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How In-Memory Affects Database Design

Louis Davidson

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Who am I?

• Been in IT for over 19 years• Microsoft MVP For 11 Years• Corporate Data Architect• Written five books on

database design• Ok, so they were all versions

of the same book. They at least had slightly different titles each time• Basically: I love Database Design, and In-

Memory technologies are changing the game

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Contact info

• Louis Davidson - louis@drsql.org• Website – http://drsql.org <-- Get slides here• Twitter – http://twitter.com/drsql(@drsql)

• SQL Blog http://sqlblog.com/blogs/louis_davidson

• Simple Talk Blog – What Counts for a DBAhttp://www.simple-talk.com/community/blogs/drsql/default.aspx

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Questions are Welcome

• Please limit questions to one’s I know the answer to.

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A tasty allegory…

• Bacon is awesome• Bacon is an extremely powerful tool for rapid

fat and calorie intake• Even bacon isn't good for everything

http://www.lazygamer.net/general-news/diablo-iii-players-burned-off-820-968-kgs-of-bacon/

https://www.flickr.com/photos/runnerone/6232183896/in/photostream/

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Attention!

• This presentation was originally based on SQL Server 2014• SQL Server 2016 promises to greatly

improve the feature set• I will note where this does and does not

affect your database design experience as I go along with asterisks *

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The process I went through

• Start with basic requirements–Sales system–Stream of customer and order data–Apply In-Memory OLTP to see how it changed things–Keep it very simple

• Learn a lot–This presentation was borne out of what I learned

from that process (and Kalen Delaney’s precon, whitepaper, and other reading that is linked throughout the slides)

• Build a test and apply what I have learned and morph until I get to what works• Build something real in my day job, if applicable

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Introduction: What exactly is In-Memory OLTP in SQL Server 2014+?

• A totally new, revamped engine for data storage, co-located in the same database with the existing engine– Obviously Enterprise Only…

• Purpose built for certain scenarios*• Terminology can be confusing –Existing tables: Home - On-Disk, but ideally cached In-Memory–In-Memory tables: Home - In-Memory: but backed up by On-Disk Structures

• If you have enough RAM, On-Disk tables are also in memory–But the implementation is very very different

• In-Memory is both very easy, and very difficult to use

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Design Basics (And no, I am not stalling for time due to lack of material)

• Designing and Coding is Like the Chicken and the Egg–Design is what you do before coding–Coding patterns can greatly affect design–Engine implementation can greatly affect design

and coding patterns–Developing software follows a natural process

• We will discuss how In-Memory technologies affect the entire design/development lifecycle

As if…

Children

I was first

Relics

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Design Basics - Separate your design mind into (minimally) three phases

• Conceptual/Logical (Overall data requirements in a data model format)• Physical Implementation Choice –Type of database system: Paper, Excel, Access, SQL Server, NoSQL, etc–Engine choices: In-Memory, On-Disk, Compression, Partitioning, etc–Note: Bad choices usually involve pointy hair and a magazine article with very little thinking

and testing• Physical (Relational Code)

• We will look at each of these phases and how in-mem may affect your design of each output

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Conceptual/Logical Design (Though Not Everyone’s Is)

• This is the easiest part of the presentation• You still need to understand the customers needs and model–Entities and Attributes–Uniqueness Conditions–General Predicates

• As I see it, nothing changes…

…to type

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Logical Data Model

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SQL Server.exe

Data Filegroup

TDS Handler and Session Management

Physical Implementation Overview

Buffer Pool for Tables & Indexes

Proc/Plan cache for ad-hoc T-SQL and

SPs

Client App

Transaction Log

Interpreter for TSQL, query plans,

expressionsAccess Methods

Parser, Catalog, Algebriz

er, Optimize

r10-30x more efficient(Real Apps see 2-30x)

Reduced log bandwidth & contention. Log latency

remains

Memory-optimized Table

Filegroup

Engine for Memory_optimized Tables & Indexes

Natively Compiled SPs and Schema

Native Compile

r

Query Interop

Checkpoints are background sequential IO

No improvements in communication stack,

parameter passing, result set generation

In-Memory OLTP

Component

KeyExisting

SQL Compone

nt

http://download.microsoft.com/documents/hk/technet/techdays2014/Day2/Session2/DBI394-SQL%20Server%202014%20In-Memory%20OLTP%20-%20Depp%20Dive.pdf

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Physical Implementation (Technically it’s all software!)

• Everything is different, and I am going to give just an overview of physical details…• In-Mem data structures coexist in the database alongside On-Disk ones• Data is housed in RAM, and backed up in Delta Files and Transaction Logs–Delta files are stored as filestream storage–The transaction log is the same one as you are used to (with lighter utilization)

• Tables and Indexes are extremely coupled• MVCC (Multi-Valued Concurrency Control) used for all isolation

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Physical Design (No, let’s not get physical)

• Your physical design will almost certainly need to be altered from “normal”• So much changes, even just changing the internal table structure• In this section, we will discuss:–Creating storage objects• Table Creation• Index Creation (which is technically part of the table creation)*• Altering a Table’s Structure*

–Accessing (Modifying/Creating) data• Using Normal T-SQL (Interop)• Using Compiled Code (Native)• Using a Hybrid Approach• No Locks, No Latches, No Waiting

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Creating Storage Objects - Tables

• The syntax is the same as on-disk, with a few additional settings• You have a durability choices– Individual In-Mem Table: SCHEMA_ONLY or SCHEMA_AND_DATA–Database level for transactions: Delayed (also for on-disk tables) • Basically Asynchronous Log Writes• Aaron Bertrand has a great article on this here:

http://sqlperformance.com/2014/04/io-subsystem/delayed-durability-in-sql-server-2014

• You also have less to work with...–Rowsize limited to 8060 bytes (Enforced at Create Time)• Not all datatypes allowed (LOB types,CLR,sql_variant, datetimeoffset, rowversion)*

–No check constraints *–No foreign keys *– Just one unique index per table *

• Every durable (SCHEMA_AND_DATA) table must have a unique index/ primary key• Note: There are memory optimized temporary tables too: See Kendra Little’s article here:

http://www.brentozar.com/archive/2014/04/table-variables-good-temp-tables-sql-2014/

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Data quality…What if?

TroublesomeTwo people are travelling to Indianapolis via train, and both order chicken from two different wait persons, but there is only one order of chicken still available

Note: The “what if?” test ought to be applied to all of your designs

Extremely TroublesomeIf Train A is given access to Location L on Track 1 at 11:30 AM, and Train B is given access to the same Location at the same time going in a different direction.

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Dealing with Un-Supported Datatypes…

• Say you have a table with 10 columns, but 1 is not allowed in a In-Memory table• First: Ask yourself if the table really fits the criteria we aren’t done covering• Second: If so, consider vertically partitioning –CREATE TABLE In_Mem (KeyValue, Column1, Column2, Column3)

CREATE TABLE On_Disk (KeyValue, Column4)• It is likely that uses of disallowed LOB types wouldn’t be good for the OLTP

aspects of the table in any case.

• Note: 2016 allows LOB (varbinary(max), nvarchar(max), varchar(max)) but it is still something you may need to consider, as memory isn’t free…

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Creating Storage Objects - Index creation

• Syntax is inline with CREATE TABLE• Indexes are linked directly to the table–8 indexes max per table due to internals–Only one unique index allowed (the primary key) *– Indexes are never persisted, but are rebuilt on restart

• String index columns must be a binary collation (case AND accent sensitive)*• Cannot index nullable column *• Two types–Hash• Ideal for single row lookups• Fixed size, you choose the number of hash buckets (approx 1-2 * # of unique values

http://msdn.microsoft.com/en-us/library/dn494956.aspx)

–Bw Tree • Best for range searches• Very similar to a BTree index as you (hopefully) know it, but optimized for MVCC and pointer connection to table

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A Taste of the Physical Structures

• Basic data record for a row

• Record Header

Begin Timestamp End Timestamp StatementId IndexCount IndexPointers ...

1 2 3 8Record Header Data For Columns (Payload)

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Hash Index - Simplified

3

1

2

4

5

Identity Column Country

0 ь Φ 1 USA

0 ь Φ Φ 2 USA

0 ь Φ Φ 3 Canada

3

1

2

6

4

5

9

7

8

10

TableNameId Country OtherColumns

1 USA Values

2 USA Values

3 Canada Values

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Hash Index - SimplifiedTableNameId Country OtherColumns1 USA Values2 Canada Values3 Canada Values

3

1

2

4

5

Identity Column Country

0 ь Φ 1 USA

0 100 Φ 2 USA

0 ь Φ 3 Canada

3

1

2

6

4

5

9

7

8

10

100 ь Φ Φ 2 Canada

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Bw Tree Index – Even More Simplified

Page 1

A B C

Page Mapping Table

Page 0

Page 1

Page 2

Page 0

C R Z

Page 2

D G J

Page 3

S T Z

Non-Leaf Pages

Leaf Pages

0 ь B OtherVals

0 240 B DifferentRow 0 ь Φ D AnotherRow

Data Pages

240 ь Φ B JustDifferent

Page 3

By Page ID

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Do you want to know more?

• For more in-depth coverage–check Kalen Delaney's white paper ... http://t.co/T6zToWc6y6 –Or for an even deeper (nerdier?) versions: “Hekaton: SQL Server’s Memory-Optimized OLTP

Engine” http://research.microsoft.com/apps/pubs/default.aspx?id=193594 or The Bw-Tree: A B-tree for New Hardware Platforms (http://research.microsoft.com/pubs/178758/bw-tree-icde2013-final.pdf)–Books Online: http://technet.microsoft.com/en-us/library/dn133186.aspx –TechDays Presentation:

http://download.microsoft.com/documents/hk/technet/techdays2014/Day2/Session2/DBI394-SQL%20Server%202014%20In-Memory%20OLTP%20-%20Depp%20Dive.pdf –Buy Kalen Delaney’s Ebook: http://

www.amazon.com/gp/product/B00QMWX8PO/ref=docs-os-doi_0 –SQL Server 2016: In-Memory OLTP Enhancementss

http://sqlperformance.com/2015/05/sql-server-2016/in-memory-oltp-enhancements

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Creating Storage Objects - Altering a Table *

• The is the second easiest slide in the deck (to write!)• No alterations allowed - Strictly Drop and

Recreate*• Cannot rename table

ALTER

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DEMO IN SLIDES – PREPARING TO (AND ACTUALLY) CREATING TABLES

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CREATE DATABASE HowInMemObjectsAffectDesignON PRIMARY ( NAME = N'HowInMemObjectsAffectDesign', FILENAME = N‘Drive:\HowInMemObjectsAffectDesign.mdf' , SIZE = 2GB , MAXSIZE = UNLIMITED, FILEGROWTH = 10% ), FILEGROUP [MemoryOptimizedFG] CONTAINS MEMORY_OPTIMIZED_DATA ( NAME = N'HowInMemObjectsAffectDesign_inmemFiles', FILENAME = N'Drive:\InMemfiles' , MAXSIZE = UNLIMITED) LOG ON ( NAME = N'HowInMemObjectsAffectDesign_log', FILENAME = N'Drive:\HowInMemObjectsAffectDesign_log.ldf' , SIZE = 1GB , MAXSIZE = 2GB , FILEGROWTH = 10%);GO

Setting the Database To Allow In-Mem

Add a filegroup to hold the delta files

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Creating a Memory Optimized Permanent Table

CREATE TABLE Customers.Customer( CustomerId integer NOT NULL IDENTITY ( 1,1 ) , CustomerNumber char(10) COLLATE Latin1_General_100_BIN2 NOT NULL, CONSTRAINT XPKCustomer PRIMARY KEY NONCLUSTERED HASH ( CustomerId) WITH ( BUCKET_COUNT = 50000),

INDEX CustomerNumber NONCLUSTERED ( CustomerNumber)

) WITH ( MEMORY_OPTIMIZED = ON , DURABILITY = SCHEMA_AND_DATA)go

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Creating a Memory Optimized Permanent Table

CREATE TABLE Customers.Customer( CustomerId integer NOT NULL IDENTITY ( 1,1 ) , CustomerNumber char(10) COLLATE Latin1_General_100_BIN2 NOT NULL, CONSTRAINT XPKCustomer PRIMARY KEY NONCLUSTERED HASH ( CustomerId) WITH ( BUCKET_COUNT = 50000),

INDEX CustomerNumber NONCLUSTERED ( CustomerNumber)

) WITH ( MEMORY_OPTIMIZED = ON , DURABILITY = SCHEMA_AND_DATA)go

Character column must be binary to index/compare in native code *

This table is as durable as the database settings

allow

Hash Index used for Primary Key. Estimated

Rows in Table 25000

This table is memory optimized

(ok, that was kind of obvious)

Bw Tree Index on Customer Number

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Accessing the Data - Using Normal T-SQL (Interop)

• Using typical interpreted T-SQL• Most T-SQL will work with no change (you may need to add isolation level hints,

particularly in explicit transaction)• A few Exceptions that will not work–TRUNCATE TABLE - This one is really annoying :)–MERGE (In-Mem table cannot be the target)–Cross Database Transactions (other than tempdb)–Locking Hints

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Accessing the Data using Compiled Code (Native)

• Instead of being interpreted, the stored procedure is compiled to machine code• Limited syntax (Like programming with both hands tied behind your back)• Allowed syntax is listed in what is available, not what isn't–http://msdn.microsoft.com/en-us/library/dn452279.aspx

• Some really extremely annoying ones:–SUBSTRING supported; LEFT, RIGHT, not so much–No Subqueries *–OR, NOT, IN, not supported in WHERE clause * –String Comparisons must be with columns of Binary Collation *–Can’t use on-disk objects (tables, sequences, views, etc)–Can’t call a stored procedures from another stored procedure *

• So you may have to write some "interesting" code

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DEMO IN SLIDES – NATIVE STORED PROCEDURE

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Creating a Natively Optimized Procedure(I write my C# the new fashioned way, with T-SQL)

CREATE PROCEDURE Customers.Customer$CreateAndReturn @Parameter1 Parameter1Type = 'defaultValue1', @Parameter2 Parameter2Type = 'defaultValue2',… @ParameterN ParameterNType = 'defaultValueN‘

WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER AS BEGIN ATOMIC WITH ( TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE = N'us_english' ) <code>

END

Alert parser that this will be a natively compiled

object

Works just like for views and functions. Can’t

change the underlying object while this object

references it

There is no Ownership chaining. All code

executes as the procedure owner

Procedures are atomic transactions

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Accessing Data Using a Hybrid Approach

•Native code is very fast but very limited (* Still true, but less so)• Use Native code where it makes sense, and not where it doesn’t• Example: Creating a sequential value–In the demo code I started out by using RAND() to create CustomerNumbers

and SalesOrderNumbers. –Using a SEQUENCE is far more straightforward–So I made one interpreted procedure that uses the SEQUENCE outside of native

code, then calls the native procedure

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Accessing the Data - No Locks, No Latches, No Waiting

•On-Disk Structures use Latches and Locks to implement isolation• In-Mem use Optimistic-MVCC • You have 3 Isolation Levels:–SNAPSHOT, REPEATABLE READ, SERIALIZABLE–Evaluated before, or when the transaction is committed–This makes manual data integrity checking "interesting"

• Essential difference, your code now must handle errors

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Concurrency is the #1 difference you will deal with

• Scenario1: 2 Connections - Update Every Row In 1 Million Rows• Any Isolation Level• On-Disk–Either: 1 connection blocks the other–Or: Deadlock

• In-Mem–One connection will fail, saying: “the row you are trying to update has been updated since

this transaction started” EVEN if it never commits.

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Another slide on Concurrency(Because if I had presented it concurrently with the other one, you wouldn’t have liked that)

• Scenario2: 1 Connection Updates All Rows, Another Reads All Rows (In an explicit transaction)• On-Disk–Either: 1 connection blocks the other–Or: Deadlock

• In-Mem–Both Queries Execute Immediately–In SNAPSHOT ISOLATION the reader will always succeed–In REPEATABLE READ or SERIALIZABLE • Commits transaction BEFORE updater commits: Success• Commits transaction AFTER updater commits: Fails

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The Difficulty of Data Integrity

• With on-disk structures, we used constraints for most issues (Uniqueness, Foreign Key, Simple Predicates)• With in-memory code, we have to implement in stored procedures–Uniqueness on > 1 column set suffers from timing (If N connections are inserting the same

data...MVCC will let them) *–Foreign Key type checks can't reliably be done because: *• In Snapshot Isolation Level, the row may have been deleted while you check• In Higher Levels, the transaction will fail if the row has been updated

–Check constraint style work can be done in stored procedures for the most part.

• Note: Constraints in 2016 will often be more important that for on disk tables because of the lack of blocking operations

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Problem: How to Implement Uniqueness on > 1 Column Set: INDEXED VIEW?

• CREATE VIEW Customers.Customers$UniquenessEnforcementWITH SCHEMABINDINGASSELECT customerId, emailAddress, customerNumberFROM customers.CustomerGO• CREATE UNIQUE CLUSTERED INDEX emailAddress ON Customers.Customers$UniquenessEnforcement (emailAddress)GO

• Msg 10794, Level 16, State 12, Line 8The operation 'CREATE INDEX' is not supported with memory optimized tables.

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Problem: How to Implement Uniqueness on > 1 Column Set: Multiple Tables?

• Wow, that seems messy… And what about duplicate customerId values in the two subordinate tables?

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Problem: How to Implement Uniqueness on > 1 Column Set: Simple code

• You can’t…exactly. But what if EVERY caller has to go through the following block:• DECLARE @CustomerId INT SELECT @CustomerId = CustomerIdFROM Customers.CustomerWHERE EmailAddress = @EmailAddress

IF @customerId is null… Do your insert• This will stop MOST duplication, but not all. Two inserters can check at the same

time, and with no blocks, app locks, or constraints even available, you may get duplicates.• Remember the term: Optimistic Concurrency Control• Even still, this sort of code is reducing the value, isn’t it?

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Foreign Keys and Unique Index/Constraints in 2016(Pure conjecture based on how things work now)

• In the traditional engine, these are implemented with locks• In the in-mem engine, you have to expect that it will be implemented much like

the isolation levels• Basically, if two transactions do operations that would have blocked, the other

connection will likely fail either:–At COMMIT (Currently PRIMARY KEY Violations fail at COMMIT)–At first sign of trouble (As is the case when you modify existing resources)

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When Should You Make Tables In-Memory Louis's Advice

• Read Microsoft’s Opinion Here: http://msdn.microsoft.com/en-us/library/dn133186.aspx

• Things to factor in–High concurrency needs/Low chance of collisions–Minimal uniqueness protection requirements *–Minimal data integrity concerns (minimal key update/deletes) *–Limited searching of data (binary comparisons only) *–Limited need for transaction isolation/Short transactions–You are able to answer all “What If?” scenarios successfully.

• Basically, the “very hot” tables in a strict OLTP workload...– I don’t see this changing, but the scenarios where it fits will expand in 2016

• NOT a way to “FIX” bad code… Not at all… • In fact, most applications will need to be re-engineered to deal with MVCC.

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The Choices I made• Louis has improved his methods for estimating performance, but your

mileage will still vary.• Louis’ tests are designed to reflect only one certain usage conditions and

user behavior, but several factors may affect your mileage significantly:– How & Where You Put Your Logs– Computer Condition & Maintenance – CPU Variations– Programmer Coding Variations– Hard Disk Break In

• Therefore, Louis’ performance ratings are a minimally useful tool for comparing the performance of different strategies but may not accurately predict the average performance you will get.

• I seriously suggest you test the heck out of the technologies yourself using my code, your code, and anyone else’s code you can to make sure you are getting the best performance possible.

• The Choices (For Me) will differ in 2016…

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Model Choices – Logical Model

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Model Choices – Physical Model

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Model Choices – Tables to Make In-Mem (First Try)

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Model Choices – Tables to Make In-Mem (Final 2014 Thinking)

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The Grand Illusion (So you think your life is complete confusion)

• Performance gains are not exactly what you may expect, even when they are massive• In my examples (which is available on my website), I discovered when loading 20000 rows

(10 connections of 2000 each)– (Captured using Adam Machanic's http://www.datamanipulation.net/SQLQueryStress/ tool)

–On-Disk Tables with FK and Instead Of Trigger - 0.0472 seconds per row - Total Time – 1:12–On-Disk Tables without FK or Instead Of Trigger - 0.0271 seconds per row - Total Time – 0:51– In-Mem Tables using Interop code - 0.0202 seconds per row - Total Time 0:44– In-Mem Tables with Native Code - 0.0050 second per row - Total Time – 0:31– In-Mem Tables, Native Code, SCHEMA_ONLY – 0.0003 seconds per row - Total Time – 00:30– In-Mem Tables (except CustomerAddress), Hybrid code – 0.0163 – Total Time – 0:42– In-Mem Tables using 2016 enhancements – Coming soon to a SQLblog near you when enough features

available

• But should it be a lot better? Don't forget the overhead... (And SQLQueryStress has extra for gathering stats)

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Contact info

• Louis Davidson - louis@drsql.org• Website – http://drsql.org <-- Get slides here• Twitter – http://twitter.com/drsql (@drsql)

• SQL Blog http://sqlblog.com/blogs/louis_davidson

• Simple Talk Blog – What Counts for a DBAhttp://www.simple-talk.com/community/blogs/drsql/default.aspx