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Internal tables in ABAP can store and process data extracted from database tables. They consist of a body containing multiple rows of data and an optional header line. Various statements like APPEND, INSERT, DELETE, and LOOP AT can be used to manipulate data in internal tables. Control structures like AT NEW and AT END allow processing data based on changes in field values between table rows.
The document discusses internal tables in ABAP/4, including defining, processing, accessing, and initializing internal tables. Internal tables are structured data types that allow programs to reorganize and perform calculations on database table contents. They exist only during program runtime and cannot be accessed outside the program environment. Lines are accessed individually using a work area interface. There are two types - with or without a header line. Internal tables are created by defining a table type and object, or referring to an existing structure. They are filled using statements like APPEND, COLLECT, INSERT, and copying from database tables. Lines are read using LOOP or READ and modified using MODIFY. DELETE removes lines individually or by condition. SORT orders the
Internal tables in ABAP allow storing multiple records of the same type. They can be defined with or without a header line, which acts as a work area. Data is accessed using statements like LOOP, READ, APPEND that place one record at a time in the work area. The MODIFY, INSERT, DELETE statements update records. The SORT, COLLECT statements rearrange records in the table.
The document discusses different methods for appending lines to internal tables in ABAP, including appending a single line, appending multiple lines from one table to another, and using the APPEND statement to create ranked lists by sorting on a particular field. Examples are provided to demonstrate appending lines to build tables with various structures and contents.
The document outlines an objective to design a warehouse layout to organize finished goods based on classification and consumption. Products are classified into A, B, and C based on consumption, with A being highly consumed. SAS coding is used to import data, combine tables, compute lags, and perform regressions to forecast inventory levels based on regression equations. The layout places classification A in lane 1, B in lane 2, and C in lane 3.
Online Statistics Gathering for Bulk Loads - the official name of the feature - was introduced in Oracle 12.1. The idea is to gather optimizer statistics "on the fly" for direct path loads. Sounds good for ETL? In certain scenarios it makes sense but even then there are many points to consider so that it becomes a reliable part of your ETL processes. When exactly will it be working and when not? Do you prevent it yourself? Documented, undocumented cases, known bugs. Which statistics are gathered and which are not? What has to be considered with partitioned tables? Interval partitioning - special case?
Building a Hierarchical Data Model Using the Latest IBM Informix FeaturesAjay Gupte
Learn about developing Hierarchical queries using Informix features such as OLAP functions, setops operators and query rewrite. This presentation will cover building the hierarchical data model using existing relational schema in IDS. You learn about customer scenarios for designing hierarchical data model, in-depth knowledge of complex hierarchical queries, performance tips and references. This talk will provide details on how to identify hierarchical relationship and take advantage of using existing relational model.
The document discusses OLAP extensions that were added to the SQL standard to enable calculations like computing percentage changes or moving averages that were previously impossible or impractical in SQL. These extensions, collectively called the 'OLAP package', include the ROLLUP and CUBE functions which improve aggregation capabilities by generating subtotals for different groupings. Window functions also allow ranking, cumulative distributions and moving aggregates to be calculated on a subset of rows.
Internal tables in ABAP can store and process data extracted from database tables. They consist of a body containing multiple rows of data and an optional header line. Various statements like APPEND, INSERT, DELETE, and LOOP AT can be used to manipulate data in internal tables. Control structures like AT NEW and AT END allow processing data based on changes in field values between table rows.
The document discusses internal tables in ABAP/4, including defining, processing, accessing, and initializing internal tables. Internal tables are structured data types that allow programs to reorganize and perform calculations on database table contents. They exist only during program runtime and cannot be accessed outside the program environment. Lines are accessed individually using a work area interface. There are two types - with or without a header line. Internal tables are created by defining a table type and object, or referring to an existing structure. They are filled using statements like APPEND, COLLECT, INSERT, and copying from database tables. Lines are read using LOOP or READ and modified using MODIFY. DELETE removes lines individually or by condition. SORT orders the
Internal tables in ABAP allow storing multiple records of the same type. They can be defined with or without a header line, which acts as a work area. Data is accessed using statements like LOOP, READ, APPEND that place one record at a time in the work area. The MODIFY, INSERT, DELETE statements update records. The SORT, COLLECT statements rearrange records in the table.
The document discusses different methods for appending lines to internal tables in ABAP, including appending a single line, appending multiple lines from one table to another, and using the APPEND statement to create ranked lists by sorting on a particular field. Examples are provided to demonstrate appending lines to build tables with various structures and contents.
The document outlines an objective to design a warehouse layout to organize finished goods based on classification and consumption. Products are classified into A, B, and C based on consumption, with A being highly consumed. SAS coding is used to import data, combine tables, compute lags, and perform regressions to forecast inventory levels based on regression equations. The layout places classification A in lane 1, B in lane 2, and C in lane 3.
Online Statistics Gathering for Bulk Loads - the official name of the feature - was introduced in Oracle 12.1. The idea is to gather optimizer statistics "on the fly" for direct path loads. Sounds good for ETL? In certain scenarios it makes sense but even then there are many points to consider so that it becomes a reliable part of your ETL processes. When exactly will it be working and when not? Do you prevent it yourself? Documented, undocumented cases, known bugs. Which statistics are gathered and which are not? What has to be considered with partitioned tables? Interval partitioning - special case?
Building a Hierarchical Data Model Using the Latest IBM Informix FeaturesAjay Gupte
Learn about developing Hierarchical queries using Informix features such as OLAP functions, setops operators and query rewrite. This presentation will cover building the hierarchical data model using existing relational schema in IDS. You learn about customer scenarios for designing hierarchical data model, in-depth knowledge of complex hierarchical queries, performance tips and references. This talk will provide details on how to identify hierarchical relationship and take advantage of using existing relational model.
The document discusses OLAP extensions that were added to the SQL standard to enable calculations like computing percentage changes or moving averages that were previously impossible or impractical in SQL. These extensions, collectively called the 'OLAP package', include the ROLLUP and CUBE functions which improve aggregation capabilities by generating subtotals for different groupings. Window functions also allow ranking, cumulative distributions and moving aggregates to be calculated on a subset of rows.
The document provides an overview of the MySQL query optimizer. It discusses how the optimizer performs logical transformations, cost-based optimizations, analyzes access methods, and optimizes join orders. The goal of the optimizer is to produce a query execution plan that uses the least resources. It considers factors like I/O and CPU costs to select optimal table access methods, join orders, and other optimizations to minimize the cost of executing the query.
The document describes the functionality and purpose of the SAP ABAP Data Dictionary. The Data Dictionary provides a platform-independent interface to database metadata. It facilitates development by eliminating the need for programmers to manage specific database details. The Data Dictionary contains objects like domains, data elements, tables and their relationships which are used to develop and maintain ABAP applications.
This document provides an overview of the MySQL query optimizer. It discusses the main phases of the optimizer including logical transformations, cost-based optimizations, analyzing access methods, join ordering, and plan refinements. Logical transformations prepare the query for cost-based optimization by simplifying conditions. Cost-based optimizations select the optimal join order and access methods to minimize resources used. Access methods analyzed include table scans, index scans, and ref access. The join optimizer searches for the best join order. Plan refinements include sort avoidance and index condition pushdown.
MySQL 8.0.18 latest updates: Hash join and EXPLAIN ANALYZENorvald Ryeng
This presentation focuses on two of the new features in MySQL 8.0.18: hash joins and EXPLAIN ANALYZE. It covers how these features work, both on the surface and on the inside, and how you can use them to improve your queries and make them go faster.
Both features are the result of major refactoring of how the MySQL executor works. In addition to explaining and demonstrating the features themselves, the presentation looks at how the investment in a new iterator based executor prepares MySQL for a future with faster queries, greater plan flexibility and even more SQL features.
This document contains questions related to data structures using Python. It covers topics like arrays, linked lists, stacks, queues, trees and their various operations. Some key points:
- It asks to define concepts like ADT, linear and non-linear data structures. Operations on linked lists, stacks and queues are also included.
- Tree related questions cover binary search trees, expression trees, AVL trees, B-trees and heaps. Operations like insertion, deletion and traversal are discussed.
- Other questions include converting between infix, prefix and postfix notation. Implementing various data structures using arrays and linked lists is also covered.
- Applications of different data structures are explored along with their advantages and
The document discusses various data structures and their operations. It begins by defining a tree as a non-linear data structure used to store hierarchical data. Binary trees are discussed, including their two implementation methods (linear and linked representations). Common tree traversals like inorder, preorder and postorder are defined. Applications of trees include syntax analysis, symbol tables and file systems. Binary search trees and AVL trees are also introduced, with AVL trees ensuring subtrees differ in height by at most one for efficient searching.
Linked lists allow for dynamic memory allocation by connecting nodes using pointers. There are several types of linked lists including singly linked, doubly linked, and circular linked lists. Singly linked lists use a single pointer to connect nodes in a linear fashion, while doubly linked lists use two pointers to allow traversal in both directions. Circular linked lists connect the last node back to the first to form a circular structure. Common operations on linked lists include insertion, deletion, traversal, and searching which are implemented through manipulating the pointers between nodes.
The document discusses the MySQL query optimizer. It covers logical transformations performed by the optimizer like negation elimination and equality propagation. It also discusses cost-based optimization where the optimizer assigns costs to different execution plans and joins/access methods to select the most efficient plan. The optimizer characteristics are also summarized, noting it produces a single left-deep linear query plan to minimize resource usage.
Analytic functions allow calculations to be performed on sets of rows and return multiple rows of data per record. They are similar to aggregate functions but do not group results. Some common analytic functions discussed include ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, FIRST_VALUE, LAST_VALUE. The document also describes functions like LISTAGG, TRANSLATE, REGEXP_LIKE, REGEXP_COUNT, COALESCE, EXTRACT, ADD_MONTHS, INITCAP, INSTR and GREATEST.
The document discusses new improvements to the parser and optimizer in MySQL 5.7. Key points include:
1) The parser and optimizer were refactored for improved maintainability and stability. Parsing was separated from optimization and execution.
2) The cost model was improved with better record estimation for joins, configurable cost constants, and additional explain output.
3) A new query rewrite plugin allows rewriting queries without changing application code.
This document discusses various SQL concepts including joins, aggregation functions, and grouping. It begins with an overview of installing MySQL Workbench and loading sample data. It then covers SELECT statements and functions like COUNT, SUM, AVG. It describes different types of joins like inner, left, right, and self joins. It provides examples of joining tables to retrieve related data and performing self joins to combine rows from the same table. It also explains how to use the GROUP BY clause to divide data into groups and apply aggregation functions.
https://github.com/ashim888/dataStructureAndAlgorithm
Stack
Concept and Definition
• Primitive Operations
• Stack as an ADT
• Implementing PUSH and POP operation
• Testing for overflow and underflow conditions
Recursion
• Concept and Definition
• Implementation of:
¬ Multiplication of Natural Numbers
¬ Factorial
¬ Fibonacci Sequences
The Tower of Hanoi
Ground Breakers Romania: Explain the explain_planMaria Colgan
This session was delivered as part of the EMEA Ground Breakers tour in Romania, Oct. 2019. The execution plan for a SQL statement can often seem complicated and hard to understand. Determining if the execution plan you are looking at is the best plan you could get or attempting to improve a poorly performing execution plan can be a daunting task even for the most experienced DBA or developer. This session examines the different aspects of an execution plan, from selectivity to parallel execution and explains what information you should be gleaming from the plan and how it affects the execution. It offers insight into what caused the Optimizer to make the decision it did as well as a set of corrective measures that can be used to improve each aspect of the plan.
This document provides an overview of SAP ABAP (Advanced Business Application Programming) and covers various topics related to data modeling and programming in SAP. It discusses the SAP data dictionary, data types, tables, views, search helps, and lock objects. It also describes how to create and maintain tables, views, and search helps using transactions codes like SE11, SE93, and SM30.
Linked allocation vs. Sequential allocation, Operations on Linked Lists realized in terms of Stacks & Queues with boundary conditions, programming errors & rectifications, Cyclic permutation in Stack push & pop, Partial Ordering, Group Activity, Case Study of Topological Sorting; Characterization, explanation, analysis & applications of Topological Sorting Algorithm, Exercise on Inverting a Linked list, Programs for practice.
Exploring Advanced SQL Techniques Using Analytic FunctionsZohar Elkayam
Session from ILOUG I presented in May, 2016
Even though DBAs and developers are writing SQL queries every day, it seems that advanced SQL techniques such as multi-dimension aggregation and analytic functions are still relatively remain unknown. In this session, we will explore some of the common real-world usages for analytic function, and understand how to take advantage of this great and useful tool. We will deep dive into ranking based on values and groups; understand aggregation of multiple dimensions without a group by; see how to do inter-row calculations, and much-much more…
Together we will see how we can unleash the power of analytics using Oracle 11g best practices and Oracle 12c new features.
A matrix is a rectangular table of numbers that can represent a system of linear equations. Matrix notation uses row operations like switching rows, multiplying rows by constants, and adding multiples of rows to other rows to transform the matrix into upper triangular form, from which the solution to the system of equations can be determined. The three main row operations are: 1) switching rows, 2) multiplying a row by a constant, and 3) adding a multiple of one row to another row.
This document provides an overview of SQL tuning and optimization techniques. It discusses various indexing options in Oracle like bitmap indexes and reverse key indexes. It also covers execution plan analysis using tools like EXPLAIN PLAN and tuning techniques like hints. The goal of SQL tuning is to identify resource-intensive queries and optimize them using better indexing, rewriting queries, and other optimization strategies.
This document introduces internal tables in ABAP, including:
1) The structure of internal tables, which store multiple data records in memory similarly to database tables.
2) Defining internal tables with and without header lines.
3) Filling internal tables by reading from database tables, appending lines, and moving tables.
4) Sorting and retrieving data from internal tables using loops, indexes, and keys.
5) Changing internal table contents by modifying, inserting, and deleting lines.
ALTER TABLE Improvements in MariaDB ServerMariaDB plc
OpenWorks 2019 Presentation
MariaDB Server 10.3 introduced ALGORITHM=NOCOPY, allowing columns to be added in an instant because adding columns no longer required rebuilding a table. In MariaDB Server 10.4, we’re taking this a step further with instant DROP COLUMN and instant ALTER TABLE to support many more instant schema changes. In this session, Marko Mäkelä explains how instant schema changes work and how MariaDB is eliminating table rebuilds.
Discover the power of Recursive SQL and query transformation with Informix da...Ajay Gupte
This presentation will provide an overview of the Recursive SQL with the CONNECT BY clause feature. We will provide examples of typical practical database problems and describe in detail how they can be solved with recursive SQL. The problems discussed include for bill of materials, obtaining the number of employees for each manager in a particular sub-organization, converting linked dimension hierarchies in a star schema to fixed dimension hierarchies, tracking packages, and generating test data. This presentation compares the new solutions with traditional solutions of these problems and discusses the advantages and disadvantages of the various methods. This presentation will also discuss the query transformation techniques with Informix 12.10 features which will focus on how query blocks are moved between different levels and optimized using examples and diagrams. Users will learn how to analyze complex examples based on various Informix 12.10 features. Examples included in this session are query block movement, table re-ordering, complex ANSI joins, sub-queries, derived tables, views, connect by, OLAP functions, setops cases.
The document provides an overview of the MySQL query optimizer. It discusses how the optimizer performs logical transformations, cost-based optimizations, analyzes access methods, and optimizes join orders. The goal of the optimizer is to produce a query execution plan that uses the least resources. It considers factors like I/O and CPU costs to select optimal table access methods, join orders, and other optimizations to minimize the cost of executing the query.
The document describes the functionality and purpose of the SAP ABAP Data Dictionary. The Data Dictionary provides a platform-independent interface to database metadata. It facilitates development by eliminating the need for programmers to manage specific database details. The Data Dictionary contains objects like domains, data elements, tables and their relationships which are used to develop and maintain ABAP applications.
This document provides an overview of the MySQL query optimizer. It discusses the main phases of the optimizer including logical transformations, cost-based optimizations, analyzing access methods, join ordering, and plan refinements. Logical transformations prepare the query for cost-based optimization by simplifying conditions. Cost-based optimizations select the optimal join order and access methods to minimize resources used. Access methods analyzed include table scans, index scans, and ref access. The join optimizer searches for the best join order. Plan refinements include sort avoidance and index condition pushdown.
MySQL 8.0.18 latest updates: Hash join and EXPLAIN ANALYZENorvald Ryeng
This presentation focuses on two of the new features in MySQL 8.0.18: hash joins and EXPLAIN ANALYZE. It covers how these features work, both on the surface and on the inside, and how you can use them to improve your queries and make them go faster.
Both features are the result of major refactoring of how the MySQL executor works. In addition to explaining and demonstrating the features themselves, the presentation looks at how the investment in a new iterator based executor prepares MySQL for a future with faster queries, greater plan flexibility and even more SQL features.
This document contains questions related to data structures using Python. It covers topics like arrays, linked lists, stacks, queues, trees and their various operations. Some key points:
- It asks to define concepts like ADT, linear and non-linear data structures. Operations on linked lists, stacks and queues are also included.
- Tree related questions cover binary search trees, expression trees, AVL trees, B-trees and heaps. Operations like insertion, deletion and traversal are discussed.
- Other questions include converting between infix, prefix and postfix notation. Implementing various data structures using arrays and linked lists is also covered.
- Applications of different data structures are explored along with their advantages and
The document discusses various data structures and their operations. It begins by defining a tree as a non-linear data structure used to store hierarchical data. Binary trees are discussed, including their two implementation methods (linear and linked representations). Common tree traversals like inorder, preorder and postorder are defined. Applications of trees include syntax analysis, symbol tables and file systems. Binary search trees and AVL trees are also introduced, with AVL trees ensuring subtrees differ in height by at most one for efficient searching.
Linked lists allow for dynamic memory allocation by connecting nodes using pointers. There are several types of linked lists including singly linked, doubly linked, and circular linked lists. Singly linked lists use a single pointer to connect nodes in a linear fashion, while doubly linked lists use two pointers to allow traversal in both directions. Circular linked lists connect the last node back to the first to form a circular structure. Common operations on linked lists include insertion, deletion, traversal, and searching which are implemented through manipulating the pointers between nodes.
The document discusses the MySQL query optimizer. It covers logical transformations performed by the optimizer like negation elimination and equality propagation. It also discusses cost-based optimization where the optimizer assigns costs to different execution plans and joins/access methods to select the most efficient plan. The optimizer characteristics are also summarized, noting it produces a single left-deep linear query plan to minimize resource usage.
Analytic functions allow calculations to be performed on sets of rows and return multiple rows of data per record. They are similar to aggregate functions but do not group results. Some common analytic functions discussed include ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, FIRST_VALUE, LAST_VALUE. The document also describes functions like LISTAGG, TRANSLATE, REGEXP_LIKE, REGEXP_COUNT, COALESCE, EXTRACT, ADD_MONTHS, INITCAP, INSTR and GREATEST.
The document discusses new improvements to the parser and optimizer in MySQL 5.7. Key points include:
1) The parser and optimizer were refactored for improved maintainability and stability. Parsing was separated from optimization and execution.
2) The cost model was improved with better record estimation for joins, configurable cost constants, and additional explain output.
3) A new query rewrite plugin allows rewriting queries without changing application code.
This document discusses various SQL concepts including joins, aggregation functions, and grouping. It begins with an overview of installing MySQL Workbench and loading sample data. It then covers SELECT statements and functions like COUNT, SUM, AVG. It describes different types of joins like inner, left, right, and self joins. It provides examples of joining tables to retrieve related data and performing self joins to combine rows from the same table. It also explains how to use the GROUP BY clause to divide data into groups and apply aggregation functions.
https://github.com/ashim888/dataStructureAndAlgorithm
Stack
Concept and Definition
• Primitive Operations
• Stack as an ADT
• Implementing PUSH and POP operation
• Testing for overflow and underflow conditions
Recursion
• Concept and Definition
• Implementation of:
¬ Multiplication of Natural Numbers
¬ Factorial
¬ Fibonacci Sequences
The Tower of Hanoi
Ground Breakers Romania: Explain the explain_planMaria Colgan
This session was delivered as part of the EMEA Ground Breakers tour in Romania, Oct. 2019. The execution plan for a SQL statement can often seem complicated and hard to understand. Determining if the execution plan you are looking at is the best plan you could get or attempting to improve a poorly performing execution plan can be a daunting task even for the most experienced DBA or developer. This session examines the different aspects of an execution plan, from selectivity to parallel execution and explains what information you should be gleaming from the plan and how it affects the execution. It offers insight into what caused the Optimizer to make the decision it did as well as a set of corrective measures that can be used to improve each aspect of the plan.
This document provides an overview of SAP ABAP (Advanced Business Application Programming) and covers various topics related to data modeling and programming in SAP. It discusses the SAP data dictionary, data types, tables, views, search helps, and lock objects. It also describes how to create and maintain tables, views, and search helps using transactions codes like SE11, SE93, and SM30.
Linked allocation vs. Sequential allocation, Operations on Linked Lists realized in terms of Stacks & Queues with boundary conditions, programming errors & rectifications, Cyclic permutation in Stack push & pop, Partial Ordering, Group Activity, Case Study of Topological Sorting; Characterization, explanation, analysis & applications of Topological Sorting Algorithm, Exercise on Inverting a Linked list, Programs for practice.
Exploring Advanced SQL Techniques Using Analytic FunctionsZohar Elkayam
Session from ILOUG I presented in May, 2016
Even though DBAs and developers are writing SQL queries every day, it seems that advanced SQL techniques such as multi-dimension aggregation and analytic functions are still relatively remain unknown. In this session, we will explore some of the common real-world usages for analytic function, and understand how to take advantage of this great and useful tool. We will deep dive into ranking based on values and groups; understand aggregation of multiple dimensions without a group by; see how to do inter-row calculations, and much-much more…
Together we will see how we can unleash the power of analytics using Oracle 11g best practices and Oracle 12c new features.
A matrix is a rectangular table of numbers that can represent a system of linear equations. Matrix notation uses row operations like switching rows, multiplying rows by constants, and adding multiples of rows to other rows to transform the matrix into upper triangular form, from which the solution to the system of equations can be determined. The three main row operations are: 1) switching rows, 2) multiplying a row by a constant, and 3) adding a multiple of one row to another row.
This document provides an overview of SQL tuning and optimization techniques. It discusses various indexing options in Oracle like bitmap indexes and reverse key indexes. It also covers execution plan analysis using tools like EXPLAIN PLAN and tuning techniques like hints. The goal of SQL tuning is to identify resource-intensive queries and optimize them using better indexing, rewriting queries, and other optimization strategies.
This document introduces internal tables in ABAP, including:
1) The structure of internal tables, which store multiple data records in memory similarly to database tables.
2) Defining internal tables with and without header lines.
3) Filling internal tables by reading from database tables, appending lines, and moving tables.
4) Sorting and retrieving data from internal tables using loops, indexes, and keys.
5) Changing internal table contents by modifying, inserting, and deleting lines.
ALTER TABLE Improvements in MariaDB ServerMariaDB plc
OpenWorks 2019 Presentation
MariaDB Server 10.3 introduced ALGORITHM=NOCOPY, allowing columns to be added in an instant because adding columns no longer required rebuilding a table. In MariaDB Server 10.4, we’re taking this a step further with instant DROP COLUMN and instant ALTER TABLE to support many more instant schema changes. In this session, Marko Mäkelä explains how instant schema changes work and how MariaDB is eliminating table rebuilds.
Discover the power of Recursive SQL and query transformation with Informix da...Ajay Gupte
This presentation will provide an overview of the Recursive SQL with the CONNECT BY clause feature. We will provide examples of typical practical database problems and describe in detail how they can be solved with recursive SQL. The problems discussed include for bill of materials, obtaining the number of employees for each manager in a particular sub-organization, converting linked dimension hierarchies in a star schema to fixed dimension hierarchies, tracking packages, and generating test data. This presentation compares the new solutions with traditional solutions of these problems and discusses the advantages and disadvantages of the various methods. This presentation will also discuss the query transformation techniques with Informix 12.10 features which will focus on how query blocks are moved between different levels and optimized using examples and diagrams. Users will learn how to analyze complex examples based on various Informix 12.10 features. Examples included in this session are query block movement, table re-ordering, complex ANSI joins, sub-queries, derived tables, views, connect by, OLAP functions, setops cases.
IBM Informix Database SQL Set operators and ANSI Hash JoinAjay Gupte
This document discusses SQL set operators like UNION, INTERSECT, and MINUS. It explains that INTERSECT returns rows common to two result sets, while MINUS returns rows in the first set not in the second. The operators support NULLs and have rules like UNION. Examples demonstrate their usage in views, derived tables, and procedures. Optimization techniques like nested loops and hash joins are covered. Scenarios illustrate uses like finding overlapping or non-overlapping supplier and order IDs. ANSI join improvements like hash joins are also summarized.
This document discusses using table controls in ABAP transactions. It introduces table controls as graphical screen elements for displaying multiple rows of tabular data on a single screen. It covers creating and adding table controls to screens, filling them from internal tables, updating the internal tables from changes to the table control, and saving updated data back to the database. Methods for handling table control flow logic, updating the internal table, and detecting and saving changes are presented.
The document discusses B-tree indexes in Oracle Database, including their structure as balanced trees, how index entries are stored for B-tree and bitmap indexes, and how the indexes are maintained during data manipulation language (DML) operations like inserts, deletes, and updates on the associated table. It also covers topics like why an optimizer may not use an index, index constraints, reorganizing indexes, and myths about index performance tuning.
The document discusses various techniques for optimizing database performance in Oracle, including:
- Using the cost-based optimizer (CBO) to choose the most efficient execution plan based on statistics and hints.
- Creating appropriate indexes on columns used in predicates and queries to reduce I/O and sorting.
- Applying constraints and coding practices like limiting returned rows to improve query performance.
- Tuning SQL statements through techniques like predicate selectivity, removing unnecessary objects, and leveraging indexes.
Internal tables are temporary tables stored in RAM that consist of rows with an identical structure. They have a body that holds the rows of data and an optional header line. Data can be added to internal tables using APPEND and read using LOOP AT. The OCCURS clause determines how much memory is initially allocated, but more is allocated if needed.
The document discusses stacks and queues as abstract data types. It describes their basic operations and implementations using arrays. Stacks follow LIFO (last-in, first-out) order and can be used for applications like undo operations. Queues follow FIFO (first-in, first-out) order and can be used where ordering of elements is important, like in printing queues. The document also discusses infix, prefix and postfix notations for arithmetic expressions and provides an algorithm to convert infix to postfix notation using a stack. Finally, it describes different types of queues including linear and circular queues.
Sap abap-data structures and internal tablesMustafa Nadim
Data structures and internal tables allow programs to store and manipulate data in memory. Structures define the layout of related data fields, while internal tables provide a way to store multiple occurrences of structured data. The document demonstrates how to declare structures and internal tables, populate them with data from database tables, and process the stored data within programs.
The document discusses different types of joins in SQL including Cartesian product, natural join, equi join, self join, inner join, left join, right join, and full outer join. It provides examples of each join type using sample tables and describes the key characteristics of each join.
Internal tables are temporary tables stored in RAM on the application server that are created and filled during program execution. They consist of rows with an identical structure and can include a header line and body. Data is added to internal tables using APPEND and read using LOOP AT, which can be restricted using FROM, TO, and WHERE clauses. The OCCURS clause determines how much memory is initially allocated but more is allocated if needed.
After completing this lesson, you should be able to do the following:
Describe the main database objects
Create tables
Describe the datatypes that can be used when specifying column definition
Alter table definitions
Drop, rename, and truncate tables
This document discusses data manipulation in Oracle databases. It describes how to insert new rows into tables using the INSERT statement, update existing rows using the UPDATE statement, and delete rows from tables using the DELETE statement. It also covers controlling transactions with commands like COMMIT, ROLLBACK, and SAVEPOINT to ensure data integrity when making multiple changes to a database.
The document provides examples of SQL commands for:
1. Selecting data from tables including top rows, random rows, joins, outer joins, and grouping.
2. Creating views and stored procedures.
3. Differences between functions and stored procedures.
4. Using triggers, cursors, and retrieving the second highest/lowest value from a column.
5. Common DDL commands like creating, copying, deleting, and altering tables.
So in summary, it covers the basics of SQL including queries, views, stored procedures, functions, triggers and DDL commands.
The document discusses data manipulation language (DML) statements in SQL. It describes how to insert rows into a table using INSERT, update rows using UPDATE, and delete rows from a table using DELETE. It also covers transaction control using COMMIT to save changes permanently and ROLLBACK to undo pending changes back to a savepoint.
Oracle 9i is changing the ETL (Extract, Transform, Load) paradigm by providing powerful new ETL capabilities within the database. Key features discussed include external tables for reading flat files directly without loading to temporary tables, the MERGE statement for updating or inserting rows with one statement, multi-table inserts for conditionally inserting rows into multiple tables, pipelined table functions for efficiently passing row sets between functions, and native compilation for improving PL/SQL performance. These new Oracle 9i capabilities allow for simpler, more efficient, and lower cost ETL processes compared to traditional third-party ETL tools.
V.6 CSPro Tabulation Application_Creating Tables with PostCalc Application.pptxEmmanuelAzuela3
This document provides instructions on how to create a CSPro tabulation application to generate a table showing average per capita monthly food consumption expenditure by barangay for a municipality. It describes how to:
1. Create a new tabulation application and input dictionary
2. Add variables to store household and indicator information
3. Specify the universe, value tallied, tabulation logic, and post-calculation for the table
4. Select the area to disaggregate the data by barangay
5. Save, run the application, and view the output table
The goal is to generate a table with monthly average food expenditure per capita by barangay using CSPro post-calculation logic.
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How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
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Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
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How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
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Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
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IntroductionIntroduction
ToTo
Internal TablesInternal Tables
3. 3
Report - Internal TablesReport - Internal Tables
Internal tables fulfill the function of arrays.
Stores data extracted from database tables.
Internal tables can be nested.
It consists of Body and Header line.
Body – Holds the rows of the internal table.
Header line – Has same structure as row of the body
holding a single row only.
Work Area :
To change or output the contents of an internal table, you need a
work area.
When processing an internal table, the system always fills the work
area with the contents of the current table line.
You can then process the work area.
Header line is the default work area for internal tables with header
line
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5. Report - Declaring Internal TablesReport - Declaring Internal Tables
5
TYPE typ OCCURS n
Defines an internal table without header line.
Example
TYPES: BEGIN OF LINE_TYPE,
NAME(20) TYPE C,
AGE TYPE I,
END OF LINE_TYPE.
DATA: PERSONS TYPE LINE_TYPE OCCURS 20,
PERSONS_WA TYPE LINE_TYPE.
PERSONS-NAME = 'Michael'.
PERSONS-AGE = 25.
APPEND PERSONS_WA TO PERSONS.
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6. 6
TYPE typ OCCURS n WITH HEADER LINE
Defines an internal table with header line. Such a table consists of any number of
table lines with the type typ and a header line.
Example
TYPES: BEGIN OF LINE_TYPE,
NAME(20) TYPE C,
AGE TYPE I,
END OF LINE_TYPE.
DATA: PERSONS TYPE LINE_TYPE OCCURS 20 WITH HEADER LINE.
PERSONS-NAME = 'Michael'.
PERSONS-AGE = 25.
APPEND PERSONS.
Report - Declaring Internal TablesReport - Declaring Internal Tables
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7. 7
Referencing data dictionary object
With header line
DATA FLIGHT_TAB LIKE SFLIGHT OCCURS 10 WITH HEADER LINE.
Without header line
DATA FLIGHT_TAB LIKE SFLIGHT OCCURS 10.
DATA FLIGHT_TAB LIKE SFLIGHT.
Including structures
You can Include another structure into Internal table.
DATA : BEGIN OF T_TAB1 OCCURS 10,
FIELDS1 LIKE BKPF-BELNR,
FIELDS2 LIKE BSEG-BUZEI,
END OF T_TAB1.
DATA : BEGIN OF T_TAB2 OCCURS 10.
INCLUDE STRUCTURE T_TAB1.
DATA : END OF T_TAB2.
In this example, T_TAB2 will also contain the fields FIELD1 & FIELD2.
Report - Declaring Internal TablesReport - Declaring Internal Tables
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8. 8
APPEND Statement
APPEND [wa TO | INITIAL LINE TO] itab.
Appends a new line to the end of the internal table itab. If you specify wa TO,
the new line is taken from the contents of the explicitly specified work area
wa.
If you use INITIAL LINE TO, a line filled with the correct value for the type is
added. If the specification before itab is omitted, the new line is taken from
the internal table itab.
After the APPEND, the system field SY-TABIX contains the index of the
newly added table entry.
Report - Filling Internal TablesReport - Filling Internal Tables
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9. 9
INSERT Statement
INSERT [wa INTO | INITIAL LINE INTO] itab [INDEX idx].
Inserts a new line into an internal table. If you specify wa INTO , the new line
is taken from the contents of the explicitly specified work area wa.
When using INITIAL LINE INTO , a line containing the appropriate initial
value for its type is inserted into the table. If you omit the specification
before itab , the new line is taken from the header line of the internal table
itab.
INDEX idx specifies the table index before which the line is inserted into the
table itab .
Report - Filling Internal TablesReport - Filling Internal Tables
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10. 10
Assigning internal tablesAssigning internal tables
Internal tables without header line
MOVE ITAB1 TO ITAB2.
ITAB2 = ITAB1
Internal tables with header line
MOVE ITAB1[ ] TO ITAB2[ ].
ITAB2[ ] = ITAB1[ ].
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11. 11
Extracting data from database tableExtracting data from database table
SELECT c1 c2 … cn|* FROM <dbtable>
INTO TABLE <itab>
WHERE <condition>.
dbtable – Name of the database table
c1,c2,…cn – columns in the database table
itab – Internal table that holds the data
condition – WHERE clause condition
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12. 12
LOOP AT Statement
LOOP AT itab.
LOOP AT itab INTO wa.
Processes an internal table (DATA) in a loop which begins with LOOP and
ends with ENDLOOP. Each of the internal table entries is sent to the output
area in turn.
When LOOP AT itab. is used, the header line of the internal table itab is used
as output area.
In the case of LOOP AT itab INTO wa , there is an explicitly specified work
area wa.
If the internal table is empty, all the statements between LOOP and
ENDLOOP are ignored.
In each loop pass, SY-TABIX contains the index of the current table entry.
After leaving a LOOP, SY-TABIX has the same value as it had before.
Report - Retrieving Internal TablesReport - Retrieving Internal Tables
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13. 13
READ Statement
READ TABLE itab INDEX idx [INTO WA].
READ TABLE itab WITH KEY <k1> = <f1>
<k2> = <f2>…
<kn> = <fn> [INTO wa]
[BINARY SEARCH].
Reads an internal table entry. An entry can be chosen using a key or its index
idx.
With "READ TABLE itab.", the header line of the internal table itab is used as
the output area; with "READ TABLE itab INTO wa." the explicitly specified
work area wa is used for this purpose.
For BINARY SEARCH, the internal table itab should be sorted by the keys
k1,k2…kn.
Report - Retrieving Internal TablesReport - Retrieving Internal Tables
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14. 14
MODIFY Statement
MODIFY itab [FROM wa] [INDEX idx].
Changes an entry in the internal table itab .
If you specify FROM wa , the line is replaced by the explicitly specified
work area wa . If the FROM specification is omitted, the line is replaced
by the header line from itab .
With INDEX idx, you can specify the table index of the line to be
changed. The index specification can be omitted in a LOOP on an
internal table.
The INDEX specification can also appear before the FROM
specification.
Note
The counting of table entries begins with 1.
Report - Modifying Internal TablesReport - Modifying Internal Tables
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15. 15
DELETE Statement
DELETE itab.
The current entry of the internal table itab is deleted in a LOOP loop.
Return code value is set to 0.
DELETE itab INDEX idx.
Deletes the idx entry from the internal table itab .
The return code value is set as follows:
SY-SUBRC = 0 The entry was deleted.
SY_SUBRC = 4 The entry does not exist.
Report - Deleting Internal TablesReport - Deleting Internal Tables
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16. 16
DELETE Statement
DELETE itab FROM idx1 TO idx2.
Deletes the line area from index idx1 to idx2 from internal table itab. At least
one of the two parameters FROM idx1 or TO idx2 should be specified.
If parameter FROM is missing, the area from the start of the table to line
idx2 is deleted.
If parameter TO is missing, the area from line idx1 to the end of the table is
deleted.
Start index idx1 must be greater than 0. The return code value is set as
follows:
SY-SUBRC = 0 At least one entry was deleted.
SY_SUBRC = 4 None of the entries were deleted.
Report - Deleting Internal TablesReport - Deleting Internal Tables
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17. 17
SORT Statement
SORT itab DESCENDING.
SORT itab ASCENDING.
SORT itab BY f1 f2 ... fi.
Sorts the entries of the internal table itab in ascending order. The default
key is used as the sort key for internal tables.
Sorts itab by the sub-fields f1, f2 , ..., fi which form the sort key. These fields
can be any type (even number fields or tables).
Unless you specify otherwise, the sort is in ascending order. You can also
use additions 1 and 2 before BY if you want all sub-fields to apply.
To change the sort sequence for each individual field, specify
DESCENDING or ASCENDING after each of the sub-fields f1 , f2 , ..., fi .
Report - Sorting Internal TablesReport - Sorting Internal Tables
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18. 18
CLEAR/REFRESHCLEAR/REFRESH
CLEAR ITAB.
If ITAB is an internal table without a header line, the entire table is
deleted together with all its entries.
If, however, ITAB is an internal table with a header line, only the
subfields in the table header entry are reset to their initial values.
To delete the entire internal table together with all its entries, you
can use CLEAR ITAB[ ] or REFRESH ITAB.
NOTE:
CLEAR f.
Clears the field contents
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19. 19
DESCRIBE Statement
DESCRIBE TABLE itab.
Returns the attributes of the internal table itab. You must use at least one of
the additions listed below.
Additions :
1. ... LINES lin
Places the number of filled lines of the table t in the field lin.
2. ... OCCURS n
Transfers the size of the OCCURS parameter from the table definition
to the variable n.
Report - Retrieving Internal Table attributesReport - Retrieving Internal Table attributes
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20. 20
All these structures begin with AT and end with ENDAT. The sequence
of statements which lies between them is then executed if a control
break occurs.
AT NEW f. / AT END OF f.
f is a sub-field of an internal table processed with LOOP.
The sequence of statements which follow it is executed if the sub-
field f or a sub-field in the current LOOP line defined (on the left)
before f has a different value than in the preceding (AT NEW) or
subsequent (AT END OF) table line.
AT FIRST. / AT LAST.
Executes the appropriate sequence of statements once during the
first (AT FIRST) or last (AT LAST) loop pass.
Report - Control Break With Internal TablesReport - Control Break With Internal Tables
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21. 21
AT FIRST
Statements are executed before any records are processed while
looping at Internal table.
Example :
LOOP AT itab.
AT FIRST.
WRITE : SY-ULINE.
ENDAT.
ENDLOOP.
Report - Control Break With Internal TablesReport - Control Break With Internal Tables
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22. 22
AT LAST
Statements are executed after all records are processed while looping
at Internal table.
Example :
LOOP AT itab.
AT LAST.
WRITE : SY-ULINE.
ENDAT.
ENDLOOP.
Report - Control Break With Internal TablesReport - Control Break With Internal Tables
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23. 23
AT NEW <Field Name>
Statements are executed at the beginning of a group of records
containing the same value for <Field Name>..
Example :
LOOP AT itab.
AT NEW I_LIFNR.
WRITE : SY-ULINE
ENDAT.
ENDLOOP.
Report - Control Break With Internal TablesReport - Control Break With Internal Tables
25. 25
AT END OF <Field Name>
Statements are executed at the end of a group of records containing
the same value for <Field Name>.
Example :
LOOP AT itab.
AT END OF I_LIFNR.
WRITE : SY-ULINE.
ENDAT.
ENDLOOP.
Note :
AT NEW and AT END OF make sense only for a sorted
table.
Report - Control Break With Internal TablesReport - Control Break With Internal Tables
26. 26
SUM Statement
SUM.
SUM calculates the control totals of all fields of type I , F and P and places them
in the LOOP output area (header line of the internal table or an explicitly specified
work area).
You can use the SUM statement both at the end and the beginning of a control
group
Example:
LOOP AT itab.
AT LAST.
SUM.
WRITE : itab-fld1.
ENDAT.
ENDLOOP.
Prints the sum of values of fld1 in all rows of itab.
Fld1 should be a numeric type field
Report - SummationReport - Summation
27. 27
COLLECT Statement
COLLECT [wa INTO] itab.
COLLECT is used to summate entries in an internal table.
COLLECT = APPEND, if no entries with the same key exists
= Adds the numeric values to their corresponding field
values, if an entry with same key exists
Used to create summarized tables.
Report - SummationReport - Summation
28. 28
COLLECT Statement Example
Timesheets per employee per day
INT_EMPTMSHT – EMPNO(char), EDATE(date), HOURS(integer)
Total time per employee for the whole period under consideration
INT_TMSHTSUM – EMPNO(char),HOURS(integer)
INT_EMPTMSHT
1000 20040102 5
1000 20040103 7
1001 20040103 8
1001 20040106 3
Report - SummationReport - Summation
INT_TMSHTSUM
1000 12
1001 8