Dibuka pendaftaran Public Training SQL Implementation & Embedded Programming- in IBM i ( 23-27 Mei 2016 ). INFO TRAINING : +6281381088767/hanypaulina7@gmail.com
A Billion Goods in a Few Categories: When Optimizer Histograms Help and When ...Sveta Smirnova
Last year this session’s speaker worked on several cases where data followed the same pattern: millions of popular products fit into a couple of categories, and the rest uses the rest. Her team had a hard time finding a solution for retrieving goods quickly. MySQL 8.0 has a feature that resolves such issues: optimizer histograms, storing statistics of an exact number of values in each data bucket. In real life, histograms don’t help with all queries accessing nonuniform data. How you write a statement, the number of rows in the table, data distribution: All of these may affect the use of histograms. This presentation shows examples demonstrating how the optimizer works in each case, describes how to create histograms, and covers differences between MySQL and Oracle implementations.
Using Spark's RDD APIs for complex, custom applicationsTejas Patil
https://spark-summit.org/east-2017/events/experiences-with-sparks-rdd-apis-for-complex-custom-applications/
In this talk, we will discuss several advantages of the Spark RDD API for developing custom applications when compared to pure SQL-like interfaces such as Hive. In particular, we will describe how to control data distribution, avoid data skew, and implement application specific optimizations in order to build performant and reliable data pipelines. In order to illustrate these ideas, we will share our experiences redesigning a large-scale, complex (100+ stage) language model training pipeline for Spark that was originally built in Hive. The final Spark based pipeline is modular, readable, and more maintainable when compared to previous set of HQL queries. In addition to the qualitative improvements, we also observed a significant reduction in both resource usage and data landing time. Finally, we will also describe Spark optimizations that we implemented for this workload that can be applied toward batch workloads in general.
A Billion Goods in a Few Categories: When Optimizer Histograms Help and When ...Sveta Smirnova
Last year this session’s speaker worked on several cases where data followed the same pattern: millions of popular products fit into a couple of categories, and the rest uses the rest. Her team had a hard time finding a solution for retrieving goods quickly. MySQL 8.0 has a feature that resolves such issues: optimizer histograms, storing statistics of an exact number of values in each data bucket. In real life, histograms don’t help with all queries accessing nonuniform data. How you write a statement, the number of rows in the table, data distribution: All of these may affect the use of histograms. This presentation shows examples demonstrating how the optimizer works in each case, describes how to create histograms, and covers differences between MySQL and Oracle implementations.
Using Spark's RDD APIs for complex, custom applicationsTejas Patil
https://spark-summit.org/east-2017/events/experiences-with-sparks-rdd-apis-for-complex-custom-applications/
In this talk, we will discuss several advantages of the Spark RDD API for developing custom applications when compared to pure SQL-like interfaces such as Hive. In particular, we will describe how to control data distribution, avoid data skew, and implement application specific optimizations in order to build performant and reliable data pipelines. In order to illustrate these ideas, we will share our experiences redesigning a large-scale, complex (100+ stage) language model training pipeline for Spark that was originally built in Hive. The final Spark based pipeline is modular, readable, and more maintainable when compared to previous set of HQL queries. In addition to the qualitative improvements, we also observed a significant reduction in both resource usage and data landing time. Finally, we will also describe Spark optimizations that we implemented for this workload that can be applied toward batch workloads in general.
Advanced MySQL Query Tuning - talk at Percona Live and MySQL Meetup tour.
Tuning Queries and Schema/Indexes can significantly increase performance of your application and decrease response times.
This year I will cover new MySQL 5.6 and 5.7 algorithms that has been designed to improve query performance and simply tuning.
Topics:
1. Group by and order by optimizations
2. MySQL temporary tables and filesort
3. Using covered indexes to optimize your queries
4. Loose and tight index scan in MySQL
5. Using summary tables to optimize your reporting queries
6. New MySQL 5.6 and 5.7 Optimizer features and improvements
Billion Goods in Few Categories: how Histograms Save a Life?Sveta Smirnova
We store data with the intention to use it: search, retrieve, group, sort... To perform these actions effectively MySQL storage engines index data and communicate statistics with the Optimizer when it compiles a query execution plan. This approach works perfectly well unless your data distribution is not even.
Last year I worked on several tickets where data follow the same pattern: millions of popular products fit into a couple of categories and rest used the rest. We had a hard time to find a solution for retrieving goods fast. Workarounds for version 5.7 were offered. However new MySQL 8.0 feature: histograms, - would work better, cleaner and faster. This is how the idea of the talk was born.
I will discuss
- how index statistics physically stored
- which data exchanged with the Optimizer
- why it is not enough to make correct index choice
In the end, I will explain which issues resolve histograms and why using index statistics is insufficient for fast retrieving of not evenly distributed data.
https://www.percona.com/live/e18/sessions/billion-goods-in-few-categories-how-histograms-save-a-life
Billion Goods in Few Categories: How Histograms Save a Life?Sveta Smirnova
We store data with an intention to use it: search, retrieve, group, sort... To do it effectively the MySQL Optimizer uses index statistics when compiles the query execution plan. This approach works excellently unless your data distribution is not even.
Last year I worked on several tickets where data follow the same pattern: millions of popular products fit into a couple of categories and rest used the rest. We had a hard time to find a solution for retrieving goods fast. We offered workarounds for version 5.7. However new MariaDB and MySQL 8.0 feature: histograms, - would work better, cleaner and faster. The idea of the talk was born.
Of course, histograms are not a panacea and do not help in all situations.
I will discuss:
how index statistics physically stored by the storage engine
which data exchanged with the Optimizer
why it is not enough to make correct index choice
when histograms can help and when they cannot
differences between MySQL and MariaDB histograms
What SQL functionality was added in the past year or so. The presentation covers default expressions, functional key parts, lateral derived tables, CHECK constraints, JSON and spatial improvements. Also some other small SQL and other improvements.
January 2016 Meetup: Speeding up (big) data manipulation with data.table packageZurich_R_User_Group
Abstract: Both practitioners and researchers spend significant amount of their time on data preparation, cleaning and exploration. It gets more complicated and interesting if a dataset is big, or if it has a lot of groups in it which require per-group analysis. In this talk I will introduce an innovative data.table package as an alternative to the standard data.frame which significantly cuts your programming and execution time with easier code. It is also the first step to working with big data in R. The talk will be beneficial for R users from all disciplines, as well as for big data professionals looking for more explicit data exploration tools.
Advanced MySQL Query Tuning - talk at Percona Live and MySQL Meetup tour.
Tuning Queries and Schema/Indexes can significantly increase performance of your application and decrease response times.
This year I will cover new MySQL 5.6 and 5.7 algorithms that has been designed to improve query performance and simply tuning.
Topics:
1. Group by and order by optimizations
2. MySQL temporary tables and filesort
3. Using covered indexes to optimize your queries
4. Loose and tight index scan in MySQL
5. Using summary tables to optimize your reporting queries
6. New MySQL 5.6 and 5.7 Optimizer features and improvements
Billion Goods in Few Categories: how Histograms Save a Life?Sveta Smirnova
We store data with the intention to use it: search, retrieve, group, sort... To perform these actions effectively MySQL storage engines index data and communicate statistics with the Optimizer when it compiles a query execution plan. This approach works perfectly well unless your data distribution is not even.
Last year I worked on several tickets where data follow the same pattern: millions of popular products fit into a couple of categories and rest used the rest. We had a hard time to find a solution for retrieving goods fast. Workarounds for version 5.7 were offered. However new MySQL 8.0 feature: histograms, - would work better, cleaner and faster. This is how the idea of the talk was born.
I will discuss
- how index statistics physically stored
- which data exchanged with the Optimizer
- why it is not enough to make correct index choice
In the end, I will explain which issues resolve histograms and why using index statistics is insufficient for fast retrieving of not evenly distributed data.
https://www.percona.com/live/e18/sessions/billion-goods-in-few-categories-how-histograms-save-a-life
Billion Goods in Few Categories: How Histograms Save a Life?Sveta Smirnova
We store data with an intention to use it: search, retrieve, group, sort... To do it effectively the MySQL Optimizer uses index statistics when compiles the query execution plan. This approach works excellently unless your data distribution is not even.
Last year I worked on several tickets where data follow the same pattern: millions of popular products fit into a couple of categories and rest used the rest. We had a hard time to find a solution for retrieving goods fast. We offered workarounds for version 5.7. However new MariaDB and MySQL 8.0 feature: histograms, - would work better, cleaner and faster. The idea of the talk was born.
Of course, histograms are not a panacea and do not help in all situations.
I will discuss:
how index statistics physically stored by the storage engine
which data exchanged with the Optimizer
why it is not enough to make correct index choice
when histograms can help and when they cannot
differences between MySQL and MariaDB histograms
What SQL functionality was added in the past year or so. The presentation covers default expressions, functional key parts, lateral derived tables, CHECK constraints, JSON and spatial improvements. Also some other small SQL and other improvements.
January 2016 Meetup: Speeding up (big) data manipulation with data.table packageZurich_R_User_Group
Abstract: Both practitioners and researchers spend significant amount of their time on data preparation, cleaning and exploration. It gets more complicated and interesting if a dataset is big, or if it has a lot of groups in it which require per-group analysis. In this talk I will introduce an innovative data.table package as an alternative to the standard data.frame which significantly cuts your programming and execution time with easier code. It is also the first step to working with big data in R. The talk will be beneficial for R users from all disciplines, as well as for big data professionals looking for more explicit data exploration tools.
SQL stands for Structured Query Language
SQL lets you access and manipulate databases
SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987
MySQL 8 -- A new beginning : Sunshine PHP/PHP UK (updated)Dave Stokes
MySQL 8 has many new features and this presentation covers the new data dictionary, improved JSON functions, roles, histograms, and much more. Updated after SunshinePHP 2018 after feedback
AWS December 2015 Webinar Series - Design Patterns using Amazon DynamoDBAmazon Web Services
If you’re familiar with relational databases, designing your app to use a NoSQL database like DynamoDB may be new to you. In this webinar, we’ll walk you through common data design patterns for a variety of applications to help you learn how to design a schema, then store and retrieve the data with DynamoDB. We will discuss the benefits of using DynamoDB to develop mobile, web, IoT, and gaming apps.
Learning Objectives:
Learn schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others
Who Should Attend:
Architects, Developers, and SysOps interested in learning how to design NoSQL schemas to support mobile, web, IoT, AdTech, and gaming apps.
Familiarity with DynamoDB is helpful
Ikuti Public Training AS/400 System Administration & Control (27-31 Augustus ...Hany Paulina
Manfaat Training ini : Belajar mengenai System AS/400 secara keseluruhan, Security Concept & Planning, Backup & Recovery and Restore System, Problem Determination.
Ingin Belajar System AS/400, Security, Back Up Recovery & Problem Determintat...Hany Paulina
Ikuti Public Training AS/400 System Administration & Control
(29 Januari - 02 Februari 2018) INFO TRAINING : hany.paulina@globaledu.co.id / +6281381088767
Ikuti Public Training AS/400 System Administration & Control (29 Januari - 02...Hany Paulina
Dalam Public Training ini peserta akan belajar mengenai : System AS/400 secara keseluruhan, Security, Back Up Recovery dan Problem Solving . INFO TRAINING : hany.paulina@globaledu.co.id / +6281381088767
Ingin Belajar AS/400 System Tuning & Performance Tips & TechniquesHany Paulina
Peserta akan belajar : Work Management, Creating a Work Environment, Special Work Management Functions, Shipped System Objects, System Values & Network Attributes, Job Structure & Execution Logic, Storage Management, System Tuning, Performance Management, Sample Report, Job Summary, Performance Tips & Techniques. Info Training : +6281381088767 / hanypaulina7@gmail.com
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Public Training SQL Implementation & Embedded Programming in IBM i
1.
2. • An alternative database interface language
• NOT a database management system
• High level, simple statement formats
• A language used for:
• Data Definition (DDL)
• Data Manipulation (DML)
• Completely interchangeable data methods
• SQL tables may be accessed with native language
• DDS created files can be access with SQL
3. • Great for selecting and manipulating groups of data
• If you want to update/delete all the records in a file matching a certain
criteria, use SQL. SQL can change or delete a group of records in a single
statement, whereas native I/O would require you to loop through a file
and issue individual update or delete statements.
• Columnar functions allow for column/field manipulation during
the record selection phase
• SQL has many columnar functions designed to tally, total, summarize,
manipulate, and calculate columns of data. Many program features such
as substringing, averaging, and math functions can be performed
during the record selection phase. Columns can even be returned that
don't exist in the file, such as counts, calculations, literals, and dates.
• Aggregate data
• if you wanted to find a list of all the different zip codes in a mailing
address file and count how many addresses were in each zip code, SQL
can easily accomplish this in a single statement. In native I/O you would
have to loop through a file and increment counter fields or use arrays
and/or multiple-occurrence data structures to aggregate like data.
4. • Interactive SQL
STRSQL
Quick ad-hoc queries
• Embedded SQL
Alternative to native file I/O
Allows for SQL functionality in RPG or COBOL
5. • STRSQL (Green Screen)
• System i Navigator
• Most Common SQL Statements
The SELECT statement is used to select data from a database. The result is
stored in a result table, called the result-set.
The UPDATE statement is used to update existing records in a table.
The DELETE statement is used to delete rows in a table.
6. • SELECT *
FROM EMPLOYEE
• SELECT EMPNO, LASTNAME, BIRTHDATE, SALARY
FROM EMPLOYEE
WHERE SALARY > 30000
• SELECT LASTNAME, SALARY, BONUS, COM
FROM EMPLOYEE
WHERE SALARY > 22000 AND BONUS = 400
OR BONUS = 500 AND COM < 1900
ORDER BY LASTNAME
7.
8.
9.
10. • UPDATE table_name
SET column1=value, column2=value2,...
WHERE some_column=some_value
• Be careful when updating records !!!
If you omit the WHERE, all records get
updated
• Example:
UPDATE EMPLOYEE
SET SALARY = SALARY + 1000
WHERE WORKDEPT = 'C01'
11. • DELETE FROM table_name
WHERE some_column=some_value
• BE CAREFUL WHEN DELETING RECORDS!
Like the UPDATE statement, if you omit the
WHERE clause, all records get deleted.
BACK IT UP!
• Example:
DELETE FROM TESTEMP
WHERE EMPNO = '000111'
12. • Static SQL
The simplest form of embedding SQL in RPG or COBOL
The SQL statement is hard coded in your program
• Dynamic SQL
The SQL statement is assembled at run-time
Requires more resource at run-time for preparing the statement
Makes an application very dynamic
Can become very sophisticated
13. • Most variables defined in RPG or COBOL can be used in a SQL
statement
• Variables are preceded by a colon, ( : ).
• Example:
To use RPG or COBOL’s variable field name
ITMNBR in SQL use it as :ITMNBR
14. COST OF ON HAND INVENTORY
ITEM# DESCRIPTION COST QTY OH COST OH
20001 Telephone, one line 15.00 10 150.00
20002 Telephone, two line 89.00 5 445.00
20003 Speaker Telephone 85.00 6 510.00
20004 Telephone Extension Cord 1.10 25 27.50
20005 Dry Erase Marker Packs 2.25 428 963.00
20006 Executive Chairs 325.00 10 3,250.00
20007 Secretarial Chairs 55.00 13 715.00
20008 Desk Calendar Pads 5.00 56 280.00
20009 Diskette Mailers .20 128 25.60
20010 Address Books 6.00 1,680 10,080.00
20011 Desk lamp, brass 20.00 3 60.00
20012 Blue pens 20.00 7 140.00
20013 Red pens 100.00 14 1,400.00
20014 Black pens 150.00 25 3,750.00
20015 Number 2 pencils 2.50 150 375.00
20016 Number 3 pencils 4.50 25 112.50
20017 Two Drawer File Cabinets 5.50 15 82.50
20018 Manilla folders 4.00 50 200.00
20019 Hanging file folders 3.00 150 450.00
20020 Metal desk 125.00 2 250.00
::::: :::::::::::::::::::: :::: ::::: ::::::
20045 Blue paper, 8 1/2 X 11 2.95 75 221.25
20046 Continuous 8,5 X 11 paper 20.00 24 480.00
20047 Yellow paper, 8 1/2 X 11 2.95 199 587.05
20048 3 hole white paper 3.35 35 117.25
20049 3 hole lined paper 3.85 10 38.50
20050 Heavy duty stapler 9.15 5 45.75
TOTAL COST OF INVENTORY ON HAND: 51,755.90
15. • Step 1 – calculate average Item cost
• Read all item records and calculate the ITMCOST average
• Put the average result in AVGCOST
• Step 2 – Select Item cost > Average cost
• Re-Read all item records again and compare the ITMCOST to
AVGCOST
• If the ITMCOST > AVGCOST calculate the amount of
ITMCOST * ITMQTYOH to ITMCOSTOH and print the record,
if NOT read next records
• Print a total of ITMCOSTOH after all records have been
read.