SQL server 2016 developer s guide

SQL Server 2016 Developer's Guide Table of Contents SQL Server 2016 Developer's Guide Credits About the Authors About the Reviewer www.PacktPub.com Why subscribe? Customer Feedback Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Errata Piracy Questions Introduction to SQL Server 2016 Security Row Level Security Dynamic Data Masking Always Encrypted Engine features Query Store Live Query Statistics Stretch Database Database scoped configuration Temporal Tables Columnstore indexes Programming Transact SQL enhancements JSON In-Memory OLTP SQL Server tools Business intelligence R in SQL Server Release cycles Summary Review of SQL Server Features for Developers The mighty Transact-SQL SELECT Core Transact-SQL SELECT statement elements Advanced SELECT techniques DDL, DML, and programmable objects Data definition language statements Data modification language statements Using triggers Data abstraction - views, functions, and stored procedures Transactions and error handling Error handling Using transactions Beyond relational Defining locations and shapes with Spatial Data CLR integration XML support in SQL Server Summary SQL Server Tools Installing and updating SQL Server tools New SSMS features and enhancements Autosave open tabs Searchable options Enhanced scroll bar Execution plan comparison Live Query Statistics SQL Server Data Tools Tools for developing R code RStudio IDE R Tools for Visual Studio Summary Transact-SQL Enhancements New and enhanced functions and expressions STRING_SPLIT STRING_ESCAPE COMPRESS DECOMPRESS CURRENT_TRANSACTION_ID SESSION_CONTEXT DATEDIFF_BIG AT TIME ZONE HASHBYTES JSON functions Enhanced DML and DDL statements The conditional DROP statement (DROP IF EXISTS) CREATE OR ALTER Online Alter Column TRUNCATE TABLE Maximum key size for nonclustered indexes New query hints NO_PERFORMANCE_SPOOL MAX_GRANT_PERCENT MIN_GRANT_PERCENT Summary JSON Support in SQL Server Why JSON? What is JSON? Why is it popular? JSON versus XML JSON objects JSON object JSON array Primitive JSON data types JSON in SQL Server prior to SQL Server 2016 JSON4SQL JSON.SQL Transact-SQL-based solution Retrieving SQL Server data in the JSON format FOR JSON AUTO FOR JSON PATH FOR JSON additional options Adding a root node to the JSON output Including null values in the JSON output Formatting a JSON output as a single object Converting data types Escaping characters Converting JSON data in a tabular format OPENJSON with the default schema Processing data from a comma-separated list of values Returning the difference between two table rows OPENJSON with an explicit schema Import the JSON data from a file JSON storage in SQL Server 2016 Validating JSON data Extracting values from a JSON text JSON_VALUE JSON_QUERY Modifying JSON data Adding a new JSON property Updating the value for a JSON property Removing a JSON property Multiple changes Performance considerations Indexes on computed columns Full-text indexes Summary Stretch Database Stretch Database architecture Is this for you? Using Data Migration Assistant Limitations of using Stretch Database Limitations that prevent you from enabling the Stretch DB feature for a table Table limitations Column limitations Limitations for Stretch-enabled tables Use cases for Stretch Database Archiving of historical data Archiving of logging tables Testing Azure SQL database Enabling Stretch Database Enabling Stretch Database at the database level Enabling Stretch Database by using wizard Enabling Stretch Database by using Transact-SQL Enabling Stretch Database for a table Enabling Stretch DB for a table by using wizard Enabling Stretch Database for a table by using Transact-SQL Filter predicate with sliding window Querying Stretch Databases Querying and updating remote data SQL Server Stretch Database pricing Stretch DB management and troubleshooting Monitoring Stretch Database Pause and resume data migration Disable Stretch Database Disable Stretch Database for tables by using SSMS Disable Stretch Database for tables using Transact-SQL Disable Stretch Database for a database Backup and restore Stretch-enabled databases Summary Temporal Tables What is temporal data? Types of temporal table Allen's interval algebra Temporal constraints Temporal data in SQL Server before 2016 Optimizing temporal queries Temporal features in SQL:2011 System-versioned tables in SQL Server 2016 How temporal tables work in SQL Server 2016 Creating temporal tables Period columns as hidden attributes Converting non-temporal to temporal tables Migration existing temporal solution to system-versioned tables Altering temporal tables Dropping temporal tables Data manipulation in temporal tables Querying temporal data in SQL Server 2016 Retrieving temporal data at a specific point in time Retrieving temporal data from a specific period Retrieving all temporal data Performance and storage considerations with temporal tables History data retention History table physical implementation History table overhead Temporal tables with memory-optimized tables What is missing in SQL Server 2016? SQL Server 2016 temporal tables and data warehouses Summary Tightening the Security SQL Server security basics Defining principals and securables Managing schemas Object and statement permissions Encrypting the data Leveraging SQL Server data encryption options Always Encrypted Row-Level security Using programmable objects to maintain security Predicate-based Row-Level Security Exploring dynamic data masking Defining masked columns Dynamic data masking limitations Summary Query Store Why Query Store? What is Query Store? Query Store architecture Enabling and configuring Query Store Enabling Query Store with SSMS Enabling Query Store with Transact-SQL Configuring Query Store Query Store default configuration Query Store Recommended Configuration Disabling and cleaning Query Store Query Store in action Capturing Query info Capturing plan info Collecting runtime statistics Query Store and migration Query Store – identifying regressed queries Query Store - fixing regressed queries Query Store reports in SQL Server management studio Regressed queries Top resource consuming queries tab Overall resource consumption Query Store use cases SQL Server version upgrades and patching Application and service releases, patching, failovers, and cumulative updates Identifying ad hoc queries Identifying unfinished queries Summary 10 Columnstore Indexes Analytical queries in SQL Server Joins and indexes Benefits of clustered indexes Leveraging table partitioning Nonclustered indexes in analytical scenarios Using indexed views Data compression and query techniques Writing efficient queries Columnar storage and batch processing Columnar storage and compression Recreating rows from columnar storage Columnar storage creation process Development of columnar storage in SQL Server Batch processing Nonclustered columnstore indexes Compression and query performance Testing the nonclustered columnstore Index Operational analytics Clustered columnstore indexes Compression and query performance Testing the clustered columnstore Index Using archive compression Adding B-Tree indexes and constraints Updating a clustered columnstore index Deleting from a clustered columnstore index Summary 11 Introducing SQL Server In-Memory OLTP In-Memory OLTP architecture Row and index storage Row structure Row header Row payload Index structure Non-clustered Index Hash indexes Creating memory-optimized tables and indexes Laying the foundation Creating a table Querying and data manipulation Performance comparisons Natively compiled stored procedures Looking behind the curtain of concurrency Classifying and predicting with decision trees Decision trees are one of the most frequently used data mining algorithms The algorithm is not very complex, yet it gives very good results in many cases In addition, you can easily understand the result You use decision trees for classification and prediction Typical usage scenarios include: Predicting which customers will leave Targeting the audience for mailings and promotional campaigns Explaining reasons for a decision Decision trees is a directed technique Your target variable is the one that holds information about a particular decision, divided into a few discrete and broad categories (yes / no; liked / partially liked / disliked, and so on) You are trying to explain this decision using other gleaned information saved in other variables (demographic data, purchasing habits, and so on) With limited statistical significance, you are going to predict the target variable for a new case using its known values of the input variables based on the results of your trained model You use recursive partitioning to build the tree The data is split into partitions using a certain value of one of the explaining variables The partitions are then split again and again Initially the data is in one big box The algorithm tries all possible breaks of both input (explaining) variables for the initial split The goal is to get purer partitions considering the classes of the target variable The tree continues to grow using the two new partitions as separate starting points and splitting them more You have to stop the process somewhere Otherwise, you could get a completely fitted tree that has only one case in each class The class would be, of course, absolutely pure and this would not make any sense; you could not use the results for any meaningful prediction, because the prediction would be 100% accurate, but for this case only This phenomenon is called overfitting The following code uses the rpart() function from the base installation to build a decision tree using all possible independent variables, with factors properly defined, as used in the last logistic regression model: TMDTree