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Indexes in Oracle :Index Scan Methods :Part 2
 
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The Video Explains when should you create indexes. The difference between Simple and composite Index, Relevance of order in composite indexes and Index Scan Methods in detail. 1.Index Unique scan 2.Index Range Scan 3. Index Skip Scan 4. Fast full Index Scan 5. Full Index Scan If you have any questions just drop in a comment
Views: 4466 Tech Coach
A Story of Indexes and Full Table Scans: Finding All the Red Sweets Part 1
 
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"Why isn't Oracle using my index?!" is a common question people have when tuning SQL queries. In this episode Chris compares two methods for finding all the red candies from party bags he's prepared. He shows how these are like a full table scan and an index range scan. He goes on to compare the performance of these two approaches. He shows when a full table scan becomes more efficient than an index range scan and vice versa. ============================ The Magic of SQL with Chris Saxon Copyright © 2015 Oracle and/or its affiliates. Oracle is a registered trademark of Oracle and/or its affiliates. All rights reserved. Other names may be registered trademarks of their respective owners. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the “Materials”). The Materials are provided “as is” without any warranty of any kind, either express or implied, including without limitation warranties or merchantability, fitness for a particular purpose, and non-infringement.
Views: 7134 The Magic of SQL
07 06 Index Skip Scan Operations
 
03:14
ORACLE
Views: 720 oracle ocm
Oracle SQL Tuning Expert Series - Understanding Indexes
 
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Ross Leishman of DWS Ltd presents the principles of Understanding Indexes for SQL Tuning. The presentation includes an entertaining demonstration of Indexes featuring DWS Alumnus Jordan Thomas as a "Buffer Cache". DWS Ltd is a leading publicly listed Australian IT Services company, providing services to blue chip organisations since 1991. With a business philosophy based upon integrity, reliability and professional service delivery, DWS provides end to end IT solutions. www.dws.com.au
Views: 43075 DWS Ltd
Oracle Performance - Indexes
 
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Oracle Performance - Indexes
Views: 239 The Silent DBA
What is BLOCK RANGE INDEX? What does BLOCK RANGE INDEX mean? BLOCK RANGE INDEX meaning
 
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What is BLOCK RANGE INDEX? What does BLOCK RANGE INDEX mean? BLOCK RANGE INDEX meaning - BLOCK RANGE INDEX definition - BLOCK RANGE INDEX explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A Block Range Index or BRIN is a database indexing technique. They are intended to improve performance with extremely large tables. BRIN indexes provide similar benefits to horizontal partitioning or sharding but without needing to explicitly declare partitions. A BRIN is applicable to an index on a table that is large and where the index key value is easily sorted and evaluated with a MinMax function. BRIN were originally proposed by Alvaro Herrera of 2ndQuadrant in 2013 as 'Minmax indexes'. Implementations thus far are tightly coupled to internal implementation and storage techniques for the database tables. This makes them efficient, but limits them to particular vendors. So far PostgreSQL is the only vendor to have announced a live product with this specific feature, in PostgreSQL 9.5. Other vendors have described some similar features, including Oracle, Netezza 'zone maps', Infobright 'data packs', MonetDB and Apache Hive with ORC/Parquet. BRIN operate by "summarising" large blocks of data into a compact form, which can be efficiently tested to exclude many of them from a database query, early on. These tests exclude a large block of data for each comparison. By reducing the data volume so early on, both by representing large blocks as small tuples, and by eliminating many blocks, BRIN substantially reduce the amount of detailed data that must be examined by the database node on a row-by-row basis. Data storage in large databases is layered and chunked, with the table storage arranged into 'blocks'. Each block contains perhaps 1MB in each chunk and they are retrieved by requesting specific blocks from a disk-based storage layer. BRIN are a lightweight in-memory summary layer above this: each tuple in the index summarises one block as to the range of the data contained therein: its minimum and maximum values, and if the block contains any non-null data for the column(s) of interest. Unlike a traditional index which locates the regions of the table containing values of interest, BRIN act as "negative indexes", showing the blocks that are definitely not of interest and thus do not need to be processed further. Some simple benchmarks suggest a five-fold improvement in search performance with an index scan, compared to the unindexed table. Compared to B-trees, they avoid their maintenance overhead. As BRIN are so lightweight, they may be held entirely in memory, thus avoiding disk overhead during the scan. The same may not be true of B-tree: B-tree requires a tree node for every approximately N rows in the table, where N is the capacity of a single node, thus the index size is large. As BRIN only requires a tuple for each block (of many rows), the index becomes sufficiently small to make the difference between disk and memory. For a 'narrow' table the B-tree index volume approaches that of the table itself; the BRIN may be only 5-15% of it. A large database index would typically use B-tree algorithms. BRIN is not always a substitute for B-tree, it is an improvement on sequential scanning of an index, with particular (and potentially large) advantages when the index meets particular conditions for being ordered and for the search target to be a narrow set of these values. In the general case, with random data, B-tree may still be superior. A particular advantage of the BRIN technique, shared with Oracle Exadata's Smart Scanning, is in the use of this type of index with Big Data or data warehousing applications, where it is known that almost all of the table is irrelevant to the range of interest. BRIN allows the table to be queried in such cases by only retrieving blocks that may contain data of interest and excluding those which are clearly outside the range, or contain no data for this column. A regular problem with the processing of large tables is that retrieval requires the use of an index, but maintaining this index slows down the addition of new records. Typical practices have been to group additions together and add them as a single bulk transaction, or to drop the index, add the batch of new records and then recreate the index. Both of these are disruptive to simultaneous read / write operations and may not be possible in some continuously-operating businesses. With BRIN, the slowdown from maintaining the index is much reduced compared to B-tree. Wong reports that B-tree slowed down additions to an unindexed 10GB table by 85%, but a comparable BRIN only had an overhead of 11%. BRIN may be created for extremely large data where B-tree would require horizontal partitioning.....
Views: 162 The Audiopedia
How Does the Phyiscal Location of Rows Affect Indexes?: Finding All the Red Sweets Part 2
 
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In part one of the red candy series, Chris compared the efficiency of using a index range scan and full table scan to access data. He found that a full table scan was more efficient when fetching more rows than there are table blocks. This analysis made a big assumption however. It worked on the presumption that there was no correlation between the order of candies in the document and which the bags they were in. In this episode tests this assumption. Chris looks at how the physical order of rows in a table can affect the efficiency of indexes on it. He discusses how Oracle tracks this via the clustering factor. ============================ The Magic of SQL with Chris Saxon Copyright © 2015 Oracle and/or its affiliates. Oracle is a registered trademark of Oracle and/or its affiliates. All rights reserved. Other names may be registered trademarks of their respective owners. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the “Materials”). The Materials are provided “as is” without any warranty of any kind, either express or implied, including without limitation warranties or merchantability, fitness for a particular purpose, and non-infringement.
Views: 2962 The Magic of SQL
Which Order Should Columns Go in an Index?: Finding All the Red Sweets Part 4
 
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When you create an index on multiple columns there's an important question you need to answer: In which order should you list the columns? This video looks at some of the factors you should consider to help answer this question. ============================ The Magic of SQL with Chris Saxon Copyright © 2015 Oracle and/or its affiliates. Oracle is a registered trademark of Oracle and/or its affiliates. All rights reserved. Other names may be registered trademarks of their respective owners. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the “Materials”). The Materials are provided “as is” without any warranty of any kind, either express or implied, including without limitation warranties or merchantability, fitness for a particular purpose, and non-infringement.
Views: 3631 The Magic of SQL
Indexing in Oracle :B-Tree,Bitmap Indexing
 
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This Video is the 1st tutorial in the video series Indexing in Oracle , The video series explains in detail, What are indexes?It's types, what index should be used in which scenario and other important thing in basic terminology. Note :You may want to watch the video with a higher playback speed(1.25 if it suits you more)
Views: 8836 Tech Coach
What is RANGE-LIST SUBPARTITIONS in Oracle via OEM ?
 
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Hi friends today in my videos I'm going to explain with you What is RANGE-LIST SUB-PARTITIONS in Oracle via OEM Oracle database Unbeatable,Unbreakable Platform..
Views: 1432 Oracle World
Oracle Database Indexes: Myths, Tips and Tricks
 
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In this tutorial, OCM John Watson will - via demonstrations - debunk these myths: Myth #1: Oracle Database does not index NULL Myth #2: A search that includes wildcards can't use an index if the wildcard precedes the string. Myth #3: Oracle will not use a function-based index unless the FBI is coded in the predicate. Myth #4: Indexes always help. The more indexes the better. See http://skillbuilders.com/free-oracle-tutorials for gigabytes of free Oracle video tutorials.
Views: 16025 SkillBuilders
When to use Oracle Database Bitmap Indexes Lesson 2
 
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This lesson demonstrates cases where a *B-Tree* index cannot be used AND shows how Oracle will use a *bitmap* index. See all lessons, free, at http://www.skillbuilders.com/when-to-use-oracle-bitmap-indexes.
Views: 577 SkillBuilders
Oracle || Indexes Part-1 by dinesh
 
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DURGASOFT is INDIA's No.1 Software Training Center offers online training on various technologies like JAVA, .NET , ANDROID,HADOOP,TESTING TOOLS ,ADF,INFORMATICA,TABLEAU,IPHONE,OBIEE,ANJULAR JS, SAP... courses from Hyderabad & Bangalore -India with Real Time Experts. Mail us your requirements to [email protected] so that our Supporting Team will arrange Demo Sessions. Ph:Call +91-8885252627,+91-7207212428,+91-7207212427,+91-8096969696. http://durgasoft.com http://durgasoftonlinetraining.com https://www.facebook.com/durgasoftware http://durgajobs.com https://www.facebook.com/durgajobsinfo......
Reverse Key Index :Types of Btree Index in Oracle
 
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Please subscribe to my new channel. https://www.youtube.com/c/AnIndianAbroadd The Videos explains how Reverse Btree Index works and in what condition they shall be used. Reverse Btree index are used to solve index block contention. You can't perform range scans in reverse btree Index.
Views: 1334 Tech Coach
Why Isn't My Query Using an Index?
 
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“Why isn’t my query using an index?” is a common question people have when tuning SQL. This session explores the factors that influence the optimizer’s decision to answer this question. It does so by comparing fetching rows from a database table to finding all the red M&Ms a packet, and contrasts using an index range scan and a full table scan. It also introduces the concepts of blocks and the clustering factor. The session offers a discussion of how these affect the optimizer's calculations, and includes a demo of how these concepts work in practice using real SQL queries. This session is intended for developers who want to learn the basics of how the optimizer chooses between an index range or full table scan. Speaker: Chris Saxon
Views: 252 Oracle Developers
SQL: Explain Plan for knowing the Query performance
 
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In this tutorial, you'll learn how to compare queries to know the better performance query..
Views: 91254 radhikaravikumar
SQL tutorial 62: Indexes In Oracle Database By Manish Sharma RebellionRider
 
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Watch and learn concepts of SQL Index In Oracle Database. In this tutorial you will learn about B-Tree Index and Function based Index. ------------------------------------------------------------------------ ►►►LINKS◄◄◄ Blog : Previous Tutorial ► ------------------------------------------------------------------------- ►►►Help Me In Getting A Job◄◄◄ ►Help Me In Getting A Good Job By Connecting With Me on My LinkedIn and Endorsing My Skills. All My Contact Info is Down Below. You Can Also Refer Me To Your Company Thanks ------------------------------------------------------------------------- Copy Cloud referral link || Use this link to join copy cloud and get 20GB of free storage https://copy.com?r=kb4rc1 -------------------------------------------------------------------------- ►Make sure you SUBSCRIBE and be the 1st one to see my videos! -------------------------------------------------------------------------- Amazon Wishlist: http://bit.ly/wishlist-amazon ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ►►►Find me on Social Media◄◄◄ Follow What I am up to as it happens on https://twitter.com/rebellionrider https://www.facebook.com/imthebhardwaj http://instagram.com/rebellionrider https://plus.google.com/+Rebellionrider http://in.linkedin.com/in/mannbhardwaj/ http://rebellionrider.tumblr.com/ http://www.pinterest.com/rebellionrider/ You can also Email me at for E-mail address please check About section Please please LIKE and SHARE my videos it makes me happy. Thanks for liking, commenting, sharing and watching more of our videos This is Manish from RebellionRider.com ♥ I LOVE ALL MY VIEWERS AND SUBSCRIBERS
Views: 38651 Manish Sharma
Using Oracle Metadata to your Advantage Explained with real scenarios.
 
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In this video I have explained who you can use the Oracle metadata tables to your advantage with real project scenarios and situations. The video is a little descriptive it gives you much more than just the syntax and the tables. All database offer a bunch of tables using which you can make smart inferences about your data,process and health of the database. I have explained the below tables in this video and how to use them smartly 1.All_Source 2.all_objects 3.All_tables 4.all_tab_cons 5.all_constraints I will be sharing more such places where we can use these metadata queries so don't forget to subscribe to my channel. If you can questions/clarifications just drop a comment and I will be more than happy to solve your queries.
Views: 2323 Tech Coach
BitMap Indexes & Examples
 
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BitMap Indexes & Examples watch more videos at https://www.tutorialspoint.com/videotutorials/index.htm Lecture By: Mr. Parth Panjab, Tutorials Point India Private Limited
Partitioning in Oracle - Performance Basics
 
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This is the 2nd video from " Partitioning in Oracle " series, It explains how oracle stores and manages data. What is single Block IO and Multi Block IO ? Why full table scan is better than index access in few cases. The video is very elaborate, I have tried my level best to keep it as simple as possible
Views: 6707 Tech Coach
When to use Oracle Database Bitmap Indexes Lesson 1
 
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This tutorial will identify some use cases for Oracle bitmap indexes, including some of the more advanced capabilities. See all lessons, free, at http://www.skillbuilders.com/when-to-use-oracle-bitmap-indexes. Indexing your Oracle Database for best performance? There are cases, depending on data structures and queries, where b-tree indexes are not useful (e.g. scan access paths perform inadequately). In these cases, bitmap indexes may be a better solution. Bitmap indexes are a powerful tool, but they need to be used with care. Inappropriate use may cause problems worse than those they solve. The tutorial covers somewhat more advanced cases such as using bitmap join indexes to denormalize a snowflake schema, and to enable star transformations in queries that join fact tables to several dimension tables. This training will benefit any Oracle DBA administering a Data Warehouse or VLDB and "power" developers working in same. Instructor: Oracle Certified Master DBA John Watson, SkillBuilders
Views: 420 SkillBuilders
07 03 Bitmap Indexes
 
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ORACLE
Views: 7139 oracle ocm
BITMAP  and  BITMAP JOIN INDEX IN ORACLE explained
 
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The video explains BITMAP and BITMAP JOIN INDEX IN ORACLE and when you should create them on a column. BITMAP INDEXES should be dealt with carefully as they can lead to serious performance issues if the table is updated by multiple processes in parallel. Indexing Basics :https://www.youtube.com/watch?v=0X9bbtwTnuE&t=1095s Star and snowflake Schema :https://www.youtube.com/watch?v=Qq4yhhAk9fc&t=17s
Views: 1832 Tech Coach
Oracle Indexes - Live Demonstration
 
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When is a Full Table Scan faster than an Index Scan? Watch Ross and Jordan act out an Oracle database reading and caching data via both methods, explaining the costs and benefits in simple and easy to understand terms. The demonstration is part of a talk by Ross Leishman of DWS Ltd on the principles of Understanding Indexes for SQL Tuning. The full lecture is at this link: https://www.youtube.com/watch?v=Z4hKomnGHFA DWS Ltd is a leading publicly listed Australian IT Services company, providing services to blue chip organisations since 1991. With a business philosophy based upon integrity, reliability and professional service delivery, DWS provides end to end IT solutions. www.dws.com.au
Views: 2630 DWS Ltd
Range Partitioning in Oracle
 
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In this video I have explained what is range partitioning along with it's 2 real project use cases. I have also explained interval Partitioning as an extension of Range partitioning If you have not watched my Initial Videos on partitioning I will recommended watching them before watching this video Apologies for the 10 second video glitch between 6 and 7 minutes :(
Views: 2854 Tech Coach
Local Vs Global Partitioned Index in Oracle 11g
 
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The Video Explains the difference between Local Partitioned Indexes(Prefixed vs Non Prefixed Indexes). and Global Partitioned Index along with the challenges in maintaining Global partitioned Indexes when the underlying tables partitioned is dropped/truncated/Merged/Moved. Local Partitioned Index Shares the same boundaries as the table and are in the same number as table partitions they are widely used in DSS and DWH systems. While Global Partitioned Index are predominantly used in OLTP systems
Views: 4083 Tech Coach
PART 5.7 Clustering Index | DBMS HINDI |
 
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This video contains the basic concepts of Clustering Index and will help students in various competitive exams like GATE , NET, PSU'S etc Following are the important topics of dbms ER‐model - entity–relationship model, strong entity set, weak entity set, single valued attribute, multivalued attribute, stored attribute, derived attribute, simple attribute composite attribute, weak relationship, strong relationship, mapping, cardinality ratios, discriminator attribute, fan trap, chasm trap Relational model – relational table, column, domain, row, tuple, relational algebra – selection, projection, union, intersection, set difference, Cartesian product, natural join, left outer join, right outer join, complete outer join, theta join, division operator, nested query, safe query tuple calculus – tuple relational calculus, domain relational calculus, SQL – select, from, where, order by, group by, max, min, avg, count, sum, having, Integrity constraints – super key, candidate key, primary key, foreign key, alternate key, secondary key, surrogate key normal forms – first normal form, second normal form, third normal form, bcnf, 4nf, 5nf, functional dependency, minimal cover, canonical collection, multivalued functional dependency, dependency preserving, lossy and lossless decomposition. File organization – indexing, B, B+ trees, key attribute, anchor attribute, primary indexing, secondary indexing, clustered indexing, multilevel indexing, block pointer, tree pointer, record pointer, top down search, sequential search, range query, index file, ordering, non-ordering Transactions and concurrency control- transaction, acid properties, atomicity, consistency, isolation, durability, life cycle of a transaction, active state, partially committed state, committed state, abort, rollback, terminated, phantom read, dirty read, unrepeatable read, lost update problem, conflict serializability, view serializability, irrecoverable schedule, cascading rollback, recoverable schedule, cascadless schedule. Lock based protocol, two phase locking, exclusive lock, shared lock, growing phase, shrinking phase, conservative two-phase locking, rigorous two phase locking, strict two phase locking, time stamping, time stamp, read, write, deadlock, granularity, tree based protocol,
Views: 57128 KNOWLEDGE GATE
Global Temporary Tables in Oracle Database - DBArch Video 21
 
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Learn about Global Temporary Tables in Oracle Database. You will learn the internals of Global Temporary tables in Oracle database, and also a demo for the same. Our Upcoming Online Course Schedule is available in the url below https://docs.google.com/spreadsheets/d/1qKpKf32Zn_SSvbeDblv2UCjvtHIS1ad2_VXHh2m08yY/edit#gid=0 Reach us at [email protected]
Views: 3933 Ramkumar Swaminathan
Oracle Exadata Smart Scan - Limitations and Best Practices
 
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Smart Scan is a wonderful capability, but you don't always get it. It's impossible for many execution plans, and this is a major restriction. If you think about what a Smart Scan actually does, it delivers individual columns, individual rows back to the instance. Now, a buffer cache can accept only blocks. Therefore, Smart Scan cannot possibly put those columns of rows into the buffer cache. It's simply not formatted appropriately. So, a Smart Scan has to return values directly into the session's PGA or, to put it another way, the only access method that can use Smart Scan is direct read. Well, what access methods can use direct read? There are only two, which are table full scan and index fast full scan. Any other access method, typically index range scan, table access by row ID, cannot use a Smart Scan. The second major issue, there are strict limitations of the type of objects that can be accessed through Smart Scan. It really is only heap tables. You can't use indexes. You can't use clusters. You can't use IOTs. Heap tables only. Perhaps hardest to track down and giving sometimes very erratic results is that Smart Scan can be interrupted by various conditions. You've met all the requirements for Smart Scan, directory and so on, got the right execution plan. The Smart Scan starts and then hits something that causes a problem. Issues that we know cause problems are, for instance, read consistency, also delayed block cleanout, change rows. Any of those issues and a few others mean that the storage tier will have to interrupt its Smart Scan, deliver complete blocks into that buffer cache, let your session then do what is necessary to the block, and only then can the Smart Scan proceed. Now, in order to maximize the use of Smart Scan, there may be quite a lot of work. Very often, you'll have to adjust your index structures. Making them invisible is a nice technique there. There are many, many, many parameters that can influence the likelihood of achieving a Smart Scan, and almost inevitably you're going to be rewriting a lot of hint SQL and putting hints in it to get the correct execution plans that can enable a Smart Scan to occur. This is all because of one fundamental problem; the optimizer is not in any way aware of the Exadata. The optimizer develops an execution plan in exactly the way it would without the Exadata storage. The use of Smart Scan, the awareness of Exadata comes at the next level down. The optimizer develops the plan through a normal pass and then passes it through to the SQL execution engine, and it's the SQL execution engine that determines, on a case-by-case basis, whether to use the Smart Scan. This means that you might develop a plan and execute the statement 50 times. Forty-nine times, you get a Smart Scan. The 50th time, for whatever reason, the SQL execution engine decides not to. This can result in somewhat erratic performance.
Views: 1595 SkillBuilders
SQL: Indexes - Bit Map & B-trees
 
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In this tutorial, you'll learn when to use b-tree and bitmap index
Views: 51685 radhikaravikumar
What is REVERSE INDEX? What does REVERSE INDEX mean? REVERSE INDEX meaning & explanation
 
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What is REVERSE INDEX? What does REVERSE INDEX mean? REVERSE INDEX meaning - REVERSE INDEX definition - REVERSE INDEX explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Database management systems provide multiple types of indexes to improve performance and data integrity across diverse applications. Index types include b-trees, bitmaps, and r-trees. In database management systems, a reverse key index strategy reverses the key value before entering it in the index. E.g., the value 24538 becomes 83542 in the index. Reversing the key value is particularly useful for indexing data such as sequence numbers, where each new key value is greater than the prior value, i.e., values monotonically increase. Reverse key indexes have become particularly important in high volume transaction processing systems because they reduce contention for index blocks. Reversed key indexes use b-tree structures, but preprocess key values before inserting them. Simplifying, b-trees place similar values on a single index block, e.g., storing 24538 on the same block as 24539. This makes them efficient both for looking up a specific value and for finding values within a range. However if the application inserts values in sequence, each insert must have access to the newest block in the index in order to add the new value. If many users attempt to insert at the same time, they all must write to that block and have to get in line, slowing down the application. This is particularly a problem in clustered databases, which may require the block to be copied from one computer's memory to another's to allow the next user to perform their insert. Reversing the key spreads similar new values across the entire index instead of concentrating them in any one leaf block. This means that 24538 appears on the same block as 14538 while 24539 goes to a different block, eliminating this cause of contention. (Since 14538 would have been created long before 24538, their inserts don't interfere with each other.) Reverse indexes are just as efficient as unreversed indexes for finding specific values, although they aren't helpful for range queries. Range queries are uncommon for artificial values such as sequence numbers. When searching the index, the query processor simply reverses the search target before looking it up. Typically, applications delete data that is older on average before deleting newer data. Thus, data with lower sequence numbers generally go before those with higher values. As time passes, in standard b-trees, index blocks for lower values end up containing few values, with a commensurate increase in unused space, referred to as "rot". Rot not only wastes space, but slows query speeds, because a smaller fraction of a rotten index's blocks fit in memory at any one time. In a b-tree, if 14538 gets deleted, its index space remains empty. In a reverse index, if 14538 goes before 24538 arrives, 24538 can reuse 14538's space.
Views: 583 The Audiopedia
sub partitioning in oracle  or composite partitioning in Oracle RANGE-LIST, RANGE-HASH
 
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sub partitioning in oracle or composite partitioning in Oracle SQL Tutorial SQL Tutorial for beginners PLSQL Tutorial PLSQL Tutorial for beginners PL/SQL Tutorial PL SQL Tutorial PL SQL Tutorial for beginners PL/SQL Tutorial for beginners Oracle SQL Tutorial
Views: 382 TechLake
01 Overview of table Partition in oracle
 
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Partitioning enhances the performance, manageability, and availability of a wide variety of applications and helps reduce the total cost of ownership for storing large amounts of data. Partitioning allows tables, indexes, and index-organized tables to be subdivided into smaller pieces, enabling these database objects to be managed and accessed at a finer level of granularity. Oracle provides a rich variety of partitioning strategies and extensions to address every business requirement. Moreover, since it is entirely transparent, partitioning can be applied to almost any application without the need for potentially expensive and time consuming application changes. Partitioning allows a table, index, or index-organized table to be subdivided into smaller pieces, where each piece of such a database object is called a partition. Each partition has its own name, and may optionally have its own storage characteristics. From the perspective of a database administrator, a partitioned object has multiple pieces that can be managed either collectively or individually. This gives the administrator considerable flexibility in managing partitioned objects. However, from the perspective of the application, a partitioned table is identical to a non-partitioned table; no modifications are necessary when accessing a partitioned table using SQL queries and DML statements. Partitioning Key ======================== Each row in a partitioned table is unambiguously assigned to a single partition. The partitioning key is comprised of one or more columns that determine the partition where each row will be stored. Oracle automatically directs insert, update, and delete operations to the appropriate partition through the use of the partitioning key. When to Partition a Table ========================== Here are some suggestions for when to partition a table: Tables greater than 2 GB should always be considered as candidates for partitioning. Tables containing historical data, in which new data is added into the newest partition. A typical example is a historical table where only the current month's data is updatable and the other 11 months are read only. When the contents of a table need to be distributed across different types of storage devices. When to Partition an Index ============================= Here are some suggestions for when to consider partitioning an index: Avoid rebuilding the entire index when data is removed. Perform maintenance on parts of the data without invalidating the entire index. Reduce the impact of index skew caused by an index on a column with a monotonically increasing value. Partitioned Index-Organized Tables =================================== Partitioned index-organized tables are very useful for providing improved performance, manageability, and availability for index-organized tables. For partitioning an index-organized table: ============================================ Partition columns must be a subset of the primary key columns Secondary indexes can be partitioned (both locally and globally) OVERFLOW data segments are always equi-partitioned with the table partitions See Also: Oracle Database Concepts for more information about index-organized tables System Partitioning System partitioning enables application-controlled partitioning without having the database controlling the data placement. The database simply provides the ability to break down a table into partitions without knowing what the individual partitions are going to be used for. All aspects of partitioning have to be controlled by the application. For example, an insertion into a system partitioned table without the explicit specification of a partition will fail. System partitioning provides the well-known benefits of partitioning (scalability, availability, and manageability), but the partitioning and actual data placement are controlled by the application. See Also: Oracle Database Data Cartridge Developer's Guide for more information about system partitioning Partitioning for Information Lifecycle Management Information Lifecycle Management (ILM) is concerned with managing data during its lifetime. Partitioning plays a key role in ILM because it enables groups of data (that is, partitions) to be distributed across different types of storage devices and managed individually.
Views: 6526 OnLinE ReSoUrCe
Differences between Clustered vs Nonclustered Indexes in SQL Server
 
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Should you use a clustered or a nonclustered index? When starting out with SQL, you might intuitively know that you should add an index to a table, but you might not be sure what kind of index to add. In this video we go over the basics of clustered and nonclustered indexes to help you get through index choice paralysis. Blog post with example queries: https://bertwagner.com/2017/09/26/clustered-vs-nonclustered-index-fundamentals-you-need-to-know/ Follow me on Twitter: https://twitter.com/bertwagner Want to receive my latest weekly blog posts and videos in your inbox? Sign up for the newsletter here: https://upscri.be/c77fc8/
Views: 4746 Bert Wagner
#Kscope16 Interview: Chris Saxon, Oracle Corporation
 
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http://kscope16.com http://kscope17.com To view Chris 's presentations go to http://odtug.com Finding All the Red M&Ms: A Story of Indexes and Full Table Scans Topic: Database - Subtopic: SQL “Why isn’t my query using an index?” is a common question people have when tuning SQL. This talk explores the factors that influence the optimizer’s decision behind this question. It does so by comparing fetching rows from a database table to finding all the red M&Ms from their bags. It contrasts using an index range scan and a full table scan to do this. It introduces the concepts of blocks and the clustering factor. It discusses how these affect the optimizer's calculations. It goes on to demonstrate how these concepts work in practice using real SQL queries. This session is intended for developers and DBAs who want to learn the basics of how the optimizer chooses between an index range or full table scan. SQL for Date Ranges and History Using Temporal Validity and Flashback Data Archive Topic: Database - Subtopic: SQL Keeping a full history of changes to a table is a common business requirement. Auditors and analysts often need to view data as it existed at some point in the past. This is to ensure regulation compliance or understand how the business is performing. This session discusses the challenges with writing the SQL to implement these requirements. It then introduces temporal validity and flashback data archive. It shows how you can use these features to simplify working with date ranges and history tables. This talk is for developers and/or DBAs who write SQL or build applications that use date ranges and/or capture past data.
Views: 22 ODTUG
Hash Partitioning in Oracle
 
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The Video explains Hash partitioning in oracle and how it focuses on equal data distribution. It explains how hash partitioning uses partition joins to improve performance in VLDB by MPP.
Views: 3621 Tech Coach
Chris Saxon - Finding All the Red M&Ms: A Story of Indexes and Full Table Scans
 
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'Why isn’t my query using an index?' is a common question people have when tuning SQL. This talk explores the factors that influence the optimizer’s decision behind this question. It does so by comparing fetching rows from a database table to finding all the red M&Ms from their bags. It contrasts using an index range scan and a full table scan to do this. It introduces the concepts of blocks and the clustering factor. It discusses how these affect the optimizer's calculations. It goes on to demonstrate how these concepts work in practice using real SQL queries. This session is intended for developers and DBAs who want to learn the basics of how the optimizer chooses between an index range or full table scan.
Views: 96 Riga Dev Days
GIN - Stronger than ever
 
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by Alexander Korotkov Heikki Linnakangas Oleg Bartunov This talk presents set of GIN advances in PostgreSQL 9.4 and further which brings GIN to new level of performance and extendability. Most important advances are: posting lists compression, fast-scan algorithm, storing additional information and index-based ranking. This talk presents set of GIN advances: Compression posting lists. Indexes become 2 times smaller without any work in opclass. pg_upgrade is supported, old indexes will be recompressed on the fly. Fast scan algorithm. Fast scan allows GIN to skip parts of large posting trees during index scan. It dramatically improve performance of hstore and json search operators as well as FTS "frequentterm & rareterm" case. In order to use this improvement three-state logic support required in "consistent" opclass method. Storing additional (opclass defined) information in posting lists. Usage of additional information for filtering enables new features for GIN opclasses: better phrase search, better array similarity search, inverse FTS search (search for tsqueries matching tsvector), inverse regex search (search for regexes matching string), better string similarity using positioned n-grams. Index based ranking. This improvement allows GIN to return results in opclass defined manner. Most important application is returning results in relevance order for FTS which dramatically reduces IO load. But there are other applications like returns arrays in similarity order. We present the results of benchmarks for FTS using several datasets (6 M and 15 M documents) and real-life load for PostgreSQL and Sphinx full-text search engines and demonstrate that improved PostgreSQL FTS (with all ACID overhead) outperforms the standalone Sphinx search engine.
Views: 1484 Andrea Ross
Analytical Functions in oracle explained with real examples
 
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This video explains analytical functions and how they are implemented in real projects. Analytical functions are somewhat similar to aggregate functions,but they offer much more. Why use analytical function ? They allow you to write fast and concise queries which otherwise will involve self join and long processing times They allow you to perform aggregate functions independently on sets of partitions. You can access values from previous rows in current row and you can restrict the window on which you want to apply this analytical function. I have given additional practice exercises along with the dataset so that you can comfortably work with analytical functions. You can find the sample problems along with dataset in the below link. http://www.internshipsfromhome.com/oracle-analytical-functions-ddl-and-dml/
Views: 12549 Tech Coach
ROWID - DBArch Video 32
 
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In this video you will understand what is ROWID in an Oracle Database, where is it stored and what are its components. Our Upcoming Online Course Schedule is available in the url below https://docs.google.com/spreadsheets/d/1qKpKf32Zn_SSvbeDblv2UCjvtHIS1ad2_VXHh2m08yY/edit#gid=0 Reach us at [email protected]
Views: 2400 Ramkumar Swaminathan
SQL: Optimizer Hints Part-2
 
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In this tutorial, you'll learn...what are optimizer hints and how to use it.. SQL (pronounced "ess-que-el") stands for Structured Query Language. SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems.
Views: 10921 radhikaravikumar
ROWID VS ROWNUM IN ORACLE
 
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This Video explains the difference between ROWID and ROWNUM using real project examples. ROWID provides the unique physical address where the row is being stored. ROWNUM indicates the order in which the data was returned from the select query.
Views: 1888 Tech Coach
Reference partitioning in Oracle 11g
 
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The video explains Reference partitioning and its advantages in Oracle with real project example. It builds on the limitations of reference partitioning in oracle 11g (Interval partitioning) and scenarios where you should implement reference partitioning
Views: 1097 Tech Coach
What is BITMAP INDEX? What does BITMAP INDEX mean? BITMAP INDEX meaning & explanation
 
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What is BITMAP INDEX? What does BITMAP INDEX mean? BITMAP INDEX meaning - BITMAP INDEX definition - BITMAP INDEX explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A bitmap index is a special kind of database index that uses bitmaps. Bitmap indexes have traditionally been considered to work well for low-cardinality columns, which have a modest number of distinct values, either absolutely, or relative to the number of records that contain the data. The extreme case of low cardinality is Boolean data (e.g., does a resident in a city have internet access?), which has two values, True and False. Bitmap indexes use bit arrays (commonly called bitmaps) and answer queries by performing bitwise logical operations on these bitmaps. Bitmap indexes have a significant space and performance advantage over other structures for query of such data. Their drawback is they are less efficient than the traditional B-tree indexes for columns whose data is frequently updated: consequently, they are more often employed in read-only systems that are specialized for fast query - e.g., data warehouses, and generally unsuitable for online transaction processing applications. Some researchers argue that bitmap indexes are also useful for moderate or even high-cardinality data (e.g., unique-valued data) which is accessed in a read-only manner, and queries access multiple bitmap-indexed columns using the AND, OR or XOR operators extensively. Bitmap indexes are also useful in data warehousing applications for joining a large fact table to smaller dimension tables such as those arranged in a star schema. Bitmap based representation can also be used for representing a data structure which is labeled and directed attributed multigraph, used for queries in graph databases.Efficient graph management based on bitmap indices article shows how bitmap index representation can be used to manage large dataset(billions of data points) and answer queries related to graph efficiently. Basic bitmap indexes use one bitmap for each distinct value. It is possible to reduce the number of bitmaps used by using a different encoding method. For example, it is possible to encode C distinct values using log(C) bitmaps with binary encoding. This reduces the number of bitmaps, further saving space, but to answer any query, most of the bitmaps have to be accessed. This makes it potentially not as effective as scanning a vertical projection of the base data, also known as a materialized view or projection index. Finding the optimal encoding method that balances (arbitrary) query performance, index size and index maintenance remains a challenge. Without considering compression, Chan and Ioannidis analyzed a class of multi-component encoding methods and came to the conclusion that two-component encoding sits at the kink of the performance vs. index size curve and therefore represents the best trade-off between index size and query performance. For high-cardinality columns, it is useful to bin the values, where each bin covers multiple values and build the bitmaps to represent the values in each bin. This approach reduces the number of bitmaps used regardless of encoding method. However, binned indexes can only answer some queries without examining the base data. For example, if a bin covers the range from 0.1 to 0.2, then when the user asks for all values less than 0.15, all rows that fall in the bin are possible hits and have to be checked to verify whether they are actually less than 0.15. The process of checking the base data is known as the candidate check. In most cases, the time used by the candidate check is significantly longer than the time needed to work with the bitmap index. Therefore, binned indexes exhibit irregular performance. They can be very fast for some queries, but much slower if the query does not exactly match a bin.
Views: 2012 The Audiopedia
Roxy: Managing Indexes
 
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Adding and removing indexes on a MarkLogic database
Views: 906 David Cassel
14.316 Covering and Composite Index, Duplicates, Overflow Pages, Composite Keys
 
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Video for my inverted classroom "Database Systems". The complete list of videos and additional material is (will be) available at http://datenbankenlernen.de Computer Science, Saarland University: Bachelor (in German): http://www.cs.uni-saarland.de/index.php?id=52&L=1 Master (in English): http://www.cs.uni-saarland.de/index.php?id=132&L=1 Ph.D./Grad School: http://gradschool.cs.uni-saarland.de/
How to think in SQL, a set-based mindset - Kevin Devine
 
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One of the main sticking points for developers when they need to write SQL is thinking about the process the incorrect way. Java, C# and others require procedural thinking for optimization, but SQL optimization requires a different tactic, set-based thinking. In this talk, Kevin Devine, takes you through a number of SQL scenarios that were originally written procedurally and shows you how they were optimized using set-based thinking. We will talk about optimizer decisions like full scan, index fast full scan, index range scan, hash joins, merge joins, nested loops anti semi joins, lazy spool, hash aggregate and more. In addition, we will examine the fallacy of process-oriented thinking for SQL and focus on results-oriented thinking. At the end of this talk, you should be able to look at SQL differently and go home ready to optimize those hard to understand queries. Copyright © 2015 Stir Trek Conference, Inc. All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law.
Views: 3217 Stir Trek
Stored Procedure Optimization Techniques
 
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Performance Tunning : Procedure : 1.Set Nocount On 2.Set Isolation level Read Uncommited / Nolock 3.Missing Indexes : 4.Scalar function 5.COvering index -- include 6.Partition . 7.Proper Index -- Column Store 9.Try to minimize Physical read 10.Fragmentation issue (Index rebuilding + Reorganizing ) 11.understand Execution planning (opertor )
Views: 8200 SqlIsEasy