Event Location:
Telerik Academy
bulevard "Aleksandar Malinov" 31
Sofia, , Bulgaria
Abstract:
Pies are definitely fine for dessert :-) – but sometimes also used for reports, even if their real use case is very narrow. Do you want deliver information in an effective and efficient way?  Even when attractiveness of a report is important, beauty is in the eye of the beholder. Research shows that we can help end-users to understand the message of the data quickly by following some simple rules. You will see how you can implement those rules with Microsoft Excel's Tables and Charts and the Power View add-on – ending up with effective and efficient information delivering through beautiful dashboards. You will learn how to identify suboptimal dashboards and what you can do to improve them.
Speaker(s): Markus Ehrenmueller-Jensen,
Abstract:
Azure DocumentDB is a new highly-scalable NoSQL database platform on Microsoft Azure, designed as a fully managed cloud service for modern web and mobile applications. Like other NoSQL platforms, DocumentDB supports schema-free designs, and scales across replicas with fast write performance and eventual consistency. But DocumentDB also offers several unique features that make it a compelling candidate for NoSQL scenarios, which you will learn about in this session. The platform is based on HTTP/REST, embraces JSON as a standard document format, and is supported by a wide range of SDKs for .NET, JavaScript, Java, Python, and Node.js. Without defining any index paths, new documents are queryable within milliseconds, using SQL grammar or LINQ. Learn these and other capabilities, including server-side JavaScript-based stored procedures, user-defined functions, triggers, tunable consistency, and more.
Speaker(s): Leonard Lobel,
Abstract:
Any data that we have today is delivered to end users as reports and dashboards. =gt; When it is more than 50 reports, 10 departments, 300 users, how to manage the department categories by folders, reports, users, groups security? =gt; What are the techniques to implement linked reports, subscriptions, report history, snapshot options? =gt; How to edit reports using browser based Report Builder and reuse some of the report parts? =gt; How to build a report that is very simple to use but has advanced capabilities? =gt; How do I see the subscription information that stores data in XML format within ReportServer database? =gt; How to take ownership of a deployed report(s) using T-SQL and is it healthy modifying the data using T-SQL directly on the DB? There are plenty of things that we can do with Reporting services, Report Manager and you can find answers for all above questions in this session. This session also helps you identify the performance bottleneck in poor performing SSRS s
Speaker(s): Dr. Subramani Paramasivam,
Abstract:
ADF provides a managed way to orchestrate the collection, movement and transformation of structured and semi-structured data together. It works both with on-premises and cloud data and it has - of course - a powershell interface, enabling you to manage it without the bling of the Azure portal. But is it useful, is it fast, is it easy and powerful to use ? And most of all is Azure Data Factory the new SSIS ? These are some of the questions that might arise, when reading about this new PaaS and that we will answer together in this demo packed session where will we go through orchestration, consumption and administration of data through the ADF pipeline.
Speaker(s): Regis Baccaro,
Abstract:
Do you know your data? Let's go on a journey into the mountains of data and see if we can reach the peak! What instruments will we use on this journey and how will we transform our data to help ourselves make the right decisions? Be prepared to see transformations of your data from a different point of view with the latest technology in this field, the Tabular Data Model. In this presentation, we will cover the main features of the Tabular Data Model, the usage of the standard reporting package, some useful applications / utilization through Share Point and exciting new features of Excel. We are enthusiastic facilitators of knowledge sharing, idea brainstorming, training and mentoring in the MS area of expertise. We are sure you will enjoy this presentation to the fullest and will get the maximum of useful tips for your everyday work! Will you accept the challenge...?
Speaker(s): Stefan Damyanov,
Abstract:
Transaction is running slow!  It’s time to isolate issues and get them resolved! There are common reasons for transaction delays like slow transaction log, page splits, blocking, over-indexing, and even some configuration settings of your database and/or instance. Query tuning tips are slightly different than those for transactions, this session is about OLTP performance troubleshooting. I will show you some typical scenarios of transaction delays and the reasons behind them. This will definitely help you in your performance troubleshooting efforts and will give you a good level of confidence.
Speaker(s): Margarita Naumova,
Abstract:
SQL Server 2014 is full of new features and improvements. Some of them are "Killer" features like InMemory OLTP, Clustered Columnstore Indexes, Buffer Pool Extensions, etc., which are discussed a lot and we always can get a lot of information about them. And in the same time, SQL Server 2014 have several fantastic features and improvements, which are more hidden from our sight. In this session we will talk in details about these features and improvements. Query Fingerprints, Cardinality Estimator, Tempdb improvements, and more features will be covered in this session.
Speaker(s): Denis Reznik,
Abstract:
Make your business happy with well performing databases. To master these approaches you are responsible for well performing indexes which may not harm the daily business of your customers. With a handy toolset of common database management objects (DMO) you can become the superstar in your SQL Server team. Impress your team members when you detect common indexing issues like a pro with dmo's.
Speaker(s): Uwe Ricken,
Abstract:
In this presentation we will start with description of the Data Mining Process, then we will Introduce the Microsoft Time Series Algorithm for Forecasting. Then we will demonstrate production of forecast by: SQL Server Data Tools (SSDT) Project. And we will show how to implement complete process as part of your application by using ADOMD on real data.
Speaker(s): Daniel Joskovski,
Abstract:
Indexes are important! They can make your life easy, but they can be a huge performance killer. No matter your role - DBA or DEV, you need to design effective indexing strategy and most importantly - know if those indexes are really effective. SQL Server gives you number of possibilities to track the use of your indexes and suggest you new ones. But what else we need to be careful for? How to keep our SQL Server in proper index shape? Join me for this one-hour session where we will have real fun on indexes and I promise you, you will leave with plenty of ideas for improving your index environment!
Speaker(s): Ivan Donev,
Abstract:
Can ETL be fun? Unfortunately, very often creation of ETL layer is time-consuming, tedious, error-prone task. What’s more, any change in requirement needs most of the work to be redone. In our session we’ll present new approach to creating BI solutions using Biml language and Mist programming environment. They greatly simplify the process, allowing for amazing flexibility, and make the ETL work more pleasant. We will show real examples of how Biml and C# generates hundreds of usable SSIS packages in just couple of minutes. We will also cover integration of ETL with TFS and CI for ETL. Everything with 100% compatibility with unmodified SQL Server. Join us and see how BI just became fun again
Speaker(s): Marcin Szeliga,
Abstract:
SQL Server 2016 brings the topic "in-memory" to a new level. Not that previous versions did not hold as much as data as possible inside of memory through caching, but now we enter a new era: in-memory row stores, in-memory column stores and compiled stored procedures are different from what we have seen so far. In this session, you will see how you can leverage those new features and how you can come by existing limitations. Come back to office with the knowledge how to boost the performance of your ETL, queries and reports into the new era.
Speaker(s): Markus Ehrenmueller-Jensen,
Abstract:
We will present in this session the concept of the transaction log, its importance and the roles it plays inside the database engine. We will look at how logging and recovery works, the checkpoint process, write-ahead logging, and steps we need to perform as a DBA to ensure proper management of the transaction log for a SQL Server database.
Speaker(s): Tsvetozar Ivanov,
Abstract:
Azure SQL Datawarehouse is the latest SQL related cloud service, Microsoft released in Preview. It uses Microsoft’s massive parallel processing (MPP) architecture where you pay for time-to-insight, not hardware, based on performance objectives for fundamental data warehousing operations like scanning, loading, and query processing. Quickly deployable, it is definitely one of the services that will change the gear in the datawarehoue world. Join me in the session where I will provide a quick overview of the service and show you how easily you can migrate and start using it.
Speaker(s): Ivan Donev,
Abstract:
The topic of SQL Server concurrency is for sure one of the most complex ones in the field. Locking, blocking, timeouts and performance issues are just some of the consequences of not choosing the proper isolation level for our applications. In this demo-intensive session we will not only talk, but actually go deep into optimistic concurrency and the two isolation levels it offers to us - Read Committed Snapshot and Snapshot isolation level. We will see how they work, how much they can help for the performance and scalability of our applications and what are the little gotchas we need to be aware of.
Speaker(s): Boris Hristov,
Abstract:
Elasticsearch doubled its popularity during the last year and this is just one of the signs that something good is happening there. Yet this DB stays far behind the usual suspects like SQL Server and MySQL but there are areas where different technologies fit much better. One of these is complex and real-time search. Elasticsearch builds on the top of the well-known Apache Lucene but is designed to easily scale and lay the foundation for modern search intensive applications. This session will draw your path into this relatively new technology, provide guidelines on its usage and practical advice on integration with your existing SQL Server solutions.
Speaker(s): Ivelin Andreev,
Abstract:
Or IoT for DBAs And we are not talking about Oracle Index Organized Tables, we are talking about Internet of Things. This buzzword you hear almost everywhere, and for sure needs databases. So let’s take the Microsoft vision of IoT and see the database side of it. We will talk about the vision of IoT and the real world practicalities of it. The presentation goal is to provide a DBA all he/she needs to know to support an IoT application. And to make you curious we will cover topics such SQL 2014 for Embedded Systems or the Azure offerings (and their SQL connections), and I will try full demos with IoT hardware.
Speaker(s): Catalin Gheorghiu,
Abstract:
The session will show how to prepare cool reports with Excel and publish them to Office 365.
Speaker(s): Boriana Petrova,
Abstract:
An overview of Data Science in cloud
Speaker(s): Miroslav Lessev,
Abstract:
Backup and Recovery are some of the most important DBA tasks. All in all it is the data that matters and having an optimized backup strategy and a great recovery plan will save your day! Let's dive into the basics and the tips and tricks you will need to optimize your SQL Server backups and restores.
Speaker(s): Kaloyan Kosev,
Abstract:
In this dynamic session, Lenni will teach you the ins-and-outs of unstructured data storage and native file streaming with FILESTREAM (introduced in SQL Server 2008) and FileTable (new in SQL Server 2012). Learn how to program FILESTREAM using T-SQL and, for maximum performance, using the OpenSqlFileStream API in C#. We’ll also cover the hierarchyid data type, which casts a hierarchical structure over every FileTable, and wrap up with the full-text search improvements and new Statistical Semantic Search in SQL Server 2012 that you can leverage to get the most out of unstructured documents contained in your databases.
Speaker(s): Leonard Lobel,
Abstract:
Keep your DBAs happy. Sounds like an impossible task ? In this session we are going to walkthrough different scenarios based on the interaction between SSDT and Team Foundation Server. Developing locally and publishing the changes to an on-premises instance of SQL Server. We will walkthrough several scenarios to get an idea of what works when and what doesn’t. At the end of this session you will be able to know how to get the best out of TFS and where the limitations are. You will also be able to pinpoint several other solutions for doing Continuous Integration with SSDT without compromising the flexibility.
Speaker(s): Regis Baccaro,
Abstract:
During the session we will review definitions of stored procedures, clr and orm, and will inspect some simple code examples.Then we will discuss practical and philosophical aspects of choice and/or combinations of these options, how they affect design and performance of databases and apps. Fun for everybody!
Speaker(s): Todor Peev,
Abstract:
Probably, all of us have received a notice that the performance of their SQL Server is not running at the expected level. There are hundreds of reasons why this is the case but we need to start our check with the most simple one - how is our maintenance organized? Are we using the most suitable schedule and setup? We will explore how you can improve your maintenance's efficiency and explore some tricks for VLDBs.
Speaker(s): Miroslav Dimitrov,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://www.linkedin.com/profile/view?id=270775013amp;trk=tab_pro
Contact: http://www.sqlmaster.de
Uwe Ricken is working with IT systems since the 90’s. The primary passion for developments with Microsoft SQL Server expanded in 2007 with his engagement as a DBA for Deutsche Bank AG in Frankfurt am Main. After 6 years of operational experiences as a DBA and over 14 years as a developer of complex database models he achieved the “Microsoft Certified Master – SQL Server 2008” certification which “was” the highest technical certification. In the same year he earned his first award as a Data Platform MVP for his support to the Microsoft SQL Server community in Germany and Europe. Uwe Ricken is a speaker on many international conferences and events and preferred topics are “Database Internals”, “Indexing” and “Development”.
LinkedIn: https://www.linkedin.com/in/markus-ehrenmueller/
Contact: http://www.savorydata.com
Markus Ehrenmueller-Jensen, as the founder of Savory Data, has a long history of providing customer solutions in the areas of data engineering, data science, and Business Intelligence. He is a certified software engineer, a graduated business educator, and professor of Databases Project Management at HTL Leonding, and is certified as an MCSE MCT. He is a published author and writes articles for well-known journals. He co-founded PASS Austria and organizes SQLSaturdays in Austria. Markus is a founding member of Power BI Usergroup Austria. Since 2017 Markus was awarded as an Microsoft Data Platform MVP.
LinkedIn: http://dk.linkedin.com/in/regisbaccaro
Contact: http://theblobfarm.wordpress.com
Regis has more than 15 years of experience with SQL Server, SharePoint and .Net as an architect and developer. R#233;gis is a SQL Server MVP since 2014 and a frequent speaker at SQL conferences, a PASS Regional Mentor and the founder of SQLSaturday Denmark community event. He is also a certified industry trainer on the APS/PDW platform. In his freetime he is an avid runner, cook and farmer.
LinkedIn: http://dk.linkedin.com/in/regisbaccaro
Contact: http://theblobfarm.wordpress.com
Regis has more than 15 years of experience with SQL Server, SharePoint and .Net as an architect and developer. R#233;gis is a SQL Server MVP since 2014 and a frequent speaker at SQL conferences, a PASS Regional Mentor and the founder of SQLSaturday Denmark community event. He is also a certified industry trainer on the APS/PDW platform. In his freetime he is an avid runner, cook and farmer.
LinkedIn: http://www.linkedin.com/pub/denis-reznik/3/502/234
Contact: http://reznik.uneta.com.ua
Denis Reznik is a Data Architect at Intapp, Inc. He has more than ten years experience in software development, database design, and performance tuning for cloud and on-premises solutions. In 2010 Denis became Microsoft Data Platform MVP and maintains MVP status today. Apart from his professional career, Denis actively participates in the development of the Ukrainian Data Community.
I held positions as system administrator, database administrator, Java developer, currently I work as database developer. My interests lie in Sql development, application development and database administration of large data sets.
LinkedIn: http://www.linkedin.com/in/lennilobel
Contact: http://lennilobel.wordpress.com
Leonard Lobel (Microsoft MVP, Data Platform) is CTO and co-founder of Sleek Technologies, Inc., a New York-based development shop with an early adopter philosophy toward new technologies. He is also a principal consultant at Tallan, Inc., a Microsoft National Systems Integrator and Gold Competency Partner. Programming since 1979, Lenni specializes in Microsoft-based solutions, with experience that spans a variety of business domains, including publishing, financial, wholesale/retail, health care, and e-commerce. Lenni has served as chief architect and lead developer for various organizations, ranging from small shops to high-profile clients. He is also a consultant, trainer, and frequent speaker at local usergroup meetings, VSLive, SQL PASS, and other industry conferences. Lenni has also authored several MS Press books and Pluralsight courses on SQL Server programming.
LinkedIn: http://www.linkedin.com/profile/view?id=270775013amp;trk=tab_pro
Contact: http://www.sqlmaster.de
Uwe Ricken is working with IT systems since the 90’s. The primary passion for developments with Microsoft SQL Server expanded in 2007 with his engagement as a DBA for Deutsche Bank AG in Frankfurt am Main. After 6 years of operational experiences as a DBA and over 14 years as a developer of complex database models he achieved the “Microsoft Certified Master – SQL Server 2008” certification which “was” the highest technical certification. In the same year he earned his first award as a Data Platform MVP for his support to the Microsoft SQL Server community in Germany and Europe. Uwe Ricken is a speaker on many international conferences and events and preferred topics are “Database Internals”, “Indexing” and “Development”.
LinkedIn: http://uk.linkedin.com/in/dageop
Contact: http://www.yoursqlman.com/index.php/blogs
Dr.SubraMANI Paramasivam is a Microsoft MVP, PhD, Microsoft Certified Trainer and Head of Data AI at DAGEOP Ltd having 21 years of DB, BI, Analytics and AI experience. Mani has also spoken in various events like MS IGNITE, PASS Summit, MS Inspire, SQLBITS, Power Platform Summit, Global AI Bootcamp, Data BI Summit, SQLRelay, Power BI World Tour, Data Platform Summit, INSIDESQL UK, Global AI Nights, SQLServerGeeks Summit, MCT Global Summits, Data Day Events and various user group events. Mani also believes in Empowering Every Person in the planet and spreads the awareness on Global challenges via www.EmpoweringEveryPerson.com. He is also a co-owner of 1 Global AI Meetup and 3 Power BI PUG PASS local / user groups.
LinkedIn: http://bg.linkedin.com/in/borishristov
Contact: http://borishristov.com/blog/
Boris is a SQL Server Consultant, but also an Author for Pluralsight and a SQL Server MVP. He is frequently speaking at conferences all accross Europe and is heavily involved in training students and organizations in what he calls quot;The Art of SQL Serverquot;. Boris is frequently blogging and is the host of SQLHangouts.
LinkedIn: https://www.linkedin.com/profile/public-profile-settings?trk=prof-edit-edit-public_profile
Tanja Vukov, PhD – research associate at Institute for Biological Research, University of Belgrade. She is an expert in biostatistics and her role is to transform data into meaningful scientific conclusions. As a bio-analyst she helped to develop new practices in a field of agriculture and medicine. She published more than 20 papers in leading international scientific journals. She was a speaker on using different Azure technologies. She holds a PhD in Evolutionary biology.
MSSQL Server 2012/2008/2005 administration: - Installation and configuration - design and implement backup solutions - design and implement disaster recovery solution (mirroring, transaction log shipping, always on availability groups) - design and implement high availability solution (clustering, replication) - security management - troubleshooting - capacity planning - Performance and health monitoring, performance tuning
LinkedIn: https://bg.linkedin.com/in/kosev
Kaloyan Kosev is a SQL Server expert working with enterprise companies for the last 9 years. His daily work routine is a mixture of troubleshooting bugs and issues, consulting and advising clients and presenting to students. Within his free time he is trying to balance between SQL and x-sports.
LinkedIn: https://www.linkedin.com/profile/public-profile-settings?trk=prof-edit-edit-public_profile
Tanja Vukov, PhD – research associate at Institute for Biological Research, University of Belgrade. She is an expert in biostatistics and her role is to transform data into meaningful scientific conclusions. As a bio-analyst she helped to develop new practices in a field of agriculture and medicine. She published more than 20 papers in leading international scientific journals. She was a speaker on using different Azure technologies. She holds a PhD in Evolutionary biology.
LinkedIn: https://www.linkedin.com/in/borianapetrova/
Contact: http://itraining.bg/category/itraining-blog/
Microsoft Certified Trainer with over 10 years experience as an IT instructor and Consultant. She has many qualifications and professional certifications, including: Excel MVP, MCT, MCITP, MCTS, MOS Master. In 2008 she is among the TOP 25 of the best instructors in Microsoft World Company business evaluation and measurement - Knowledge Advisors.
LinkedIn: https://pl.linkedin.com/in/marcinszeliga
Data Philosopher. Since 2006 invariably awarded Microsoft Most Valuable Professional title in the Data Platform category. A speaker at numerous conferences across Europe, as well as at user groups meetings. The author of many books and articles devoted to Microsoft Data Platform. An independent consultant, database-centric application practitioner and architect totally focused on SQL Server and Azure. Huge fan of Data Science and Machine Learning.
Daniel Joskovski work in IT since 1985, he is a database-centric application developer and architect, instructor, course author, user group leader. Over the Past 17 Years as MCT he#39;s been teaching Microsoft SQL Server and .NET Development courses to premier customers in Macedonia, Serbia, Norway, Kosovo and Albania. From 2009 He is being awarded with Microsoft MVP award for SQL Server 8 times in a row. At the moment Daniel works for CloudCoder DOOEL Skopje.
LinkedIn: http://dk.linkedin.com/in/regisbaccaro
Contact: http://theblobfarm.wordpress.com
Regis has more than 15 years of experience with SQL Server, SharePoint and .Net as an architect and developer. R#233;gis is a SQL Server MVP since 2014 and a frequent speaker at SQL conferences, a PASS Regional Mentor and the founder of SQLSaturday Denmark community event. He is also a certified industry trainer on the APS/PDW platform. In his freetime he is an avid runner, cook and farmer.
LinkedIn: https://bg.linkedin.com/in/ivelin
Contact: https://www.slideshare.net/ivoandreev
Ivelin is a Microsoft Azure MVP and software architect in one of the leading and highly innovative Bulgarian IT brands - Interconsult Bulgaria Ltd. He is an external ICT expert in the European Commission, Eurostars-Eureka programme and Innovation Fund Denmark, Microsoft professional developer, senior project manager and Data Platform specialist. With 17+ years of strong experience in design and development of web systems, SOA, enterprise integration, machine learning, web security and data management, he is a practical man who always tries to balance between risk and flexibility. Never scared of work he has turned the weighing of pros and cons into own marked style that urges to make conscious decisions.
LinkedIn: https://www.linkedin.com/in/ivandonev/
I am a Data platform MVP with more than 10 years of real-life experience with SQL Server and its stack of services. Besides the support and project work, I am also a trainer, conference speaker and user group organizer (and volunteer).
LinkedIn: https://www.linkedin.com/in/markus-ehrenmueller/
Contact: http://www.savorydata.com
Markus Ehrenmueller-Jensen, as the founder of Savory Data, has a long history of providing customer solutions in the areas of data engineering, data science, and Business Intelligence. He is a certified software engineer, a graduated business educator, and professor of Databases Project Management at HTL Leonding, and is certified as an MCSE MCT. He is a published author and writes articles for well-known journals. He co-founded PASS Austria and organizes SQLSaturdays in Austria. Markus is a founding member of Power BI Usergroup Austria. Since 2017 Markus was awarded as an Microsoft Data Platform MVP.
LinkedIn: https://www.linkedin.com/in/ivandonev/
I am a Data platform MVP with more than 10 years of real-life experience with SQL Server and its stack of services. Besides the support and project work, I am also a trainer, conference speaker and user group organizer (and volunteer).
LinkedIn: http://bg.linkedin.com/pub/margarita-naumova/9/965/206
Contact: www.maginaumova.com; http://blogs.technet.com/b/magi/
Magi Naumova is SQL Server Architect and Consultant, speaker and trainer, Microsoft Certified Master, MVP Data Platform, MCT, Founder of SQL Master Academy, founder and leader of the Bulgarian SQL User group. She has more than 15 years SQL Server training and consulting experience. Magi is a former member of Microsoft Services Worldwide Technical Leadership Team, а Microsoft Women worth watching http://zd.net/1lwNYKH Currently she speaks, writes, trains and consults people on SQL Server technology. She runs a Data Platform consulting company part of which is SQL Master Academy training program. It helps hundreds of specialists to feel knowledgeable in their daily work or to find an inspirational career path in the world of SQL Server.
LinkedIn: ?https://ro.linkedin.com/in/catalingheorghiu?
Contact: http://ronua.ro/author/catalin/
Catalin Gheorghiu is a solution architect from Romania. He has more than 15 years of experience in developing solutions, especially on Microsoft technologies in very demanding environments. Is contributing articles and blogs to several user groups (MrSmersh), lecturing all over Romania and abroad, is also RONUA Timisoara (PASS Chapter) user group leader. Since 2011, every year he was awarded the Microsoft MVP Award.
Stefan Damyanov - a versatile BA/BI Expert and co-presenter Diana Tisheva – a senior DA/DWH Expert @ Adastra Bulgaria. With over 10 years of hands-on experience, they have led and participated in many local and international projects. Enthusiastic followers of the latest developments in the area of MS technologies, they spend endless hours in researching and analyzing the possibilities of making maximum use of the tools, in order to achieve excellent client satisfaction and efficiency.
LinkedIn: http://www.linkedin.com/in/lennilobel
Contact: http://lennilobel.wordpress.com
Leonard Lobel (Microsoft MVP, Data Platform) is CTO and co-founder of Sleek Technologies, Inc., a New York-based development shop with an early adopter philosophy toward new technologies. He is also a principal consultant at Tallan, Inc., a Microsoft National Systems Integrator and Gold Competency Partner. Programming since 1979, Lenni specializes in Microsoft-based solutions, with experience that spans a variety of business domains, including publishing, financial, wholesale/retail, health care, and e-commerce. Lenni has served as chief architect and lead developer for various organizations, ranging from small shops to high-profile clients. He is also a consultant, trainer, and frequent speaker at local usergroup meetings, VSLive, SQL PASS, and other industry conferences. Lenni has also authored several MS Press books and Pluralsight courses on SQL Server programming.
LinkedIn: https://bg.linkedin.com/in/dimitrovmiroslav
Contact: http://www.sqlshack.com/author/miroslav-dimitrov/
Miroslav Dimitrov is a Senior Big Data and Analytics Engineer at DXC Technology, regular speaker at various events and Microsoft Certified Trainer. He is MCSE: Data Platform for SQL Server and MCITP on SQL 2008. In the last years he had worked on great variety of customers' environments and involved in complex transitions and transformation projects as well as developing private and public cloud solutions. Miroslav is also leading database courses at Sofia University.
LinkedIn: https://bg.linkedin.com/pub/miroslav-lessev/62/238/34
I am a PhD candidate at Faculty of Economics and Business Administration, University of Sofia quot;St. Kliment Ohridskiquot; in Implementation of modern computer engineering in economic studies at. I have study on cloud computing and its effect on economics and people. I have more than 20 years of experience in designing, developing, implementing and supporting databases. At the moment I#39;m a Big Data Developer in DataCore Software.
LinkedIn: https://pl.linkedin.com/in/marcinszeliga
Data Philosopher. Since 2006 invariably awarded Microsoft Most Valuable Professional title in the Data Platform category. A speaker at numerous conferences across Europe, as well as at user groups meetings. The author of many books and articles devoted to Microsoft Data Platform. An independent consultant, database-centric application practitioner and architect totally focused on SQL Server and Azure. Huge fan of Data Science and Machine Learning.
The following is a list of sponsors that helped fund the event.


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API Timestamp Docs
1) You pass a string as a parameter, and it will check to see whether that string contains either a unix timestamp or a natural language date (Month Day, Year | Month Day Year)
2) If it does, it returns both the Unix timestamp and the natural language form of that date.
3) If it does not contain a date or Unix timestamp, it returns null for those properties.
Example
https://brusbilis.com/freecodecamp/5-api/allApis/timestamp/July%2024,%201995
https://brusbilis.com/freecodecamp/5-api/allApis/timestamp/July 24 1995
https://brusbilis.com/freecodecamp/5-api/allApis/timestamp/806536800
Output:
{
"unix": 806536800,
"natural": "July 24, 1995"
}


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Big brother watch you!
monday tuesday wednesday
adult
man
woman
CHECK


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166. Editor
time limit per test: 0.3 sec.
memory limit per test: 1024
KB
input: standard
output: standard
You are to write a simple text editor. This editor must supply
next commands:
- english lowercase letters or spaces: insert this letter in the current
position of the cursor.
- L: move cursor one position left. If current position in the beginning of
the line, cursor mustn't move.
- R: move cursor one position right. If the current position if after the
end of the line, cursor must go beyond this end. If an english lowercase
letter or space follows in command line, additional spaces must be added to
the text.
- U: move cursor a line up. If the current line is the first, cursor mustn't
move.
- D: move cursor a line down. If the current line is last, cursor mustn't
move.
- N: a new line must be added after the current. The part of the current
line, which goes after the cursor position, must go on the new line. The
cursor must go on the first position of the new line.
- E: a cursor must move to a position after the end of the curent line.
- H: a cursor must move to the first position of the current line.
- X: current symbol must be deleted. The remaining part of the line must
shift left. If the current position is after the end of the current line,
additional spaces must be added to the current line and the next line must be
attached to the current line; if the current position is after the end of the
last line, nothing must happen.
- B: the symbol before the the cursor must be deleted and the remaining part
of the line must shift left. If the current position if after the end of the
line, cursor must shift left one position. If the current symbol is the first
of the line, then current line must be attached to the previous and remaining
part must shift up; if the current symbol is the first symbol of the first
line, nothing must happen.
- Q: the editor must finish the work.
The editor starts
from the blank file.
It is guaranted that in any moment while working
length of each string does not exceed 1000 chars, and total number of commands
does not exceed 20480.
Input
On the input there is the only line --- the list of commands of
the editor.
Output
Write to the output the result of working of the editor. The
EOLn (symbols with ASCII numbers 13 and 10) chars must be only between lines
generated by editor (NOT after the last string!)
Sample test(s)
Input
lineRoUNLLLLline dDDDNline tline
cLLLLLLNEUULLXRRRBXHBBHBBBBQsome left output
Output
line lined line t
line c
| Author: |
NNSU #2 team |
| Resource: |
|
| Date: |
|
| Server time: 2004-01-13 20:41:12 |
Online Contester Team © 2002 - 2003. All
rights
reserved. | | |


Currently only images available via an HTTP URL are supported. The image location must be accessible to the Image Service. Compressed image binaries are supported (.zip and .tar.gz.)
Please note: The Image Location field MUST be a valid and direct URL to the image binary. URLs that redirect or serve error pages will result in unusable images.


Enter The Details For Internal
Name:
Usn:
Enter Marks For Subject1:
Enter Marks For Subject2:
Enter Marks For Subject3:
Enter Marks For Subject4:
Enter Marks For Subject5:
Enter Marks For Subject6:


My Basic Blog
blog Entry #1
10/5/15
-
The benefits of version control:
- track all the changes that were done by multiple people on the same project
- able to restore a section of code if you think you messed up
- a place to store your work
Git helps track changes by listing all of the modifications to the file as they are being saved. It keeps record of every change from the smallest period so there isn't a chancge that won't get tracked.
You should use github to store your code so others can see it. The git community is a great place to see other codes and project that interest you and see how they did their code.


Nools is a rules engine for node based on the rete network.
npm install nools
To get started with nools the examples and tests are a great place to get started.
When using nools you define a flow which acts as a container for rules that can later be used to get an engine session
var nools = require("nools");
var Message = function (message) {
this.message = message;
};
var flow = nools.flow("Hello World", function (flow) {
//find any message that start with hello
this.rule("Hello", [Message, "m", "m.message =~ /^hello(\\s*world)?$/"], function (facts) {
facts.m.message = facts.m.message + " goodbye";
this.modify(facts.m);
});
//find all messages then end in goodbye
this.rule("Goodbye", [Message, "m", "m.message =~ /.*goodbye$/"], function (facts) {
console.log(facts.m.message);
});
});
In the above flow definition 2 rules were defined
You may also use the nools
rules language to define your rules
define Message {
message : '',
constructor : function(message){
this.message = message;
}
}
rule Hello {
when {
m : Message m.message =~ /^hello(\\s*world)?$/;
}
then {
modify(m, function(){this.message += " goodbye";});
}
}
rule Goodbye {
when {
m : Message m.message =~ /.*goodbye$/;
}
then {
console.log(m.message);
}
}
To use the flow
var flow = nools.compile(__dirname + "/helloworld.nools"),
Message = flow.getDefined("message");
A session is an instance of the flow that contains a working memory and handles the assertion, modification, and retraction of facts from the engine.
To obtain an engine session from the flow invoke the getSession method.
var session = flow.getSession();
Facts are items that the rules should try to match.
To add facts to the session use assert method.
session.assert(new Message("hello"));
session.assert(new Message("hello world"));
session.assert(new Message("goodbye"));
As a convenience any object passed into getSession will also be asserted.
flow.getSession(new Message("hello"), new Message("hello world"), new Message("goodbye"));
To retract facts from the session use the retract method.
var m = new Message("hello");
//assert the fact into the engine
session.assert(m);
//remove the fact from the engine
session.retract(m);
To modify a fact use the modify method.
Note modify will not work with immutable objects (i.e. strings).
var m = new Message("hello");
session.assert(m);
m.message = "hello goodbye";
session.modify(m);
assert is typically used pre engine execution and during the execution of the rules.
modify and retract are typically used during the execution of the rules.
When you get a session from a flow no rules will be fired until the match method is called.
var session = flow.getSession();
//assert your different messages
session.assert(new Message("goodbye"));
session.assert(new Message("hello"));
session.assert(new Message("hello world"));
//now fire the rules
session.match(function(err){
if(err){
console.error(err);
}else{
console.log("done");
}
})
The match method returns a promise that is invoked once there are no more rules to activate.
Example of the promise api
session.match().then(
function(){
console.log("Done");
},
function(err){
//uh oh an error occurred
console.error(err);
});
When working with a lot of facts it is wise to call the dispose method which will purge the current session of all facts, this will help prevent the process growing a large memory footprint.
session.dispose();
Lets look at the "Calculate" rule in the Fibonacci example
//flow.rule(type[String|Function], constraints[Array|Array[[]]], action[Function]);
flow.rule("Calculate", [
//Type alias pattern store sequence to s1
[Fibonacci, "f1", "f1.value != -1", {sequence:"s1"}],
[Fibonacci, "f2", "f2.value != -1 && f2.sequence == s1 + 1", {sequence:"s2"}],
[Fibonacci, "f3", "f3.value == -1 && f3.sequence == s2 + 1"],
[Result, "r"]
], function (facts) {
var f3 = facts.f3, f1 = facts.f1, f2 = facts.f2;
var v = f3.value = f1.value + facts.f2.value;
facts.r.result = v;
this.modify(f3);
this.retract(f1);
});
Or using the nools DSL
rule Calculate{
when {
f1 : Fibonacci f1.value != -1 {sequence:s1};
f2 : Fibonacci f2.value != -1 && f2.sequence == s1 + 1 {sequence:s2};
f3 : Fibonacci f3.value == -1 && f3.sequence == s2 + 1;
}
then {
modify(f3, function(){
this.value = f1.value + f2.value;
});
retract(f1);
}
}
Constraints define what facts the rule should match. The constraint is a array of either a single constraint (i.e. Bootstrap rule) or an array of constraints(i.e. Calculate).
Prgamatically
[
//Type alias pattern store sequence to s1
[Fibonacci, "f1", "f1.value != -1", {sequence:"s1"}],
[Fibonacci, "f2", "f2.value != -1 && f2.sequence == s1 + 1", {sequence:"s2"}],
[Fibonacci, "f3", "f3.value == -1 && f3.sequence == s2 + 1"],
[Result, "r"]
]
Using nools DSL
when {
f1 : Fibonacci f1.value != -1 {sequence:s1};
f2 : Fibonacci f2.value != -1 && f2.sequence == s1 + 1 {sequence:s2};
f3 : Fibonacci f3.value == -1 && f3.sequence == s2 + 1;
}
String
- "string", "String", StringNumber
- "number", "Number", NumberBoolean
- "boolean", "Boolean", BooleanDate
- "date", "Date", DateRegExp
- "regexp", "RegExp", RegExpArray
- "array", "Array", [], ArrayObject
- "object", "Object", "hash", ObjectPattern(optional) - The pattern that should evaluate to a boolean, the alias that was used should be used to reference the object in the pattern. Strings should be in single quotes, regular expressions are allowed. Any previously define alias/reference can be used within the pattern. Available operators are.
&&
, AND
, and
||
, OR
, or
>
, <
, >=
, <=
, gt
, lt
, gte
, lte
==
, !=
, =~
, eq
, neq
, like
+
, -
, *
, /
-
(unary minus).
(member operator)in
(check inclusion in an array)now
- the current dateDate(year?, month?, day?, hour?, minute?, second?, ms?)
- creates a new Date
objectlengthOf(arr, length)
- checks the length of an arrayisTrue(something)
- check if something === trueisFalse(something)
- check if something === falseisRegExp(something)
- check if something is a RegExp
isArray(something)
- check if something is an Array
isNumber(something)
- check if something is an Number
isHash(something)
- check if something is strictly an Object
isObject(something)
- check if something is any type of Object
isDate(something)
- check if something is a Date
isBoolean(something)
- check if something is a Boolean
isString(something)
- check if something is a String
isUndefined(something)
- check if something is a undefined
isDefined(something)
- check if something is Defined
isUndefinedOrNull(something)
- check if something is a undefined
or null
isPromiseLike(something)
- check if something is a "promise" like (containing then
, addCallback
, addErrback
)isFunction(something)
- check if something is a Function
isNull(something)
- check if something is null
isNotNull(something)
- check if something is not nulldateCmp(dt1, dt2)
- compares two dates return 1, -1, or 0years|months|days|hours|minutes|seconds``Ago
/FromNow``(interval)
- adds/subtracts the date unit from the current timeReference(optional) - An object where the keys are properties on the current object, and values are aliases to use. The alias may be used in succeeding patterns.
The action is a function that should be fired when all patterns in the rule match. The action is called in the scope of the engine so you can use this to assert, modify, or retract facts. An object containing all facts and references created by the alpha nodes is passed in as the first argument to the action.
So calculate's action modifies f3 by adding the value of f1 and f2 together and modifies f3 and retracts f1.
function (facts) {
var f3 = facts.f3, f1 = facts.f1, f2 = facts.f2;
var v = f3.value = f1.value + facts.f2.value;
facts.r.result = v;
this.modify(f3);
this.retract(f1);
}
The engine is also passed in as a second argument so alternatively you could do the following.
function (facts, engine) {
var f3 = facts.f3, f1 = facts.f1, f2 = facts.f2;
var v = f3.value = f1.value + facts.f2.value;
facts.r.result = v;
engine.modify(f3);
engine.retract(f1);
}
If you have an async action that needs to take place an optional third argument can be passed in which is a function to be called when the action is completed.
function (facts, engine, next) {
//some async action
process.nextTick(function(){
var f3 = facts.f3, f1 = facts.f1, f2 = facts.f2;
var v = f3.value = f1.value + facts.f2.value;
facts.r.result = v;
engine.modify(f3);
engine.retract(f1);
next();
})
}
If any arguments are passed into next it is assumed there was an error and the session will error out.
To define the action with the nools DSL
then {
modify(f3, function(){
this.value = f1.value + f2.value;
});
retract(f1);
}
For rules defined using the rules language nools will automatically determine what parameters need to be passed in based on what is referenced in the action.
"use strict";
var nools = require("nools");
var Fibonacci = function (sequence, value) {
this.sequence = sequence;
this.value = value || -1;
};
var Result = function (result) {
this.result = result || -1;
};
var flow = nools.flow("Fibonacci Flow", function (flow) {
flow.rule("Recurse", {priority:1}, [
["not", Fibonacci, "f", "f.sequence == 1"],
[Fibonacci, "f1", "f1.sequence != 1"]
], function (facts) {
var f2 = new Fibonacci(facts.f1.sequence - 1);
this.assert(f2);
});
flow.rule("Bootstrap", [
Fibonacci, "f", "f.value == -1 && (f.sequence == 1 || f.sequence == 2)"
], function (facts) {
var f = facts.f;
f.value = 1;
this.modify(f);
});
flow.rule("Calculate", [
[Fibonacci, "f1", "f1.value != -1", {sequence:"s1"}],
[Fibonacci, "f2", "f2.value != -1 && f2.sequence == s1 + 1", {sequence:"s2"}],
[Fibonacci, "f3", "f3.value == -1 && f3.sequence == s2 + 1"],
[Result, "r"]
], function (facts) {
var f3 = facts.f3, f1 = facts.f1, f2 = facts.f2;
var v = f3.value = f1.value + facts.f2.value;
facts.r.result = v;
this.modify(f3);
this.retract(f1);
});
});
var r1 = new Result(),
session1 = flow.getSession(new Fibonacci(10), r1),
s1 = new Date;
session1.match().then(function () {
console.log("%d [%dms]", r1.result, new Date - s1);
session1.dispose();
});
var r2 = new Result(),
session2 = flow.getSession(new Fibonacci(150), r2),
s2 = new Date;
session2.match().then(function () {
console.log("%d [%dms]", r2.result, new Date - s2);
session2.dispose();
});
var r3 = new Result(),
session3 = flow.getSession(new Fibonacci(1000), r3),
s3 = new Date;
session3.match().then(function () {
console.log("%d [%dms]", r3.result, new Date - s3);
session3.dispose();
});
Output
55 [43ms]
9.969216677189305e+30 [383ms]
4.346655768693743e+208 [3580ms]
Fiboncci with nools DSL
//Define our object classes, you can
//also declare these outside of the nools
//file by passing them into the compile method
define Fibonacci {
value:-1,
sequence:null
}
define Result {
value : -1
}
rule Recurse {
priority:1,
when {
//you can use not or or methods in here
not(f : Fibonacci f.sequence == 1);
//f1 is how you can reference the fact else where
f1 : Fibonacci f1.sequence != 1;
}
then {
assert(new Fibonacci({sequence : f1.sequence - 1}));
}
}
rule Bootstrap {
when {
f : Fibonacci f.value == -1 && (f.sequence == 1 || f.sequence == 2);
}
then{
modify(f, function(){
this.value = 1;
});
}
}
rule Calculate {
when {
f1 : Fibonacci f1.value != -1 {sequence : s1};
//here we define constraints along with a hash so you can reference sequence
//as s2 else where
f2 : Fibonacci f2.value != -1 && f2.sequence == s1 + 1 {sequence:s2};
f3 : Fibonacci f3.value == -1 && f3.sequence == s2 + 1;
r : Result
}
then {
modify(f3, function(){
this.value = r.result = f1.value + f2.value;
});
retract(f1);
}
}
And to run
var flow = nools.compile(__dirname + "/fibonacci.nools");
var Fibonacci = flow.getDefined("fibonacci"), Result = flow.getDefined("result");
var r1 = new Result(),
session1 = flow.getSession(new Fibonacci({sequence:10}), r1),
s1 = +(new Date());
session1.match().then(function () {
console.log("%d [%dms]", r1.result, +(new Date()) - s1);
session1.dispose();
});
var r2 = new Result(),
session2 = flow.getSession(new Fibonacci({sequence:150}), r2),
s2 = +(new Date());
session2.match().then(function () {
console.log("%d [%dms]", r2.result, +(new Date()) - s2);
session2.dispose();
});
var r3 = new Result(),
session3 = flow.getSession(new Fibonacci({sequence:1000}), r3),
s3 = +(new Date());
session3.match().then(function () {
console.log("%d [%dms]", r3.result, +(new Date()) - s3);
session3.dispose();
});
MIT https://github.com/C2FO/nools/raw/master/LICENSE
Code: git clone git://github.com/C2FO/nools.git


HARD DRIVE (1994) A Film Review Copyright Dragan Antulov 2000
"Get a life", words spoken by Sharon Stone's character at the end of SLIVER, might be a good advice to protagonist of HARD DRIVE, 1994 erotic thriller directed by James Marandino.   Lately he developed on-line relationship with mysterious woman named Delilah. They exchange their bizarre sexual fantasies, and once Delilah suggests that they try one of them in real life, Will agrees. However, this fantasy is rather dark and in might not be as harmless in real life as it is in cyberspace.
Made in a time when Internet only began to establish itself as global phenomenon, this film tries to predict some of its after-effects - alienation and addiction to cyberspace as preferable form or reality. Unfortunately, when screenwriter and lead actor Leo Damian tries to explore the ultimate consequences of such alienation, HARD DRIVE begins to fall apart. The story is stuffed with plenty of totally unnecessary characters, although at least one of them, Will's real life girlfriend Laura, is played by quite capable Belinda Waymouth. Unfortunately, despite rather nice photography by Sead Muhtarevic, HARD DRIVE in the end falls apart because of cliches and director James Marandino's inability to prevent confusion among the audience. Although HARD DRIVE at least tries to deal with serious subjects, the audience is left with the impression of failed effort, and those subjects seem too lofty for unsuitable for something so exploitative like erotic thriller.
RATING: 3/10 (+)
Review written on October 25th 2000
Dragan Antulov a.k.a. Drax Fido: 2:381/100 E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>Filmske recenzije na hrvatskom/Movie Reviews in Croatian http://film.purger.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
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Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


According to numerous studies on child development, learning languages at a young age stimulates children's brains by providing educational challenges that lead to being able to retain information easier, better memorization, develop open-mindedness, and generate curiosity. Shinnpo - meaning "advance" in Japanese - advocates this by providing a child-friendly (and adult!) adventure-style game that lets you learn basic communication around the world.


We couldn't search for that because search doesn't work yet.
stories are for the world. the world is for everyone.


Detailed course offerings (Time Schedule) are available for
B CMU 301 Strategic Business Communication (4)
Strategic approach to communications as a management tool. Analysis of the psychology, semantics, planning, and principles of effective business writing, presenting, and interpersonal and team communication. Practical applications include written and oral messages, inform and persuade, individual and team presentations, and tools and processes to increase collaboration among members of a team.
View course details in MyPlan: B CMU 301
B CMU 410 Business Reports and Other Specialized Communications (4)
Covers internal and external, written and oral business reporting. Considers communications strategies within the context of rapidly changing technologies. Students learn to apply primary and secondary research to quarter-long, individual projects resulting in a variety of reports: proposals, progress reports, feasibility studies, business plans, etc. Prerequisite: B CMU 301 or B CMU 302.
View course details in MyPlan: B CMU 410
B CMU 490 Special Topics in Business Communications (1-6, max. 12)
Students and faculty focus on current topics of concern. Prerequisite: either B CMU 301 or B CMU 302.
View course details in MyPlan: B CMU 490
B CMU 499 Research in Business Communications (1-6, max. 9)
View course details in MyPlan: B CMU 499
B CMU 509 Finding your Voice (2)
Develop a leadership communication style by discovering core values and learning how to translate those values into effective messages. Students give speeches as well as critique speeches of classmates, produce written evaluation of speeches given in class and receive instructor feedback. Prerequisite: B A 501.
View course details in MyPlan: B CMU 509
B CMU 510 Business Communications for Managers (4)
Develops understanding of communication theories, describes strategies for planning managerial communications, and builds skills in oral and written reporting and persuading. Looks at how new technologies are changing the way people in business communicate, and the implications those changes have for organizations. Prerequisite: B A 501.
View course details in MyPlan: B CMU 510
B CMU 579 Special Topics in Business Communication (2/4, max. 12)
Business and managerial communication topics of current interest to faculty and students.
View course details in MyPlan: B CMU 579
B CMU 600 Independent Study or Research (*)
Offered: AWSp.
View course details in MyPlan: B CMU 600


I have done a number of projects, one being the greenhomed website, then the greenhomed blog on wordpress, and the greenhomed facebook page.


Welcome
Today,
18 Sept 2013
Robert Martin
22 Feb 1953
Male
Simple List
Interactive Table
Timeline
Back to the Medication List chapter
Contact
Impact
This prototype requires javascript to function properly.


Bijou.js is a library of super helpful JavaScript snippets!  If there's something that's not in here currently but you use frequently, feel free to submit an issue! I'd be happy to add it! Bijou's goal is to eliminate the need to copy paste the same thing from StackOverflow, another project or anywhere else! So if you ever find yourself typing the same thing over and over again, copy pasting the same snippet as you did a few days ago, use Bijou.js! If the snippet you're using isn't in Bijou.js, just submit an issue with the code!
Thanks for using Bijou.js! It's pretty simple to use, but here's a guide:
jsDelivr:
<script<EMAIL_ADDRESS>UNPKG
<script<EMAIL_ADDRESS>require()
const _$ = require("bijou.js");
To use Bijou.js simply call one of the many functions built into it!
_$.anyFunction(); //If you're using node you can name bijou anything.
Such as this one!
_$.uuid(); //Results in something like this: c3435c88-0a20-491f-9391-3afde9c4a2d1
Me (@Explosion-Scratch) and my good friend @GrahamSH-LLK with a lot of help from other people such as retron and theColaber!
Well, a few reasons:
(You can probably see I'm pretty desperate for people to use this.)
You guessed it…… npm i bijou.js


"A fabulous story of a little girl Alice who came back home."


vignettes/UsingSqlRender.Rmd
UsingSqlRender.Rmd
One of the main functions of the package is to support
parameterization of SQL. Often, small variations of SQL need to be
generated based on some parameters. SqlRender offers a simple markup
syntax inside the SQL code to allow parameterization. Rendering the SQL
based on parameter values is done using the render()
function.
The @
character can be used to indicate parameter names
that need to be exchange for actual parameter values when rendering. In
the following example, a variable called a
is mentioned in
the SQL. In the call to the render function the value of this parameter
is defined:
sql <- "SELECT * FROM table WHERE id = @a;"
render(sql, a = 123)
## [1] "SELECT * FROM table WHERE id = 123;"
Note that, unlike the parameterization offered by most database management systems, it is just as easy to parameterize table or field names as values:
sql <- "SELECT * FROM @x WHERE id = @a;"
render(sql, x = "my_table", a = 123)
## [1] "SELECT * FROM my_table WHERE id = 123;"
The parameter values can be numbers, strings, booleans, as well as vectors, which are converted to comma-delimited lists:
## [1] "SELECT * FROM table WHERE id IN (1,2,3);"
For some or all parameters, it might make sense to define default
values that will be used unless the user specifies another value. This
can be done using the {DEFAULT @parameter = value}
syntax:
sql <- "{DEFAULT @a = 1} SELECT * FROM table WHERE id = @a;"
render(sql)
## [1] "SELECT * FROM table WHERE id = 1;"
render(sql, a = 2)
## [1] "SELECT * FROM table WHERE id = 2;"
Defaults for multiple variables can be defined:
sql <- "{DEFAULT @a = 1} {DEFAULT @x = 'my_table'} SELECT * FROM @x WHERE id = @a;"
render(sql)
## [1] "SELECT * FROM my_table WHERE id = 1;"
Sometimes blocks of codes need to be turned on or off based on the
values of one or more parameters. This is done using the
{Condition} ? {if true} : {if false}
syntax. If the
condition evaluates to true or 1, the if true block is
used, else the if false block is shown (if present).
sql <- "SELECT * FROM table {@x} ? {WHERE id = 1}"
render(sql, x = FALSE)
## [1] "SELECT * FROM table "
render(sql, x = TRUE)
## [1] "SELECT * FROM table WHERE id = 1"
Simple comparisons are also supported:
sql <- "SELECT * FROM table {@x == 1} ? {WHERE id = 1};"
render(sql, x = 1)
## [1] "SELECT * FROM table WHERE id = 1;"
render(sql, x = 2)
## [1] "SELECT * FROM table ;"
As well as the IN
operator:
sql <- "SELECT * FROM table {@x IN (1,2,3)} ? {WHERE id = 1};"
render(sql, x = 2)
## [1] "SELECT * FROM table WHERE id = 1;"
Clauses can combined with boolean operators:
sql <- "SELECT * FROM table {@x IN (1,2,3) | @y != 3} ? {WHERE id = @x AND value = @y};"
render(sql, x = 4, y = 4)
## [1] "SELECT * FROM table WHERE id = 4 AND value = 4;"
sql <- "SELECT * FROM table {(@x == 1 | @x == 3) & @y != 3} ? {WHERE id = @x AND val = @y};"
render(sql, x = 3, y = 4)
## [1] "SELECT * FROM table WHERE id = 3 AND val = 4;"
SQL for one platform (e.g. Microsoft SQL Server) will not always
execute on other platforms (e.g. Oracle). The translate()
function can be used to translate between different dialects, but there
are some limitations.
A first limitation is that the starting dialect has to be SQL
Server. The reason for this is that this dialect is in general
the most specific. For example, the number of days between two dates in
SQL Server has to be computed using the DATEDIFF function:
DATEDIFF(dd,a,b)
. In other languages one can simply
subtract the two dates: b-a
. Since you’d need to know a and
b are dates, it is not possible to go from other languages to SQL
Server, only the other way around.
A second limitation is that currently only these dialects are supported as targets: Oracle, PostgreSQL, Microsoft PDW (Parallel Data Warehouse), Impala, Netezza, Google BigQuery, Amazon Redshift, Snowflake, Azure Synapse, Apache Spark and SQLite.
A third limitation is that only a limited set of translation rules have currently been implemented, although adding them to the list should not be hard.
A last limitation is that not all functions supported in one dialect have an equivalent in other dialects.
Below an example:
sql <- "SELECT DATEDIFF(dd,a,b) FROM table; "
translate(sql,targetDialect = "oracle")
## [1] "SELECT CEIL(CAST(b AS DATE) - CAST(a AS DATE)) FROM table ; "
## attr(,"sqlDialect")
## [1] "oracle"
The targetDialect
parameter can have the following
values:
These SQL Server functions have been tested and were found to be translated correctly to the various dialects:
## Warning in matrix(funs, ncol = 4): data length [53] is not a sub-multiple or
## multiple of the number of rows [14]
| Function | Function | Function | Function |
|---|---|---|---|
| ABS | DATEFROMPARTS | LOG10 | RTRIM |
| ACOS | DATETIMEFROMPARTS | LOWER | SIN |
| ASIN | DAY | LTRIM | SQRT |
| ATAN | EOMONTH | MAX | SQUARE |
| AVG | EXP | MIN | STDEV |
| CAST | FLOOR | MONTH | SUM |
| CEILING | GETDATE | NEWID | TAN |
| CHARINDEX | HASHBYTES* | PI | UPPER |
| CONCAT | IIF | POWER | VAR |
| COS | ISNULL | RAND | YEAR |
| COUNT | ISNUMERIC | RANK | |
| COUNT_BIG | LEFT | RIGHT | ABS |
| DATEADD | LEN | ROUND | ACOS |
| DATEDIFF | LOG | ROW_NUMBER | ASIN |
Similarly, many SQL syntax structures are supported.  In SQL Server, one would write
SELECT first_name + ' ' + last_name AS full_name FROM table
,
but this should be
SELECT first_name || ' ' || last_name AS full_name FROM table
in PostgreSQL and Oracle. SqlRender tries to guess when values that are
being concatenated are strings. In the example above, because we have an
explicit string (the space surrounded by single quotation marks), the
translation will be correct. However, if the query had been
SELECT first_name + last_name AS full_name FROM table
,
SqlRender would have had no clue the two fields were strings, and would
incorrectly leave the plus sign. Another clue that a value is a string
is an explicit cast to VARCHAR, so
SELECT last_name + CAST(age AS VARCHAR(3)) AS full_name FROM table
would also be translated correctly. To avoid ambiguity altogether, it is
probable best to use the CONCAT()
function to concatenate
two or more strings.
Many SQL dialects allow the use of the AS
keyword when
defining a table alias, but will also work fine without the keyword. For
example, both these SQL statements are fine for SQL Server, PostgreSQL,
RedShift, etc.:
-- Using AS keyword
SELECT *
FROM my_table AS table_1
INNER JOIN (
SELECT * FROM other_table
AS table_2
) ON table_1.person_id = table_2.person_id;
-- Not using AS keyword
SELECT *
FROM my_table table_1
INNER JOIN (
SELECT * FROM other_table
) table_2ON table_1.person_id = table_2.person_id;
However, Oracle will throw an error when the AS
keyword
is used. In the above example, the first query will fail. It is
therefore recommended to not use the AS
keyword when
aliasing tables. (Note: we can’t make SqlRender handle this, because it
can’t easily distinguish between table aliases where Oracle doesn’t
allow AS
to be used, and field aliases, where Oracle
requires AS
to be used.)
Temp tables can be very useful to store intermediate results, and when used correctly can be used to dramatically improve performance of queries. In platforms like Postgres, PDW, RedShift and SQL Server temp tables have very nice properties: they’re only visible to the current user, are automatically dropped when the session ends, and can be created even when the user has no write access. Unfortunately, in Oracle temp tables are basically permanent tables, with the only difference that the data inside the table is only visible to the current user. Other platforms, like Impala and Spark, don’t support temp tables at all. This is why for some platforms SqlRender will try to emulate temp tables by
For example:
sql <- "SELECT * FROM #children;"
translate(sql, targetDialect = "oracle", tempEmulationSchema = "temp_schema")
## [1] "SELECT * FROM temp_schema.dx224bvxchildren ;"
## attr(,"sqlDialect")
## [1] "oracle"
Note that the user will need to have write privileges on
temp_schema
. You can also set the
tempEmulationSchema
globally by using
options(sqlRenderTempEmulationSchema = "temp_schema")
Also note that because Oracle has a limit on table names of 30 characters, temp table names are only allowed to be at most 22 characters long because else the name will become too long after appending the session ID.
Furthermore, remember that emulated temp tables are not automatically
dropped, so you will need to explicitly TRUNCATE
and
DROP
all temp tables once you’re done with them to prevent
orphan tables accumulating in the Oracle temp schema.
If possible, try to avoid using temp tables altogether. Sometimes one could use Common Table Expressions (CTEs) when one would normally use a temp table. For example, instead of
SELECT * INTO #children FROM person WHERE year_of_birth > 2000;
SELECT * FROM #children WHERE gender = 8507;
you could use
WITH children AS (SELECT * FROM person WHERE year_of_birth > 2000)
SELECT * FROM children WHERE gender = 8507;
One of the few points where SQL Server is less explicit than other dialects is that it allows implicit casts. For example, this code will work on SQL Server:
CREATE TABLE #temp (txt VARCHAR);
INSERT INTO #temp
SELECT '1';
SELECT * FROM #temp WHERE txt = 1;
Even though txt
is a VARCHAR field and we are comparing
it with an integer, SQL Server will automatically cast one of the two to
the correct type to allow the comparison. In contrast, other dialects
such as PosgreSQL will throw an error when trying to compare a VARCHAR
with an INT.
You should therefore always make casts explicit. In the above example, the last statement should be replaced with either
SELECT * FROM #temp WHERE txt = CAST(1 AS VARCHAR);
or
SELECT * FROM #temp WHERE CAST(txt AS INT) = 1;
Some DBMS platforms such as SQL Server always perform string comparisons in a case-insensitive way, while others such as PostgreSQL are always case sensitive. It is therefore recommended to always assume case-sensitive comparisons, and to explicitly make comparisons case-insensitive when unsure about the case. For example, instead of
SELECT * FROM concept WHERE concep_class_id = 'Clinical Finding'
it is preferred to use
SELECT * FROM concept WHERE LOWER(concep_class_id) = 'clinical finding'
In SQL Server, tables are located in a schema, and schemas reside in
a database. For example, cdm_data.dbo.person
refers to the
person
table in the dbo
schema in the
cdm_data
database. In other dialects, even though a similar
hierarchy often exists they are used very differently. In SQL Server,
there is typically one schema per database (often called
dbo
), and users can easily use data in different databases.
On other platforms, for example in PostgreSQL, it is not possible to use
data across databases in a single session, but there are often many
schemas in a database. In PostgreSQL one could say that the equivalent
of SQL Server’s database is the schema.
We therefore recommend concatenating SQL Server’s database and schema
into a single parameter, which we typically call
@databaseSchema
. For example, we could have the
parameterized SQL
SELECT * FROM @databaseSchema.person
where on SQL Server we can include both database and schema names in
the value: databaseSchema = "cdm_data.dbo"
. On other
platforms, we can use the same code, but now only specify the schema as
the parameter value: databaseSchema = "cdm_data"
.
The one situation where this will fail is the USE
command, since USE cdm_data.dbo;
will throw an error. It is
therefore preferred not to use the USE
command, but always
specify the database / schema where a table is located. However, if one
wanted to use it anyway, we recommend creating two variables, one called
@database
and the other called
@databaseSchema
. For example, for this parameterized
SQL:
SELECT * FROM @databaseSchema.person;
USE @database;
SELECT * FROM person
we can set database = "cdm_data"
and the other called
databaseSchema = "cdm_data.dbo"
. On platforms other than
SQL Server, the two variables will hold the same value and only on SQL
Server will they be different. Within an R function, it is even possible
to derive one variable from the other, so the user of your function
would need to specify only one value:
foo <- function(databaseSchema, dbms) {
database <- strsplit(databaseSchema ,"\\.")[[1]][1]
sql <- "SELECT * FROM @databaseSchema.person; USE @database; SELECT * FROM person;"
sql <- render(sql, databaseSchema = databaseSchema, database = database)
sql <- translate(sql, targetDialect = dbms)
return(sql)
}
foo("cdm_data.dbo", "sql server")
## [1] "SELECT * FROM cdm_data.dbo.person; USE cdm_data; SELECT * FROM person;"
## attr(,"sqlDialect")
## [1] "sql server"
foo("cdm_data", "postgresql")
## [1] "SELECT * FROM cdm_data.person; SET search_path TO cdm_data; SELECT * FROM person;"
## attr(,"sqlDialect")
## [1] "postgresql"
Both PDW and RedShift are massively parallel processing platforms,
meaning they consist of many nodes that work together. In such an
environment, significant increases in performance can be achieved by
fine-tuning the SQL for these platforms. Probably most importantly,
developers can specify the way data is distributed over the nodes.
Ideally, data in a node only needs to be combined with data in the same
node. For example, if I have two tables with the field
person_id
, I would like all records with the same person ID
to be on the same node, so a join on person_id
can be
performed locally without exchanging data between nodes.
SQL Server SQL, our source dialect, does not allow for these optimizations, so we’ve introduced the notion of hints. In the following example, a hint is provided on which field should be used for the distribution of data across nodes:
--HINT DISTRIBUTE_ON_KEY(person_id)
SELECT * INTO one_table FROM other_table;
which will translate into the following on PDW:
--HINT DISTRIBUTE_ON_KEY(person_id)
IF XACT_STATE() = 1 COMMIT;
CREATE TABLE one_table WITH (DISTRIBUTION = HASH(person_id)) AS
SELECT * FROM other_table;
Another tuning parameter is the key to sort a table on. This can be also be specified in a hint:
--HINT SORT_ON_KEY(INTERLEAVED:start_date)
CREATE TABLE cdm.my_table (row_id INT, start_date);
translates to the following on RedShift:
--HINT SORT_ON_KEY(INTERLEAVED:start_date)
CREATE TABLE cdm.my_table (row_id INT, start_date)
INTERLEAVED SORTKEY(start_date);
The hints should be formatted exactly as shown above, and directly precede the statement where the table is created.
Debugging parameterized SQL can be a bit complicated; Only the rendered SQL can be tested against a database server, but changes to the code should be made in the parameterized (pre-rendered) SQL.
A Shiny app is included in the SqlRender package for interactively editing source SQL and generating rendered and translated SQL. The app can be started using:
Which will open the default browser with the app.
In addition, two functions have been developed to aid the debugging
process: renderSqlFile()
and
translateSqlFile()
. These can be used to read SQL from
file, render or translate it, and write it back to file. For
example:
renderSqlFile("parameterizedSql.txt","renderedSql.txt")
will render the file, using the default parameter values specified in the SQL. What works well for us is editing in the parameterized file, (re)running the command above, and have the rendered SQL file open in a SQL client for testing. Any problems reported by the server can be dealt with in the source SQL, and can quickly be re-rendered.
Often, the SQL code will become part of an R package, where it might
be used to perform initial data-preprocessing and extraction before
further analysis. We’ve developed the following practice for doing so:
The parameterized SQL should be located in the inst/sql/ folder
of the package. The parameterized SQL for SQL Server should be in the
inst/sql/sql_server/ folder. If for some reason you do not want
to use the translation functions to generate the SQL for some dialect
(e.g because dialect specific code might be written that gives better
performance), a dialect-specific version of the parameterized SQL should
be placed in a folder with the name of that dialect, for example
inst/sql/oracle/. SqlRender has a function
loadRenderTranslateSql()
that will first check if a
dialect-specific version is available for the target dialect. If it is,
that version will be rendered, else the SQL Server version will be
rendered and subsequently translated to the target dialect.
The createRWrapperForSql()
function can be used to
create an R wrapper around a rendered SQL file, using the
loadRenderTranslateSql()
function . For example, suppose we
have a text file called test.sql containing the following
parameterized SQL:
DEFAULT @selected_value = 1}
{SELECT * FROM table INTO result where x = @selected_value;
Then the command
createRWrapperForSql(sqlFilename = "test.sql",
rFilename = "test.R",
packageName = "myPackage")
would result in the file test.R being generated containing this R code:
#' Todo: add title
#'
#' @description
#' Todo: add description
#'
#' @details
#' Todo: add details
#'
#' @param connectionDetails An R object of type \code{ConnectionDetails} created ...
#' @param selectedValue
#'
#' @export
test <- function(connectionDetails,
selectedValue = 1) {
renderedSql <- loadRenderTranslateSql("test.txt",
packageName = "myPackage",
dbms = connectionDetails$dbms,
selected_value = selectedValue)
conn <- connect(connectionDetails)
writeLines("Executing multiple queries. This could take a while")
executeSql(conn,renderedSql)
writeLines("Done")
dummy <- dbDisconnect(conn)
}
This code expects the file test.sql to be located in the inst/sql/sql_server/ folder of the package source.
Note that the parameters are identified by the declaration of default values, and that snake_case names (our standard for SQL) are converted to camelCase names (our standard for R).
Spark SQL v3.1 improves the ANSI SQL compliance of Spark, however, at this time, INSERT statements are a bit limited, compared to other dialects:
INSERT commands must feature a full column list in both the parenthetical column list and in the actual data to be inserted. A full column list here means that every column in the destination table must be present.
The column list does not need to be in the correct order as the destination table, provided the parenthetical column list explicitly states the column names you’re targeting.
To mitigate this issue, SqlRender has a Java function with R wrapper
named sparkHandleInsert()
, which will connect to the
database and interrogate it for the full column list of a destination
table used in an INSERT command, to then reconstitute the command to
have the full column list.
For example:
Suppose we were inserting a record to the COHORT table with this call:
sql <- "insert into results_schema.cohort (subject_id, cohort_definition_id) values (1, 2)'
In Spark SQL, this would fail, as the COHORT table consists of cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date.
Instead, we can run this:
sql <- "insert into results_schema.cohort (subject_id, cohort_definition_id) values (1, 2)'
connection <- DatabaseConnector::connect(connectionDetails = connectionDetails)
SqlRender::sparkHandleInsert(sql, connection)
and obtain:
INSERT INTO results_schema.cohort VALUES (2, 1, NULL, NULL);
,
which places the columns in correct table order and pads it out with
NULL values.


LASTCALL Issue 1 Prerelease
From a bar story cobbled together over drinks in Las Vegas bar, into your hands arrives the story of a mild mannered bartender turned superhero defending truth, justice and the inebriated way!
Author A.P. Delchi and artist James Liswed present the tale of LASTCALL, in which an ordinary Las Vegas bartender is endowed with amazing powers by way of an alien bottle opener whisked away from Area 51 in a series of events all too familiar to anyone who has spent time in sin city. Set to the music of local sensation Thee Swank Bastards, the adventure follows LASTCALL with his faithful friends Al "The Mountain" McMurphy, Sarah "The Brains of the outfit" Smalls, and a Jager laden ex-hacker for hire, only known as Delchi, as they raise their glasses to the solemn oath of their calling:
Where there are drunk people, they will live to party another day.
Where there are anti-party people, they will be silenced
Where there is sorrow, there will be liquor
Where there are hangovers, there will be burritos and Gatorade
Where there is unemployment, there will be cheap drinks
And there there is sunlight, there will be a dark, air conditioned bar.
By the power of the GROGNOX I swear this oath:
Ergo Bibamus to one and all!
I am the hero known as LASTCALL!
LASTCALL Issue #1 is available in both standard comics size and a limited edition magazine size for you back to the pillow fort days of comics, soda pop, science fiction, and rock & roll!


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NCSACW - Drug Testing in Child Welfare
Drug testing is one tool that child welfare workers often use to facilitate decision- making with families affected by substance use disorders. Child welfare workers .
http://www.ncsacw.samhsa.gov/resources/resources-Drug-Testing-in-Child-Welfare.aspx
-
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Georgia to require drug testing for welfare recipients - Atlanta child ...
Jan 3, 2012 . With a new year comes a new legislative session, and it's important for child welfare advocates to understand the implication of each bill that .
http://www.examiner.com/article/georgia-to-require-drug-testing-for-welfare-recipients
-
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Drug Testing in Child Welfare - National Center on Substance ...
i. Drug Testing in Child Welfare: Practice and Policy Considerations. Substance Abuse and Mental Health Services Administration. Center for Substance Abuse .
http://www.ncsacw.samhsa.gov/files/DrugTestinginChildWelfare.pdf
-
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Substance Abuse Screening Practice Points - Arizona Department of ...
ARIZONA DEPARTMENT OF ECONOMIC SECURITY. CHILD PROTECTIVE SERVICES. Substance Abuse Screening. Practice Points. In our work with families, .
https://www.azdes.gov/InternetFiles/InternetProgrammaticForms/pdf/ACY-1173E.pdf
-
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Drug Testing Welfare Recipients: Recent Proposals and Continuing ...
What impact does drug testing of TANF applicants and recipients .
http://aspe.hhs.gov/hsp/11/DrugTesting/ib.shtml
-
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Michigan Substance Abuse/Child Welfare Protocol for Screening
from training conducted by the National Center on Substance Abuse and Child Welfare in February 2004, on the National Screening and Assessment for Family .
http://courts.mi.gov/Administration/SCAO/Resources/Documents/bestpractice/SAFERR-Protocol.pdf
-
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DCF: Positive drug test will lead to child abuse hotline referral | TBO ...
Jun 28, 2011 . Starting next month, Florida's social service agency will refer every welfare applicant who fails a drug test to a child abuse hotline. State officials .
http://www2.tbo.com/news/breaking-news/2011/jun/28/dcf-positive-drug-test-will-lead-to-child-abuse-ho-ar-240285/
-
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National Advocates for Pregnant Women: Civil Child Welfare Cases ...
Civil Child Welfare Cases and Issues. Drug use, real and merely alleged, has increasingly become a basis for child welfare interventions against poor families.
http://www.advocatesforpregnantwomen.org/issues/civil_child_welfare_cases_and_issues/
-
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Page Not Found - National Center on Substance Abuse and Child ...
U.S Department of Health and Human Services, Substance Abuse and Mental . Children and Family Services Reviews · Drug Testing in Child Welfare · Family .
http://www.ncsacw.samhsa.gov/files/Substance-Exposed-Infants.pdf
-
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Guidelines for Testing and Reporting Drug Exposed Newborns in ...
drug screening, laboratory testing and reporting of drug-exposed newborns . Health care providers remain mandated reporters of child abuse and neglect .
http://here.doh.wa.gov/materials/guidelines-drug-exposed-newborns/15_BabyDrugTest_E12L.pdf
-
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Nebraska' Response to Substance Abuse in Child Welfare
Majority of Child Welfare Cases have Substance Abuse Related. Problems: 56% . Drug Testing is Frequently Used without Treatment: Over a third of the cases .
http://www.throughtheeyes.org/files/SA%20Report%20Final.pdf
-
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Daily Kos: Drug testing those on welfare: still unconstitutional and a ...
Jun 20, 2012 . It's still true that, as I wrote in October, 2011, Drug testing welfare . Another 2 percent declined to take the test, Department of Children and .
http://www.dailykos.com/story/2012/06/20/1101296/-Drug-testing-those-on-welfare-still-unconstitutional-and-a-waste-of-money
-
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Known Factors That Increase Risk - Florida Department of Children ...
indicate that between one-third and two-thirds of substantiated child abuse and neglect reports involve substance abuse. A good screening tool for child welfare .
http://www.dcf.state.fl.us/admin/publications/fsp/trainingbulletin/August07TrainingBulletin.pdf
-
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Welfare drug-test bill deepens stigma - Bangor Daily News
Mar 3, 2013 . It also would allow municipalities to drug-test people seeking emergency aid . constitutes sound child welfare and substance abuse prevention policy making. . Maine Democrat submits bill to drug-test welfare recipients .
http://bangordailynews.com/2013/03/03/opinion/welfare-drug-test-bill-deepens-stigma/
-
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Arizona child welfare: Traces of drugs marked child's life
May 27, 2012 . Arizona child welfare: Traces of drugs marked child's life, Patrick Smith's . drug tests six times, according to Child Protective Services reports.
http://www.azcentral.com/news/articles/2012/05/27/20120527arizona-child-welfare-drugs.html
-
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National Center on Substance Abuse and Child Welfare: NCSACW
Children and Family Services Reviews · Drug Testing in Child Welfare · Family . Free tutorials that focus on the subjects of substance abuse and child welfare.
http://www.ncsacw.samhsa.gov/
-
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State Of Maine Child Welfare/Substance Abuse Committee
and the Maine State District Court. Universal Substance Abuse Screening for Families in the Child Welfare System: Policy and Practice for Family Assessments .
http://muskie.usm.maine.edu/helpkids/telefiles/050608tele/Maine%20-%20Pamphlet-%20Universal%20Substance%20Abuse%20Screening%20for%20Families%20in%20the%20Child%20Welfare%20System.pdf
-
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Texas Plan to Test Welfare Applicants for Drugs Would Hurt ...
A plan that would require applicants for welfare and unemployment benefits in Texas to be tested for drugs would hurt children, critics argue. The plan is backed .
http://www.drugfree.org/uncategorized/texas-plan-to-test-welfare-applicants-for-drugs-would-hurt-children-critics-say
-
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Michigan Substance Abuse/Child Welfare Protocol For Screening ...
from training conducted by the National Center on Substance Abuse and Child Welfare in February 2004, on the National Screening and Assessment for Family .
http://www.ncsacw.samhsa.gov/files/Michigan_Protocol.pdf
-
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Nebraska' Response to Substance Abuse in Child Welfare
Majority of Child Welfare Cases have Substance Abuse Related. Problems: 56% . Drug Testing is Frequently Used without Treatment: Over a third of the cases .
http://www.throughtheeyes.org/files/SA%20Report%20Final.pdf


Django
The web framework for perfectionists with deadlines.
Writing your first Django app
Start by creating a new project on desktop
Things which make sense
- Project directory structure
- Create an app and it's directory structure
- Run the server
Views
- Python function that takes a Web request and returns a Web response
- Views are responsible for processing incoming requests, handling input, saving data, and sending a response to send back to the client.
Writing one function in view
using render object
Step by step writing view
- request to be sent - Write the url
- define the dictionary(dummy one)
- Create a template
Let's talk about what API is?
Application Programming Interface
it's only about REST api
REpresentational State Transfer
Lets see Facebook api
All you get this Gibberish language - It is JSON
Json
Javascript Object Notation - Key Value pairs
Let's try out filtering the data
Two Api's we will use today
Google Maps Api
Instagram Api
Enough of theory!! It's time to go back to our app
urllib.request,json will help you to play with url's
- Get the query parameter using request.GET.get('location','')
- Open,read and decode data of the url
- Open - urllib.request.urlopen(url)
- Read - content.read()
- Load and decode - json.loads(data.decode("utf8"))
Same with Instagram Api too
- Get the query parameter using request.GET.get('location','')
- Open,read and decode data of the url
- Open - urllib.request.urlopen(url)
- Read - content.read()
- Load and decode - json.loads(data.decode("utf8"))


All variables should be enclosed in {} (braces) when being interpolated in a string.
{}
warning "Variable $var is undefined"
warning "Variable ${var} is undefined"


Trump impeachment inquiry: powerful testimony on final day of public hearings – video
by ReutersThe fifth day of public hearings in the impeachment inquiry into Donald Trump saw powerful testimony from Fiona Hill, a former National Security Council official and former top Russia expert in the White House, and David Holmes, a state department aide in Kyiv.
Both spoke of Gordon Sondland, the ambassador to the European Union, as Holmes described a cellphone conversation in which he overheard Trump ask Sondland about 'investigations' and heard Sondland tell Trump the Ukrainians had agreed to them.


Isn't it beautiful.
This text is marked.
GNU is a recursive acronym.
Some of the worst mistakes of my life have been haircuts.
| Firstname | Lastname |
|---|---|
| Spongebob | Squarepants |
| Patrick | Star |
| Squidward | Tentacles |
| Firstname | Lastname |
|---|---|
| Spongebob | Squarepants |
| Patrick | Star |
| Squidward | Tentacles |
| Firstname | Lastname |
|---|---|
| Spongebob | Squarepants |
| Patrick | Star |
| Squidward | Tentacles |


titan¶
- pilot.resource.titan.command_fix(command, job_scratch_dir)[source]¶
Modification of payload parameters, to be executed on Titan on RAM disk. Some cleanup.
- Parameters
command – payload command (string).
job_scratch_dir – local path to input files (string).
- Returns
updated/fixed payload command (string).
- pilot.resource.titan.get_job(harvesterpath)[source]¶
Return job description in dictionary and MPI rank (if applicable)
- Parameters
harvesterpath – path to config.Harvester.jobs_list_file (string).
- Returns
job object, rank (int).
- pilot.resource.titan.get_setup(job=None)[source]¶
Return the resource specific setup.
- Parameters
job – optional job object.
- Returns
setup commands (list).
- pilot.resource.titan.postprocess_workdir(workdir)[source]¶
Post-processing of working directory. Unlink paths.
- Parameters
workdir – path to directory to be processed (string).
- Raises
FileHandlingFailure – in case of IOError.
- pilot.resource.titan.process_jobreport(payload_report_file, job_scratch_path, job_communication_point)[source]¶
Copy job report file to make it accessible by Harvester. Shrink job report file.
- Parameters
payload_report_file – name of job report (string).
job_scratch_path – path to scratch directory (string).
job_communication_point – path to updated job report accessible by Harvester (string).
- Raises
FileHandlingFailure – in case of IOError.
- pilot.resource.titan.set_job_workdir(job, path)[source]¶
Point pilot to job working directory (job id).
- Parameters
job – job object.
path – local path to Harvester access point (string).
- Returns
job working directory (string).
- pilot.resource.titan.set_scratch_workdir(job, work_dir, args)[source]¶
Copy input files and some db files to RAM disk.
- Parameters
job – job object.
work_dir – job working directory (permanent FS) (string).
args – args dictionary to collect timing metrics.
- Returns
job working directory in scratch (string).


Facebook Post
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I'm feeling great today!
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Section 11.3 Climate History
Subsection 11.3.1 Introduction
Have the climates always been the same? What mechanisms have caused the climate to change?
-
Two categories of past climate
Historical, past few thousand years
Paleoclimate, conditions before human civilization
-
Historical and natural records reflect climate; climate changes and causes can be determined from careful study
Dates of freezes of lakes and rivers, farmers’ logs, diaries, newspapers
Animals in cave paintings indicate climate and climate change
But historical records only go so far; we need to turn to proxy data to reconstruct past climates
Subsection 11.3.2 Reconstructing past climates
A number of different methods can be used to estimate the climates of the past, including:
Tree-ring data
Pollen records
Ice sheets and trapped air
Marine sediments
Glacial features and geologic evidence
Radiometric and stable isotope data
Dendrochronology (tree-ring data)
Diameter of tree trunk increases with growth
In regions with distinct growing seasons, tree grown appears as concentric rings
Trees generally produce one ring per year; width of ring indicates how fast growth was
Growth varies with temperature, precipitation, solar radiation
Pollen Records
-
Pollen is useful to paleoclimatologists
Degrades slowly
Distinctive shapes for each species
Can accumulate in sediments in lakes, providing record of past vegetation (and thereby, climate)
Radiocarbon dating of pollen grains extends information back tens of thousands of years
Isotopic Applications
radiocarbon dating of organic materials or ice core air bubbles
-
stable isotope measurements:
deuterium and O-18 content
colder : higher O-18 concentrations in seawater
O isotopes also trapped in fossil shells
radiometric dating of fossil-bearing rock layers
Stable Isotopes (D and O-18)
Materials have been deposited in layers on ocean floor for millions of years
-
Animal shells are made of CaCO\(_3\)
May contain O-18, which is sensitive to temperature
Colder: higher O-18 concentrations in seawater
Can sequester lighter O-16 in continental ice
This is the benthic oxygen isotope record
This method works back to 2–3 million years
Warm periods about every 100,000 years
Carbon-14
produced in upper atmosphere (cosmic rays) and then incorporated (via CO\(_2\)) into plants by photosynthesis
photosynthetic products are consumed by animals (and by animals eating animals) so C-14 goes through food chain
C-14 abundance is maintained as long as the organism is alive. There are at least a few thousand C-14 atoms decaying in your body every second.
C-14 is unstable and decays with a half-life of 5730 years (Beta decay of C-14 into N-14)
effective for dating 100-70,000 yrs
useful for organic material
Radiometric Age Dating
Provide a means to track life through the ages
Integral part of rocks in which they are found
Types of plants and animals give climate clues
Some plants have particular requirements; quantity of a fossil can indicate whether it was thriving in the climate
Subsection 11.3.3 Past Climate Observations
Putting these proxies together to reconstruct paleoclimate
-
Several major ice ages have occurred throughout Earth’s history:
Precambrian Eon (2.2 Ga)
late Proterozoic Eon (700 Ma; Rodinia snowball Earth?)
Paleozoic Era (500 Ma)
Permian Period (250 Ma)
-
We are currently in the Holecene epoch (0.01 Ma-present) of the Quaternary Period (2.6 Ma-present)
Earth had been cooling into an ice age throughout the Paleogene (65-23 Ma) and Neogene (23-2.6 Ma
intensification of the ice age occurred around 2.6 Ma (Quaternary)
-
recent continental glacial activity occurred during Plestocene epoch (1.6-0.1 Ma)
widespread geologic evidence of Pleistocene glaciation
most recently the Illinoian (200-130 ka) and Wisconsinian (110-10 ka) glaciation events
-
The Holecene epoch (10,000 years ago to the present) represents an interglacial period of the Quaternary ice age
this means that there are polar caps but no continental glaciation into the mid-latitudes
sea level rise of nearly 40 meters over this timescale
rise of agricultural and civilization in the Neolithic (stone age)
Little Ice Age
period of cooler temperatures, 1300-1800
Maunder Minimum: period of low solar activity, 1610-1680


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Hi there!
My name is Richy Teas and I am a graduate from University of California, Irvine with a degree in Computer Science and Engineering (CSE).
Looking to challenge and grow my skills by tackling interesting software problems and would like to keep my mind sharp with learning new technologies. Having completed a chunk of my game, I am currently looking to expand my software development skills by looking for new techniques/technologies to implement.


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Snippets is a collection of elements and combined components that you will find on demo pages.
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[gecode-users] Re: Var indexes-help
penche21 penche21
penche21 at hotmail.com
Wed Feb 7 12:49:41 CET 2007
Hi!
(Sorry for so much questions on var indexes..)
I have an IntVar, say I, to specify an index of a VarArray and
I want to post some constraints (like element, linear) for indexes between
0(zero) and I.
is there a way to express;
if (current_index < I ) then element(....) + linear(....)
And generally speaking, I cant see the way to tell the
if___then___ structure.
Thanks a lot,
penche
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Trump campaign rapid response director Steven Cheung tells ABC News that Carter Page “has no role” in the campaign. “We are not aware of any of his activities; past or present,” he said, using the same statement as that made the day before by Miller.
Source: USA Today


WITNESS LIST Financial Institutions Committee September 17, 1998-1:30P Home Equity Lending For: Gillespie, Robin (Fannie Mae) On: Norcross, Rob (Lenders Associations) Pettijohn, Leslie (OCCC) Schneider, Rob (Consumers Union) Interstate Branch Banking For: Rodgers, Kelly (Texas Assoc. of Bankers) Williston, Chris (Indep. Bankers Assoc.) On: Feeney, Harold (Credit Union Dept.) Ghiglieri, Catherine (Department of Banking) Pledger, Jim (Savings & Loan Dept.)


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/lean.20failing.20quietly.html ---
Lean has got worse and worse recently, at least for me. I am sorely tempted to just check out a version from a week or two ago and compile that and use it instead. I have a largeish file open (350 lines) and after Lean: Restart in VS Code I get the orange "compiling" lines and then they just disappear, there are no red failure lines, nothing in lean messages, and lean isn't running any more in the sense that none of my #check
s are underlined in green.
I am getting those memory overflow messages on a regular basis and having to restart
I appreciate it's super-complex software but it didn't used to be like this
I am actually missing the overflow messages now I am having to deal with silent failures. I removed all olean files and hopefully things will be a bit better (I thought we did not have to do this any more but it seems to have made some difference).
Is there any way I can give user feedback about all the overflow errors? They strike all the time.
excessive memory consumption detected at 'replace' (potential solution: increase memory consumption threshold)
that one
Have you increased your memory limit, or are you just running with the default? I think most people are running with more than the default.
Note that it’s not just the file you have open that affects memory requirements: imports matter too, although I don’t have any sense of the relative memory requirements of stuff loaded from olean files vs processed in this session.
Probably the default memory limit in the VS Code and emacs extensions should just be raised, so newcomers don’t get tripped up by this.
On the other hand, Lean silently failing is bad news. If you’ve got something that reproducibly causes VS Code’s Lean extension to silently fail, you should 1. Check if lean —make
also fails (it may tell you something more informative) 2. Post it here!
Last updated: Apr 24 2020 at 19:02 UTC


{% extends 'nova/base.html' %} {% load i18n %} {% block page_header %} {# to make searchable false, just remove it from the include statement #} {% include "horizon/common/_page_header.html" with title=_("Create Keypair") %} {% endblock page_header %} {% block dash_main %}
{% trans "Keypairs are ssh credentials which are injected into images when they are launched. Creating a new key pair registers the public key and downloads the private key (a .pem file)."%}
{% trans "Protect and use the key as you would any normal ssh private key."%}