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SQL SERVER ENTERPRISE AWARENESS
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About MeHamid J. Fard
I am SQL Server Data Platform Expert with more than 9 years’ of professionalexperience, I am currently Microsoft Certified Master: SQL Server 2008,Microsoft Certified Solutions Master: Charter-Data Platform, Microsoft DataPlatform MVP and CIW Database Design Specialist. After a few years of being aproduction database administrator I jumped into the role of Data PlatformExpert. Being a consultant allows me to work directly with customers to helpsolve questions regarding database issues for SQL Server.
Agenda• What Matters To You?
• Do You Believe in High End Hardware?
• SQL Server Enterprise Edition (Internal Features)
• SQL Server 2016 Enterprise Edition New Features
• Questions and Answers
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What Matters To You?
Manageability Performance Storage Space Disaster Recovery Cost
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Do You Believe in High End Hardware?
YES
NO
Without High End Software, Your High End Hardware Is Worthless.
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SQL Server Internal Features vs. EditionsFeature Enterprise Standard
Advance Scan (Merry-Go-Round)
Fast Recovery
Prefetching
Single Scatter Scan
Automation Matching
Role Reversal
Partitioning
Data Compression
Deferred Transaction
Resource Governor
Vardecimal Storage Formatting
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Advance Scan (Marry-Go-Round)
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Table A
User A
User B
SQL Server Optimizer Engine
Query Buffer
Query Buffer
Query BufferRead
Fast Recovery
Analysis Phase Redo Phase Undo Phase
Database Recovery Lifecycle
Database is AvailableBy Lock-Logging
Database is Under Recovery Uncommitted Transactionsare Roll-Backed
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PrefetchingSelect E.ID, E.FN, E.LN, D.Designation From Employee E Inner Join Designation D On E.Designation_ID = D.ID
Designation
Employee
Read Synchronously
Read Asynchronously
Project Resultset
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Single Scatter Scan
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465
308
488
489
Lis
t o
f L
eaf
Pag
es
Automation MatchingCreate Table dbo.Test (ID Bigint, Batch Char(10), BatchID As Cast(ID As Char) + Batch Persisted)
Select ID, Batch From dbo.Test Where Cast ( ID As Char ) + Batch = ‘1045623’
Select ID, Batch From dbo.Test Where BatchID = ‘1045623’
Converts To
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Role Reversal
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Build Input
Probe Input
Hash MatchResultset
Partitioning
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Dbo.Sales2016-2017Filegroup 1
Dbo.Sales2015-2016Filegroup 2
Dbo.Sales2014-2015Filegroup 3
Query
Dbo.Sales
Data Compression
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dbo.Sales1 Billion Rows186GB Data Size23,250,000,000,000 Data Pages
dbo.Sales1 Billion Rows108GB Data Size1,4557,760,000 Data Pages
Data Compression Up to 40%
Deferred Transaction
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In Other Editions, A Corrupted Transaction Causes Database Startup To Fail
Analysis Phase Roll Forward Phase Roll Back Phase
Transaction Recovery Lifecycle
Transaction Uncommitted Transaction Not Rolled BackDue to Corrupted Transaction Log I/O
Resource Governor
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Memory: 64GBProcessor: 8 Logical Processor
DB_AMax Memory: 12GBMax Processor: 2 Logical ProcessorsMax IOPS: 500
DB_BMax Memory: 24GBMax Processor: 4 Logical ProcessorsMax IOPS: 2000
App A
App B
Vardecimal Storage Formatting
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ID Full Name Savings
1 John Smith 50,000
2 Bill Gates 97,000,000,000
3 Kevin Wood 500
20 Bytes20 Bytes20 Bytes
ID Full Name Savings
1 John Smith 50,000
2 Bill Gates 97,000,000,000
3 Kevin Wood 500
5 Bytes20 Bytes5 Bytes
SQL SERVER 2016ENTERPRISE EDITION
NEW FEATURES
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Columnstore Index
• Batch execution for single-threaded queries.
• Snapshot isolation and read-committed snapshot isolation.
• Specify columnstore index when creating a table.
• AlwaysOn readable secondary supports updateable columnstore indexes.
• Updateable nonclustered columnstore index on heap or btree.
• Btree index on a clustered columnstore index.
• Columnstore index on a memory-optimized table. ***
• Nonclustered columnstore index definition supports using a filtered condition.
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In-Memory OLTP• ALTER operations for memory-optimized tables & natively compiled stored
procedures.
• Use MARS connections to access memory-optimized tables and natively compiled stored procedures.
• Support for natively compiled, scalar user-defined functions.
• Complete support for collations (No more limited to code page 1252).
• Memory-Optimized Table size increase to 2TB.
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What is In-Memory OLTP?
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In-Memory Table
Disk Based Table
Benefits:• Eliminate contention.• Reduce logging.• Lower latency data retrieval.• Minimize code execution time.• Efficient data retrieval.• Optional IO reduction or removal, when
using non-durable tables
Implementation Scenarios:• High data insertion rate from multiple
concurrent connections.• Read performance and scale with periodic
batch inserts and updates.• Intensive business logic processing in the
database server.• Low latency.• Session state management.
In-Memory OLTP Components
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In-MemoryPerformance
Memory-Optimized Table Requirements.
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Hardware Software
• X64 Architecture Processor• <60 Processor Cores• Enough Memory (x2 Table Size)• Enough Storage (x2 Table Size)• Processor Needs to Support cmpxchg16b
• X64 Architecture Edition• Enterprise or Developer Edition• Hyper-V to Support cmpxchg16b (If needed)• Enable Instant File Initialization
Memory Optimized Tables.
SQL Server CLR In-Memory Table
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Memory Optimized Indexes.
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Made for point lookups Made for range scans and ordered scans
In-Memory Query Processing
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Disk Based Query Plan SQL Server Query Processing for Disk-Based Tables
INTerpreted Transact-SQL Access to Memory-Optimized TablesMemory Optimized Query Plan
In-Memory Query Processing (Cont.)
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Native compilation of stored procedures
Execution of natively compiled stored procedures
In-Memory Transactions
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Lifetime of a transaction that accesses memory-optimized tables
Regular Processing Validation Phase Commit Processing
• Commit Dependency• Repeatable Read Validation• Serialization Validation
In-Memory Memory EstimationCREATE TABLE IMOLTP (col1 INT NOT NULL PRIMARY KEY NONCLUSTERED,col2 INT NOT NULL INDEX INDEX_1 HASH WITH (BUCKET_COUNT = 5000000),col3 INT NOT NULL INDEX INDEX_2 HASH WITH (BUCKET_COUNT = 5000000),col4 INT NOT NULL INDEX INDEX_3 HASH WITH (BUCKET_COUNT = 5000000),col5 INT NOT NULL INDEX INDEX_4 NONCLUSTERED,col6 CHAR (50) NOT NULL, col7 CHAR (50) NOT NULL, col8 CHAR (30) NOT NULL, col9 CHAR (50) NOT NULL)WITH (MEMORY_OPTIMIZED = ON)
Memory for Table: Timestamps + Index Pointers + Row Data => 24 + (8+8+8+8) + (4+4+4+4+4+50+50+30+50) => 256 x 5000000 => 1.28GB
Memory for Hash Indexes: Round Up Bucket Count x Index Pointer => 223 x 8 => 67,108,864 Bytes => 64MB x 3 => 192MB
Memory for Non-Clustered Index: (Index Pointer + SUM(Key Column Data Type Size)) x Unique Rows => (8 + 4) x 5000000 => 57MB
Memory for Row Versioning: (Longest Trans. Duration in Sec. x Peak No. of Row Updates & Deletes in Sec.) x Row Size=> ( 1 x 10) x 256 => 2560 Bytes => 2560 x (0.3 x 5000000) => 3.66GB
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http://fard-solutions.com/sql-server-memory-table-size-estimation-script/
In-Memory Filegroup Configuration
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In-Memory Data & Delta File Population
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Live Query Statistics
Query Store
Temporal Tables
JSON Format Query Result
Always Encrypted
Stretch DB
Row Level Security
Dynamic Data Masking
Questions and Answers
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Contact for Expert Advice
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Fard Solutions Sdn Bhd1-1C, Incubator 1, Technology Park Malaysia, Bukit Jalil, 57000 Kuala Lumpur, Malaysia.
Tel: +603-89946788 - +603-79725433
Web: www.Fard-Solutions.com
Email: Enquiry@Fard-Solutions.com
Contact Us For 30 Minutes Free Consultation.
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