug hunting abilities
1. Overview
1.1 What is a web service?
1.2 What is a web service?(cont.)
2. Working with SOAP services
2.1 What is SOAP?
2.2 What is SOAP? (cont.)
2.3 Why is SOAP Needed?
2.4 SOAP Building Blocks
2.5 SOAP Building Blocks (cont.)
3. Working with XML
3.1 What is XML?
3.2 What is XML Parser?
3.3 The main types of parsers?
3.4 What is SAX parser?
3.5 What is SAX parser? (cont.)
3.6 What is DOM parser?
3.7 What is DOM parser? (cont.)
3.8 What is Pull parser?
3.9 What is Pull parser? (cont.)
4. Using KSoap2 Library
4.1 What is KSoap2?
4.2 Why is KSoap2 Needed?
5. Working with Restful web services
6. Working with JSON
6.1 What is JSON?
6.2 JSON’s basic types
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and ...Databricks
Uber has real needs to provide faster, fresher data to data consumers & products, running hundreds of thousands of analytical queries everyday. Uber engineers will share the design, architecture & use-cases of the second generation of ‘Hudi’, a self contained Apache Spark library to build large scale analytical datasets designed to serve such needs and beyond. Hudi (formerly Hoodie) is created to effectively manage petabytes of analytical data on distributed storage, while supporting fast ingestion & queries. In this talk, we will discuss how we leveraged Spark as a general purpose distributed execution engine to build Hudi, detailing tradeoffs & operational experience. We will also show to ingest data into Hudi using Spark Datasource/Streaming APIs and build Notebooks/Dashboards on top using Spark SQL.
Structured, Unstructured and Streaming Big Data on the AWSAmazon Web Services
Using AWS has never been easier or more affordable to solve business problems and uncover new opportunities using data. Now, businesses of all sizes and across all industries can take advantage of big data technologies and easily collect, store, process, analyze, and share their data. Gain a thorough understanding of what AWS offers across the big data lifecycle and learn architectural best practices for applying these technologies to your projects. We will also deep dive into how to use AWS services such as Kinesis, DynamoDB, Redshift, and Quicksight to optimize logging, build real-time applications, and analyze and visualize data at any scale.
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
NOSQL101, Or: How I Learned To Stop Worrying And Love The Mongo!Daniel Cousineau
Lets learn the philosophy NOSQL takes (from a developer's standpoint), the changes you'll (not) have to take, discuss mongo, and see some practical examples!
These are my first revision of this talk and will be making some organizational improvements late.
Ekoparty 2017 - The Bug Hunter's Methodologybugcrowd
Goals of this Presentation:
- Outline and provide an actionable methodology for effectively and efficiently testing for, and finding security vulnerabilities in web applications
- Cover common vulnerability classes/types/categories from a high level
- Provide useful tools and processes that you can take right out into the world to immediately improve your own bug hunting abilities
1. Overview
1.1 What is a web service?
1.2 What is a web service?(cont.)
2. Working with SOAP services
2.1 What is SOAP?
2.2 What is SOAP? (cont.)
2.3 Why is SOAP Needed?
2.4 SOAP Building Blocks
2.5 SOAP Building Blocks (cont.)
3. Working with XML
3.1 What is XML?
3.2 What is XML Parser?
3.3 The main types of parsers?
3.4 What is SAX parser?
3.5 What is SAX parser? (cont.)
3.6 What is DOM parser?
3.7 What is DOM parser? (cont.)
3.8 What is Pull parser?
3.9 What is Pull parser? (cont.)
4. Using KSoap2 Library
4.1 What is KSoap2?
4.2 Why is KSoap2 Needed?
5. Working with Restful web services
6. Working with JSON
6.1 What is JSON?
6.2 JSON’s basic types
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and ...Databricks
Uber has real needs to provide faster, fresher data to data consumers & products, running hundreds of thousands of analytical queries everyday. Uber engineers will share the design, architecture & use-cases of the second generation of ‘Hudi’, a self contained Apache Spark library to build large scale analytical datasets designed to serve such needs and beyond. Hudi (formerly Hoodie) is created to effectively manage petabytes of analytical data on distributed storage, while supporting fast ingestion & queries. In this talk, we will discuss how we leveraged Spark as a general purpose distributed execution engine to build Hudi, detailing tradeoffs & operational experience. We will also show to ingest data into Hudi using Spark Datasource/Streaming APIs and build Notebooks/Dashboards on top using Spark SQL.
Structured, Unstructured and Streaming Big Data on the AWSAmazon Web Services
Using AWS has never been easier or more affordable to solve business problems and uncover new opportunities using data. Now, businesses of all sizes and across all industries can take advantage of big data technologies and easily collect, store, process, analyze, and share their data. Gain a thorough understanding of what AWS offers across the big data lifecycle and learn architectural best practices for applying these technologies to your projects. We will also deep dive into how to use AWS services such as Kinesis, DynamoDB, Redshift, and Quicksight to optimize logging, build real-time applications, and analyze and visualize data at any scale.
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
NOSQL101, Or: How I Learned To Stop Worrying And Love The Mongo!Daniel Cousineau
Lets learn the philosophy NOSQL takes (from a developer's standpoint), the changes you'll (not) have to take, discuss mongo, and see some practical examples!
These are my first revision of this talk and will be making some organizational improvements late.
MongoDB: Optimising for Performance, Scale & AnalyticsServer Density
MongoDB is easy to download and run locally but requires some thought and further understanding when deploying to production. At scale, schema design, indexes and query patterns really matter. So does data structure on disk, sharding, replication and data centre awareness. This talk will examine these factors in the context of analytics, and more generally, to help you optimise MongoDB for any scale.
Presented at MongoDB Days London 2013 by David Mytton.
Presented by Gregg Donovan, Senior Software Engineer, Etsy.com, Inc.
Understanding the impact of garbage collection, both at a single node and a cluster level, is key to developing high-performance, high-availability Solr and Lucene applications. After a brief overview of garbage collection theory, we will review the design and use of the various collectors in the JVM.
At a single-node level, we will explore GC monitoring -- how to understand GC logs, how to monitor what % of your Solr request time is spend on GC, how to use VisualGC, YourKit, and other tools, and what to log and monitor. We will review GC tuning and how to measure success.
At a cluster-level, we will review how to design for partial availability -- how to avoid sending requests to a GCing node and how to be resilient to mid-request GC pauses.For application development, we will review common memory leak scenarios in custom Solr and Lucene application code and how to detect them.
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...MongoDB
This will cover what to consider for high write throughput performance from hardware configuration through to the use of replica sets, multi-data centre deployments, monitoring and sharding to ensure your database is fast and stays online.
Rapid and Scalable Development with MongoDB, PyMongo, and MingRick Copeland
This talk, given at PyGotham 2011, will teach you techniques using the popular NoSQL database MongoDB and the Python library Ming to write maintainable, high-performance, and scalable applications. We will cover everything you need to become an effective Ming/MongoDB developer from basic PyMongo queries to high-level object-document mapping setups in Ming.
Ring: 프로그래밍 언어와 가까운 캐시 인터페이스
#
user에 item을 추가해야 한다고 생각해 봅시다.
클래스가 없는 언어라면 아마도 user_add_item(user, item) 같은 코드를 쓸 것입니다.
아마 user_delete_item도 있고 user_clear_items도 있겠지요.
하지만 우리는 파이썬 프로그래머니까 보통 user.add_item(item) 같은 코드를 씁니다.
#
user에 속한 item들을 가져오는 함수가 있고 이 함수는 결과를 캐시하고 있다고 생각해 봅시다.
user.get_items() 같은 코드를 쓸 수도 있고 user.get_cached_items(storage) 같은 코드를 쓸 수도 있겠지요.
item의 목록이 업데이트 되었습니다. 이제 캐시를 무효화해야 합니다.
아마도 user.invalidate_items()나 user.delete_cached_items(storage) 같은 코드를 만들어야 하겠지요.
Ring에서는 user.get_items.delete() 를 호출합니다.
#
Ring은 이 아이디어에서부터 출발합니다.
Living with Garbage by Gregg Donovan at LuceneSolr Revolution 2013Gregg Donovan
Understanding the impact of garbage collection, both at a single node and a cluster level, is key to developing high-performance, high-availability Solr and Lucene applications. After a brief overview of garbage collection theory, we will review the design and use of the various collectors in the JVM.
At a single-node level, we will explore GC monitoring -- how to understand GC logs, how to monitor what % of your Solr request time is spend on GC, how to use VisualGC, YourKit, and other tools, and what to log and monitor. We will review GC tuning and how to measure success.
At a cluster-level, we will review how to design for partial availability -- how to avoid sending requests to a GCing node and how to be resilient to mid-request GC pauses.For application development, we will review common memory leak scenarios in custom Solr and Lucene application code and how to detect them.
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
SparkR - Play Spark Using R (20160909 HadoopCon)wqchen
1. Introduction to SparkR
2. Demo
Starting to use SparkR
DataFrames: dplyr style, SQL style
RDD v.s. DataFrames
SparkR on MLlib: GLM, K-means
3. User Case
Median: approxQuantile()
ID Match: dplyr style, SQL style, SparkR function
SparkR + Shiny
4. The Future of SparkR
Webinar: Simplifying Persistence for Java and MongoDBMongoDB
Jeff Yemin will host a webinar covering the design and major features of Morphia, an Object Document Mapper (ODM) for Java and MongoDB. This webinar will start with a short introduction to MongoDB and the various options for building MongoDB applications on the JVM before taking a deep dive into Morphia. Morphia will be presented as an extended example format that demonstrates, for each feature, the domain model, a test driver, and the results as they appear in MongoDB.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
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.
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.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
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
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.
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.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
3. ORM approaches
• Object to schema (code first)
• Schema to object (database first)
• Meet in the middle
4. ORM in Go
Name Stars ⭐️ DBMS support Approach Notes
gorm 15153
MySQL, PostgreSQL, Sqlite3,
MS SQL Server
Code first
xorm 5356
MySQL, PostgreSQL, Sqlite3,
MS SQL Server, Oracle, …
Code first, DB first Codegen, sync
go-pg 3106 PostgreSQL only Code first, DB first
gorp 3099
MySQL, PostgreSQL, Sqlite3,
MS SQL Server, Oracle
Code first
SQLBoiler 2336
MySQL, PostgreSQL, Sqlite3,
MS SQL Server, CockroachDB
DB first Full codegen
reform 815
MySQL, PostgreSQL, Sqlite3,
MS SQL Server
Code first Codegen
go-queryset 457
MySQL, PostgreSQL, Sqlite3,
MS SQL Server
Code first Codegen, on top of GORM
8. Query result caching
• About GCache: LRU, LFU, ARC support
• Why GCache is used in current project
9. Caching in GORM: after query
db.Callback().Query().After("gorm:query").Register(”mykey:after_query", func(scope *gorm.Scope) {
key := fmt.Sprintf(“%s”, scope.DB().QueryExpr())
if gch.Has(key) {…}
if value, ok = scope.Get(“cachemarker”); !ok {
value = scope.Value
}
if value == nil {
scope.Log("nothing to cache")
}
if err := gch.SetWithExpire(key, value, 30 * time.Minute); err != nil {…}
})
10. Caching in GORM: before query
db.Callback().Query().Before("gorm:query").Register("my:before_query", func(scope *gorm.Scope) {
key := fmt.Sprintf(“%s”, scope.DB().QueryExpr())
value, err := gch.GetIFPresent(key)
if err == gcache.KeyNotFoundError {…}
dst, ok := scope.Get(“cachemarker”)
if !ok {…}
srcValue, dstValue := indirect(reflect.ValueOf(value)), indirect(reflect.ValueOf(dst))
if !dstValue.CanSet() {…}
dstValue.Set(srcValue)
scope.InstanceSet("gorm:skip_query_callback", nil)
})
11. Caching in GORM: caching marker
var target []struct {
FileName string `gorm:"column:file_name"`
}
db := c.DB().Table("files").
Where("file_id = ?", fileID).
Select("file_name").
Set(“cachemarker”, &target).
Scan(&target)