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Main Memory database systems store data primarily in main memory for faster access compared to disk-based systems. The T tree is proposed as an index structure for main memory databases that provides fast search, insert and delete performance while using relatively little memory space. The Dali storage manager is designed for main memory databases and provides persistence, availability and recovery guarantees similar to disk-based databases through the use of logging, locking, checkpointing and other techniques while leveraging the speed of main memory.
This document discusses different methods for organizing and indexing data stored on disk in a database management system (DBMS). It covers unordered or heap files, ordered or sequential files, and hash files as methods for physically arranging records on disk. It also discusses various indexing techniques like primary indexes, secondary indexes, dense vs sparse indexes, and multi-level indexes like B-trees and B+-trees that provide efficient access to records. The goal of file organization and indexing in a DBMS is to optimize performance for operations like inserting, searching, updating and deleting records from disk files.
This document discusses storage techniques used in databases, including various file organization methods and RAID levels. It describes primary memory, secondary memory, and tertiary memory. It explains fixed length records and variable length records approaches to file organization. Common file organization methods include heap files, hashing, B+ trees, clustered files, sequential files, and piles. RAID levels such as RAID 0, 1, 2, 3, and 4 are also summarized, explaining how they provide redundancy and performance improvements through techniques like mirroring, striping, and parity bits.
This document discusses different types of data storage used in database management systems, including primary storage (main memory and cache), secondary storage (flash memory and magnetic disk storage), and tertiary storage (optical storage and tape storage). It also covers various file organization methods like sequential, heap, hash, and cluster, as well as indexing methods like ordered indices, primary indexing, and secondary indexing. The goal of file organization is to optimize data access and storage efficiency while indexing is used to minimize disk accesses during queries.
Lec20.pptx introduction to data bases and information systemssamiullahamjad06
The document provides an overview of databases and information systems. It defines what a database is, how data is organized in a hierarchy from bits to files, and the different types of database models including hierarchical, network, and relational. It also discusses how structured query language and query by example are used to retrieve data in relational databases. Finally, it outlines different types of computer-based information systems used in organizations like transaction processing systems, management information systems, and decision support systems.
Web indexing involves creating metadata to provide keywords for websites and intranets to improve searchability. It collects, parses, and stores data to facilitate fast information retrieval. The purpose is to optimize speed in finding relevant documents by indexing all content, though this requires significant computing resources. Index design incorporates concepts from various fields to balance factors like size, speed, and maintenance over time.
The document describes the process of building an inverted index for information retrieval. Key points:
- Documents are parsed to extract terms which are sorted in a vocabulary file along with document frequency and collection frequency.
- A postings file stores the document IDs and term frequencies for each unique term. This separates the small vocabulary file for fast searching from the large postings file.
- The process involves tokenizing documents, removing stopwords, stemming terms, and counting term frequencies to build the inverted index files for efficient searching of documents based on terms.
Denormalization involves transforming normalized database relations into unnormalized physical tables to improve performance. This is done by reducing the number of necessary joins. While it improves speed, it also risks data duplication, wasted storage, and integrity issues. Common situations for denormalization include one-to-one relationships, many-to-many relationships with attributes, and reference data. Physical files are portions of disk storage allocated for storing records. File organization techniques determine how records are physically arranged, such as sequentially or through indexing, and affect retrieval speed, storage usage, and data protection.
Main Memory database systems store data primarily in main memory for faster access compared to disk-based systems. The T tree is proposed as an index structure for main memory databases that provides fast search, insert and delete performance while using relatively little memory space. The Dali storage manager is designed for main memory databases and provides persistence, availability and recovery guarantees similar to disk-based databases through the use of logging, locking, checkpointing and other techniques while leveraging the speed of main memory.
This document discusses different methods for organizing and indexing data stored on disk in a database management system (DBMS). It covers unordered or heap files, ordered or sequential files, and hash files as methods for physically arranging records on disk. It also discusses various indexing techniques like primary indexes, secondary indexes, dense vs sparse indexes, and multi-level indexes like B-trees and B+-trees that provide efficient access to records. The goal of file organization and indexing in a DBMS is to optimize performance for operations like inserting, searching, updating and deleting records from disk files.
This document discusses storage techniques used in databases, including various file organization methods and RAID levels. It describes primary memory, secondary memory, and tertiary memory. It explains fixed length records and variable length records approaches to file organization. Common file organization methods include heap files, hashing, B+ trees, clustered files, sequential files, and piles. RAID levels such as RAID 0, 1, 2, 3, and 4 are also summarized, explaining how they provide redundancy and performance improvements through techniques like mirroring, striping, and parity bits.
This document discusses different types of data storage used in database management systems, including primary storage (main memory and cache), secondary storage (flash memory and magnetic disk storage), and tertiary storage (optical storage and tape storage). It also covers various file organization methods like sequential, heap, hash, and cluster, as well as indexing methods like ordered indices, primary indexing, and secondary indexing. The goal of file organization is to optimize data access and storage efficiency while indexing is used to minimize disk accesses during queries.
Lec20.pptx introduction to data bases and information systemssamiullahamjad06
The document provides an overview of databases and information systems. It defines what a database is, how data is organized in a hierarchy from bits to files, and the different types of database models including hierarchical, network, and relational. It also discusses how structured query language and query by example are used to retrieve data in relational databases. Finally, it outlines different types of computer-based information systems used in organizations like transaction processing systems, management information systems, and decision support systems.
Web indexing involves creating metadata to provide keywords for websites and intranets to improve searchability. It collects, parses, and stores data to facilitate fast information retrieval. The purpose is to optimize speed in finding relevant documents by indexing all content, though this requires significant computing resources. Index design incorporates concepts from various fields to balance factors like size, speed, and maintenance over time.
The document describes the process of building an inverted index for information retrieval. Key points:
- Documents are parsed to extract terms which are sorted in a vocabulary file along with document frequency and collection frequency.
- A postings file stores the document IDs and term frequencies for each unique term. This separates the small vocabulary file for fast searching from the large postings file.
- The process involves tokenizing documents, removing stopwords, stemming terms, and counting term frequencies to build the inverted index files for efficient searching of documents based on terms.
Denormalization involves transforming normalized database relations into unnormalized physical tables to improve performance. This is done by reducing the number of necessary joins. While it improves speed, it also risks data duplication, wasted storage, and integrity issues. Common situations for denormalization include one-to-one relationships, many-to-many relationships with attributes, and reference data. Physical files are portions of disk storage allocated for storing records. File organization techniques determine how records are physically arranged, such as sequentially or through indexing, and affect retrieval speed, storage usage, and data protection.
overview of storage and indexing BY-Pratik kadam pratikkadam78
The document provides an overview of storage and indexing in databases. It discusses how data is stored on external storage devices like disks and tapes. It also describes different file organizations like heap files and cluster files that arrange records on storage. Finally, it covers indexing, explaining that indexes allow efficient retrieval of records based on key fields and common types of indexes include primary, secondary, and clustering indexes.
This document discusses file structure and organization. It defines what a file is and different types of file organization including sequential, hashed/direct access, and indexed sequential access.
It also covers logical vs physical files, basic file operations, record types, indexing, and different index types like primary, secondary, dense, sparse, and clustered indexes. Indexing improves query performance but decreases performance for insert/update/delete operations due to additional space required.
The document summarizes the architecture and internal structure of Oracle databases. It describes that an Oracle database consists of an Oracle instance and database. The database contains physical structures like datafiles, redo logs, and control files, as well as logical structures like tablespaces, schemas, tables, indexes, and views. It also explains the various components that make up an Oracle instance, including the system global area (SGA) and background processes that manage the database.
This document discusses different types of database management systems and file structures, including sequential files, indexed sequential files, random access files, hierarchical databases, network databases, and relational databases. It provides details on the characteristics and applications of each type. For sequential files, it describes ordered vs unordered files and the processing methods for each. It also covers database management systems and their role in structuring and managing database systems.
The document discusses file management and organization. It covers topics like file structures, directories, file sharing, blocking, and secondary storage management. Specifically, it describes:
1) The main file structures like sequential, indexed sequential, and hashed files and how they organize records in files.
2) How directories store metadata about files like their location, attributes, and access permissions to map file names to files.
3) Methods for sharing files between users through access rights and managing simultaneous access.
4) Techniques for blocking records into units for storage like fixed, variable spanned, and unspanned blocking.
5) Secondary storage management including file allocation methods like contiguous, chained, and indexed allocation
Indexed sequential files store records sequentially in the order they are written, allowing both sequential and random access via a numeric index. The index provides fast retrieval while the sequential storage requires less disk space than keyed files. As inserts and deletes are performed, records may be stored in overflow chains which can gradually get large and slow down retrieval. Data warehouses integrate data from multiple sources for analysis and informed decision making, providing advantages like competitive insights but also challenges in data integration and meeting expanding user needs.
This document discusses how databases physically organize and access data through different file organizations and indexing methods. It describes three main file organizations (heap, ordered, and hash files), how each supports insert, search, and delete operations, and when each performs best. It also explains what indexing is, different index types like primary and secondary indexes, and how to create indexes using SQL. The document aims to explain how databases optimize data storage and access.
files,indexing,hashing,linear and non linear hashingRohit Kumar
The document discusses different file organization techniques used in database management systems (DBMS) to store data on hard disks. It describes three main types of file organization - unordered or heap files, ordered or sequential files, and hash files. For each type, it explains how record insertion, searching, and deletion operations are performed, and the relative speeds of each operation for the different file organization methods. It also discusses indexing techniques like primary and secondary indexing that can be used to improve search performance.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
This document discusses storage virtualization techniques. It covers what can be virtualized (file system and block levels), where virtualization can occur (host-based, network-based, storage-based), and how virtualization is implemented (in-band and out-of-band). Examples of storage virtualization include logical volume management (LVM) on Linux hosts, SAN volume controllers, and virtualization features in disk arrays. Key benefits are improved manageability, availability, scalability and security of storage resources.
The document discusses various physical storage media used in computers including cache, main memory, flash memory, magnetic disks, optical disks, and magnetic tapes. It classifies storage based on characteristics like speed of access, cost, and reliability. RAID systems are described which provide storage virtualization through techniques like mirroring and striping across disks to improve performance and reliability. Different RAID levels are outlined including RAID 0, 1, 2, 3, 4, 5, and 6.
This document discusses different file organization structures including sequential, random access, indexed sequential, and partially and fully indexed files. It provides definitions of key concepts and compares the structures in terms of data entry order, duplicate records, access speed, availability of keys, storage location, and frequency of use. Logical and physical data organization and updating sequential files are also covered.
The document discusses file systems and deadlocks. It covers key aspects of file systems like space management, file names, directories, and metadata. It also discusses different types of file systems and file operations. The document then covers deadlocks, characterizing them and describing methods to handle deadlocks through prevention, avoidance, detection, and recovery.
Fundamental file structure concepts & managing files of recordsDevyani Vaidya
This document discusses fundamental concepts for structuring and managing files containing records of data. It covers topics such as stream files, field structures, record structures using length indicators, record access, file access and organization, and considerations for portability and standardization. The key ideas are that files can be organized into logical records and fields to group related data elements together and allow random access within files.
Cache memory is a small, fast memory located close to the CPU that stores frequently accessed instructions and data from main memory. It improves performance by reducing access time compared to main memory. There are three main characteristics of cache memory: 1) it uses the principle of locality of reference, where data that is accessed once is likely to be accessed again soon; 2) it is organized into blocks that are transferred between cache and main memory as a unit; and 3) it uses mapping and tagging to determine if requested data is in cache or needs to be fetched from main memory.
The audience will get to learn how to use ElasticSearch efficiently and reliably, when data scales up in their applications. It will be about tuning your ElasticSearch and configuring ElasticSearch internal queues and buffers for heavy indexing. Another takeaway will be some insight to internals of ElasticSearch.
Virtual memory allows for larger logical address spaces than physical memory by storing portions of programs and data on disk when not actively in use. Demand paging loads pages into memory only when accessed, reducing memory usage. When a page fault occurs and no frames are free, page replacement algorithms select a victim page to swap out based on policies like FIFO, LRU, or optimal. File systems organize data on storage using structures like directories with file attributes, allocation methods like contiguous or chained, and access methods like sequential or direct.
The document discusses file system implementation and mass storage structures. It describes the on-disk and in-memory structures used to manage files and free space on disks. These include the boot block, volume control block, file control blocks, directory structures, allocation methods like contiguous, linked and indexed allocation, and free space management using bitmaps, linked lists and counting. It also covers disk organization, scheduling algorithms like FCFS, SSTF, SCAN and CSCAN, and failure modes and consistency in networked file systems.
The document summarizes different file organization techniques used in database management systems. It discusses sequential, direct access, indexed sequential access, and hash file organizations. Sequential access file organization stores records sequentially and allows sequential retrieval but not random access. Direct access organization allows random retrieval by storing records randomly, while indexed sequential access combines both sequential and direct access organizations. Hash file organization uses a hash function to map records to storage locations, allowing direct access via the hash key.
overview of storage and indexing BY-Pratik kadam pratikkadam78
The document provides an overview of storage and indexing in databases. It discusses how data is stored on external storage devices like disks and tapes. It also describes different file organizations like heap files and cluster files that arrange records on storage. Finally, it covers indexing, explaining that indexes allow efficient retrieval of records based on key fields and common types of indexes include primary, secondary, and clustering indexes.
This document discusses file structure and organization. It defines what a file is and different types of file organization including sequential, hashed/direct access, and indexed sequential access.
It also covers logical vs physical files, basic file operations, record types, indexing, and different index types like primary, secondary, dense, sparse, and clustered indexes. Indexing improves query performance but decreases performance for insert/update/delete operations due to additional space required.
The document summarizes the architecture and internal structure of Oracle databases. It describes that an Oracle database consists of an Oracle instance and database. The database contains physical structures like datafiles, redo logs, and control files, as well as logical structures like tablespaces, schemas, tables, indexes, and views. It also explains the various components that make up an Oracle instance, including the system global area (SGA) and background processes that manage the database.
This document discusses different types of database management systems and file structures, including sequential files, indexed sequential files, random access files, hierarchical databases, network databases, and relational databases. It provides details on the characteristics and applications of each type. For sequential files, it describes ordered vs unordered files and the processing methods for each. It also covers database management systems and their role in structuring and managing database systems.
The document discusses file management and organization. It covers topics like file structures, directories, file sharing, blocking, and secondary storage management. Specifically, it describes:
1) The main file structures like sequential, indexed sequential, and hashed files and how they organize records in files.
2) How directories store metadata about files like their location, attributes, and access permissions to map file names to files.
3) Methods for sharing files between users through access rights and managing simultaneous access.
4) Techniques for blocking records into units for storage like fixed, variable spanned, and unspanned blocking.
5) Secondary storage management including file allocation methods like contiguous, chained, and indexed allocation
Indexed sequential files store records sequentially in the order they are written, allowing both sequential and random access via a numeric index. The index provides fast retrieval while the sequential storage requires less disk space than keyed files. As inserts and deletes are performed, records may be stored in overflow chains which can gradually get large and slow down retrieval. Data warehouses integrate data from multiple sources for analysis and informed decision making, providing advantages like competitive insights but also challenges in data integration and meeting expanding user needs.
This document discusses how databases physically organize and access data through different file organizations and indexing methods. It describes three main file organizations (heap, ordered, and hash files), how each supports insert, search, and delete operations, and when each performs best. It also explains what indexing is, different index types like primary and secondary indexes, and how to create indexes using SQL. The document aims to explain how databases optimize data storage and access.
files,indexing,hashing,linear and non linear hashingRohit Kumar
The document discusses different file organization techniques used in database management systems (DBMS) to store data on hard disks. It describes three main types of file organization - unordered or heap files, ordered or sequential files, and hash files. For each type, it explains how record insertion, searching, and deletion operations are performed, and the relative speeds of each operation for the different file organization methods. It also discusses indexing techniques like primary and secondary indexing that can be used to improve search performance.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
This document discusses storage virtualization techniques. It covers what can be virtualized (file system and block levels), where virtualization can occur (host-based, network-based, storage-based), and how virtualization is implemented (in-band and out-of-band). Examples of storage virtualization include logical volume management (LVM) on Linux hosts, SAN volume controllers, and virtualization features in disk arrays. Key benefits are improved manageability, availability, scalability and security of storage resources.
The document discusses various physical storage media used in computers including cache, main memory, flash memory, magnetic disks, optical disks, and magnetic tapes. It classifies storage based on characteristics like speed of access, cost, and reliability. RAID systems are described which provide storage virtualization through techniques like mirroring and striping across disks to improve performance and reliability. Different RAID levels are outlined including RAID 0, 1, 2, 3, 4, 5, and 6.
This document discusses different file organization structures including sequential, random access, indexed sequential, and partially and fully indexed files. It provides definitions of key concepts and compares the structures in terms of data entry order, duplicate records, access speed, availability of keys, storage location, and frequency of use. Logical and physical data organization and updating sequential files are also covered.
The document discusses file systems and deadlocks. It covers key aspects of file systems like space management, file names, directories, and metadata. It also discusses different types of file systems and file operations. The document then covers deadlocks, characterizing them and describing methods to handle deadlocks through prevention, avoidance, detection, and recovery.
Fundamental file structure concepts & managing files of recordsDevyani Vaidya
This document discusses fundamental concepts for structuring and managing files containing records of data. It covers topics such as stream files, field structures, record structures using length indicators, record access, file access and organization, and considerations for portability and standardization. The key ideas are that files can be organized into logical records and fields to group related data elements together and allow random access within files.
Cache memory is a small, fast memory located close to the CPU that stores frequently accessed instructions and data from main memory. It improves performance by reducing access time compared to main memory. There are three main characteristics of cache memory: 1) it uses the principle of locality of reference, where data that is accessed once is likely to be accessed again soon; 2) it is organized into blocks that are transferred between cache and main memory as a unit; and 3) it uses mapping and tagging to determine if requested data is in cache or needs to be fetched from main memory.
The audience will get to learn how to use ElasticSearch efficiently and reliably, when data scales up in their applications. It will be about tuning your ElasticSearch and configuring ElasticSearch internal queues and buffers for heavy indexing. Another takeaway will be some insight to internals of ElasticSearch.
Virtual memory allows for larger logical address spaces than physical memory by storing portions of programs and data on disk when not actively in use. Demand paging loads pages into memory only when accessed, reducing memory usage. When a page fault occurs and no frames are free, page replacement algorithms select a victim page to swap out based on policies like FIFO, LRU, or optimal. File systems organize data on storage using structures like directories with file attributes, allocation methods like contiguous or chained, and access methods like sequential or direct.
The document discusses file system implementation and mass storage structures. It describes the on-disk and in-memory structures used to manage files and free space on disks. These include the boot block, volume control block, file control blocks, directory structures, allocation methods like contiguous, linked and indexed allocation, and free space management using bitmaps, linked lists and counting. It also covers disk organization, scheduling algorithms like FCFS, SSTF, SCAN and CSCAN, and failure modes and consistency in networked file systems.
The document summarizes different file organization techniques used in database management systems. It discusses sequential, direct access, indexed sequential access, and hash file organizations. Sequential access file organization stores records sequentially and allows sequential retrieval but not random access. Direct access organization allows random retrieval by storing records randomly, while indexed sequential access combines both sequential and direct access organizations. Hash file organization uses a hash function to map records to storage locations, allowing direct access via the hash key.
Similar to Data Indexing Presentation-My.pptppt.ppt (20)
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
3. Magnetic Hard Disk Mechanism
NOTE: Diagram is schematic, and simplifies the structure of actual disk drives
4. Performance Measures of Disks
• Access time – the time it takes from when a read or
write request is issued to when data transfer begins.
Consists of:
• Seek time – time it takes to reposition the arm over the
correct track.
• 4 to 10 milliseconds on typical disks
• Rotational latency – time it takes for the sector to be
accessed to appear under the head.
• 4 to 11 milliseconds on typical disks (5400 to 15000 r.p.m.)
• Data-transfer rate – the rate at which data can be
retrieved from or stored to the disk.
• 25 to 100 MB per second max rate, lower for inner tracks
6. File Organization
• The database is stored as a collection of files.
Each file is a sequence of records. A record is a
sequence of fields.
• We first consider fixed length records, then extend
to variable length records.
7. Fixed-Length Records
• Simple approach:
• Store record i starting from byte n (i – 1), where n is the size of
each record.
• Record access is simple but records may cross blocks
• Modification: do not allow records to cross block boundaries
• Deletion of record i:
alternatives:
• move records i + 1, . . ., n
to i, . . . , n – 1
• move record n to i
• do not move records, but
link all free records on a
free list
9. Free Lists
• Store the address of the first deleted record in the file header.
• Use this first record to store the address of the second deleted record,
and so on
• Can think of these stored addresses as pointers since they “point” to the
location of a record.
• More space efficient representation: reuse space for normal attributes of
free records to store pointers. (No pointers stored in in-use records.)
10. Variable-Length Records
• Variable-length records arise in database systems in several ways:
• Storage of multiple record types in a file.
• Record types that allow variable lengths for one or more fields such as
strings (varchar)
• Record types that allow repeating fields (used in some older data
models).
• Attributes are stored in order
• Variable length attributes represented by fixed size (offset, length),
with actual data stored after all fixed length attributes
• Null values represented by null-value bitmap
11. Variable-Length Records: Slotted Page Structure
• Slotted page header contains:
• number of record entries
• end of free space in the block
• location and size of each record
• Records can be moved around within a page to keep
them contiguous with no empty space between them;
entry in the header must be updated.
• Pointers should not point directly to record — instead
they should point to the entry for the record in header.
12. Organization of Records in Files
• Heap – a record can be placed anywhere in the
file where there is space
• Sequential – store records in sequential order,
based on the value of the search key of each
record
• Hashing – a hash function computed on some
attribute of each record; the result specifies in
which block of the file the record should be
placed
13. Data Dictionary Storage
• Information about relations
• names of relations
• names, types and lengths of attributes of each relation
• names and definitions of views
• integrity constraints
• User and accounting information, including passwords
• Statistical and descriptive data
• number of tuples in each relation
• Physical file organization information
• How relation is stored (sequential/hash/…)
• Physical location of relation
• Information about indices
The Data dictionary (also called system catalog) stores metadata; that
is, data about data, such as
14. Storage Access
• A database file is partitioned into fixed-length storage units called
blocks. Blocks are units of both storage allocation and data
transfer.
• Database system seeks to minimize the number of block transfers
between the disk and memory. We can reduce the number of disk
accesses by keeping as many blocks as possible in main memory.
• Buffer – portion of main memory available to store copies of disk
blocks.
• Buffer manager – subsystem responsible for allocating buffer space
in main memory.
16. Purposes of Data Indexing
• What is Data Indexing?
• A database index is a data structure that improves the speed of data
retrieval operations on a database table at the cost of additional writes
and storage space to maintain the index data structure
• Why is it important?
17. Concept of File Systems
• Stores and organizes data into computer files.
• Makes it easier to find and access data at any given time.
18. How DBMS Accesses Data?
• The operations read, modify, update, and delete are used
to access data from database.
• DBMS must first transfer the data temporarily to a buffer
in main memory.
• Data is then transferred between disk and main memory
into units called blocks.
19. Time Factors
• The transferring of data into blocks is a very slow
operation.
• Accessing data is determined by the physical storage
device being used.
20. Physical Storage Devices
• Random Access Memory – Fastest to access memory, but
most expensive.
• Direct Access Memory – In between for accessing
memory and cost
• Sequential Access Memory – Slowest to access memory,
and least expensive.
21. More Time Factors
• Querying data out of a database requires more time.
• DBMS must search among the blocks of the database file
to look for matching tuples.
22. Purpose of Data Indexing
• It is a data structure that is added to a file to provide faster
access to the data.
• It reduces the number of blocks that the DBMS has to
check.
23. Properties of Data Index
• It contains a search key and a pointer.
• Search key - an attribute or set of attributes that
is used to look up the records in a file.
• Pointer - contains the address of where the data
is stored in memory.
• It can be compared to the card catalog system
used in public libraries of the past.
24. Two Types of Indices
• Ordered index (Primary index or clustering index) – which
is used to access data sorted by order of values.
• Hash index (secondary index or non-clustering index ) -
used to access data that is distributed uniformly across a
range of buckets.
27. Choosing Indexing Technique
• Five Factors involved when choosing the indexing
technique:
• access type
• access time
• insertion time
• deletion time
• space overhead
28. Indexing Definitions
• Access type is the type of access being used.
• Access time - time required to locate the data.
• Insertion time - time required to insert the new
data.
• Deletion time - time required to delete the data.
• Space overhead - the additional space occupied
by the added data structure.
29. Types of Ordered Indices
• Dense index - an index record appears for every search-
key value in the file.
• Sparse index - an index record that appears for only some
of the values in the file.
32. Index Choice
• Dense index requires more space overhead and
more memory.
• Data can be accessed in a shorter time using
Dense Index.
• It is preferable to use a dense index when the file
is using a secondary index, or when the index file
is small compared to the size of the memory.
33. Choosing Multi-Level Index
• In some cases an index may be too large for efficient
processing.
• In that case use multi-level indexing.
• In multi-level indexing, the primary index is treated as a
sequence file and sparse index is created on it.
• The outer index is a sparse index of the primary index
whereas the inner index is the primary index.
35. Hashing
• Bucket − A hash file stores data in bucket format. Bucket
is considered a unit of storage. A bucket typically stores
one complete disk block, which in turn can store one or
more records.
• Hash Function − A hash function, h, is a mapping
function that maps all the set of search-keys K to the
address where actual records are placed. It is a function
from search keys to bucket addresses.
• Hash function types
• Uniform
• Random
36. • That is, the hash function assigns each bucket the same
number of search-key values from the set of all possible
search-key values.
• That is, in the average case, each bucket will have nearly
the same number of values assigned to it, regardless of
the actual distribution of search-key values
37.
38. Types of hashing
• Static hashing- In static hashing, when a search-key value is
provided, the hash function always computes the same
address.
• Dynamic hashing-The problem with static hashing is that it
does not expand or shrink dynamically as the size of the
database grows or shrinks. Dynamic hashing provides a
mechanism in which data buckets are added and removed
dynamically and on-demand. Dynamic hashing is also known
as extended hashing.
39. Bucket Overflows(Collision )
• If the bucket does not have enough space, a bucket
overflow is said to occur.
• Reasons:
Insufficient buckets
Skew
40. • Insufficient buckets. The number of buckets, which we
denote nB,
• must be chosen such that nB > nr / fr,
• where nr denotes the total number of records that will be stored
and
• fr denotes the number of records that will fit in a bucket.
41. • Skew. Some buckets are assigned more records than are
others, so a bucket may overflow even when other
buckets still have space.
• 1. Multiple records may have the same search key.
• 2. The chosen hash function may result in nonuniform distribution of
search keys.
42. Solution 1
• So that the probability of bucket overflow is reduced, the
number of buckets is chosen to be (nr / fr ) ∗ (1 + d),
where d is a fudge factor, typically around 0.2.
• Some space is wasted: About 20 percent of the space in
the buckets will be empty.
• But the benefit is that the probability of overflow is
reduced.
43. overflow buckets –solution2
• overflow buckets- The condition of bucket-overflow is
known as collision.
• Solution:
• Overflow Chaining − When buckets are full, a new
bucket is allocated for the same hash result and is linked
after the previous one. This mechanism is called Closed
Hashing.
• Linear Probing − When a hash function generates an
address at which data is already stored, the next free
bucket is allocated to it. This mechanism is called Open
Hashing.
44. • The form of hash structure that we have just described is
sometimes referred to as closed hashing.
45. • Under an alternative approach, called open hashing, the
• set of buckets is fixed, and there are no overflow chains.
Instead, if a bucket is
• full, the system inserts records in some other bucket in the
initial set of buckets B.
47. • for such a database, we have three classes of options:
• 1 Choose a hash function based on the current file size.
This option will result in performance degradation as the
database grows.
48. • 2 Choose a hash function based on the anticipated size of
the file at some point in the future. Although performance
degradation is avoided, a significant amount of space may
be wasted initially.
49. • 3 Periodically reorganize the hash structure in
response to file growth. Such a reorganization
involves choosing a new hash function,
• Re-computing the hash function on every record
in the file, and generating new bucket
assignments
• This reorganization is a massive, time-consuming
operation.
51. Example
• Suppose A company with 250 employees assign a 5-digit
employee number to each employee which is used as
primary key in company’s employee file.
• We can use employee number as a address of record in memory.
• The search will require no comparisons at all.
• Unfortunately, this technique will require space for 1,00,000
memory locations, where as fewer locations would actually used.
• So, this trade off for time is not worth the expense.
52. Hashing
• The general idea of using the key to determine the
address of record is an excellent idea, but it must be
modified so that great deal of space is not wasted.
• This modification takes the form of a function H from the
set K of keys in to set L of memory address.
• H: K L , Is called a Hash Function or
• Unfortunately, Such a function H may not yield distinct values: it is
possible that two different keys k1 and k2 will yield the same hash
address. This situation is called Collision, and some method must be
used to resolve it.
53. Hash Functions
• the two principal criteria used in selecting a hash function
H: K L are as follows:
1. The function H should be very easy and quick
to compute.
2. The function H should as far as possible,
uniformly distribute the hash address through
out the set L so that there are minimum number
of collision.
54. Hash Functions
1. Division method: choose a number m larger than the number n of
keys in K. (m is usually either a prime number or a number without
small divisor) the hash function H is defined by
H(k) = k (mod m) or H(k) = k (mod m) + 1.
here k (mod m) denotes the reminder when k is divided by m. the
second formula is used when we want a hash address to range
from 1 to m rather than 0 to m-1.
2. Midsquare method: the key k is squared. Then the hash function H is
defined by H(k) = l. where l is obtained by deleting digits from
both end of k^2.
3. Folding Method: the key k is portioned into a number of parts, k1, k2,
……,kr, where each part is added togather, ignoring the last carry.
H(k) = k1+k2+ ……………+Kr.
Sometimes, for extra “milling”, the even numbered parts, k2, k4, …. Are
each reversed befor addition.
55. Example of Hash Functions
Consider a company with 68 employees assigns a 4-digit employee
number to each employee. Suppose L consists of 100 two-digit
address: 00, 01, 02 , ……….99. we apply above hash functions to each
of following employee numbers: 3205, 7148,2345.
1. Division Method:
choose a prime number m close to 99, m=97.
H(k)=k(mod m): H(3205)=4, H(7148)=67, H(2345)=17.
2. Midsquare Method:
k= 3205 7148 2345
k^2= 10272025 51093904 5499025
H(k)= 72 93 99
3. Folding Method: chopping the key k into two parts and adding yield
the following hash address:
H(3205)=32+05=37, H(7148)=71+48=19, H(2345)=23+45=68
Or,
H(3205)=32+50=82, H(7148)=71+84=55, H(2345)=23+54=77
56. Collision Resolution
• Suppose we want to add a new record R with key K to our file F, but
suppose the memory location address H(k) is already occupied. This
situation is called Collision.
• There are two general ways to resolve collisions :
• Open addressing,(array method)
• Separate Chaining (linked list method)