1. There are several algorithms to implement joins, including nested-loop, block nested-loop, indexed nested-loop, merge-join, and hash-join. The choice depends on the estimated cost.
2. Nested-loop join examines every pair of tuples from the outer and inner relations, making it expensive. Block nested-loop join reduces cost by iterating over blocks rather than tuples.
3. Indexed nested-loop join uses an index on the inner relation to lookup matching tuples, improving performance over nested-loop joins when an index is available.
Different algorithms can be used to implement joins in a database, including nested loop, block nested loop, indexed nested loop, merge, and hash joins. The optimal algorithm depends on factors like whether indexes are available on the joined attributes and the relative sizes and block distributions of the relations. Database tuning involves monitoring performance and adjusting aspects like indexes, queries, and design to improve response times and throughput.
This document provides an overview of query processing costs, selection operations, join operations, and concurrency control in database systems. It discusses how the costs of queries are estimated based on factors like disk accesses and seeks. It then describes algorithms for common operations like selection, join, and concurrency control protocols. Selection algorithms include file scan, binary search, and using indexes. Join algorithms include nested loops, block nested loops, indexed nested loops, merge join, and hash join. Concurrency control protocols help manage concurrent transaction executions and maintain consistency.
This document discusses query processing in a database system. It covers parsing queries, optimization to choose the most efficient evaluation plan, and executing the plan. Query optimization aims to minimize costs like I/O by choosing plans with the lowest estimated execution time. The document describes different algorithms for operations like selection, sorting, joins, and expression evaluation, and how equivalence rules and heuristics can transform queries into more efficient forms.
Hey friends, here is my "query tree" assignment. :-) I have searched a lot to get this master piece :p and I can guarantee you that this one gonna help you In Sha ALLAH more than any else document on the subject. Have a good day :-)
The document discusses query processing and optimization. It describes several key activities in query processing including translating queries to a format executable by the database, applying optimization techniques, and evaluating the queries. It then provides details on three specific operations: selection using linear searches and indices, sorting, and join operations. It explains different algorithms for implementing each operation and factors to consider when choosing algorithms such as indexing and data sizes.
The document provides an introduction to Bloom filters, which are a space-efficient probabilistic data structure used to represent a set in a way that supports membership queries. The key properties of Bloom filters are that they allow insertion of elements and probabilistic membership queries with no false negatives but possible false positives. Variants of Bloom filters have been developed to support operations like deletions, counting of elements, and space efficiency. Bloom filters and their variants have numerous applications in distributed systems, including caching, peer-to-peer networks, and network monitoring.
The document discusses different types of tree traversals including preorder, inorder, postorder, and level order traversal. It provides examples of how to traverse a binary tree using each method and describes their visiting order. Additionally, it discusses applications of trees like binary search trees, expression trees, Huffman coding trees, and using trees to represent sets and perform union/find operations. The Huffman algorithm for data compression is explained in detail with an example.
The document discusses different algorithms for performing joins between two database tables:
1. Simple nested loops join compares each tuple in one table to every tuple in the other table, resulting in very high I/O costs.
2. Block nested loops join partitions one table into blocks that fit in memory, joining each block to the other table to reduce I/O.
3. Index nested loops join uses an index on the join column to lookup matching tuples, reducing I/O costs compared to nested loops. The document provides examples comparing the I/O costs of applying different join algorithms to sample tables.
Different algorithms can be used to implement joins in a database, including nested loop, block nested loop, indexed nested loop, merge, and hash joins. The optimal algorithm depends on factors like whether indexes are available on the joined attributes and the relative sizes and block distributions of the relations. Database tuning involves monitoring performance and adjusting aspects like indexes, queries, and design to improve response times and throughput.
This document provides an overview of query processing costs, selection operations, join operations, and concurrency control in database systems. It discusses how the costs of queries are estimated based on factors like disk accesses and seeks. It then describes algorithms for common operations like selection, join, and concurrency control protocols. Selection algorithms include file scan, binary search, and using indexes. Join algorithms include nested loops, block nested loops, indexed nested loops, merge join, and hash join. Concurrency control protocols help manage concurrent transaction executions and maintain consistency.
This document discusses query processing in a database system. It covers parsing queries, optimization to choose the most efficient evaluation plan, and executing the plan. Query optimization aims to minimize costs like I/O by choosing plans with the lowest estimated execution time. The document describes different algorithms for operations like selection, sorting, joins, and expression evaluation, and how equivalence rules and heuristics can transform queries into more efficient forms.
Hey friends, here is my "query tree" assignment. :-) I have searched a lot to get this master piece :p and I can guarantee you that this one gonna help you In Sha ALLAH more than any else document on the subject. Have a good day :-)
The document discusses query processing and optimization. It describes several key activities in query processing including translating queries to a format executable by the database, applying optimization techniques, and evaluating the queries. It then provides details on three specific operations: selection using linear searches and indices, sorting, and join operations. It explains different algorithms for implementing each operation and factors to consider when choosing algorithms such as indexing and data sizes.
The document provides an introduction to Bloom filters, which are a space-efficient probabilistic data structure used to represent a set in a way that supports membership queries. The key properties of Bloom filters are that they allow insertion of elements and probabilistic membership queries with no false negatives but possible false positives. Variants of Bloom filters have been developed to support operations like deletions, counting of elements, and space efficiency. Bloom filters and their variants have numerous applications in distributed systems, including caching, peer-to-peer networks, and network monitoring.
The document discusses different types of tree traversals including preorder, inorder, postorder, and level order traversal. It provides examples of how to traverse a binary tree using each method and describes their visiting order. Additionally, it discusses applications of trees like binary search trees, expression trees, Huffman coding trees, and using trees to represent sets and perform union/find operations. The Huffman algorithm for data compression is explained in detail with an example.
The document discusses different algorithms for performing joins between two database tables:
1. Simple nested loops join compares each tuple in one table to every tuple in the other table, resulting in very high I/O costs.
2. Block nested loops join partitions one table into blocks that fit in memory, joining each block to the other table to reduce I/O.
3. Index nested loops join uses an index on the join column to lookup matching tuples, reducing I/O costs compared to nested loops. The document provides examples comparing the I/O costs of applying different join algorithms to sample tables.
Relational database management systems process queries through translation, optimization, and evaluation. Query processing involves activities like translating queries into expressions executable by the physical file system, optimizing queries through transformations, and evaluating queries. Selection operations retrieve records matching conditions through techniques like linear scanning, indexing on key attributes for equality selections, and using indices and comparisons for range selections. Joins are implemented through algorithms like nested loops, merge and hash joins. Query optimization considers alternative evaluation plans by choosing different algorithms and operations ordering.
Query Processing, Query Optimization and TransactionPrabu U
This document provides an overview of query processing and optimization techniques in database management systems. It discusses measures of query cost, various query operations like selection, sorting, joining, and aggregation. It also covers transaction processing concepts like atomicity, durability, and isolation levels. Specific algorithms covered include nested-loop join, merge join, hash join, and their cost analysis. The document is divided into sections on query processing, transaction processing, and covers various operations involved in query evaluation and optimization.
The document discusses query optimization in databases. It explains that the goal of query optimization is to determine the most efficient execution plan for a query to minimize the time needed. It outlines the typical steps in query optimization, including parsing/translation, applying relational algebra, and optimizing the query plan. It also discusses techniques like generating alternative execution plans using equivalence rules, estimating plan costs based on statistical data, and using heuristics or dynamic programming to choose the optimal plan.
The document discusses several topics:
1. It explains the stream data model architecture with a diagram showing streams entering a processing system and being stored in an archival store or working store.
2. It defines a Bloom filter and describes how to calculate the probability of a false positive.
3. It outlines the Girvan-Newman algorithm for detecting communities in a graph by calculating betweenness values and removing edges.
4. It mentions PageRank and the Flajolet-Martin algorithm for approximating the number of unique objects in a data stream.
The document discusses various steps and algorithms for processing database queries. It covers parsing and optimizing queries, estimating query costs, and algorithms for operations like selection, sorting, and joins. Selection algorithms include linear scans, binary searches, and using indexes. Sorting can use indexes or external merge sort. Join algorithms include nested loops, merge join, and hash join.
This document discusses various sorting algorithms that can be used on parallel computers. It begins with an overview of sorting and comparison-based sorting algorithms. It then covers sorting networks like bitonic sort, which can sort in parallel using a network of comparators. It discusses how bitonic sort can be mapped to hypercubes and meshes. It also covers parallel implementations of bubble sort variants, quicksort, and shellsort. For each algorithm, it analyzes the parallel runtime and efficiency. The document provides examples and diagrams to illustrate the sorting networks and parallel algorithms.
1. This chapter focuses on dynamic versions of R-trees and examines various R-tree variants, their similarities and differences, advantages and disadvantages.
2. The R*-tree considers additional factors like overlap minimization and storage utilization to improve performance compared to the original R-tree.
3. The Hilbert R-tree is a hybrid of an R-tree and B+-tree that stores entries based on their Hilbert values and can outperform other variants in some cases.
This chapter discusses several dynamic versions of R-trees that were proposed to address disadvantages of the original R-tree structure, including the R+-tree, R*-tree, Hilbert R-tree, and PR-tree. It covers aspects like node splitting algorithms, storage utilization improvements in variants like the compact R-tree and cR-tree, and concludes that the PR-tree has guaranteed worst-case performance and is the best reported variant so far.
The first lecture of the ACM Aleppo CPC training. The local contest of ICPC. This lecture will help you get started in programming contests word with the lower bound techniques. The lectures focus on the C++ programming language and the STL library to solve programming problems.
Four main types of probabilistic data structures are described: membership, cardinality, frequency, and similarity. Bloom filters and cuckoo filters are discussed as membership data structures that can tell if an element is definitely not or may be in a set. Cardinality structures like HyperLogLog are able to estimate large cardinalities with small error rates. Count-Min Sketch is presented as a frequency data structure. MinHash and locality sensitive hashing are covered as similarity data structures that can efficiently find similar documents in large datasets.
This document discusses different cache mapping schemes. It begins by explaining the goals of cache mapping and some cache design challenges. It then describes the three main mapping schemes: direct mapping, set associative mapping, and fully associative mapping. For each scheme it provides examples to illustrate how an address is broken down into tag, set or line, and offset fields. The document also discusses cache replacement policies and provides a comparison of the different mapping schemes.
The document discusses access paths in database management systems. It covers hashing and B-trees as the two main techniques used. Hashing maps attribute values to database addresses using a hash function, but requires reorganization if the file size changes. B-trees support efficient retrieval, range queries, and dynamic resizing through a balanced tree structure with index and leaf nodes. The document provides details on properties, implementation, and optimizations of hashing and B-trees.
The document provides 29 sample questions on data structures and algorithms along with their answers. Some of the key questions covered include:
1. What is a data structure and examples of areas where they are applied extensively such as compiler design, operating systems, etc.
2. Major data structures used in relational databases, network and hierarchical data models.
3. Data structures used to perform recursion and evaluate arithmetic expressions.
4. Sorting algorithms like quicksort illustrated through an example.
5. Properties of different trees including the number of possible trees with a given number of nodes and number of null branches in a binary tree.
The summary hits the main topics covered in the document such as common
Hash Tables
The memory available to maintain the symbol table is assumed to be sequential. This memory is referred to as the hash table, HT. The term bucket denotes a unit of storage that can store one or more records. A bucket is typically one disk block size but could be chosen to be smaller or larger than a disk block.
If the number of buckets in a Hash table HT is b, then the buckets are designated HT(0), ... HT(b-1). Each bucket is capable of holding one or more records. The number of records a bucket can store is known as its slot-size. Thus, a bucket is said to consist of s slots, if it can hold s number of records in it.
A function that is used to compute the address of a record in the hash table, is known as a hash function. Usually, s = 1 and in this case each bucket can hold exactly 1 record.
Writing a TSDB from scratch_ performance optimizations.pdfRomanKhavronenko
I'm one of maintainers of the open source TimeSeries database VictoriaMetrics, written in Go. It is used for APM or Kubernetes monitoring. The average VictoriaMetrics installation is processing 2-4 million samples/s on the ingestion path, 20-40 million samples/s on the read path. The biggest installations have more than 100 million samples/s on the ingestion path for a single cluster. This requires being clever with data processing to keep it efficient and scalable. In the talk, I'll cover the following optimizations for keeping the database fast:
1. String interning for lowering GC pressure. We use string interning for storing time series metadata (aka labels). However, this approach has downside of increased memory usage. When it is worth it to use string interning?
2. Metadata processing may require many regular expression matching and strings modification operations. Caching results of such operations helps to save CPU. But the downside could be increased memory usage. Which operations should be cached and which are not?
3. Limiting the number of concurrently running goroutines with CPU-bound load by the number of available CPU cores. This helps to control the memory usage on load spikes (which is a frequent event in monitoring). The limit also improves the processing speed of each goroutine, since it reduces the number of context switches. The downside of the approach is its complexity - it is easy to make a mistake and end up with a deadlock or inefficient resource utilization.
4. The better understanding of `sync.Pool`. For us, `sync.Pool` shows itself the best when used in CPU-bound code, while in IO-bound code it leads to excessive memory usage. The CPU-bound code has short ownership over the objects retrieved from the pool. In combination with p.3 (limited number of goroutines processing CPU-bound code) it gives the most efficient processing speed and memory usage since the chance to get a "hot" object from the pool is much higher.
- Binary trees are a data structure where each element has up to two children, allowing elements to be organized in a hierarchical tree-like structure. This allows items to be stored and searched more efficiently than linear data structures like linked lists.
- Searching and inserting items into a balanced binary tree takes O(log n) time, where n is the number of nodes, as each operation requires traversing a branching path proportional to the log of the number of elements. For large n, this provides much faster access than the O(n) time required to search a linked list.
- Binary trees can be traversed in different orders like in-order, pre-order and post-order to process or output the elements
This document discusses representing Geneve encapsulation metadata in OpenFlow flows. It proposes mapping each Geneve option to an OpenFlow experimenter OXM field, allowing multiple tun_metadata matches per flow. It outlines changes needed to OVS internals to support parsing variable length tun_metadata and indexing into the match field. Finally it discusses additional considerations like supporting large Geneve option payloads and handling critical vs. non-critical options.
Enhancing Spark SQL Optimizer with Reliable StatisticsJen Aman
This document discusses enhancements to the Spark SQL optimizer through improved statistics collection and cost-based optimization rules. It describes collecting table and column statistics from Hive metastore and developing 1D and 2D histograms. New rules estimate operator costs based on output rows and size. Join order, filter statistics, and handling unique columns are discussed. Future work includes faster histogram collection, expression statistics, and continuous feedback optimization.
Linear data structures include arrays, strings, stacks, queues, and lists. Arrays store elements contiguously in memory, allowing efficient random access. Linked lists store elements non-contiguously, with each element containing a data field and pointer to the next element. This allows dynamic sizes but less efficient random access. Linear data structures are ordered, with each element having a single predecessor and successor except for the first and last elements.
The document discusses the entity-relationship (E-R) data model. It was proposed by Dr. Peter Chen and represents data graphically using entity sets, attributes, and relationships. Entity sets are collections of abstract objects, attributes are concrete data used to define entity sets, and relationships specify connections between entity sets. The E-R model uses a diagram with rectangles for entity sets, ovals for attributes, and diamonds for relationships. It allows modeling of one-to-one, many-to-one, and many-to-many connections between entity sets. The model aims to faithfully represent specifications simply without redundancy.
1. Magnetic disks are the primary storage medium for databases due to their large storage capacity and reliability. Disks store data in circular tracks divided into sectors, with read/write heads positioning over tracks to access data.
2. RAID (Redundant Arrays of Independent Disks) organizes multiple disks for improved performance, capacity, and reliability. Techniques like mirroring duplicate data across disks for fault tolerance, while striping distributes data across disks to enable parallel access.
3. Database designers must choose an appropriate RAID level based on factors like update frequency, capacity needs, and performance requirements to optimize the physical storage structure.
Relational database management systems process queries through translation, optimization, and evaluation. Query processing involves activities like translating queries into expressions executable by the physical file system, optimizing queries through transformations, and evaluating queries. Selection operations retrieve records matching conditions through techniques like linear scanning, indexing on key attributes for equality selections, and using indices and comparisons for range selections. Joins are implemented through algorithms like nested loops, merge and hash joins. Query optimization considers alternative evaluation plans by choosing different algorithms and operations ordering.
Query Processing, Query Optimization and TransactionPrabu U
This document provides an overview of query processing and optimization techniques in database management systems. It discusses measures of query cost, various query operations like selection, sorting, joining, and aggregation. It also covers transaction processing concepts like atomicity, durability, and isolation levels. Specific algorithms covered include nested-loop join, merge join, hash join, and their cost analysis. The document is divided into sections on query processing, transaction processing, and covers various operations involved in query evaluation and optimization.
The document discusses query optimization in databases. It explains that the goal of query optimization is to determine the most efficient execution plan for a query to minimize the time needed. It outlines the typical steps in query optimization, including parsing/translation, applying relational algebra, and optimizing the query plan. It also discusses techniques like generating alternative execution plans using equivalence rules, estimating plan costs based on statistical data, and using heuristics or dynamic programming to choose the optimal plan.
The document discusses several topics:
1. It explains the stream data model architecture with a diagram showing streams entering a processing system and being stored in an archival store or working store.
2. It defines a Bloom filter and describes how to calculate the probability of a false positive.
3. It outlines the Girvan-Newman algorithm for detecting communities in a graph by calculating betweenness values and removing edges.
4. It mentions PageRank and the Flajolet-Martin algorithm for approximating the number of unique objects in a data stream.
The document discusses various steps and algorithms for processing database queries. It covers parsing and optimizing queries, estimating query costs, and algorithms for operations like selection, sorting, and joins. Selection algorithms include linear scans, binary searches, and using indexes. Sorting can use indexes or external merge sort. Join algorithms include nested loops, merge join, and hash join.
This document discusses various sorting algorithms that can be used on parallel computers. It begins with an overview of sorting and comparison-based sorting algorithms. It then covers sorting networks like bitonic sort, which can sort in parallel using a network of comparators. It discusses how bitonic sort can be mapped to hypercubes and meshes. It also covers parallel implementations of bubble sort variants, quicksort, and shellsort. For each algorithm, it analyzes the parallel runtime and efficiency. The document provides examples and diagrams to illustrate the sorting networks and parallel algorithms.
1. This chapter focuses on dynamic versions of R-trees and examines various R-tree variants, their similarities and differences, advantages and disadvantages.
2. The R*-tree considers additional factors like overlap minimization and storage utilization to improve performance compared to the original R-tree.
3. The Hilbert R-tree is a hybrid of an R-tree and B+-tree that stores entries based on their Hilbert values and can outperform other variants in some cases.
This chapter discusses several dynamic versions of R-trees that were proposed to address disadvantages of the original R-tree structure, including the R+-tree, R*-tree, Hilbert R-tree, and PR-tree. It covers aspects like node splitting algorithms, storage utilization improvements in variants like the compact R-tree and cR-tree, and concludes that the PR-tree has guaranteed worst-case performance and is the best reported variant so far.
The first lecture of the ACM Aleppo CPC training. The local contest of ICPC. This lecture will help you get started in programming contests word with the lower bound techniques. The lectures focus on the C++ programming language and the STL library to solve programming problems.
Four main types of probabilistic data structures are described: membership, cardinality, frequency, and similarity. Bloom filters and cuckoo filters are discussed as membership data structures that can tell if an element is definitely not or may be in a set. Cardinality structures like HyperLogLog are able to estimate large cardinalities with small error rates. Count-Min Sketch is presented as a frequency data structure. MinHash and locality sensitive hashing are covered as similarity data structures that can efficiently find similar documents in large datasets.
This document discusses different cache mapping schemes. It begins by explaining the goals of cache mapping and some cache design challenges. It then describes the three main mapping schemes: direct mapping, set associative mapping, and fully associative mapping. For each scheme it provides examples to illustrate how an address is broken down into tag, set or line, and offset fields. The document also discusses cache replacement policies and provides a comparison of the different mapping schemes.
The document discusses access paths in database management systems. It covers hashing and B-trees as the two main techniques used. Hashing maps attribute values to database addresses using a hash function, but requires reorganization if the file size changes. B-trees support efficient retrieval, range queries, and dynamic resizing through a balanced tree structure with index and leaf nodes. The document provides details on properties, implementation, and optimizations of hashing and B-trees.
The document provides 29 sample questions on data structures and algorithms along with their answers. Some of the key questions covered include:
1. What is a data structure and examples of areas where they are applied extensively such as compiler design, operating systems, etc.
2. Major data structures used in relational databases, network and hierarchical data models.
3. Data structures used to perform recursion and evaluate arithmetic expressions.
4. Sorting algorithms like quicksort illustrated through an example.
5. Properties of different trees including the number of possible trees with a given number of nodes and number of null branches in a binary tree.
The summary hits the main topics covered in the document such as common
Hash Tables
The memory available to maintain the symbol table is assumed to be sequential. This memory is referred to as the hash table, HT. The term bucket denotes a unit of storage that can store one or more records. A bucket is typically one disk block size but could be chosen to be smaller or larger than a disk block.
If the number of buckets in a Hash table HT is b, then the buckets are designated HT(0), ... HT(b-1). Each bucket is capable of holding one or more records. The number of records a bucket can store is known as its slot-size. Thus, a bucket is said to consist of s slots, if it can hold s number of records in it.
A function that is used to compute the address of a record in the hash table, is known as a hash function. Usually, s = 1 and in this case each bucket can hold exactly 1 record.
Writing a TSDB from scratch_ performance optimizations.pdfRomanKhavronenko
I'm one of maintainers of the open source TimeSeries database VictoriaMetrics, written in Go. It is used for APM or Kubernetes monitoring. The average VictoriaMetrics installation is processing 2-4 million samples/s on the ingestion path, 20-40 million samples/s on the read path. The biggest installations have more than 100 million samples/s on the ingestion path for a single cluster. This requires being clever with data processing to keep it efficient and scalable. In the talk, I'll cover the following optimizations for keeping the database fast:
1. String interning for lowering GC pressure. We use string interning for storing time series metadata (aka labels). However, this approach has downside of increased memory usage. When it is worth it to use string interning?
2. Metadata processing may require many regular expression matching and strings modification operations. Caching results of such operations helps to save CPU. But the downside could be increased memory usage. Which operations should be cached and which are not?
3. Limiting the number of concurrently running goroutines with CPU-bound load by the number of available CPU cores. This helps to control the memory usage on load spikes (which is a frequent event in monitoring). The limit also improves the processing speed of each goroutine, since it reduces the number of context switches. The downside of the approach is its complexity - it is easy to make a mistake and end up with a deadlock or inefficient resource utilization.
4. The better understanding of `sync.Pool`. For us, `sync.Pool` shows itself the best when used in CPU-bound code, while in IO-bound code it leads to excessive memory usage. The CPU-bound code has short ownership over the objects retrieved from the pool. In combination with p.3 (limited number of goroutines processing CPU-bound code) it gives the most efficient processing speed and memory usage since the chance to get a "hot" object from the pool is much higher.
- Binary trees are a data structure where each element has up to two children, allowing elements to be organized in a hierarchical tree-like structure. This allows items to be stored and searched more efficiently than linear data structures like linked lists.
- Searching and inserting items into a balanced binary tree takes O(log n) time, where n is the number of nodes, as each operation requires traversing a branching path proportional to the log of the number of elements. For large n, this provides much faster access than the O(n) time required to search a linked list.
- Binary trees can be traversed in different orders like in-order, pre-order and post-order to process or output the elements
This document discusses representing Geneve encapsulation metadata in OpenFlow flows. It proposes mapping each Geneve option to an OpenFlow experimenter OXM field, allowing multiple tun_metadata matches per flow. It outlines changes needed to OVS internals to support parsing variable length tun_metadata and indexing into the match field. Finally it discusses additional considerations like supporting large Geneve option payloads and handling critical vs. non-critical options.
Enhancing Spark SQL Optimizer with Reliable StatisticsJen Aman
This document discusses enhancements to the Spark SQL optimizer through improved statistics collection and cost-based optimization rules. It describes collecting table and column statistics from Hive metastore and developing 1D and 2D histograms. New rules estimate operator costs based on output rows and size. Join order, filter statistics, and handling unique columns are discussed. Future work includes faster histogram collection, expression statistics, and continuous feedback optimization.
Linear data structures include arrays, strings, stacks, queues, and lists. Arrays store elements contiguously in memory, allowing efficient random access. Linked lists store elements non-contiguously, with each element containing a data field and pointer to the next element. This allows dynamic sizes but less efficient random access. Linear data structures are ordered, with each element having a single predecessor and successor except for the first and last elements.
The document discusses the entity-relationship (E-R) data model. It was proposed by Dr. Peter Chen and represents data graphically using entity sets, attributes, and relationships. Entity sets are collections of abstract objects, attributes are concrete data used to define entity sets, and relationships specify connections between entity sets. The E-R model uses a diagram with rectangles for entity sets, ovals for attributes, and diamonds for relationships. It allows modeling of one-to-one, many-to-one, and many-to-many connections between entity sets. The model aims to faithfully represent specifications simply without redundancy.
1. Magnetic disks are the primary storage medium for databases due to their large storage capacity and reliability. Disks store data in circular tracks divided into sectors, with read/write heads positioning over tracks to access data.
2. RAID (Redundant Arrays of Independent Disks) organizes multiple disks for improved performance, capacity, and reliability. Techniques like mirroring duplicate data across disks for fault tolerance, while striping distributes data across disks to enable parallel access.
3. Database designers must choose an appropriate RAID level based on factors like update frequency, capacity needs, and performance requirements to optimize the physical storage structure.
This document discusses indexing and hashing techniques for improving data access performance in databases. It covers basic concepts of indexing, different index structures like B-trees and hash indices, and algorithms for insertion, deletion and searching in these index structures. B-trees are described as self-balancing tree structures commonly used for indexing database files. The document provides examples and details on B-tree structure, algorithms and performance.
This document provides an introduction to structured query language (SQL). It outlines the basic commands and functions of SQL for data administration and manipulation. These include using SQL to create tables, indexes and views; and to add, modify, delete and retrieve data. The document also discusses SQL queries, constraints, indexes, joins and aggregate functions to extract useful information from databases.
The document discusses different techniques for generating animation frames between two key frames. These include:
1. Interpolating positions of objects between key frames using motion paths defined by spline curves or physical forces.
2. Separating complex scenes into individual components called "cels" and interpolating their positions.
3. Morphing the shapes of objects overtime through techniques that adjust polygon edges and vertex positions between key frames.
4. Using curve-fitting techniques to specify animation paths by fitting key frame vertex positions with linear or nonlinear splines.
The document discusses Fourier series and related concepts:
- Fourier series decompose a function into a weighted sum of sinusoids, with the weights determined by the Fourier transform.
- They can represent both real and complex functions as a sum of coefficients and a particular series.
- Fourier series generally converge everywhere except at discontinuities and converge to the function at almost every point, according to Dirichlet's condition.
The document discusses animation design and control. Animation functions include a graphics editor, key frame generator, in-between generator, and standard graphics routines. The graphics editor allows designing and modifying object shapes. Scene description in animation specification includes object and light positioning, photometric and camera parameters. Action specification involves motion paths for objects and cameras. Standard graphics routines include viewing transformations and rendering operations. Key-frame systems generate in-betweens from user-specified key frames. Parameterized systems allow specifying object motion characteristics as part of object definitions. Scripting systems define object specifications and animation sequences through a user-input script.
Three-dimensional objects have height, width, and depth, like real-world objects that possess these three dimensions. For example, the human body is three-dimensional. Three-dimensional objects are also commonly referred to as 3D objects. In contrast, two-dimensional objects only have height and width.
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.
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
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.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
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A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
1. Join Operation
• Several different algorithms to implement joins exist (not
counting with the ones involving parallelism)
– Nested-loop join
– Block nested-loop join
– Indexed nested-loop join
– Merge-join
– Hash-join
• As for selection, the choice is based on cost estimate
• Examples in next slides use the following information
– Number of records of customer: 10.000 depositor: 5.000
– Number of blocks of customer: 400 depositor: 100
2. Nested-Loop Join
• The simplest join algorithms, that can be used independently of
everything (like the linear search for selection)
• To compute the theta join r s
for each tuple tr in r do begin
for each tuple ts in s do begin
test pair (tr,ts) to see if they satisfy the join condition
if they do, add tr • ts to the result.
end
end
• r is called the outer relation and s the inner relation of the join.
• Quite expensive in general, since it requires to examine every
pair of tuples in the two relations.
3. Nested-Loop Join (Cont.)
• In the worst case, if there is enough memory only to hold
one block of each relation, the estimated cost is
nr bs + br
block transfers, plus
nr + br
seeks
• If the smaller relation fits entirely in memory, use that as
the inner relation.
– Reduces cost to br + bs block transfers and 2 seeks
• In general, it is much better to have the smaller relation
as the outer relation
– The number of block transfers is multiplied by the number of
blocks of the inner relation
• However, if the smaller relation is small enough to fit in
memory, one should use it as the inner relation!
• The choice of the inner and outer relation strongly
depends on the estimate of the size of each relation
4. Nested-Loop Join Cost in Example
• Assuming worst case memory availability cost estimate is
– with depositor as outer relation:
• 5.000 400 + 100 = 2.000.100 block transfers,
• 5.000 + 100 = 5100 seeks
– with customer as the outer relation
• 10.000 100 + 400 = 1.000.400 block transfers, 10.400 seeks
• If smaller relation (depositor) fits entirely in memory, the
cost estimate will be 500 block transfers and 2 seeks
• Instead of iterating over records, one could iterate over
blocks. This way, instead of nr bs + br we would have br bs + br
block transfers
• This is the basis of the block nested-loops algorithm
(next slide).
5. Block Nested-Loop Join
• Variant of nested-loop join in which every block
of inner relation is paired with every block of
outer relation.
for each block Br of r do begin
for each block Bs of s do begin
for each tuple tr in Br do begin
for each tuple ts in Bs do begin
Check if (tr,ts) satisfy the join
condition
if they do, add tr • ts to the result.
end
end
end
end
6. Block Nested-Loop Join Cost
• Worst case estimate: br bs + br block transfers and 2
* br seeks
– Each block in the inner relation s is read once for each
block in the outer relation (instead of once for each tuple
in the outer relation
• Best case (when smaller relation fits into memory): br +
bs block transfers plus 2 seeks.
• Some improvements to nested loop and block nested
loop algorithms can be made:
– Scan inner loop forward and backward alternately, to make
use of the blocks remaining in buffer (with LRU
replacement)
– Use index on inner relation if available to faster get the
tuples which match the tuple of the outer relation at hands
7. Indexed Nested-Loop Join
• Index lookups can replace file scans if
– join is an equi-join or natural join and
– an index is available on the inner relation’s join attribute
• In some cases, it pays to construct an index just to compute a join.
• For each tuple tr in the outer relation r, use the index on s to look
up tuples in s that satisfy the join condition with tuple tr.
• Worst case: buffer has space for only one page of r, and, for each
tuple in r, we perform an index lookup on s.
• Cost of the join: br (tT + tS) + nr c
– Where c is the cost of traversing index and fetching all matching s
tuples for one tuple or r
– c can be estimated as cost of a single selection on s using the join
condition (usually quite low, when compared to the join)
• If indices are available on join attributes of both r and s,
use the relation with fewer tuples as the outer relation.
8. Example of Nested-Loop Join Costs
• Compute depositor customer, with depositor as the outer
relation.
• Let customer have a primary B+-tree index on the join attribute
customer-name, which contains 20 entries in each index node.
• Since customer has 10.000 tuples, the height of the tree is 4,
and one more access is needed to find the actual data
• depositor has 5.000 tuples
• For nested loop: 2.000.100 block transfers and 5.100 seeks
• Cost of block nested loops join
– 400*100 + 100 = 40.100 block transfers + 2 * 100 = 200 seeks
• Cost of indexed nested loops join
– 100 + 5.000 * 5 = 25.100 block transfers and seeks.
– CPU cost likely to be less than that for block nested loops join
– However in terms of time for transfers and seeks, in this case
using the index doesn’t pay (this is specially so because the
relations are small)
9. Merge-Join
1. Sort both relations on their join attribute (if not already sorted on
the join attributes).
2. Merge the sorted relations to join them
1. Join step is similar to the merge stage of the sort-merge algorithm.
2. Main difference is handling of duplicate values in join attribute —
every pair with same value on join attribute must be matched
3. Detailed algorithm may be found in the book
10. Merge-Join (Cont.)
• Can be used only for equi-joins and natural joins
• Each block needs to be read only once (assuming that all tuples
for any given value of the join attributes fit in memory)
• Thus the cost of merge join is (where bb is the number of blocks
in allocated in memory):
br + bs block transfers + br / bb + bs / bb seeks
– + the cost of sorting if relations are unsorted.
• hybrid merge-join: If one relation is sorted, and the other has a
secondary B+-tree index on the join attribute
– Merge the sorted relation with the leaf entries of the B+-tree .
– Sort the result on the addresses of the unsorted relation’s tuples
– Scan the unsorted relation in physical address order and merge with
previous result, to replace addresses by the actual tuples
• Sequential scan more efficient than random lookup
11. Hash-Join
• Also only applicable for equi-joins and natural joins.
• A hash function h is used to partition tuples of both
relations
• h maps JoinAttrs values to {0, 1, ..., n}, where JoinAttrs
denotes the common attributes of r and s used in the
natural join.
– r0, r1, . . ., rn denote partitions of r tuples
• Each tuple tr r is put in partition ri where i = h(tr [JoinAttrs]).
– s0, s1. . ., sn denotes partitions of s tuples
• Each tuple ts s is put in partition si, where i = h(ts [JoinAttrs]).
• General idea:
– Partition the relations according to this
– Then perform the join on each partition ri and si
• There is no need to compute the join between different
partitions since an r tuple and an s tuple that satisfy the join
condition will have the same value for the join attributes. If that
value is hashed to some value i, the r tuple has to be in ri and
the s tuple in si.
13. Hash-Join Algorithm
1. Partition the relation s using hashing function h.
When partitioning a relation, one block of memory is
reserved as the output buffer for each partition.
2. Partition r similarly.
3. For each i:
(a) Load si into memory and build an in-memory hash
index on it using the join attribute. This hash index uses
a different hash function than the earlier one h.
(b) Read the tuples in ri from the disk one by one. For each
tuple tr locate each matching tuple ts in si using the in-
memory hash index. Output the concatenation of their
attributes.
The hash-join of r and s is computed as follows.
Relation s is called the build input and
r is called the probe input.
14. Hash-Join algorithm (Cont.)
• The number of partitions n for the hash function h is
chosen such that each si should fit in memory.
– Typically n is chosen as bs/M * f where f is a “fudge
factor”, typically around 1.2, to avoid overflows
– The probe relation partitions ri need not fit in memory
• Recursive partitioning required if number of
partitions n is greater than number of pages M of
memory.
– instead of partitioning n ways, use M – 1 partitions for s
– Further partition the M – 1 partitions using a different
hash function
– Use same partitioning method on r
– Rarely required: e.g., recursive partitioning not needed
for relations of 1GB or less with memory size of 2MB, with
block size of 4KB.
• So is not further considered here (see the book for details on
the associated costs)
15. Cost of Hash-Join
• The cost of hash join is
3(br + bs) +4 nh block transfers +
2( br / bb + bs / bb) seeks
• If the entire build input can be kept in main memory no
partitioning is required
– Cost estimate goes down to br + bs.
• For the running example, assume that memory size is 20 blocks
• bdepositor= 100 and bcustomer = 400.
• depositor is to be used as build input. Partition it into five
partitions, each of size 20 blocks. This partitioning can be done
in one pass.
• Similarly, partition customer into five partitions, each of size 80.
This is also done in one pass.
• Therefore total cost, ignoring cost of writing partially filled
blocks:
– 3(100 + 400) = 1.500 block transfers +
2( 100/3 + 400/3) = 336 seeks
• The best we had up to here was 40.100 block transfers plus 200
seeks (for block nested loop) or 25,100 block transfers and
seeks (for index nested loop)
16. Complex Joins
• Join with a conjunctive condition:
r 1 2... n s
– Either use nested loops/block nested loops, or
– Compute the result of one of the simpler joins r i s
• final result comprises those tuples in the intermediate
result that satisfy the remaining conditions
1 . . . i –1 i +1 . . . n
• Join with a disjunctive condition
r 1 2 ... n s
– Either use nested loops/block nested loops, or
– Compute as the union of the records in individual
joins r i s:
(r 1 s) (r 2 s) . . . (r n s)