How In-Memory Affects Database Design Louis Davidson Certified Nerd

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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, and In-Memory technologies are changing the game Change it to planning the next version

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

A tasty allegory… Bacon is awesome https://www.flickr.com/photos/runnerone/6232183896/in/photostream/ 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/

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

Attention: There Is Homework (lots of it) I can’t teach you everything about In-Memory in 1 mere hour, particularly the internals The code will be available/demonstrated, but it is still very rudimentary It will get you started, but is only just the tip of the iceberg

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

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 I was first As if… Children Relics

Design Basics - Separate your design mind into three phases 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) 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

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…

Logical Data Model

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 (Real Apps see 2-30x) 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 http://download.microsoft.com/documents/hk/technet/techdays2014/Day2/Session2/DBI394-SQL%20Server%202014%20In-Memory%20OLTP%20-%20Depp%20Dive.pdf

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

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 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

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

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.

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

A Taste of the Physical Structures Basic data record for a row Record Header

Hash Index - Simplified TableNameId Country OtherColumns 1 USA Values 2 3 Canada

Hash Index - Simplified TableNameId Country OtherColumns 1 USA Values 2 Canada 3

Bw Tree Index – Even More Simplified

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

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

Demo In Slides – Preparing to (and actually) Creating tables

Setting the Database To Allow In-Mem CREATE DATABASE HowInMemObjectsAffectDesign ON 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 Add a filegroup to hold the delta files

Creating a Memory Optimized Permanent Table Character column must be binary to index/compare in native code 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 Hash Index used for Primary Key. Estimated Rows in Table 25000-50000 Bw Tree Index on Customer Number This table is memory optimized (ok, that was kind of obvious) This table is as durable as the database settings allow

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

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 Can’t use on-disk objects (tables, sequences, views, etc) String Comparisons must be with columns of Binary Collation So you may have to write some "interesting" code

Demo In Slides – Native Stored Procedure

Creating a Natively Optimized (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 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 Alert parser that this will be a natively compiled object Procedures are atomic transactions

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 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

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

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.

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

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.

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

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?

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 = CustomerId FROM Customers.Customer WHERE 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

When Should You Make Tables In-Memory - Microsoft's Advice From http://msdn.microsoft.com/en-us/library/dn133186.aspx 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

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 workload...

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.

Model Choices – Logical Model

Model Choices – Physical Model

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

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

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 20000 rows (10 connections of 2000 each) (Captured using Adam Machanic's http://www.datamanipulation.net/SQLQueryStress/ tool) On-Disk Tables with FK, Instead Of Trigger - 0.0472 seconds per row - Total Time – 1:12 On-Disk Tables withOUT FK, 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 – 0:55 But should it be a lot better? Don't forget the overhead... (And SQLQueryStress has extra for gathering stats)

Contact info Louis Davidson - louis@drsql.org Website – http://drsql.org <-- Get slides here Twitter – http://twitter.com/drsql SQL Blog http://sqlblog.com/blogs/louis_davidson Simple Talk Blog – What Counts for a DBA http://www.simple-talk.com/community/blogs/drsql/default.aspx [twitter] Slides will be on drsql.org in the presentations area for this and the keynote as soon as I can get them out there. @rmtechtrifecta [/twitter]

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