This document discusses the architecture and optimization of database management systems (DBMS). It covers:
1) The main components of a DBMS architecture including the query executor, buffer manager, storage manager, transaction manager, and more.
2) Query optimization techniques including rule-based optimization, cost-based optimization using a dynamic programming algorithm to search the plan space, and reducing the plan space.
3) Cost estimation including estimating selectivity factors, output sizes, and costs of different query execution plans without executing them.
introduction to data structures and typesankita946617
A ppt on primitive and non primitive data structures, abstract data types , performance analysis(time and space complexity) using asymptotic notations-(Big O, Theta, Omega), introduction to arrays and their memory representation, types of arrays- single dimension, two dimension, multi-dimensions, basic operations on arrays- insertion, deletion, searching, sorting, sparse matrix and its representation, applications of array
The file "Lecture21-Query-Optimization-1April-2018.pptx" seems to be a PowerPoint presentation focused on the topic of query optimization. Here's a brief description of what you might expect based on the title:
Title: Lecture 21 - Query Optimization
Date: 1st April 2018
This PowerPoint presentation likely covers the concept of query optimization, which is a crucial aspect of database management and performance tuning. Query optimization involves the process of selecting the most efficient execution plan for a given query, aiming to minimize resource usage (such as CPU, memory, and disk I/O) and maximize the speed of query execution.
Topics that might be covered in the presentation could include:
Introduction to query optimization
Importance of query optimization in database systems
Factors affecting query performance
Techniques for query optimization (e.g., index selection, join strategies, query rewriting)
Cost-based optimization vs. rule-based optimization
Tools and methods for analyzing query performance
Case studies or examples illustrating the impact of query optimization
Best practices for optimizing database queries
Overall, this presentation is likely to provide valuable insights into the principles and techniques used to enhance the performance of database systems by optimizing queries.
Cost-Based Optimizer in Apache Spark 2.2 Ron Hu, Sameer Agarwal, Wenchen Fan ...Databricks
Apache Spark 2.2 ships with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Leveraging these reliable statistics helps Spark to make better decisions in picking the most optimal query plan. Examples of these optimizations include selecting the correct build side in a hash-join, choosing the right join type (broadcast hash-join vs. shuffled hash-join) or adjusting a multi-way join order, among others. In this talk, we’ll take a deep dive into Spark’s cost based optimizer and discuss how we collect/store these statistics, the query optimizations it enables, and its performance impact on TPC-DS benchmark queries.
Cost-Based Optimizer in Apache Spark 2.2 Databricks
Apache Spark 2.2 ships with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Leveraging these reliable statistics helps Spark to make better decisions in picking the most optimal query plan. Examples of these optimizations include selecting the correct build side in a hash-join, choosing the right join type (broadcast hash-join vs. shuffled hash-join) or adjusting a multi-way join order, among others. In this talk, we’ll take a deep dive into Spark’s cost based optimizer and discuss how we collect/store these statistics, the query optimizations it enables, and its performance impact on TPC-DS benchmark queries.
introduction to data structures and typesankita946617
A ppt on primitive and non primitive data structures, abstract data types , performance analysis(time and space complexity) using asymptotic notations-(Big O, Theta, Omega), introduction to arrays and their memory representation, types of arrays- single dimension, two dimension, multi-dimensions, basic operations on arrays- insertion, deletion, searching, sorting, sparse matrix and its representation, applications of array
The file "Lecture21-Query-Optimization-1April-2018.pptx" seems to be a PowerPoint presentation focused on the topic of query optimization. Here's a brief description of what you might expect based on the title:
Title: Lecture 21 - Query Optimization
Date: 1st April 2018
This PowerPoint presentation likely covers the concept of query optimization, which is a crucial aspect of database management and performance tuning. Query optimization involves the process of selecting the most efficient execution plan for a given query, aiming to minimize resource usage (such as CPU, memory, and disk I/O) and maximize the speed of query execution.
Topics that might be covered in the presentation could include:
Introduction to query optimization
Importance of query optimization in database systems
Factors affecting query performance
Techniques for query optimization (e.g., index selection, join strategies, query rewriting)
Cost-based optimization vs. rule-based optimization
Tools and methods for analyzing query performance
Case studies or examples illustrating the impact of query optimization
Best practices for optimizing database queries
Overall, this presentation is likely to provide valuable insights into the principles and techniques used to enhance the performance of database systems by optimizing queries.
Cost-Based Optimizer in Apache Spark 2.2 Ron Hu, Sameer Agarwal, Wenchen Fan ...Databricks
Apache Spark 2.2 ships with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Leveraging these reliable statistics helps Spark to make better decisions in picking the most optimal query plan. Examples of these optimizations include selecting the correct build side in a hash-join, choosing the right join type (broadcast hash-join vs. shuffled hash-join) or adjusting a multi-way join order, among others. In this talk, we’ll take a deep dive into Spark’s cost based optimizer and discuss how we collect/store these statistics, the query optimizations it enables, and its performance impact on TPC-DS benchmark queries.
Cost-Based Optimizer in Apache Spark 2.2 Databricks
Apache Spark 2.2 ships with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Leveraging these reliable statistics helps Spark to make better decisions in picking the most optimal query plan. Examples of these optimizations include selecting the correct build side in a hash-join, choosing the right join type (broadcast hash-join vs. shuffled hash-join) or adjusting a multi-way join order, among others. In this talk, we’ll take a deep dive into Spark’s cost based optimizer and discuss how we collect/store these statistics, the query optimizations it enables, and its performance impact on TPC-DS benchmark queries.
Week-3 – System RSupplemental material1Recap •.docxhelzerpatrina
Week-3 – System R
Supplemental material
1
Recap
• R - workhorse data structures
• Data frame
• List
• Matrix / Array
• Vector
• System-R – Input and output
• read() function
• read.table and read.csv
• scan() function
• typeof() function
• Setwd() function
• print()
• Factor variables
• Used in category analysis and statistical modelling
• Contains predefined set value called levels
• Descriptive statistics
• ls() – list of named objects
• str() – structure of the data and not the data itself
• summary() – provides a summary of data
• Plot() – Simple plot
2
Descriptive statistics - continued
• Summary of commands with single-value result. These commands will work on variables
containing numeric value.
• max() ---- It shows the maximum value in the vector
• min() ----- It shows the minimum value in the vector
• sum() ----- It shows the sum of all the vector elements.
• mean() ---- It shows the arithmetic mean for the entire vector
• median() – It shows the median value of the vector
• sd() – It shows the standard deviation
• var() – It show the variance
3
Descriptive statistics - single value results -
example
temp is the name of the vector
containing all numeric values
4
• log(dataset) – Shows log value for each
element.
• summary(dataset) –shows the summary
of values
• quantile() - Shows the quantiles by
default—the 0%, 25%, 50%, 75%, and
100% quantiles. It is possible to select
other quantiles also.
Descriptive statistics - multiple value results -
example
5
Descriptive Statistics in R for Data Frames
• Max(frame) – Returns the largest value in the entire data frame.
• Min(frame) – Returns the smallest value in the entire data frame.
• Sum(frame) – Returns the sum of the entire data frame.
• Fivenum(frame) – Returns the Tukey summary values for the entire
data frame.
• Length(frame)- Returns the number of columns in the data frame.
• Summary(frame) – Returns the summary for each column.
6
Descriptive Statistics in R for Data Frames -
Example
7
Descriptive Statistics in R for Data Frames –
RowMeans example
8
Descriptive Statistics in R for Data Frames –
ColMeans example
9
Graphical analysis - simple linear regression model
in R
• Logistic regression is implemented to understand if the dependent
variable is a linear function of the independent variable.
• Logistic regression is used for fitting the regression curve.
• Pre-requisite for implementing linear regression:
• Dependent variable should conform to normal distribution
• Cars dataset that is part of the R-Studio will be used as an example to
explain linear regression model.
10
Creating a simple linear model
• cars is a dataset preloaded into
System-R studio.
• head() function prints the first
few rows of the list/df
• cars dataset contains two major
columns
• X = speed (cars$speed)
• Y = dist (cars$dist)
• data() function is used to list all
the active datasets in the
environment.
• ...
Cost-Based Optimizer Framework for Spark SQL: Spark Summit East talk by Ron H...Spark Summit
In Spark SQL’s Catalyst optimizer, many rule based optimization techniques have been implemented, but the optimizer itself can still be improved. For example, without detailed column statistics information on data distribution, it is difficult to accurately estimate the filter factor, cardinality, and thus output size of a database operator. With the inaccurate and/or misleading statistics, it often leads the optimizer to choose suboptimal query execution plans.
We added a Cost-Based Optimizer framework to Spark SQL engine. In our framework, we use Analyze Table SQL statement to collect the detailed column statistics and save them into Spark’s catalog. For the relevant columns, we collect number of distinct values, number of NULL values, maximum/minimum value, average/maximal column length, etc. Also, we save the data distribution of columns in either equal-width or equal-height histograms in order to deal with data skew effectively. Furthermore, with the number of distinct values and number of records of a table, we can determine how unique a column is although Spark SQL does not support primary key. This helps determine, for example, the output size of join operation and multi-column group-by operation.
In our framework, we compute the cardinality and output size of each database operator. With reliable statistics and derived cardinalities, we are able to make good decisions in these areas: selecting the correct build side of a hash-join operation, choosing the right join type (broadcast hash-join versus shuffled hash-join), adjusting multi-way join order, etc. In this talk, we will show Spark SQL’s new Cost-Based Optimizer framework and its performance impact on TPC-DS benchmark queries.
C++ Is One Of The widely used programming language. Here is the complete presentation PPT notes of C++ programming language. hope it will be helpful to you.
Ranking and Diversity in Recommendations - RecSys Stammtisch at SoundCloud, B...Alexandros Karatzoglou
Slides from my talk at the RecSys Stammtisch at SoundCloud in Berlin. The presentation is split in two part one focusing on ranking and relevance and one on diversity and how to achieve it using genres. We introduce a novel diversity metric called Binomial Diversity.
Attached here is a presentation that I made covering some bits and pieces of what I got to discover about Data Science and Machine Learning using R Programming Language.
• Process for heuristics optimization
1. The parser of a high-level query generates an initial internal representation;
2. Apply heuristics rules to optimize the internal representation.
3. A query execution plan is generated to execute groups of operations based on the access paths available on the files involved in the query.
ESSAY #4In contrast to thinking of poor people as deserving of bei.docxLinaCovington707
ESSAY #4
In contrast to thinking of poor people as deserving of being poor, use the sociological perspective to explain poverty
without
“blaming the victim.” In other words, what conditions in society create poverty? You should use the Newman book extensively to help you with this question.
Your response should be about 500 words.
Essay 4 Rubric
Essay 4 Rubric
标准
等级
得分
此标准已链接至学习结果
Clarity and professionalism
查看较长的说明
Paper is well-written, free of typos and grammatical errors, and well-organized; it's clear that the student spent some time editing the paper
3.0
得分
Poorly written; many typos and mistakes; difficult to follow or understand; appears that little time was spent on crafting a professional essay
0.0
得分
3.0
分
此标准已链接至学习结果
Sociological Understanding
查看较长的说明
Paper uses a sociological approach to explaining the causes of poverty. Paper pulls often from the Newman material. No 'victim blaming' in the paper.
27.0
得分
Paper is not sociological. Paper does not identify social structural causes of poverty. Paper contains elements of 'victim blaming,' or individual explanations for poverty.
15.0
得分
No paper submitted
0.0
得分
27.0
分
总得分:
30.0
,满分 30.0
上一页
下一页
.
Essay # 3 Instructions Representations of War and Genocide .docxLinaCovington707
Essay # 3 Instructions
Representations of War and Genocide
:
In 1000-1200 words, discuss the novel, Edwidge Danticat’s
Farming of the Bones
, represent genocide and massacre. Focus on why in history, The Parsley massacre is not called a genocide, rather a massacre.
Even though the parsley massacre was clearly an act of genocide, history calls it a massacre. Before discussing the novel, explain in your words the definitions of “massacre” and “genocide”?
This is the time you should refer to the documentary and discuss why does the author mention genocides in history as far back as the Armenian genocide but do not mention the Parsley massacre. What are the factors that might contribute to its absence in history? This is the first part of your essay.
The second part is to discuss testimonies of survivors of the genocide.
In many ways,
The Farming of Bones
is also a meditation on survival. Each character in the novel—Amabelle, Sebastien, Father Romain, Man Denise, Man Rapadou, just to name a few—have different methods of survival. Can you discuss these? Are there any characters in particular that have survived with a better quality of life than others? What does it mean to survive?
How does the novel differ from the documentaries in terms of survival testimony? Why do you think the author chose to write a historical fiction novel versus a non-fiction novel like I am Malala or Persepolis?
Length: 1000-1200 words
Style: Times New Roman, Double-space, Size 12
please use the PowerPoint
.
More Related Content
Similar to DBMS ArchitectureQuery ExecutorBuffer ManagerStora
Week-3 – System RSupplemental material1Recap •.docxhelzerpatrina
Week-3 – System R
Supplemental material
1
Recap
• R - workhorse data structures
• Data frame
• List
• Matrix / Array
• Vector
• System-R – Input and output
• read() function
• read.table and read.csv
• scan() function
• typeof() function
• Setwd() function
• print()
• Factor variables
• Used in category analysis and statistical modelling
• Contains predefined set value called levels
• Descriptive statistics
• ls() – list of named objects
• str() – structure of the data and not the data itself
• summary() – provides a summary of data
• Plot() – Simple plot
2
Descriptive statistics - continued
• Summary of commands with single-value result. These commands will work on variables
containing numeric value.
• max() ---- It shows the maximum value in the vector
• min() ----- It shows the minimum value in the vector
• sum() ----- It shows the sum of all the vector elements.
• mean() ---- It shows the arithmetic mean for the entire vector
• median() – It shows the median value of the vector
• sd() – It shows the standard deviation
• var() – It show the variance
3
Descriptive statistics - single value results -
example
temp is the name of the vector
containing all numeric values
4
• log(dataset) – Shows log value for each
element.
• summary(dataset) –shows the summary
of values
• quantile() - Shows the quantiles by
default—the 0%, 25%, 50%, 75%, and
100% quantiles. It is possible to select
other quantiles also.
Descriptive statistics - multiple value results -
example
5
Descriptive Statistics in R for Data Frames
• Max(frame) – Returns the largest value in the entire data frame.
• Min(frame) – Returns the smallest value in the entire data frame.
• Sum(frame) – Returns the sum of the entire data frame.
• Fivenum(frame) – Returns the Tukey summary values for the entire
data frame.
• Length(frame)- Returns the number of columns in the data frame.
• Summary(frame) – Returns the summary for each column.
6
Descriptive Statistics in R for Data Frames -
Example
7
Descriptive Statistics in R for Data Frames –
RowMeans example
8
Descriptive Statistics in R for Data Frames –
ColMeans example
9
Graphical analysis - simple linear regression model
in R
• Logistic regression is implemented to understand if the dependent
variable is a linear function of the independent variable.
• Logistic regression is used for fitting the regression curve.
• Pre-requisite for implementing linear regression:
• Dependent variable should conform to normal distribution
• Cars dataset that is part of the R-Studio will be used as an example to
explain linear regression model.
10
Creating a simple linear model
• cars is a dataset preloaded into
System-R studio.
• head() function prints the first
few rows of the list/df
• cars dataset contains two major
columns
• X = speed (cars$speed)
• Y = dist (cars$dist)
• data() function is used to list all
the active datasets in the
environment.
• ...
Cost-Based Optimizer Framework for Spark SQL: Spark Summit East talk by Ron H...Spark Summit
In Spark SQL’s Catalyst optimizer, many rule based optimization techniques have been implemented, but the optimizer itself can still be improved. For example, without detailed column statistics information on data distribution, it is difficult to accurately estimate the filter factor, cardinality, and thus output size of a database operator. With the inaccurate and/or misleading statistics, it often leads the optimizer to choose suboptimal query execution plans.
We added a Cost-Based Optimizer framework to Spark SQL engine. In our framework, we use Analyze Table SQL statement to collect the detailed column statistics and save them into Spark’s catalog. For the relevant columns, we collect number of distinct values, number of NULL values, maximum/minimum value, average/maximal column length, etc. Also, we save the data distribution of columns in either equal-width or equal-height histograms in order to deal with data skew effectively. Furthermore, with the number of distinct values and number of records of a table, we can determine how unique a column is although Spark SQL does not support primary key. This helps determine, for example, the output size of join operation and multi-column group-by operation.
In our framework, we compute the cardinality and output size of each database operator. With reliable statistics and derived cardinalities, we are able to make good decisions in these areas: selecting the correct build side of a hash-join operation, choosing the right join type (broadcast hash-join versus shuffled hash-join), adjusting multi-way join order, etc. In this talk, we will show Spark SQL’s new Cost-Based Optimizer framework and its performance impact on TPC-DS benchmark queries.
C++ Is One Of The widely used programming language. Here is the complete presentation PPT notes of C++ programming language. hope it will be helpful to you.
Ranking and Diversity in Recommendations - RecSys Stammtisch at SoundCloud, B...Alexandros Karatzoglou
Slides from my talk at the RecSys Stammtisch at SoundCloud in Berlin. The presentation is split in two part one focusing on ranking and relevance and one on diversity and how to achieve it using genres. We introduce a novel diversity metric called Binomial Diversity.
Attached here is a presentation that I made covering some bits and pieces of what I got to discover about Data Science and Machine Learning using R Programming Language.
• Process for heuristics optimization
1. The parser of a high-level query generates an initial internal representation;
2. Apply heuristics rules to optimize the internal representation.
3. A query execution plan is generated to execute groups of operations based on the access paths available on the files involved in the query.
ESSAY #4In contrast to thinking of poor people as deserving of bei.docxLinaCovington707
ESSAY #4
In contrast to thinking of poor people as deserving of being poor, use the sociological perspective to explain poverty
without
“blaming the victim.” In other words, what conditions in society create poverty? You should use the Newman book extensively to help you with this question.
Your response should be about 500 words.
Essay 4 Rubric
Essay 4 Rubric
标准
等级
得分
此标准已链接至学习结果
Clarity and professionalism
查看较长的说明
Paper is well-written, free of typos and grammatical errors, and well-organized; it's clear that the student spent some time editing the paper
3.0
得分
Poorly written; many typos and mistakes; difficult to follow or understand; appears that little time was spent on crafting a professional essay
0.0
得分
3.0
分
此标准已链接至学习结果
Sociological Understanding
查看较长的说明
Paper uses a sociological approach to explaining the causes of poverty. Paper pulls often from the Newman material. No 'victim blaming' in the paper.
27.0
得分
Paper is not sociological. Paper does not identify social structural causes of poverty. Paper contains elements of 'victim blaming,' or individual explanations for poverty.
15.0
得分
No paper submitted
0.0
得分
27.0
分
总得分:
30.0
,满分 30.0
上一页
下一页
.
Essay # 3 Instructions Representations of War and Genocide .docxLinaCovington707
Essay # 3 Instructions
Representations of War and Genocide
:
In 1000-1200 words, discuss the novel, Edwidge Danticat’s
Farming of the Bones
, represent genocide and massacre. Focus on why in history, The Parsley massacre is not called a genocide, rather a massacre.
Even though the parsley massacre was clearly an act of genocide, history calls it a massacre. Before discussing the novel, explain in your words the definitions of “massacre” and “genocide”?
This is the time you should refer to the documentary and discuss why does the author mention genocides in history as far back as the Armenian genocide but do not mention the Parsley massacre. What are the factors that might contribute to its absence in history? This is the first part of your essay.
The second part is to discuss testimonies of survivors of the genocide.
In many ways,
The Farming of Bones
is also a meditation on survival. Each character in the novel—Amabelle, Sebastien, Father Romain, Man Denise, Man Rapadou, just to name a few—have different methods of survival. Can you discuss these? Are there any characters in particular that have survived with a better quality of life than others? What does it mean to survive?
How does the novel differ from the documentaries in terms of survival testimony? Why do you think the author chose to write a historical fiction novel versus a non-fiction novel like I am Malala or Persepolis?
Length: 1000-1200 words
Style: Times New Roman, Double-space, Size 12
please use the PowerPoint
.
Essay 1 What is the role of the millennial servant leader on Capito.docxLinaCovington707
Essay 1: What is the role of the millennial servant leader on Capitol Hill in the 21st century?
Essay 2: Identify the most pressing public policy issue affecting your community. If you were a Member of Congress, what measures would you take to address this issue? (I want the public policy issue to focus on the school to prison pipeline in Mississippi)
Responses should equal to a total of two pages for each essay which is four pages in total.
.
ESSAY #6Over the course of the quarter, you have learned to apply .docxLinaCovington707
ESSAY #6
Over the course of the quarter, you have learned to apply the sociological perspective to the world around you. How has taking a sociological perspective changed the way you view our social environment and/or society? In other words, how has the sociological imagination changed your view of things? Provide at least two examples to illustrate.
Your response should be about 500-750 words.
Essay 6 Rubric
Essay 6 Rubric
标准
等级
得分
此标准已链接至学习结果
Sociological Understanding
查看较长的说明
Paper demonstrates that student learned at least two key ideas/concepts/themes this quarter. Paper is reflective.
27.0
得分
Paper includes fewer than two examples of key themes that the student learned. Little reflection.
15.0
得分
No paper submitted
0.0
得分
27.0
分
此标准已链接至学习结果
Clarity and professionalism
查看较长的说明
Paper is well-written, free of typos and grammatical errors, and well-organized; it's clear that the student spent some time editing the paper
3.0
得分
Poorly written; many typos and mistakes; difficult to follow or understand; appears that little time was spent on crafting a professional essay
0.0
得分
3.0
分
总得分:
30.0
,满分 30.0
上一页
下一页
.
Errors
Keyboarding Errors
Capitlalization Errors
Abbreviation errors
Number Expression Errors
Scholarship Search
Subject Verb Agreement
Pronoun Problems
Sentence Construction
Comma Errors
Other punctuation errors
Format Errors: Letters and Memos
Format Errors: Report and job search documents
Editing for content, clarity and conciseness
.
Epidemiological ApplicationsDescribe how the concept of multifacto.docxLinaCovington707
Epidemiological Applications
Describe how the concept of multifactorial etiology relates to the natural history of disease and the different levels of prevention. How should the nurse incorporate these concepts into health promotion of clients in community settings? How should the nurse approach client risk in these health promotion activities?
Disease Outbreak
Select an infectious disease and research the CDC website for information about the disease, its natural history, presenting symptoms, and outbreak characteristics. Identify an occurrence of the disease by searching the Internet for recent reports of this disease, and compare that episode or occurrence with information from the CDC website. How closely did that outbreak resemble the case definition?
.
Epidemic, Endemic, and Pandemic Occurrence of Disease(s)One aspect.docxLinaCovington707
Epidemic, Endemic, and Pandemic Occurrence of Disease(s)
One aspect of epidemiology is the study of the epidemic, endemic, and pandemic occurrence of disease(s).
Some critics may argue diseases and conditions such as bird flu are endemic in many countries, and some may argue human immunodeficiency virus (HIV) or AIDS is a series of epidemics.
Using the South University Online Library or the Internet, research about the various epidemic, endemic, and pandemic occurrence of disease(s).
Based on your research and understanding, answer the following questions:
At what point does a disease become an epidemic, endemic, or pandemic? What are the parameters that define each of these states of a disease's effect?
Do you agree that bird flu, HIV, or AIDS could be described as a series of epidemics? Why or why not?
Should we study epidemiology and disease control as a complement to the provision of healthcare services? Why or why not?
Disease control has evolved since the discoveries and achievements of these epidemiological pioneers
—
Hippocrates, John Snow, Pasteur, and Koch. Explain the impact of at least one major historical contribution on the current status of epidemiological practices. How can history potentially shape and impact our future work in public health and clinical medicine? Explain.
.
ENVIRONMENTShould the US support initiatives that restrict carbo.docxLinaCovington707
ENVIRONMENT
Should the US support initiatives that restrict carbon emissions (or carbon pollution)?
1000 - 1200 words persuasive essay
Must include minimum of three sources with in-text citations
Microsoft word document in APA format including Title page, Reference page
.
ePortfolio Completion
Resources
Discussion Participation Scoring Guide
.
Throughout this course, we have addressed the following areas:
Helping relationships.
Human services theory and practice.
Theoretical models of practice.
The multidisciplinary approach.
Professional development goals.
Pick
one
of these areas to share with your peers. Your initial post in this discussion may be a draft of one portion of the assignment in this unit. Address why you chose this particular area and its significance to your work in the field.
.
eproduction and Animal BehaviorReproduction Explain why asexually.docxLinaCovington707
eproduction and Animal Behavior
Reproduction: Explain why asexually reproducing organisms are generally found in environments that do not change very much through time, while sexually reproducing organisms are very successful in environments that change dramatically through time.
Animal Behavior: How does an animal’s behavior aid survival and reproduction? Provide an example to illustrate your comments. In your response, be sure to include information from the reading to support your answer.
Copyright
.
Envisioning LeadershipIdentifying a challenge that evokes your pas.docxLinaCovington707
Envisioning Leadership
Identifying a challenge that evokes your passion, understanding its historical and contemporary contexts, and bringing together the community of people needed to respond to this challenge—these are essential steps that make change possible. What kind of person is needed to lead such efforts? What characteristics make an effective leader?
Throughout your program of study, you have been encouraged to think about leadership. You have met, via video and audio podcasts, many inspiring and committed leaders in the early childhood field. This week, the Learning Resources have encouraged you to delve even deeper into the characteristics of leaders.
For this Discussion, without hesitation, jot down at least 10 characteristics that come to mind when you think of a leader. Put your list aside, and review this week's Learning Resources on leadership.
Now, think about the early childhood field and the various situations that call for leaders to interact and work effectively with families, colleagues, organizations, government agencies, etc. Consider the thinking and characteristics that stood out for you from the readings you just reviewed. Then, identify four characteristics you believe to be the most essential for leaders in the early childhood field today.
By Wednesday, post
:
Your list of four leadership characteristics selected from this week's Learning Resources that you think are essential for leaders in the early childhood field today and why you think each is vital.
Three mind-opening realizations about leadership that struck you from the Learning Resources this week. (Be sure to tell the reason[s] these caught your attention, and cite your sources.)
.
EnvironmentOur environment is really important. We need to under.docxLinaCovington707
Environment
Our environment is really important. We need to understand it and then would we be able to look after it. To manage our natural environment responsibly, governments, industry and the community need detailed, trusted and timely environmental information.
Good information is essential to make sound decisions (individually and/or collectively) on issues affecting our environment.
View/review information in the below attached power point then answer questions that follows prompt!
Week 2 Env. Samp ppt(2).pptx
Questions
Give 2 definitions of “Environment”?
Give 4 reasons why we are so concern about the Environment?
Give 2 definitions of Pollution?
Give 5 effects of pollution on Human?
Give 5 effects of pollution on Animals
Give 5 effects of pollution on plants, fruits and vegetables?
Explain pollution effects on outer space? (what is the name of the effect)
Explain Urban Pollution?
Explain outer space pollution?
.
Environmental Awareness and Organizational Sustainability Please .docxLinaCovington707
"Environmental Awareness and Organizational Sustainability" Please respond to the following:
Use the Internet to research one (1) environmentally aware organization and its actions. Next, examine the selected organization’s relationship between sustainability, ethical decision making, and social responsibility. Provide one (1) example of this organization demonstrating environmental awareness.
Determine the major effects that an organization’s environmental awareness has on its sustainability. Recommend one (1) approach that HR can take to use an organization’s environmental awareness in order to attract and retain top talent.
.
EnterobacteriaceaeThe family Enterobacteriaceae contains some or.docxLinaCovington707
Enterobacteriaceae
The family Enterobacteriaceae contains some organisms living in the intestines without harming the host and some organisms that are harmful to the host.
Research Enterobacteriaceae.
Based on your research, respond to the following:
What is meant by the term "enteric pathogen"?
Why are anaerobic organisms generally not seen in a routine fecal specimen or culture?
What are the indole test, methyl red test, voges-proskauer test, and citrate test (IMViC) reactions? Describe in detail all four reactions (what media is used, important ingredients, what each reaction measures, and what positive and negative results mean).
Create a flowchart for the isolation and identification of specific enteric bacteria from fecal samples.
.
Ensuring your local region is prepared for any emergency is a comp.docxLinaCovington707
Ensuring your local region is prepared for any emergency is a complex task requiring the coordination and collaboration of multiple stakeholders. What are the greatest challenges to coordination and collaboration in your area? What needs to be done to overcome those challenges in order to facilitate improved multi-agency coordination and collaboration?
.
ENG 2480 Major Assignment #3Essay #2 CharacterAnaly.docxLinaCovington707
ENG
2480
Major Assignment #
3
Essay #2
:
Character
Analysis Essay
Paper Specifications:
2
Full Pages
, excluding Work
s
Cited page. Typed. Double Spaced.
One-inch
Margins.
12pt. Font
.
Times New Roman. Proper MLA
.
Submit
.doc,
.
docx
,
odt
.,
or .rtf Files Only
***Do not paste the essay into the assignment forum
text box
. Attach the document instead***
Due Date: Monday,
June
1
9
, 201
7
in Blackboard by
11
:
00
pm
Using the STEAL method or Foil Characters
concept
, a
nalyze how the author
constructs a
character.
Your analytical argument should focus on how
the author creates
the character
and how the author uses the character
to embody
the theme of the work.
Find one scholarly source to help support your essay’s thesis.
Choose
only one character
from the following list
as your main point of analysis
:
•
Oscar Wilde’s
The Importance of Being Earnest
:
o
Lady
Bracknell
o
Miss Prism
o
Cecily
•
Robert Louis Stevenson’s
The Strange Case of Dr. Jekyll and Mr. Hyde
:
o
Mr. Poole
o
Mr. Gabriel John
Utterson
o
Dr. Hastie Lanyon
Remember, always establish clear criteria during your argumentation. You need a clear thesis to guide the essay and argumentative topic sentences to guide each paragraph. You are essentially discussing
how
an author creates the personality of a fictional character and how
that
character helps develop the meaning and significance of a work
, so make sure you assert your interpretation.
Do not summarize!
Consider that your audience has read the work
and
has
been exposed to the key literary
te
rms, so you do not need to define them.
Do not evaluate!
Avoid judging how well the author
writes or how good or bad the poem is
. Analyze the importance of the
literary device and remain objective
.
***
Numerous essays exist about these works. Do not be tempted to plagiarize! Use close reading and your critical thinking skills to approach your selected topic
***
Grading Scale
Title Is Helpful, Informative, and Reflective
0 to
5
Points
Presentation and Strength of the Introduction, Body, and Conclusion.
0 to 10 Points
Clearly Stated Thesis.
Must Be Analytical and Reflect the Assignment.
0 to 10 Points
Focus: Staying on Topic. Always Developing and Sticking to the Thesis
and Assignment
.
0 to 10 Points
Every Paragraph Has an Argumentative Topic Sentence. Every Paragraph Has Support or Examples or Details Explaining the Topic Sentence.
0 to 10 Points
Flow: Transitions (not simply transitional words) and Logical Progressions or Movements Between Paragraphs and Sentences Connecting Their Different Ideas.
0 to 10 Points
Organization, Order, and Structure.
0 to 10 Points
Using and Developing a Logical and In-depth Approach to Claims.
Strong Analysis without Over-Summarization.
0 to 10 Points
Vivid Descriptions. “Show. Do Not Tell.” Substantial, In-depth Detail
and Textual / Visual Evidence
.
0 to 10 Points
Clear Language that Explains and Expresses Each Idea in an Und.
English EssayMLA format500 words or moreThis is Caue types of .docxLinaCovington707
English Essay
MLA format
500 words or more
This is Caue types of essay (Only the causes/ not the effect)
Do not cite anything from outside source
Topic: what are the causes of Divorce?
Download the File Below to see the Form of the Essay.
Due By 4/26/2017 11 pm
*** Important note: Do not use hard or complicated words. Simple essay with easy word. ***
.
Eng 2480 British Literature after 1790NameApplying Wilde .docxLinaCovington707
Eng
2480 British Literature after 1790
Name:
Applying Wilde to Wilde (100 points)
Instructions:
Discuss how Wilde applies the ideas of aestheticism and the arguments from
The Critic as Artist
to
The Importance of Being Earnest
. What notions of living to the fullest exist in the play? What notions of living intensely and passionately do the characters reinforce? How is the play (as a creative work) acting as a critical work, as well? What does the work critique?
This response should
be around 250 to 300 words,
not
including the quotes.
Always cite specifics from the texts
.
*NEED IT COMPLETED BY 8pm eastern
.
English 1C Critical Thinking Essay (6 - 6 12 pages, MLA 12pt font .docxLinaCovington707
English 1C: Critical Thinking Essay (6 - 6 1/2 pages, MLA 12pt font times new roman)
Due Date: (8/2/17)
Assignment: Consider one of the topics: I choose to propose my own topic. (received teacher's approval)
Requirements: Use 1-2 in class philosophical texts (I have them in the attachment) and 3-4 academic sources (requires research) to analyze, explore, and make connections to each other. Needs to have at least one quote in each body paragraph.
My proposed topic:
In class, my teacher he talks about a scenario where people from different cultures tend to have different views and values, but people who were raised in both cultures can have an internal conflict between their cultures, causing to choose one over the other, have a mix of both (as in a hybrid form of culture), or identify themselves to another culture that lies somewhere in between, or maybe even reject both cultures.
In Nietzsche's essay "On Truth and Lying in an Extra-Moral Sense", he says "for between two absolutely different spheres such as subject and object, there can be no expression, but as most an aesthetic stance, I mean an allusive transference, a stammering translation into a completely foreign medium. For this, however, in any case a freely fictionalizing and freely inventive middle sphere and middle faculty is necessary." In connection to people who have lived in two different cultures this inventive "middle ground” and “aesthetic stance” is essential for them to embrace their own set of values and beliefs.
For the research part of the essay, I wanted to explore people who have immigrated to another country from their own home country since a young age, for their development is heavily influenced by the struggles of living in multiple cultures. (I’m one of them myself). In sociology, Ruben Rumbaut was the first to coin the term “1.5 generation immigrant”, which means the people who have arrived in another country before their adolescence. Based on the age in which they immigrated, some of these immigrants might feel a stronger connection to a particular culture where some might feel they belong right in the middle, being unable to identify themselves to either of their ethnicities. (Just providing possible examples)
Optional (If there isn’t enough topics): Also for immigrants who might choose one culture over another. It can possibly relate to another philosophical text. In Plato’s “The Allegory of the Cave,” Aristotle argues that there are two mediums of knowledge that exists: the physical/sensory world(cave), where people(prisoners) are living happily in an illusion, and the intelligible world, where people can achieve a perfect form of knowledge through learning philosophy. For people, who have acquired the “perfect knowledge” of philosophy, when they go back to the sensory world, they will have a better and clearer perception of the world than those in the sensory world. They also have developed a responsibility of “quietly ruling” the people in the sensor.
ENGL 227World FictionEssay #2Write a 2-3 page essay (with work.docxLinaCovington707
ENGL 227
World Fiction
Essay #2
Write a 2-3 page essay (with works cited page) on one of the following topics:
1.
D.H. Lawrence “The Rocking Horse Winner”
·
Describe the relationship between mother and son in this story.
How is this relationship central to the story’s themes of luck,
money, and dysfunctional families?
2.
Shirley Jackson “The Lottery”
·
Describe the importance of tradition in the community depicted in this story. What does the author appear to be saying about its effects upon society?
3.
Franz Kafka “A Hunger Artist”
·
What is Kafka suggesting about the nature of the relationship between the artist and society?
Cite examples of the artist’s attitude toward his “art” and regulations as well as society’s changing attitude toward the artist.
4.
Clarice Lispector “The Smallest Woman in the World”
·
What does the story appear to be implying about the nature of human love?
Be sure to examine love as it is described in the narrator’s depiction of Little Flower as well as in her depiction of the various readers’ reactions to the story of Little Flower.
Relate this to the overall theme of the story.
5.
Jack London “To Build a Fire”
·
Examine the difference between actions based on knowledge and those based on instinct as depicted in the behaviors of the man and the dog.
What does London seem to be saying about the nature and the value of both approaches to navigating the world?
Relate this to Naturalism.
6.
Ernest Hemingway “Hills Like White Elephants”
·
Hemingway is famous for his “iceberg theory” of narrative in which sparse prose suggests deeper elements of character and theme.
What does the dialogue suggest about the two protagonists?
What is the attitude of each toward their predicament?
·
What will change, depending on how the predicament is resolved? How does each envision the possibility of a shared future? Be sure to support your interpretation with quotations and connect character with theme.
·
Examine how the story’s setting is related to character, theme, and action (conflict).
7.
Flannery O’Connor “A Good Man is Hard to Find”
·
Discuss O’Connor’s use of humor in this story.
What kind of tone is developed at the beginning of the story through humor?
How does the tone change as we move toward the story’s conclusion?
8.
Jorge Luis Borges “Emma Zunz”
·
Examine Emma’s attitude toward sexuality.
How does this attitude relate to the crime she commits?
Why does she decide to add a sexual component to her set-up of Loewenthal?
Consider the element of sacrifice.
9.
Raymond Carver “A Small, Good Thing”
·
Discuss the theme of communication in relationships in the story, including the Weisses, the baker, Doctor Francis, and Franklin’s family.
10.
Yukio Mishima “Patriotism”
While Takeyama waits for his wife to take a bath, he thinks, “Was it death he was now waiting for? Or wild ecstasy of the senses?
The two seemed to overlap, almost as if the object of his bodily desire was death itself.
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.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
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.
2. Past lectures
Today’s
lecture
Many query plans to execute a SQL query
3
S UTR
SR
T
U
SR
T
U
• Even more plans: multiple algorithms to execute each
operation
SR
T
U
Sort-merge
Sort-merge
3. hash join
Table-scan
index-scan
Table-scanindex-scan
• Compute the join of R(A,B) S(B,C) T(C,D) U(D,E)
Explain command in PostgreSQL
4
SELECT C.STATE, SUM(O.NETAMOUNT),
SUM(O.TOTALAMOUNT)
FROM CUSTOMERS C
JOIN CUST_HIST CH ON C.CUSTOMERID =
CH.CUSTOMERID
JOIN ORDERS O ON CH.ORDERID = O.ORDERID
GROUP BY C.STATE
Communication between operators:
iterator model
• Each physical operator implements three functions:
– Open: initializes the data structures.
– GetNext: returns the next tuple in the result.
– Close: ends the operation and frees the resources.
4. • It enables pipelining
• Other option: compute the result of the operator in full
and store it in disk or memory:
– inefficient.
5
Query optimization: picking the fastest plan
• Optimal approach plan
– enumerate each possible plan
– measure its performance by running it
– pick the fastest one
– What’s wrong?
• Rule-based optimization
– Use a set of pre-defined rules to generate a fast plan
• e.g., If there is an index over a table, use it for scan and join.
6
Review Definitions
• Statistics on table R:
– T(R): Number of tuples in R
– B(R): Number of blocks in R
• B(R) = T(R ) / block size
– V(R,A): Number of distinct values of attribute A in R
5. 7
Plans to select tuples from R: σA=a(R)
• We have an unclustered index on R
• Plans:
– (Unclustered) indexed-based scan
– Table-scan (sequential access)
• Statistics on R
– B(R)=5000, T(R)=200,000
– V(R,A) = 2, one value appears in 95% of tuples.
• Unclustered indexed scan vs. table-scan ?
8
Presenter
Presentation Notes
Unclustered indexed scan vs. table-scan ?
table-scan is the winner!
Query optimization methods
• Rule-based optimizer fails
– It uses static rules
– The rules do not consider the distribution of the data.
• Cost-based optimization
– predict the cost of each plan
6. – search the plan space to find the fastest one
– do it efficiently
• Optimization itself should be fast!
9
Cost-based optimization
• Plan space
– which plans to consider?
– it is time consuming to explore all alternatives.
• Cost estimator
– how to estimate the cost of each plan without executing it?
– we would like to have accurate estimation
• Search algorithm
– how to search the plan space fast?
– we would like to avoid checking inefficient plans
10
Space of query plans: basic operators
• Selection
– algorithms: sequential, index-based
– Ordering
• Projection
– Ordering
7. • Join
– algorithms: nested loop, sort-merge, hash
– ordering
11
Reducing plan space
• Multiple logical query plan for each SQL query
Star(name,BD,bio), StarsIn(movie, sname, year, plot)
SELECT movie,name
FROM Stars, StarsIn
WHERE Star.name = StarsIn.sname AND year = 1950
12
Generally Faster
StarsIn Star
StarsIn.sname = Star.name
σ year=1950
StarsIn
Star
StarsIn.sname = Star.name
year=1950
movie, name
movie, name
8. Reducing plan space
• Push selection down to reduce # of rows
• Push projection down to reduce # of columns
SELECT movie, name, BD
FROM Stars, StarsIn
WHERE Star.name = StarsIn.sname
13
StarsIn Star
StarsIn.sname = Star.name
movie, name, BD
StarsIn Star
StarsIn.sname = Star.name
movie, name, BD
movie, sname name,BD
Less effective than pushing down selection.
14
• Queries with multiple joins may have exponentially
many possible plans.
9. • System-R style considers only left-deep joins
Reducing plan space
SR
T
U
SR
T
U
T USR
• Left-deep trees allow us to generate fully pipelined plans
15
• System R-style avoids plans with Cartesian products
– Cartesian products are generally larger than joins.
• Example:
– Join of R(A,B), S(B,C), U(C,D)
– (R ⋈ U) ⋈ S has a Cartesian product
– Pick (R ⋈ S) ⋈ U instead
• If cannot avoid Cartesian products, delay them.
Reducing plan space
10. Components of a cost-based optimizer
• Plan space
– Which plans to consider?
– Time-consuming to explore every possible plan.
• Cost estimator
– How to estimate the cost of a plan without executing it?
– Accurate estimations.
• Search algorithm
– How to search the plan space fast?
– Avoid checking inefficient plans.
16
17
• Our goal is to maximize relative accuracy
– Compare plans, not to predict exact costs.
• For each operator in the plan:
– Find input size
– Estimate its cost based on the input size
• Example: sort-merge join of R ⋈ S is 3 B(R) + 3 B(S)
– Estimate output size or selectivity
• Selectivity: ratio of output to input
Cost estimation
11. 18
• Why estimate output size?
– Output of an operator = Input to the next one in the plan
– Used to estimate of the cost of the next operator
• Cost of a plan
– Sum of the cost of its operators
Cost estimation
Cost estimation: Selinger Style
• Input: stats on each table
– T(R): Number of tuples in R
– B(R): Number of blocks in R
• B(R) = T(R ) / block size
– V(R,A): Number of distinct values of attribute A in R
• We assume that attributes and predicates are
independence.
• When no estimate available, use magic numbers.
19
Presenter
Presentation Notes
too much information to keep, use histogram
12. 20
Selectivity factors: selection
• Point selection: S = σA=a(R)
– T(S) ranges from 0 to T(R) – V(R,A) + 1
– consider its mean: F = 1 / V (R,A)
• Range selection: S = σA>a(R)
– F = (max(A) – a) / (max(A) – min(A))
– If not arithmetic inequality, use magic number
• F = 1 / 3
• Range selection: S = σ b <A<a(R)
– F = (a - b) / (max(A) – min(A))
– If not arithmetic, use magic number
• F = 1 / 4
21
Examples: selection with multiple conditions
• S = σA=1 AND B>10(R)
– Given T(R) = 10,000, V(R,A) = 50
– No information on B
– Multiply 1/V(R,A) for equality and 1/3 for inequality
• T(S) = 10000 / (50 * 3) = 66
• S = σA=1 OR B>10(R)
13. – Sum of estimates of predicates minus their product
• T(S) = 200 + 3333 – 66 = 3467
22
• Containment of values assumption
– If V(S,A) <= V (R,A), then A-values in S are a subset of A-
values in R
• Let’s assume V(S,A) <= V (R,A)
– Each tuple t in S joins x tuple(s) in R
– consider its mean: x = T(R) / V (R,A)
– T(R ⋈A S) = T (S) * T(R) / V(R,A)
• General formula:
T(R ⋈A S) = T(R) * T(S) / max(V(R,A), V(S,A))
Selectivity factors: join predicates
Presenter
Presentation Notes
Typical join: A is a key in R and a foreign key in S
23
• What if we join over more than one attribute?
T(R S) =
T(R) T(S) / max(V(R,A),V(S,A) max(V(R,B),V(S,B))
14. A,B
Selectivity factors: join predicates
Components of a cost-based optimizer
• Plan space
– Which plans to consider?
– Time-consuming to explore every possible plan.
• Cost estimator
– How to estimate the cost of a plan without executing it?
– Accurate estimations.
• Search algorithm
– How to search the plan space fast?
– Avoid checking inefficient plans.
24
Search the plan space
• Baseline: exhaustive search
– Enumerate all (left-deep) trees and compare their costs
• Enormous space to search => time-consuming!
25
15. Plan search: System-R style
• A.K.A: Selinger style optimization
• Bottom-up
– Start from the ground relation (in FROM)
– Work up the tree to form a plan
– Compute the cost of larger plans based on its sub-trees.
• Dynamic programming
– greedily remove sub-trees that are costly (useless)
26
27
• Step 1: For each {Ri}:
– Plans({Ri}): algorithms to access Ri
• Table-Scan, Index-Scan
– Costs({Ri}): Costs of accessing to Ri
• e.g., B(Ri) for table-scan on Ri
– Plan({Ri}): access method with lowest cost
– Cost({Ri}): lowest cost in Costs({Ri})
– Output-size ({Ri}): B(Ri)
Dynamic programming algorithm
28
• Step 2: For each {Ri, Rj}:
– Plans({Ri,Rj}): join algorithms to compute Ri ⋈ Rj
16. • E.g., nested-loop and sort-merge based algorithms
– Costs({Ri,Rj}): function of size of Ri and Rj
• #I/O access of the chosen join algorithm
• E.g., 5 B(R) + 5 B(S) for sort-merge join
– Plan({Ri,Rj}): the join algorithm with lowest cost
– Cost({Ri,Rj}): lowest cost in Costs({Ri,Rj})
– Output-size ({Ri,Rj}): estimate of the size of join
Dynamic programming algorithm
29
• Step i: For each S ⊆ {R1, …, Rn} of cardinality i:
– for every S1 ,S2 s.t. S = S1 ∪ S2 compute
C = cost(S1) + cost(S2) + cost(S1 ⋈ S2)
– Cost(S) = the smallest C
– Plan(S) = the plan with cost(S)
– Output-size(S): estimate the size of S
• Return Plan({R1, …, Rn})
Dynamic programming algorithm
30
• R ⋈ S ⋈ T ⋈ U on common attribute A
• T(R)= 2000, T(S)=5000, T(T)=3000, T(U)=1000
• We assume that each block contains a single tuple.
17. – B(R)= 2000, B(S)=5000, B(T)=3000, B(U)=1000
• No index on relations
– Table-scan to access each relation
• The only join algorithm is sort-merge
– Cost for R ⋈ S: 5 B(R) + 5 B(S)
– There is enough memory for sort-merge joins.
Example
31
• Relations: R, S, T, U
• T(R)= 2000, T(S)=5000, T(T)=3000, T(U)=1000
• B(R)= 2000, B(S)=5000, B(T)=3000, B(U)=1000
• To simplify our example, we assume
– V(R,A)=V(S,A)=V(T,A)=V(U,A)= 100
– T(R ⋈ S) = 0.01 * T(R) * T(S)
• Same formula for joins of every pair of relations.
Example
32
Query Output-Size Cost Plan
R⋈ S
18. R⋈ T
R⋈ U
S⋈ T
S⋈ U
T⋈ U
R⋈ S⋈ T
R⋈S⋈U
R⋈T⋈U
S⋈T⋈U
R⋈S⋈T⋈U
33
Query Output-Size Cost Plan
R⋈S 100K 35K R⋈S
R⋈T 60K 25K R⋈T
R⋈U 20K 15K R⋈U
S⋈T 150K 40K S⋈T
S⋈U 50K 30K S⋈U
21. • Order By
– E.g., Order By based on A for the result R⋈AS
• Sorted order based on A is an interesting order
• Multi-relation joins
– E.g., T⋈A (R⋈AS)
• Sorted output of R⋈AS helps fast join of T⋈A (R⋈AS).
• Sorted order based on A is an interesting order
• Grouping
36
Interesting Orders
• Plans with sorted results save sorting time/cost.
– E.g., sort-merge join for R⋈AS with Order By on A.
– E.g., sort-merge join for R⋈AS for query T⋈A (R⋈AS).
• The dynamic programming algorithm always keeps
– Fastest overall plan
– Fastest plan for each interesting order
37
Nested subqueries
• Subqueries are optimized separately
• Correlation: order of evaluation
– uncorrelated queries
22. • nested subqueries do not reference outer subqueries
• evaluate the most deeply nested subquery first
– correlated queries: nested subqueries reference the outer
subqueries
Select name
From employee X
Where salary > (Select salary
From employee
Where employee_num = X.manager)
38
Nested subqueries
• correlated queries: nested subqueries reference the outer
subqueries
Select name
From employee X
Where salary > (Select salary
From employee
Where employee_num = X.manager)
• The nested subquery is evaluated once for each tuple in the
outer query.
• If there are small number of distinct values in the outer
relation, it is worth sorting the tuples.
– reduces the #evaluation of the nested query.
39
23. Optimization: all operations
• Reduce plan space
– Push down selections and projections
– Choose good plans, discard bad ones
• Base relations access
– find all plans for accessing each base relations
• Join ordering
– Bottom-up dynamic programming algorithm
– Consider only left-deep plans
– Remove/ postpone Cartesian product
– Keep the cheapest plan for unordered and each interesting
order
40
Summary: optimization
• Ideal goal
– Map a declarative query to the most efficient plan
• Plan space
– Many semantically equivalent alternatives
• Why important?
– Difference between good/bad plans is order of magnitude
• Conventional wisdom:
– At least avoid bad plans
24. 41
State of the art
• Academia: always a core database research topic
– Optimizing for interactive querying
– Optimizing for novel parallel frameworks
• Industry: most optimizers use System-R style
– They started with rule-based.
• Oracle 7 and its prior versions used rule-based
• Oracle 7 – 10: rule-based, and cost based
• Oracle 10g (2003): cost-based
42
CS 540 �Database Management SystemsDBMS
ArchitectureMany query plans to execute a SQL queryExplain
command in PostgreSQLCommunication between operators: �
iterator modelQuery optimization: picking the fastest
planReview Definitions�Plans to select tuples from R: sA=a(R)
�Query optimization methodsCost-based optimizationSpace of
query plans: basic operatorsReducing plan spaceReducing plan
spaceReducing plan spaceReducing plan spaceComponents of a
cost-based optimizerCost estimationCost estimationCost
estimation: Selinger Style Selectivity factors:
selectionExamples: selection with multiple
conditionsSelectivity factors: join predicatesSelectivity factors:
join predicatesComponents of a cost-based optimizerSearch the
plan spacePlan search: System-R styleDynamic programming
algorithmDynamic programming algorithmDynamic
programming algorithmExampleExampleSlide Number 32Slide
Number 33Slide Number 34Slide Number 35Interesting
25. OrdersInteresting OrdersNested subqueriesNested
subqueriesOptimization: all operationsSummary:
optimizationState of the art
1
1: Query optimization (2 points)
Consider the following relations:
R(A, B, C, D, E)
S(F, D)
T(G, B, D, H)
U(I, J, K)
V(L, J, M)
W(L, J, N)
Suggest an optimized logical query plan for the above query and
explain why your proposed plan
may be faster than other possible plans. You should find the
best guess(es) for the join order in
your plan without knowing the statistics of the join attributes
and base relations.
SELECT B,C
FROM R, S, T, U, V, W
WHERE R.D = S.D and R.D = T.D
26. and R.B = T.B and U.J <= V.J and U.J = W.J and R.E <= 200
and W.N <= 100
Please note that your answer may not always be more efficient
than other plans, but it should run
faster than other plans for most input relations.
2: Query optimization (4 points)
For the four relations in the following table, find the best join
order according to the dynamic
programming algorithm used in System-R. You should give the
dynamic programming table
entries for evaluating the join orders. The cost of each join is
the number of I/O accesses the
database system performs to execute the join. Assume that the
database system uses the
two-pass sort-merge join algorithm to perform the join
operations. Each block contains 4 tuples
and tuples of all relations have the same size. We are interested
only in left-deep join trees. Note
that you should use the System-R optimizer formula to compute
the size of each join output.
R(A,B,C) S(B,C) W(B,D) U(A,D)
T(R)=4000 T(S)=3000 T(W)=2000 T(U)=1000
V(R,A) =100
V(R,B) =200
V(R,C) =100
27. V(S,B) =100
V(S,C) = 300
V(W,B) =100
V(W,D) =50
V(U,A) =100
V(U,D) =100
2
3: Query optimization (2 points)
What is the time-complexity of the dynamic programming
algorithm you used in question (3) of
this assignment? You should explain your answer.
4: Serializability and 2PL (5 points)
(a) Consider the following classes of schedules: serializable and
28. 2PL. For each of the following
schedules, state which of the preceding classes it belongs to. If
you cannot decide whether a
schedule belongs in a certain class based on the listed actions,
explain briefly. Also, for each 2PL
schedule, identify whether a cascading rollback (abort) may
happen. A cascading rollback will
happen in a schedule if a given transaction aborts at some point
in the schedule, and at least one
other transaction must be aborted by the system to keep the
database consistent. (4 points)
The actions are listed in the order they are scheduled and
prefixed with the transaction name. If
a commit or abort is not shown, the schedule is incomplete;
assume that abort or commit must
follow all the listed actions.
1. T1:R(X), T2:R(Y), T3:W(X), T2:R(X), T1:R(Y)
2. T1:R(X), T1:R(Y), T1:W(X), T2:R(Y), T3:W(Y), T1:W(X),
T2:R(Y)
3. T1:W(X), T2:R(X), T1:W(X)
4. T1:R(X), T2:W(X), T1:W(X), T3:R(X)
(b) Consider a database DB with relations R1 and R2. The
relation R1 contains tuples t1 and t2
and the relation R2 contains tuples t3, t4, and t5. Assume that
the database DB, relations, and
29. tuples form a hierarchy of lockable database elements. Explain
the sequence of lock requests and
the response of the locking scheduler to the following schedule.
You may assume all lock requests
occur just before they are needed, and all unlocks occur at the
end of the transaction, i.e., EOT.
(1 point)
• T1:R(t1), T2:W(t2), T2:R(t3), T1:W(t4)
5: Degrees of Consistency (4 points)
(a) Consider the schedule shown in Table 1.
What are the maximum degrees of consistency for T1 and T2 in
this schedule? You must find the
maximum degrees of consistency for T1 and T2 that makes this
schedule possible. (2 points)
(b) Consider a transaction that reads the information about a set
of accounts from a CSV file
and writes them in a database. What degree of consistency will
you choose for this transaction?
3
30. T1 T2
0 start
1 read X
2 write X
3 start
4 read X
5 write X
6 Commit
7 read Y
8 write Y
9 Commit
Table 1: Transaction schedule
You should justify your answer. Next, consider another
transaction in a banking system that
reads the balances of all bank accounts in a branch and
computes their average. What degree of
consistency will you choose for this transaction? You should
justify your answer. (2 points)
6: Serializability (6 points)
Consider the following protocol for concurrency control. The
database system assigns each
transaction a unique and strictly increasingly id at the start of
the transaction. For each data
item, the database system also keeps the id of the last
transaction that has modified the data
item, called the transaction-id of the data item. Before a
31. transaction T wants to read or write on
a data item A, the database system checks whether the
transaction-id of A is greater than the id
of T . If this is the case, the database system allows T to
read/write A. Otherwise, the database
system aborts and restarts T .
(a) Does this protocol allow only serializable schedules for
transactions? If not, you may suggest
a change to the protocol so that all schedules permitted by this
protocol are serializable. You
should justify your answer. (3 points)
(b) Propose a change to this protocol or the modified version
you have designed for part (a) that
increases its degree of concurrency, i.e., it allows more
serializable schedules. (3 points)
1: Query optimization (2 points)2: Query optimization (4
points)3: Query optimization (2 points)4: Serializability and
2PL (5 points)5: Degrees of Consistency (4 points)6:
Serializability (6 points)