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Search results “Oracle push predicates”
Part 2 Predicate Pushdown
 
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Oracle Big Data SQL - Learn about partition pruning, storage indexes and predicate push down. ================================= For more information, see http://www.oracle.com/goto/oll Copyright © 2017 Oracle and/or its affiliates. Oracle is a registered trademark of Oracle and/or its affiliates. All rights reserved. Other names may be registered trademarks of their respective owners. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the “Materials”). The Materials are provided “as is” without any warranty of any kind, either express or implied, including without limitation warranties or merchantability, fitness for a particular purpose, and non-infringement.
Query Push Down to MySQL Database
 
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In this video lecture we will see how to push down the query to databases like mysql, oracle or teradata and get the results back from the databases. In this approach execution will be done at JDBC source which can be MySQL or Oracle or Teradata. Books I Follow: Apache Spark Books: Learning Spark: https://amzn.to/2pCcn8W High Performance Spark: https://amzn.to/2Goy9ac Advanced Analytics with Spark: https://amzn.to/2pD57Ke Apache Spark 2.0 Cookbook: https://amzn.to/2pEbAUp Mastering Apache Spark 2.0: https://amzn.to/2udDEUg Scala Programming: Programming in Scala: https://amzn.to/2uiTGfl Hadoop Books: Hadoop: The Definitive Guide: https://amzn.to/2pDheH4 Hive: Programming Hive: https://amzn.to/2Gqwz7o HBase: HBase The Definitive Guide: https://amzn.to/2Gj9rI2 Python Books: Learning Python: https://amzn.to/2pDqo6m
Views: 707 Talent Origin
오라클힌트&SQL튜닝강좌 #19:옵티마이저 Query Transformation Predicate Pushing Predicate Pushdown
 
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오라클 힌트 강좌 #19 : 옵티마이저 Query Transformation Predicate Pushing Predicate Pushdown
Views: 94 이종철
Java 2 (1) ||  Introduction To JFrame
 
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Views: 2389 Smart Team
오라클힌트/튜닝 #23 Query Transformation Query Rewrite with Materialized Views 구체화뷰, REWRITE, NOREWRITE 힌트
 
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오라클힌트/튜닝 #23 Query Transformation Query Rewrite with Materialized Views 구체화뷰, REWRITE, NOREWRITE 힌트
Views: 50 이종철
오라클힌트/튜닝 #25 : Sub Query Factoring WITH 구문, _WITH_SUBQUERY 파라미터 Materialize, Inline 힌트구문
 
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오라클힌트/튜닝 #25 : Sub Query Factoring WITH 구문, _WITH_SUBQUERY 파라미터 Materialize, Inline 힌트구문
Views: 128 이종철
How to understand and use the query optimizer – Couchbase Connect 2016
 
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Every flight has a flight plan. Every query has a query plan. You must have seen its text form, called EXPLAIN PLAN. Query optimizer is responsible for creating this query plan for every query, and it tries to create an optimal plan for every query. In Couchbase, the query optimizer has to choose the most optimal index for the query, decide on the predicates to push down to index scans, create appropriate spans (scan ranges) for each index, understand the sort (ORDER BY) and pagination (OFFSET, LIMIT) requirements, and create the plan accordingly. When you think there is a better plan, you can hint the optimizer with USE INDEX. This talk will teach you how the optimizer selects the indices, index scan methods, and joins. It will teach you the analysis of the optimizer behavior using EXPLAIN plan and how to change the choices optimizer makes. Speaker: Keshav Murthy, Director, Query Development, Couchbase Slideshare: http://www.slideshare.net/Couchbase/how-to-understand-and-use-the-query-optimizer Visit our website for more information: https://www.couchbase.com/
Views: 502 Couchbase
Java 8 || Demo Program to Increment Employee Salary by using BiFunction & BiConsumer
 
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Oracle Java Certification: Shortest Way To Crack OCA 1Z0-808 Just @ Rs 640/- -------------------------------------------------------------------------------------------------------- 1. For Java SE 8 Programmer I Exam 2. Topic wise Tests and Grand Tests 3. 280 Realistic Questions With Clear Explanation 4. Study Material (408 Pages) 5. Question Bank (107 Pages) 6. Videos (63 Sessions) Use the below links to access Durga Sir Videos and Downloadable Materials and Topic wise Tests and Grand Tests with Life-Time Access. 1. Oracle Java Certification: Shortest Way To Crack OCA 1Z0-808 Link: https://goo.gl/vcMKjz 2. Java 8 New Features In Simple Way Link: https://goo.gl/F2NfZi 3. Java 9 New Features In Simple Way: JShell, JPMS and More Link: https://goo.gl/s9PP1p 4. Complete JDBC Programming Part-1 Link: https://goo.gl/uT9sav 5. Complete JDBC Programming Part-2 Link: https://goo.gl/VmhM7t
Turning Relational Database Tables into Spark Datasources
 
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https://developer.oracle.com/code/online | Kuassi Mensah: This session presents a Spark data source for joining big data with master data in an RDBMS. It describes how such an implementation: Allows parallel and direct access to the RDBMS database (with the option of controlling the number of concurrent connections). Introspects the RDBMS table, generates partitions of Spark JDBCRDDs based on the split pattern, and rewrites Spark SQL queries into the RDBMS SQL dialect. Uses hooks in the JDBC driver for faster type conversions. Pushes down predicates to the RDBMS, prunes partitions based on the where clause, and projects columns to the RDBMS to reduce the amount of data returned and processed on Spark. Writes back the result set to the RDBMS table for use by traditional BI tools
Views: 834 Oracle Developers
Lecture - 14 Query Processing and Optimization
 
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Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 109370 nptelhrd
IDUG Tech Talk: How We Code SQL - Does It Matter?
 
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Presented by Jacek Rafalak, ASSECO POLAND SA This presentation would be based on application developer experiences with coding efficient SQL. Introduction to basic concepts on how data can be accessed, what is Indexable , stage1, stage2, boolean term predicates. There will be examples of re-writing queries according to few rules of thumb so it execute more efficiently, What SQL to use, what trying to avoid, how you can influence access path with adding extra predicates, with special 'tricks', etc. This joined presentation with IBM, will also mention how DB2 can do query rewrite for you. With focus on V10 and V11 query re-write possibility: V10: How DB2 modifies IN predicates, simplification of join operations, removal of pre-evaluated predicates, predicates that DB2 generates and predicates generated through transitive closure V11 - How DB2 11 simplify YEAR(col), DATE(col), and SUBSTR(col,1,len) Simplify stage 2 BETWEEN predicate Improve indexability of CASE expression predicate Simplify always true or always false predicate Enhance correlated to non-correlated subquery transformation Predicate push-down enhancement Jacek Rafalak has 16 years' experience working with DB2 on the mainframe platform, database administration and software support. Since 2001 he has worked for DB2 for z/OS Center of Competency and Operation of Systems for Social Security (KSI) in Standard Environments Maintenance Team in Warsaw, Poland at Asseco Poland SA. He is responsibile for DB2 for z/OS Utilities and applications performance. IBM Certified Database Administrator DB2 z/OS. IBM Champion for Information Management. Poland DB2 Users Group leader.
scale.bythebay.io: Kuassi Mensah, Turning a Relational RDBMS Table into a Spark Datasource
 
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This session presents a Spark DataSource implementation for integrating (joining) Big Data in HDFS or NoSQL DBMS with Master Data in RDBMS table. The session describes how to allow parallel and direct access to RDBMS tables from Spark, generate partitions of Spark JDBCRDDs based on the split pattern and rewrites Spark SQL queries into the RDBMS SQL dialect. The session also describes the performance optimizations including hooks in the JDBC driver for faster type conversions, pushing down predicates to the RDBMS, pruning partition based on the where clause, and projecting columns to the RDBMS table to reduce the amount of data returned and processed on Spark. Kuassi Mensah Oracle Corporation Director, Product Management Kuassi Mensah is Director of Product Management at Oracle; his scope includes:(i) Java performance, scalability, HA, and Security with Oracle database.(ii) Hadoop and Spark integration with the Oracle database (iii) Java & JavaScript integration with the Oracle database (OJVM, Nashorn) Mr Mensah holds a MS in CS from the Programming Institute of University of Paris.He is is a frequent speaker at IT events (Oracle Open World, JavaOne, Sangam, OTNYathra, UKOUG, DOAG, OUGN, BGOUG, etc) & author. @kmensah, http://db360.blogspot.com/, https://www.linkedin.com/in/kmensah
Views: 163 FunctionalTV
Extending Apache Spark SQL Data Source APIs with Join Push Down - Ioana Delaney &  Jia Li
 
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"When Spark applications operate on distributed data coming from disparate data sources, they often have to directly query data sources external to Spark such as backing relational databases, or data warehouses. For that, Spark provides Data Source APIs, which are a pluggable mechanism for accessing structured data through Spark SQL. Data Source APIs are tightly integrated with the Spark Optimizer. They provide optimizations such as filter push down to the external data source and column pruning. While these optimizations significantly speed up Spark query execution, depending on the data source, they only provide a subset of the functionality that can be pushed down and executed at the data source. As part of our ongoing project to provide a generic data source push down API, this presentation will show our work related to join push down. An example is star-schema join, which can be simply viewed as filters applied to the fact table. Today, Spark Optimizer recognizes star-schema joins based on heuristics and executes star-joins using efficient left-deep trees. An alternative execution proposed by this work is to push down the star-join to the external data source in order to take advantage of multi-column indexes defined on the fact tables, and other star-join optimization techniques implemented by the relational data source. Session hashtag: #EUdev7"
Views: 1066 Databricks
[KOR] Oracle Deep Internal 4-1강
 
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ODI(Oracle Deep Internal) 4-1강 비효율적인 블록 클린 아웃으로 인한 오라클 성능 저하 현상에 대해 알아보겠습니다.
Views: 405 엑셈TV
Oracle OTG Highlights - demo
 
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This video was automatically created based on vinja project 8v1bVW93
Views: 12 Pavel Myuller
오라클힌트&SQL튜닝교육#21  옵티마이저 Query Transformation  OR-Expansion  USE_CONCAT 힌트 NO_EXPAND 힌트
 
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오라클 힌트 강좌#21 옵티마이저 Query Transformation OR-Expansion USE_CONCAT 힌트 NO_EXPAND 힌트
Views: 103 이종철
오라클힌트&SQL튜닝동영상강의:옵티마이저쿼리변환,JPPD PUSH_PRED, NO_PUSH_PRED, NO_QUERY_TRANSFORMATION
 
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오라클 힌트 강좌#20 : 옵티마이저 Query Transformation Predicate Pushing Join Predicate Pushdown(JPPD) PUSH_PRED, NO_PUSH_PRED 힌트 NO_QUERY_TRANSFORMATION 힌트 Join Predicate Pushdown은 뷰가 인덱스 기반의 중첩루프 조인에서 사용되어 다른 테이블이나 쿼리블럭과 조인되는 경우 조인조건을 뷰안으로 밀어 넣는 것을 이야기 한다. 조인을 수행중 드라이빙 테이블에서 읽은 값을 뷰안에 조건으로 밀어 넣는 것으로 조인 조건 컬럼을 뷰안으로 병합하는 것이다. Join Predicate Pushdown은 대체로 성능을 개선시키지만 일부 복잡한 쿼리문에서는 그렇지 못하는 경우가 있어 힌트 또는 오라클 내부 히든 파라미터인 “_push_join_predicate” 값을 제어함으로서 조절이 가능하다. alter session set "_PUSH_JOIN_PREDICATE“ = FALSE; push_pred, no_push_pred 힌트로 join predicate pushdown 사용여부를 제어 Join Predicate Pushdown은 뷰 외부의 조인조건에 의해 많은 데이터가 추출되는 경우 좋지않은 성능을 유발시키므로 해시조인, 소트-머지조인이 효울적일 수도 있다. UNION ALL/UNION뷰, Outer-joined뷰, Anti-joined뷰, Semi-joined뷰, DISTINCT뷰, GROUP-BY뷰 등에서 사용가능 하다.
Views: 74 이종철
오라클힌트동영상 : 오라클 옵티마이저 쿼리변환, 뷰머징,View Merging  - Optimizer Query Transformation Hint(merge, no_merge)
 
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오라클 힌트강좌 #17 : 오라클 옵티마이저 쿼리변환, 뷰머징 Simple, Complex View Merging - Optimizer Query Transformation - Query Transformation Hint(merge, no_merge) 오라클 옵티마이저는 인라인뷰, 서브쿼리등 복잡한 쿼리문에 대해 한번에 실행계획을 세우지 않고 각각의 쿼리 블록 단위로 실행계획을 세운 후 이를 하나로 합치는 과정을 진행하는데 이 과정에서 각각의 쿼리 블록을 따로따로 실행하는 계획보다는 하나로 묶어서 변경된 실행계획을 수립하기를 선호한다. 이것이 Query Transformation이며 이 경우 다양한 접근경로(Index 또는 Full Table Scan), 조인순서(어떤 테이블을 먼저 드라이빙을 할 것인지), 조인방법(중첩루프, 해시, 머지조인중 어떤 방법으로?)을 선택할 수 있으므로 효율적인 실행계획을 수립할 가능성이 높다. 하지만 늘 최선이지는 않다. 쿼리문을 블럭화 하기 위해 서브쿼리, 인라인 뷰등을 자주 사용하는데 오라클 옵티마이저가 인라인뷰, 서브쿼리를 해석할 때 독자적으로 실행하지 않고 메인쿼리와 함께 실행되는 경우, 즉 쿼리블럭을 풀어서 기존 쿼리와 함께 최적화를 수행하는 것을 뷰 머징(View Merging)이라고 한다. 인라인뷰나 서브쿼리등이 많아 지면 옵티마이저가 뷰 머징을 해서 쿼리 성능이 안좋아질 수가 있다. 서브쿼리, 인라인 뷰등에서 ROWNUM을 사용하면 뷰 머징을 방지하는 효과가 있다.
Views: 191 이종철
What Makes the Oracle Exadata DB Machine Special
 
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What Makes the DB Machine Special A Database Machine consists of two sets of Linux* machines. There's one set of Linux machines that we call compute nodes, and these have masses of CPU, masses of RAM, and they run your database instances. There's a second set of Linux machines that have a vast amount of disk space. These are your storage tier, and it's the Exadata software that links the compute nodes to the cell nodes, the cell nodes of the storage machines. Now, you cannot install the Exadata software independently. It comes pre-installed on your DB machine. I think, with earlier releases, it was possible to license Exadata software independently and install it on your own box, but certainly with the current release, you cannot get it unless you buy a DB machine. Well, what does Exadata deliver? It delivers certain capabilities that you cannot get in any other environments, and these are the abilities that we'll be studying in the next few slides. The Smart Scan, that's the one that gets all the publicity. It's the ability to offload a large amount of the SQL processing from the database tier from the compute nodes to the storage tier to the cell nodes. You have a storage tier that is aware of the database and can take over some of the workload of executing your SQL.
Views: 3823 SkillBuilders
Developing Monitoring Plans for Investigator-Initiated Clinical Trials - November 14, 2017
 
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Sandra SAM Sathers, MS, BSN, CCRA, CCRC presented a ReGARDD educational seminar entitled "Developing Monitoring Plans for Investigator-Initiated Clinical Trials" on November 14, 2017 at the Duke University School of Medicine. This event was organized by the Office of Regulatory Affairs and Quality in collaboration with ReGARDD (ReGARDD.org).
Java 8 || Creation of Student object by taking name and rollno as input with BiFunction
 
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Oracle Java Certification: Shortest Way To Crack OCA 1Z0-808 Just @ Rs 640/- -------------------------------------------------------------------------------------------------------- 1. For Java SE 8 Programmer I Exam 2. Topic wise Tests and Grand Tests 3. 280 Realistic Questions With Clear Explanation 4. Study Material (408 Pages) 5. Question Bank (107 Pages) 6. Videos (63 Sessions) Use the below links to access Durga Sir Videos and Downloadable Materials and Topic wise Tests and Grand Tests with Life-Time Access. 1. Oracle Java Certification: Shortest Way To Crack OCA 1Z0-808 Link: https://goo.gl/vcMKjz 2. Java 8 New Features In Simple Way Link: https://goo.gl/F2NfZi 3. Java 9 New Features In Simple Way: JShell, JPMS and More Link: https://goo.gl/s9PP1p 4. Complete JDBC Programming Part-1 Link: https://goo.gl/uT9sav 5. Complete JDBC Programming Part-2 Link: https://goo.gl/VmhM7t
The roadmap for SQL Server - BRK2416
 
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SQL Server 2017 has brought to market a new modern data platform including support for Linux, Docker Containers and rich features in intelligent performance, HADR, machine learning, and graph database. Come learn about the roadmap and new functionality planned for SQL Server including intelligent query processing, data virtualization, new features for mission critical security and HADR, and new scenarios for Linux and Docker Containers.
Views: 775 Microsoft Ignite
RailsConf 2017: How to Write Better Code Using Mutation Testing by John Backus
 
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RailsConf 2017: How to Write Better Code Using Mutation Testing by John Backus Mutation testing is a silver bullet for assessing test quality. Mutation testing will help you: Write better tests Produce more robust code that better handles edge cases Reveal what parts of your legacy application are most likely to break before you dive in to make new changes Learn about features in Ruby and your dependencies that you didn’t previously know about This talk assumes a basic knowledge of Ruby and testing. The examples in this talk will almost certainly teach you something new about Ruby!
Views: 2657 Confreaks
Deep dive on SQL Server and big data - BRK4021
 
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Many customers have investments in data lakes with big data storage and infrastructure. Come explore a deep dive behind the technology for big data integration with SQL Server including Polybase futures and scalable performance.
Views: 2571 Microsoft Ignite
Migration experience from an on-premises enterprise data warehouse to Azure - BRK3327
 
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In this session, we take you through the challenge, lesson learned, and best practices from migrating an on-premises enterprise data warehouse workload to the Azure services.
Views: 223 Microsoft Ignite
"55 New Features in Java SE 8" by Simon Ritter
 
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IRC Log - https://docs.google.com/document/d/1wWDYquiVyl1NQX-n5sQA0PD5NKxJGc3ugRtaPNI2lHQ/edit Slides - http://www.slideshare.net/SimonRitter/javase8-55thingsv2-sritter Abstract Java SE 8 is the next release of the core Java platform and contains lots of exciting new features. In addition to the big features like Lambda expressions, extension methods for interfaces and a new Date and Time API there are plenty of smaller features as well. This session will rapidly cover fifty-five new features that are scheduled for inclusion in Java SE 8 when it is released in March. Speaker Simon Ritter is Manager of the Java Technology Evangelist team at Oracle Corporation. Simon has been in the IT business since 1984 and holds a Bachelor of Science degree in Physics from Brunel University in the U.K. Originally working in the area of UNIX development for AT&T UNIX System Labs and then Novell, Simon moved to Sun in 1996. At this time he started working with Java technology and has spent time working both in Java development and consultancy. Having moved to Oracle as part of the Sun acquisition he now focuses on the core Java platform, Java for client applications and embedded Java. He also continues to develop demonstrations that push the boundaries of Java for applications like gestural interfaces, embedded robot controllers and in-car systems. Follow him on Twitter,@speakjava, and his blog at blogs.oracle.com/speakjava.
Views: v JUG
Yelawolf - Punk ft. Travis Barker, Juicy J
 
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Yelawolf “PUNK” feat. Juicy J & Travis Barker is Out Now! http://smarturl.it/PunkYelawolf Follow Yelawolf: http://www.yelawolf.com https://www.instagram.com/yelawolf https://www.facebook.com/yelawolf Music video by Yelawolf performing Punk. (C) 2017 Interscope Records http://vevo.ly/0FA6l9
Views: 2402000 YelawolfVEVO
Project Lambda: Functional Prog. Constructs and Simpler Concurrency in Java SE 8
 
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Abstract The big language features for Java SE 8 are lambda expressions (closures) and default methods (formerly called defender methods or virtual extension methods). Adding lambda expressions to the language opens up a host of new expressive opportunities for applications and libraries. You might assume that lambda expressions are simply a more syntactically compact form of inner classes, but, in fact, the implementation of lambda expressions is substantially different and builds on the invokedynamic feature added in Java SE 7. This session will explain the ideas behind lambda expressions, how they will be used in Java SE 8 and look at some of the details of their implementation. Speaker Simon Ritter is Manager of the Java Technology Evangelist team at Oracle Corporation. Simon has been in the IT business since 1984 and holds a Bachelor of Science degree in Physics from Brunel University in the U.K. Originally working in the area of UNIX development for AT&T UNIX System Labs and then Novell, Simon moved to Sun in 1996. At this time he started working with Java technology and has spent time working both in Java development and consultancy. Having moved to Oracle as part of the Sun acquisition he now focuses on the core Java platform, Java for client applications and embedded Java. He also continues to develop demonstrations that push the boundaries of Java for applications like gestural interfaces, embedded robot controllers and in-car systems. Follow him on Twitter,@speakjava, and his blog atblogs.oracle.com/speakjava.
Views: v JUG
Jean-Pierre Dijcks, Oracle - On the Ground - #theCUBE
 
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Jean-Pierre Dijcks, Master Product Manager at Oracle, sits down with host Peter Burris at Oracle’s Redwood Shores Headquarters for a special On the Ground segment. @theCUBE
05 CloudKit - Query - Expenses App - Private iCloud Database
 
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This is the fifth video in a series about using CloudKit and iCloud to persist data for an Expenses app using a single entity type of Expense in a private iCloud database. In the video Expenses are queried from the database and displayed.
Views: 316 Tech Innovator
Running open-source Databases on Google Cloud Platform (Google Cloud Next '17)
 
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Learn about the various options for running open-source databases on GCP, both self-managed and fully-managed. We will also do a deep dive with Quizlet about how to run MySQL effectively and efficiently on GCP. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Application Development here: https://goo.gl/YFgZpl
28 JAVA INHERITANCE OVERRIDING WITH EXAMPLE (IN HINDI)
 
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Buy JAVA books (affiliate): Java - The Complete Reference https://amzn.to/2oB4jVg Programming with Java https://amzn.to/2wBFInL Head First Java: A Brain-Friendly Guide https://amzn.to/2wDNWvo Core Java: An Integrated Approach https://amzn.to/2PUjAwV Java - A Beginner’s Guide https://amzn.to/2PXwx93 Core Java - Fundamentals https://amzn.to/2MFOaMJ Java 8 in Action https://amzn.to/2N8KYbQ Programmer's Guide to Java SE 8 Oracle Certified Associate (OCA) https://amzn.to/2PXGYcG ------------------------------------- JAVA INHERITANCE OVERRIDING WITH EXAMPLE (IN HINDI)
Views: 1887 LearnEveryone
(오라클인덱스에서 데이터 스캐닝을 위한 힌트)order by의 잘못된 사용으로 인한 테이블  FULL SCAN은 최악의 상황을 만들죠^^ Index Access Path 관련 힌트
 
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DB에서 인덱스를 잘다루시면 성능향상에 짱이죠^^ order by등의 잘못된 사용으로 인한 테이블 FULL SCAN은 최악의 상황을 만들죠^^ Index Access Path 관련 힌트,인덱스에서 데이터 스캐닝을 위한 힌트, INDEX SKIP SCAN INDEX_SS, NO_INDEX_SS INDEX_SS_ASC, INDEX_SS_DESC INDEX_RS, INDEX_FFS
Views: 121 이종철
Versioning and Migrating with Core Data - Intermediate Core Data Tutorial - raywenderlich.com
 
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Relationships between data is critical to be successful in Core Data. In this video, you'll learn how to create them in Xcode. Watch the full series over here: https://videos.raywenderlich.com/courses/intermediate-core-data/lessons/1 ---- About www.raywenderlich.com: raywenderlich.com is a website focused on developing high quality programming tutorials. Our goal is to take the coolest and most challenging topics and make them easy for everyone to learn – so we can all make amazing apps. We are also focused on developing a strong community. Our goal is to help each other reach our dreams through friendship and cooperation. As you can see below, a bunch of us have joined forces to make this happen: authors, editors, subject matter experts, app reviewers, and most importantly our amazing readers! ---- About Core Data (from Wikipedia) Core Data is an object graph and persistence framework provided by Apple in the macOS and iOS operating systems. It was introduced in Mac OS X 10.4 Tiger and iOS with iPhone SDK 3.0. It allows data organised by the relational entity–attribute model to be serialized into XML, binary, or SQLite stores. The data can be manipulated using higher level objects representing entities and their relationships. Core Data manages the serialised version, providing object lifecycle and object graph management, including persistence. Core Data interfaces directly with SQLite, insulating the developer from the underlying SQL. Just as Cocoa Bindings handle many of the duties of the controller in a model–view–controller design, Core Data handles many of the duties of the data model. Among other tasks, it handles change management, serializing to disk, memory footprint minimization and queries against the data. Core Data owes much of its design to an early NeXT product, Enterprise Objects Framework (EOF). EOF was specifically aimed at object-relational mapping for high-end SQL database engines such as Microsoft SQL Server and Oracle. EOF's purpose was twofold: first, to connect to the database engine and hide the implementation details; second, to read the data out of the simple relational format and translate that into a set of objects. Developers typically interacted with the objects only, which dramatically simplifies development of complex programs, at the cost of some "setup". The EOF object model was deliberately designed to make the resulting programs "document like", in that the user could edit the data locally in memory, and then write out all changes with a single Save command. Throughout its history, EOF "contained" a number of bits of extremely useful code that were not otherwise available under NeXTSTEP/OpenStep. For instance, EOF required the ability to track which objects were "dirty" so the system could later write them out. This was presented to the developer not only as a document-like system, but also in the form of an unlimited "Undo" command stack. Many developers complained that this state management code was far too useful to be isolated in EOF, and it was later moved into the Cocoa API during the transition to Mac OS X. Oddly, what was not translated was EOF itself. EOF was used primarily along with another OpenStep-era product, WebObjects, which was an application server originally based on Objective-C. At the time, Apple was in the process of porting WebObjects to the Java programming language, and as part of this conversion, EOF became much more difficult to use from Cocoa. Enough developers complained about this that Apple apparently decided to do something about it. One critical realization is that the object state management system in EOF did not really have anything to do with relational databases. The same code could be, and was, used by developers to manage graphs of other objects as well. In this role, the really useful parts of EOF were those that automatically built the object sets from the raw data, and then tracked them. It is this concept, and perhaps code, that forms the basis of Core Data. About Swift (from Wikipedia) Swift is a general-purpose, multi-paradigm, compiled programming language developed by Apple Inc. for iOS, macOS, watchOS, tvOS, and Linux. Swift is designed to work with Apple's Cocoa and Cocoa Touch frameworks and the large body of extant Objective-C (ObjC) code written for Apple products. Swift is intended to be more resilient to erroneous code ("safer") than Objective-C, and more concise. It is built with the LLVM compiler framework included in Xcode 6 and later and, on platforms other than Linux, uses the Objective-C runtime library, which allows C, Objective-C, C++ and Swift code to run within one program. Swift supports the core concepts that made Objective-C flexible, notably dynamic dispatch, widespread late binding, extensible programming and similar features. These features also have well-known performance and safety trade-offs, which Swift was designed to address.
Views: 4547 raywenderlich.com
Cloud-Based Automated Software Reliability Services
 
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Google Tech Talk July 22, 2010 ABSTRACT Presented by Professor George Candea http://people.epfl.ch/george.candea This talk proposes cloud-based automated software reliability services (SRS), a step toward making testing and debugging of code as easy as using webmail. SRS is automatic, without human involvement from the service user's or provider's side; this is unlike today's "testing as a service" businesses, which employ humans to write tests. First, I will outline four of the SRS components we envision: a "home edition" on-demand testing service for consumers to verify the software they are about to install on their PC or mobile device; a "programmer's sidekick" enabling developers to thoroughly and promptly test their code with minimal upfront resource investment; a public "certification service," akin to Underwriters Labs, that independently assesses the reliability, safety, and security of software; and an "automated debugging" service that helps developers fix code based on bug reports from the field. Then I will present in detail execution synthesis, the technique that makes automated debugging (the latter SRS component) a reality. Given a program and a bug report, execution synthesis combines static analysis and symbolic execution to "synthesize" a thread schedule and various required program inputs that cause the reported bug to manifest. The synthesized execution can then be played back deterministically in a regular debugger, like gdb. We have found this determinism to be particularly useful in debugging concurrency bugs. Our technique requires no runtime tracing or program modifications, thus incurring no runtime overhead and being practical for use in production systems. We evaluate it on popular software (e.g., the SQLite database, ghttpd Web server, HawkNL network library, UNIX utilities) and find that, starting from mere bug reports, it can reproduce on its own several real concurrency and memory safety bugs in less than three minutes.
Views: 7389 GoogleTechTalks
Refactoring a 1000 Lines of Code Method into Clean(er) Code (in Serbian)
 
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Long functions in even longer classes can often be found in mature code bases. Even though every programmer knows it's wrong to keep such a beast in production, every one of us has been feeding one of those for at least some time. In this lecture, we will show the process of building large functions from scratch. We will then turn attention to one such function, which has about 1000 lines of code. You will see why we need to break such monstrous functions into smaller chunks and then we will embark on a voyage to refactor and redesign it into smaller chunks of code. If you have passion for Sudoku, then the example we present will surely amuse you. The program we will be dealing with is setting up Sudoku problems and then it solves each problem, verbalizing all decisions and explaining the solution in common English sentences. But, the way in which this interesting program does its task is, at the same time, the greatest impediment to its further development. That is the point at which this lecture begins. Before watching this recording, you may wish to try fixing the same code on your own. Please download the initial solution from GitHub repository: https://github.com/zoran-horvat/sudoku-kata
Views: 2535 Zoran Horvat
What's new in Azure SQL Database - your operational database in the cloud - BRK3166
 
01:10:13
Azure SQL Database, Microsoft's fully managed, database-as-a-service offering solves the demands of today’s data estate involving omnipresence, heterogeneity and data explosion. Built on the world’s top relational database management system, SQL Server, the platform offers multiple deployment options to our customers to meet their data needs – singleton, elastic pools, and managed instances. Hear directly from the Azure SQL Database engineering team on some of the latest and upcoming capabilities involving advanced intelligence, enterprise-grade performance, high-availability, and industry-leading security in the fully managed Azure SQL Database service that will enable you to focus on your business.
Views: 98 Microsoft Ignite
Neo4j Online Meetup #37 :GQL: It’s Time for a Single Property Graph Query Language
 
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The time has come to create a single, unified property graph query language. Different languages for different products help no one. We’ve heard from the graph community that a common query language would be powerful: more developers with transferable expertise; portable queries; solutions that leverage multiple graph options; and less vendor lock-in. One language, one skill set. In this session Amy Hodler and Alastair Green will explain the GQL proposal and run a Q&A session. https://gql.today/#vote
Views: 660 Neo4j
#156: Enterprise Decision Making with Anthony Scriffignano, Chief Data Scientist, Dun & Bradstreet
 
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#156: Enterprise Decision Making with Anthony Scriffignano, Chief Data Scientist, Dun & Bradstreet How can we use data to make better business decisions? In this episode, we address this question with Anthony Scriffignano, the Chief Data Scientist at Dun & Bradstreet. Anthony Scriffignano has over 35 years experience in information technologies, Big-4 management consulting, and international business. Sciffignano leverages deep data expertise and global relationships to position Dun & Bradstreet with strategic customers, partners, and governments. A key thought leader in D&B’s worldwide efforts to discover, curate, and synthesize business information in multiple languages, geographies, and contexts, he has also held leadership positions in D&B’s Technology and Operations organizations. Dr. Scriffignano has extensive background in linguistics and advanced computer algorithms, leveraging that background as primary inventor on multiple patents and patents pending for D&B. Scriffignano regularly presents at various business and academic venues in the U.S., Europe, Latin America, and Asia as a keynote speaker, guest instructor, and forum panelist. Topics have included emerging trends in data and information stewardship relating to the “Big Data” explosion of data available to organizations, multilingual challenges in business identity, and strategies for change leadership in organizational settings. Scriffignano also confers with key customers on emerging trends in global data science. He was profiled by InformationWeek in a special coverage series “Big Data. Big Decisions” and by BizCloud regarding big data problem formulation and data privacy. He was also published in the May, 2014 edition of CIO review (“The Future Belongs to the Informed”). Scriffignano has also held senior positions with other multinational organizations. This experience includes extremely large ERP implementations and worldwide organizational change and technology adaptation efforts. He has advised firms in financial services, manufacturing (chemicals and pharmaceuticals) and information technologies. He maintains CPIM certification from APICS, the internationally-recognized Association for Operations Management, in production and inventory management. For more information, see https://www.cxotalk.com/enterprise-decision-making-anthony-scriffignano-chief-data-scientist-dun-bradstreet ------------------ Check out all the CXOTALK episodes: https://cxotalk.com/episodes ------------------ Follow us on Twitter: https://twitter.com/cxotalk ------------------
Views: 2363 CXOTALK
Michael Stonebraker, 2014 ACM Turing Award Recipient
 
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Discusses his life, research, and business experience in the relational database management field. Topics cover his early life and education, development of INGRES and founding of the Ingres Corporation and later he extended the use of the relational structure into areas other than business data. For more information: http://amturing.acm.org/award_winners/stonebraker_1172121.cfm
Rebuilding the Getty Provenance Index as Linked Data
 
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Rebuilding the Getty Provenance Index as Linked Data Joshua Gomez Getty Research Institute Emily Pugh Getty Research Institute For more information see https://wp.me/p1LncT-6kw CNI Spring 2016 Membership Meeting April 4-5, 2016 San Antonio, TX https://www.cni.org/
Caller Disagrees On Voter ID
 
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Thom talks with a caller who says a state id should be required of every voter. If you liked this clip of The Thom Hartmann Program, please do us a big favor and share it with your friends... and hit that "like" button! http://www.thomhartmann.com Follow Us on Twitter: http://www.twitter.com/thom_hartmann Subscribe to The Thom Hartmann Program for more: http://www.youtube.com/subscription_center?add_user=thomhartmann
Lecture -2 Conceptual Designs
 
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Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 98240 nptelhrd
Jean Grey Chapter 6: Bad Tuesday: Part 1: Naughty Robin
 
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Part 1 of Chapter 6 of Jean Grey. The Cast: Robin as Michael Jean Grey as Mary Poppins Supergirl as Ellen Batgirl as Mrs. Brill Arella as Mrs. Banks Flash as Robinson Eye Krypto as Andrew Emma Frost as Miss Lark Raven as Jane Little Raven as Barbara Disclaimer: The pictures belong to TitansGo.net, Worlds Finest Online, and Marvel Toonzone.net. The audio belongs to Listening Library, and the story belongs to PL Travers. I don't own this or profit from this, so please don't take it down.
Views: 1092 DogoHalibar
Cypher Everywhere: Neo4j, Hadoop/Spark and the Unexpected — A. Green, M. Rydberg, D. Solovyov, Neo4j
 
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Cypher started in Neo4j. It's now used by SAP HANA Graph, Redis Graph and Agens Graph over PostgreSQL, among others. The Neo4j Graph Platform will include Cypher for Apache Spark, with Hadoop and other integrations, allowing the data lake to be projected into graphs. Speakers: Alastair Green, Mats Rydberg, Dimitry Solovyov Location: GraphConnect NYC 2017
Views: 428 Neo4j
DB2 for z/OS Best Practice: Application Best Practices Specific to DB2 for z/OS Data Sharing
 
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Running applications in a data sharing environment generally follows the same best practices as running any DB2 for z/OS application. Applications have a role to play in continuous availability. Only relatively minor changes are required to avoid locking hot spots. This presentation explores specific best practices of application design and database design in a data sharing environment. It drills into the topics of locking considerations, high-volume inserts, sequential keys, bind options, and batch processing. It also describes how to deploy applications in a data sharing environment with minimal down time.
Views: 1071 World of Db2

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