File Organization and Indexing

فهرست عناوین اصلی در این پاورپوینت

فهرست عناوین اصلی در این پاورپوینت

● File Organization and Indexing
● How expensive are these operations?
● Important Point
● 1) Heap Files
● 2) Sorted File Tree Based Indexing
● 3) Hash Based Indexing
● Cost Model for Our Analysis
● Assumptions in Our Analysis
● Comparison of all Techniques
● Basic Concepts
● Ordered Indices
● Index Update: Deletion
● Index Update: Insertion
● Dense Index Files
● Sparse Index Files
● Example of Sparse Index Files

نوع زبان: انگلیسی حجم: 0.3 مگا بایت
نوع فایل: اسلاید پاورپوینت تعداد اسلایدها: 26 صفحه
سطح مطلب: نامشخص پسوند فایل: ppt
گروه موضوعی: زمان استخراج مطلب: 2019/06/05 07:18:17

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عبارات مهم استفاده شده در این مطلب

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datum, page, ., index, search, file, key, record, bd, base, order, scan,

توجه: این مطلب در تاریخ 2019/06/05 07:18:17 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

http://www.cs.ucr.edu/~eamonn/teaching/cs166materials/indexing.ppt

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عبارات پرتکرار و مهم در این اسلاید عبارتند از: datum, page, ., index, search, file, key, record, bd, base, order, scan,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

chapter ۸ file organization and indices file organization and indexing assume that we have a large amount of data in our database which lives on a hard drive s what are some of the things we might wish to do with the data scan fetch all records from disk equality search range search insert a record delete a record how expensive are these operations in terms of execution time how expensive are these operations the cost of operations listed below depends on how we organize data. there are three main ways we could organize the data heap files sorted file tree based indexing hash based indexing scan equality search range selection insert a record delete a record important point data which is organized based on one field may be difficult to search based on a different field. consider a phone book. the data is well organized if you want to find eamonn keogh’s phone number suppose to want to find out whose number ۲۳۴ ۲۳۴۲ is informally the attribute we are most interested in searching is called the search key or just key we will formalize this notation later . note that the search key can be a combination of fields for example phone books are organized by last name first name unfortunately the word key is overloaded in databases the word key in this context has nothing to do with primary key candidate key etc. ۱ heap files we have already seen heap files. recall that the data is unsorted. we can initially build the database in sorted order but if our application is dynamic our database will become unsorted very quickly so we assume that heap files are unsorted. header page data page data page data page data page data page data page pages with free space full pages ۲ sorted file tree based indexing if we are willing to pay the overhead of keeping the data sorted on some field we can index the data on that field. data page data page data page data page data page data page ۳ ۱۳ ۵ ۱۸ entries ۱۷ entries ۱۷ ۱۴ ۱۶ ۳۵ ۴۳ ۱۷ ۳ hash based indexing with hash based indexing we assume that we have a function h which tells us where to place any given record. data page data page data page data page data page data page h cost model for our analysis we ignore cpu costs for simplicity b the number of data pages d average time to read or write disk page measuring number of page i o’s ignores gains of pre fetching a sequence of pages thus even i o cost is only approximated. average case analysis based on several simplistic assumptions. as we have already seen a single disk access takes thousands of times longer than the cpu time for most operations. furthermore the trend is for this gap to increase. assumptions in our analysis heap files equality selection on search key exactly one match. sorted files files compacted after deletions. indexes hash no overflow buckets. ۸ page occupancy file size ۱.۲۵ data size we only consider the cost of getting the right page s into the buffer we don’t consider how the records are arranged in the buffer. data page header page data page data page data page data page data page data page pages with free space full pages scan equality search range insert delete heap bd ۱ ۲bd bd ۲d search d data page data page data page data page data page data page ۳ ۱۳ ۵ ۱۸ entries ۱۷ entries ۱۷ ۱۴ ۱۶ ۳۵ ۴۳ ۱۷ if the search key is not a candidate key equality search takes ۱ ۲bd scan equality search range insert delete tree bd dlog۲ b dlog۲ b matches search bd search bd data page data page data page data page data page data page h scan equality search range insert delete hash bd ۱.۲۵ d bd ۱.۲۵ ۲d search d comparison of all techniques b the number of data pages d average time to read or write disk page a heap is great if all you do is insert delete and scan the data. but if you have to do any kind of search it is too slow. if you are only interest in equality searches a hash index looks very promising although it does waste some space disk space is relatively cheap. if you need to do range search use a tree. scan equality search range insert delete heap bd ۱ ۲bd bd ۲d search d tree bd dlog۲ b dlog۲ b matches search bd search bd hash bd ۱.۲۵ d bd ۱.۲۵ ۲d search d basic concepts indexing mechanisms used to speed up access to desired data. e.g. author catalog in library search key attribute or set of attributes used to look up records in a file. an index file consists of records called index entries or data entries of the form index files are typically much smaller than the original file two basic kinds of indices ordered indices search keys are stored in sorted order i.e tree based hash indices search keys are distributed uniformly across buckets using a hash function . search key pointer ordered indices in an ordered index i.e tree based index entries are stored sorted on the search key value. e.g. author catalog in library. primary index in a sequentially ordered file the index whose search key specifies the sequential order of the file. also called clustering index the search key of a primary index is usually but not necessarily the primary key. note that we can have at most one primary index secondary index an index whose search key specifies an order different from the sequential order of the file. also called non clustering index. we can have as many secondary indices as we like. index sequential file ordered sequential file with a primary index. primary index secondary index data page data page data page data page data page data page data page data page data page data page data page data page primary and secondary indices secondary indices have to be dense. indices offer substantial benefits when searching for records. when a file is modified every index on the file must be updated updating indices imposes overhead on database modification. sequential scan using primary index is efficient but a sequential scan using a secondary index is expensive probably worse that no index at all . each record access may fetch a new block from disk index update deletion if deleted record was the only record in the file with its particular search key value the search key is deleted from the index also. single level index deletion dense indices – deletion of search key is similar to file record deletion. sparse indices – …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: datum, page, ., index, search, file, key, record, bd, base, order, scan,

این فایل پاورپوینت شامل 26 اسلاید و به زبان انگلیسی و حجم آن 0.3 مگا بایت است. نوع قالب فایل ppt بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/06/05 07:18:17 استخراج شده است.

http://www.cs.ucr.edu/~eamonn/teaching/cs166materials/indexing.ppt

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