1. How In-Memory Affects Database Design
Louis Davidson
Certified Nerd

2. Who am I? Been in IT for over 19 years Microsoft MVP For 10 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
Change it to planning the next version

3. Questions are Welcome
Please limit questions to one’s I know the answer to.

4. Attention: There Is Homework (lots of it)
I can’t teach you everything about In-Memory in 1 hour
The code will be available, but it is still very rudimentary
It will get you started, but is only just the tip of the iceberg
Do lots of thinkin’ and testin’ before divin’ in

5. 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

6. 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
We will discuss how In-Memory technologies affect the entire design/development lifecycle
I was first
As if…
Children
Relics

7. Design Basics - Separate your design mind into three phases
Logical (Overall data requirements in a data model format)
Physical Implementation Choice (Indexes, Physical Structures, etc)
Physical (Relational Code)
Before the engine choice I always suggested 3 before 2
We will look at each of these phases and how in-mem may affect your design of each output

8. Logical Design (Though Not Everyone’s Is)
This is the easiest part of the presentation
You still need to model
Entities and Attributes
Uniqueness Conditions
General Predicates
As I see it, nothing changes…

9. Logical Data Model

10. Physical Implementation Overview
Client App
SQL Server.exe
TDS Handler and Session Management
Key
No improvements in communication stack, parameter passing, result set generation
Memory-optimized Table Filegroup
Engine for Memory_optimized Tables & Indexes
Natively Compiled SPs and Schema
Hekaton Compiler
Query Interop
Existing SQL Component
Parser, Catalog, Algebrizer, Optimizer
Proc/Plan cache for ad-hoc T-SQL and SPs
Hekaton Component
Interpreter for TSQL, query plans, expressions
10-30x more efficient
Generated .dll
Access Methods
Buffer Pool for Tables & Indexes
Reduced log bandwidth & contention. Log latency remains
Reference how the demo took advantage of each of these areas of performance.
Also note in the last build that if you spend all your time going through the TDS layers, you won’t get as much benefit from Hekaton as you might otherwise.
Transaction Log
Data Filegroup
Checkpoints are background sequential IO

11. Physical Implementation (Or DBA stuff that I only slightly care about)
Everything is different, and I am not here to cover these 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
Tables and Indexes are extremely coupled
MVCC (Multi-Valued Concurrency Control) used for all isolation

12. Physical Design (No, let’s not get physical)
Your physical design will almost certainly need to be affected
So much changes, even just changing the 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

13. Creating Storage Objects - Tables
The syntax is the same as on-disk, with a few additional settings
You have a durability choices
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:
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
Limited unique constraints (just one unique index per table)
Every durable (Schema_and_Data) table must have a primary key
Note: There are memory optimized temporary tables too: See Kendra Little’s article here:

14. 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 types wouldn’t be good for the OLTP aspects of the table in any case.

15. 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
Indexes are never persisted, but are rebuilt on restart
String index columns must be a binary collation (case AND access sensitive)
Two types
Hash
Ideal for single row lookups
Fixed size, you choose the number of hash buckets (approx 1-2 * # of unique values
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

16. A Taste of the Physical Structures
A table with two hash indexes
From Kalen’s Whitepaper:

17. Do you want to know more? For more in-depth coverage
check Kalen Delaney's white paper ...
Or for an even deeper (nerdier?) versions: “Hekaton: SQL Server’s Memory-Optimized OLTP Engine” or The Bw-Tree: A B-tree for New Hardware Platforms (
Books Online:

18. Creating Storage Objects - Altering a Table
The is the second easiest slide in the deck
No alterations allowed - Strictly Drop and Recreate
You can rename a table, which makes this at east easier
ALTER

19. Demo - Creating tables

20. 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)
A few Exceptions
TRUNCATE TABLE - This one is really annoying :)
MERGE (In-Mem table cannot be the target)
Cross Database Transactions (other than tempdb)
Locking Hints

21. 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
Some really extremely annoying ones:
SUBSTRING supported; LEFT, RIGHT, not so much
No Subqueries
OR, NOT, IN, not supported in WHERE clause
Can’t use on-disk objects (tables, sequences, views, etc)
So you may have to write some "interesting" code

22. Accessing Data Using a Hybrid Approach
Native code is very fast but very limited
Use Native code where it makes sense, and not where it doesn’t
Example: Creating a sequential value
In the demo code I used 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

23. 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 data integrity checking "interesting"
Essential difference, your code now must handle errors

24. 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.

25. 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

26. 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 procedure
Uniqueness on > 1 column set suffers from timing (If N connections are inserting the same data...MVCC will let them)
Foreign Key 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.

27. Problem: How to Implement Uniqueness on > 1 Column Set: INDEXED VIEW?
CREATE VIEW Customers.Customers$UniquenessEnforcement WITH SCHEMABINDING AS SELECT customerId, Address, customerNumber FROM customers.Customer GO
CREATE UNIQUE CLUSTERED INDEX Address ON Customers.Customers$UniquenessEnforcement ( Address) GO
Msg 10794, Level 16, State 12, Line 8 The operation 'CREATE INDEX' is not supported with memory optimized tables.

28. 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?

29. 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:
INT = CustomerId FROM Customers.Customer WHERE Address 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

30. When Should You Make Tables In-Memory - Microsoft's Advice
From
Implementation Scenario
Benefits of In-Memory OLTP
High data insertion rate from multiple concurrent connections.
Primarily append-only store.
Unable to keep up with the insert workload.
Eliminate contention.
Reduce logging.
Read performance and scale with periodic batch inserts and updates.
High performance read operations, especially when each server request has multiple read operations to perform.
Unable to meet scale-up requirements.
Eliminate contention when new data arrives.
Lower latency data retrieval.
Minimize code execution time.
Intensive business logic processing in the database server.
Insert, update, and delete workload.
Intensive computation inside stored procedures.
Read and write contention.
Minimize code execution time for reduced latency and improved throughput.
Low latency.
Require low latency business transactions which typical database solutions cannot achieve.
Low latency code execution.
Efficient data retrieval.
Session state management.
Frequent insert, update and point lookups.
High scale load from numerous stateless web servers.
Optional IO reduction or removal, when using non-durable tables

31. When Should You Make Tables In-Memory Louis's Advice
More or less the same as Microsoft's really (duh!)
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
Basically, the “hot” tables in a strict OLTP workloads...

32. 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.

33. Model Choices – Logical Model

34. Model Choices – Physical Model

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

36. Model Choices – Tables to Make In-Mem (Final)

37. 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 you have seen), I discovered when loading rows (10 connections of 2000 each)
(Captured using Adam Machanic's tool)
On-Disk Tables with FK, Instead Of Trigger seconds per row - Total Time – 1:12
On-Disk Tables withOUT FK, Instead Of Trigger seconds per row - Total Time – 0:51
In-Mem Tables using Interop code seconds per row - Total Time 0:44
In-Mem Tables with Native Code second per row - Total Time – 0:31
In-Mem Tables, Native Code, SCHEMA_ONLY – seconds per row - Total Time – 00:30
In-Mem Tables (not CustomerAddress), Hybrid code – – Total Time – 0:55
But should it be a lot better? Don't forget the overhead... (And SQLQueryStress has extra for gathering stats)

38. Contact info
Louis Davidson -
Website – <-- Get slides here
Twitter –
SQL Blog
Simple Talk Blog – What Counts for a DBA
[twitter] Slides will be on drsql.org in the presentations area for this and the keynote as soon as I can get them out [/twitter]

39. As Much Code Review As We Have Time For!
Demo
As Much Code Review As We Have Time For!