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ویرایش: 1
نویسندگان: Kim Berg Hansen
سری:
ISBN (شابک) : 1484256166, 9781484256169
ناشر: APress
سال نشر: 2020
تعداد صفحات: 467
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 9 مگابایت
در صورت تبدیل فایل کتاب Practical Oracle SQL: Mastering the Full Power of Oracle Database به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب عملی Oracle SQL: تسلط کامل بر قدرت کامل پایگاه داده اوراکل نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Table of Contents About the Author Acknowledgments Introduction Part I: Core SQL Chapter 1: Correlating Inline Views Brewery products and sales Scalar subqueries and multiple columns Correlating inline view Outer joining correlated inline view Lessons learned Chapter 2: Pitfalls of Set Operations Sets of beer Set operators Set concatenation The three set operators Multiset operators Multiset union Multiset intersect Multiset except Minus vs. multiset except Lessons learned Chapter 3: Divide and Conquer with Subquery Factoring Products and sales data Best-selling years of the less strong beers Modularization using the with clause Multiple uses of the same subquery Listing column names Lessons learned Chapter 4: Tree Calculations with Recursion Bottles in boxes on pallets Multiplying hierarchical quantities Recursive subquery factoring Dynamic SQL in PL/SQL function Lessons learned Chapter 5: Functions Defined Within SQL Table with beer alcohol data Blood alcohol concentration Function with PRAGMA UDF Function in the with clause Encapsulated in a view Lessons learned Chapter 6: Iterative Calculations with Multidimensional Data Conway’s Game of Life Live neighbor count with the model clause Iterating generations Lessons learned Chapter 7: Unpivoting Columns to Rows Data received in columns Unpivoting to rows Do-it-yourself unpivoting More than one dimension and/or measure Using dimension tables Dynamic mapping to dimension tables Lessons learned Chapter 8: Pivoting Rows to Columns Tables for pivoting Pivoting single measure and dimension Do-it-yourself manual pivoting Multiple measures Multiple dimensions as well Lessons learned Chapter 9: Splitting Delimited Text Customer favorites and reviews Delimited single values Pipelined table function Built-in APEX table function Straight SQL with row generators Treating the string as a JSON array Delimited multiple values Custom ODCI table function Combining apex_string.split and substr Row generators and regexp_substr Transformation to JSON Lessons learned Chapter 10: Creating Delimited Text Delimited lists of products String aggregation Aggregate function listagg Aggregate function collect Custom aggregate function stragg Aggregate function xmlagg When it doesn’t fit in a VARCHAR2 Get just the first part of the result Try to make it fit with reduced data Use a CLOB instead of a VARCHAR2 Lessons learned Part II: Analytic Functions Chapter 11: Analytic Partitions, Ordering, and Windows Sums of quantities Analytic syntax Partitions Ordering and windows Flexibility of the window clause Windows on value ranges The danger of the default window Lessons learned Chapter 12: Answering Top-N Questions Top-N of sales data Which kind of Top-3 do you mean? The sales data for the beer Traditional rownum method Analytic functions for ranking Fetch only the first rows Handling of ties What the row limiting clause cannot do Top-N in multiple partitions The lateral trick for the row limiting clause Lessons learned Chapter 13: Ordered Subsets with Rolling Sums Data for goods picking Building the picking SQL Solving picking an order by FIFO Easy switch of picking principle Solving optimal picking route Solving batch picking Finalizing the complete picking SQL Lessons learned Chapter 14: Analyzing Activity Logs with Lead Picking activity log Analyzing departures and arrivals Analyzing picking activity Complete picking cycle analysis Teaser: row pattern matching Lessons learned Chapter 15: Forecasting with Linear Regression Sales forecasting Time series Calculating the basis for regression Linear regression Final forecast Lessons learned Chapter 16: Rolling Sums to Forecast Reaching Minimums Inventory, budget, and order The data Accumulating until zero Restocking when minimum reached Lessons learned Part III: Row Pattern Matching Chapter 17: Up-and-Down Patterns The stock ticker example Classifying downs and ups Downs + ups = V shapes Revisiting if SAME is needed V + V = W shapes Overlapping W shapes Lessons learned Chapter 18: Grouping Data Through Patterns Two sets of data to group Three grouping conditions Group consecutive data Analytic Tabibitosan vs. match_recognize Consecutive dates instead of integers Gap detection Group until gap too large Group until fixed limit Lessons learned Chapter 19: Merging Date Ranges Job hire periods Temporal validity Merging overlapping ranges Attempts comparing to the previous row Better comparing to the maximum end date Handling the null dates Lessons learned Chapter 20: Finding Abnormal Peaks Web page counter history The counter data Patterns in the raw counter data Looking at daily visits Patterns in daily visits data More complex patterns Lessons learned Chapter 21: Bin Fitting Inventory to be packed in boxes Bin fitting with unlimited number of bins of limited capacity Showing where box capacity is too small Bin fitting with limited number of bins of unlimited capacity Lessons learned Chapter 22: Counting Children in Trees Hierarchical tree of employees Counting subordinates of all levels Counting with row pattern matching The details of each match Fiddling with the output Lessons learned Index