This document provides an overview of MS SQL Server tips covering topics such as relationship databases, database design including normalization, indexes, and useful queries. Relationship databases organize information into tables that can be related through primary and foreign keys. Database design involves normalization to eliminate anomalies and improve performance. Indexes help optimize queries and common types include clustered, nonclustered, unique and full-text. Useful queries are provided to check index fragmentation and monitor currently running processes.
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An introduction to database architecture, design and development, its relation to Object Oriented Analysis & Design in software, Illustration with examples to database normalization and finally, a basic SQL guide and best practices
Presented by,
Mr. Abhilash K
Database Architect, Livares Technologies
Introduction
About DBMS
A database management system (DBMS) is a software for
creating and managing databases. DBMS provides
users/programmers with a systematic way to create,
retrieve, update and manage data.
What is RDBMS
A type of DBMS in which the database is organized and
accessed according to the relationships between data
values. RDBMS are designed to take care of large amounts
of data and also the security of this data
These slides cover the following concepts:
~ RDBMS vs DBMS
~ RDBMS structure
~ RDBMS basics for beginners
~ RELATIONAL DATABASE MANAGEMENT SYSTEM
~ DATA, SCHEMA, AND DATABASE
~ WHAT IS RDBMS?
~ FEATURES OF RDBMS
~ RELATIONSHIPS IN DATABASE
~ RULES OF RDBMS
~ ELEMENTS OF RDBMS
~ SQL COMMANDS
~ SQL CONSTRAINTS
~ COMMON SQL CONSTRAINTS
~ DATA DEFINITION LANGUAGE SCRIPT (DDL)
~ DATA MANIPULATION LANGUAGE SCRIPT (DML)
~ DATA CONTROL LANGUAGE SCRIPT (DCL)
~ PRIMARY KEY, FOREIGN KEY
~ EXAMPLE OF PRIMARY AND FOREIGN KEY
~ DBMS VS RDBMS
~ RDBMS NORMALIZATION
~ BENEFITS OF NORMALIZING
~ SQL JOINS
~ INNER JOIN
~ LEFT OUTER JOIN
~ RIGHT OUTER JOIN
~ FULL OUTER JOIN
~ CROSS JOIN
~ SELF JOIN
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3. RELATIONSHIP DATABASE MANAGEMENT SYSTEM
Collection of information organized in tables
Tables are also “Relations”
Tables are constructed and associated to each other through shared fields.
Fields are also “Columns” or “Attributes”
A set of attributes comprises a record
Records are also “Rows” or “Tuples”
Tables are related through common fields designated as primary key and foreign keys.
Allow us to find, update, and delete data quickly, and help to ensure accuracy
4. RELATIONSHIP DATABASE MANAGEMENT SYSTEM
Primary Key :
Primary key fields must be unique and cannot contain a null value
Each table should have a primary key field[s].
Composite Key: Using more than one field as a primary key.
Foreign Key :
Fields in a table that refer to the primary key in another table.
The data in this field must exactly match data contained in the primary key
7. DATABASE DESIGN NORMALIZATION 1NF
First normal form (1NF)
I. It should only have single(atomic) valued attributes/columns.
II. Values stored in a column should be of the same domain
III. All the columns in a table should have unique names.
IV. And the order in which data is stored, does not matter.
8. DATABASE DESIGN NORMALIZATION 2NF
Second normal form (2NF)
I. It should be in the First Normal form.
II. And, it should not have Partial Dependency.
9. DATABASE DESIGN NORMALIZATION 3NF
Third normal form (3NF)
I. It is in the Second Normal form.
II. And, it doesn't have Transitive Dependency.
10. DATABASE DESIGN NORMALIZATION BCNF
Boyce-Codd Normal Form (BCNF)
I. It should be in the Third Normal Form.
II. And, for any dependency A → B, A should be a super key.
12. INDEXES TYPES
Regulations
1 Clustered Index per Table
249 Non Clustered (2005)
999 Non Clustered (2008+)
16 Columns or 900 Bytes
Clustered Index
Nonclustered Index
Unique index
Filtered index
Full-Text
XML
14. CLUSTERED INDEX
Clustered Index – sort order of the data within the table on the hard
drive and in memory.
One per table
Desired Qualities
Narrow Data Type
Unique
Static
Increasing
15. NONCLUSTERED INDEX
Cover Common Queries
Link to Clustered Indexes through Key
Bookmark (Key/RID) Lookup
16. FRAGMENTATION
Identifying Fragmentation
2005
DBCC SHOWCONTIG
Above 2005
sys.dm_db_index_physical_stats
Required Actions
avg_fragmentation_in_percent value Corrective statement
> 5% and < = 30% ALTER INDEX REORGANIZE
> 30% ALTER INDEX REBUILD WITH (ONLINE = ON)
17. USEFUL QUERIES
Check index fragmentation using Transact-SQL
2012
2005
DBCC SHOWCONTIG WITH TABLERESULTS, ALL_INDEXES;
Currently running process
SELECT sqltext.TEXT,
req.session_id, req.status,req.command,
req.cpu_time, req.total_elapsed_time
,blocking_session_id ,wait_time ,wait_type
,logical_reads
FROM sys.dm_exec_requests req
CROSS APPLY sys.dm_exec_sql_text(sql_handle) AS
sqltext
SELECT a.index_id, name, avg_fragmentation_in_percent
FROM sys.dm_db_index_physical_stats (DB_ID(N'UniversoDB2018'),
OBJECT_ID(N'dbo.tblindividual'), NULL, NULL, NULL) AS a
JOIN sys.indexes AS b
ON a.object_id = b.object_id AND a.index_id = b.index_id;
18. USEFUL QUERIES
Check index fragmentation using Transact-SQL
SELECT TOP 10 SUBSTRING(qt.TEXT, (qs.statement_start_offset/2)+1,
((CASE qs.statement_end_offset
WHEN -1 THEN DATALENGTH(qt.TEXT)
ELSE qs.statement_end_offset
END - qs.statement_start_offset)/2)+1),
qs.execution_count,
qs.total_logical_reads, qs.last_logical_reads,
qs.total_logical_writes, qs.last_logical_writes,
qs.total_worker_time, qs.last_worker_time,
qs.total_elapsed_time/1000000 total_elapsed_time_in_S,
qs.last_elapsed_time/1000000 last_elapsed_time_in_S,
qs.last_execution_time, qp.query_plan
FROM sys.dm_exec_query_stats qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) qt
CROSS APPLY sys.dm_exec_query_plan(qs.plan_handle) qp
ORDER BY qs.total_logical_reads DESC -- logical reads
-- ORDER BY qs.total_logical_writes DESC -- logical writes
-- ORDER BY qs.total_worker_time DESC -- CPU time
19. READ TYPES
Index Scan
Table Scan (Heap)
Index Seek
Key Lookup
RID Lookup (heap)
20. RULES FOR EXECUTION PLAN COMPILATION
Build up most resource efficient plan
AS little page reads as possible
Fastest Operation possible
Choosing Indexes:
Prefer read range over scan
Prefer small indexes over large
Editor's Notes
Student and Branch Example
Insertion Anomaly
Updation Anomaly
Deletion Anomaly
Student and Branch Example
Insertion Anomaly
Updation Anomaly
Deletion Anomaly
Student and Branch Example
Insertion Anomaly
Updation Anomaly
Deletion Anomaly
Student and Branch Example
Insertion Anomaly
Updation Anomaly
Deletion Anomaly
In the table above, student_id, subject form primary key, which means subject column is a prime attribute.
But, there is one more dependency, professor → subject.
And while subject is a prime attribute, professor is a non-prime attribute, which is not allowed by BCNF.
For a dependency A → B, if for a single value of A, multiple value of B exists, then the table may have multi-valued dependency.
Also, a table should have at-least 3 columns for it to have a multi-valued dependency.
And, for a relation R(A,B,C), if there is a multi-valued dependency between, A and B, then B and C should be independent of each other.
HEAP
Data is not stored in any particular order
Specific data can not be retrieved quickly, unless there are also non-clustered indexes
Data pages are not linked, so sequential access needs to refer back to the index allocation map (IAM) pages
Since there is no clustered index, additional time is not needed to maintain the index
Since there is no clustered index, there is not the need for additional space to store the clustered index tree
These tables have a index_id value of 0 in the sys.indexes catalog view
Clustered Indexed Table
Data is stored in order based on the clustered index key
Data can be retrieved quickly based on the clustered index key, if the query uses the indexed columns
Data pages are linked for faster sequential access
Additional time is needed to maintain clustered index based on INSERTS, UPDATES and DELETES
Additional space is needed to store clustered index tree
These tables have a index_id value of 1 in the sys.indexes catalog view
Clustered
Clustered indexes sort and store the data rows in the table or view based on their key values. These are the columns included in the index definition. There can be only one clustered index per table, because the data rows themselves can be stored in only one order.
The only time the data rows in a table are stored in sorted order is when the table contains a clustered index. When a table has a clustered index, the table is called a clustered table. If a table has no clustered index, its data rows are stored in an unordered structure called a heap.
Nonclustered
Nonclustered indexes have a structure separate from the data rows. A nonclustered index contains the nonclustered index key values and each key value entry has a pointer to the data row that contains the key value.
The pointer from an index row in a nonclustered index to a data row is called a row locator. The structure of the row locator depends on whether the data pages are stored in a heap or a clustered table. For a heap, a row locator is a pointer to the row. For a clustered table, the row locator is the clustered index key.
You can add nonkey columns to the leaf level of the nonclustered index to by-pass existing index key limits, and execute fully covered, indexed, queries.