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IN-MEMORY OLTPBy

Manohar PunnaSQL Server Geeks – Regional Mentor, Hyderabad

Blogger, SpeakerSr. DBA at S&P Capital IQ (McGraw Hill Financials)


Speaker Details

Manohar Punna (Regional Mentor, Hyderabad)

5+ Years of experience as Core SQL DBA

Blogger and Speaker at SQL Server Geeks

Worked in various business models supporting databases including Microsoft GTSC

MCITP Certified Currently working at McGraw Hill

Financials as a Sr SQL DBA in Escalation team


Agenda

Why In-Memory?

Disk Based Tables vs. Memory Optimized Tables

How Stuff Works

Demo – Create, Insert, Read Row Format Garbage Collection Data Storage Transaction Logging

Considerations

Summary


Memory is getting cheaperEfficient processing of instructionsWith Native compiled SP execution is just a DLL entry

pointAggressively optimized at compile time

Scale-Up IssuesNo PagingLock free structureOptimistic concurrency with full ACID support

Why In-Memory


Disk Based Tables

Memory

Buffer Pool

Physical Reads

SQL DB Engine Process

Checkpoint Lazy Writer

Consider a scenario with NCI scan followed by a key lookup.


Memory Optimized Tables

Memory

Buffer Pool

Asynchronous data Sync

SQL DB Engine Process

Hekaton DB Engine

Recovery

R1 R3

R2


Lets LEARN

this by a Demo

How Stuff Works – Create, Insert, Read


How Stuff Works – Row Format

Row Header Payload (data in columns)

Begin Ts End Ts Stmnt ID Ind Link Count8 bytes 8 bytes 4 bytes 2 bytes 8b*(IndLinkCount – 1)

Begin Ts and End Ts determine the validity of the recordRow size limited to 8060 bytesRow linking leads to collisions. Can be avoided by providing more hash buckets.Off-Row storage types are not allowed (XML, BLOB, Spatial)


How Stuff Works – Garbage Collection

Row Versions are created for Update, Delete and Aborted transactionsGC is non blocking, cooperative and efficient

Dedicated GC background thread Active Transactions work cooperatively to perform GC Based on oldest Active Transaction Hint

100 200 1 FN1 MN1 LN1

300 ∞ 1 FN1 MN1 LN1

50 100 1 FN1 MN1 LN1

200 ∞ 1 FN1 MN1 LN1

Hash Index Tran 5 = 210Oldest Active Hint = 175


How Stuff Works – Data Storage

Data storage is two types – Durable and Non-DurableStorage is a dedicated filestream in a Memory Optimized filegroupThe data access is sequentialTwo types of files as pairs – Data and DeltaData Files

128 MB file size with 256 kb pages Stores only Inserted rows

Delta Files Grows by 4kb pages over time Contains only IDs of deleted rows

Data files are merged overtime to reuse the space. Manually - sys.sp_merge_xtp_checkpoint_files


How Stuff Works – Data Storage (2)

TS(Ins) TableID RowID Row Payload

TS(Ins) TS(Del) RowID

Data File

Delta File

Checkpoint File Pair

0

100


How Stuff Works – Transaction Logging

The log records are logicalPhysical structure modifications are not loggedIndex specific or Index maintenance operations not loggedUndo information is not loggedTransaction are logged only when the commit occurs

SQL Log Header Opaque Hekaton specific log content


Considerations

Space needed in Memory Table data - 2X the data size Index data - Bucket_Count x Pointer_Size (8 bytes)

Space needed on Disk Data 2X-3X the table size Log - Less than disk based tables. If latency is important use SSD

Backups Differential is not supported Piecemeal Restore should include Hekaton File Group restored with

Primary CTP1 includes Data and Delta files with t-log backups


Summary

Covered creation of In-Memory table

Advantages of In-Memory table over disk-based table

Storage structure of Hekaton

Logging with Memory Optimized tables

What next Native Compiled Stored Procedures Deep dive with more demos


Q & A


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Thank you

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twitter: @sqlservergeeks in-memory oltp by manohar punna sql server geeks – regional mentor,...

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