1. In Memory OLTP
Not Just for OLTP

2. Russ Thomas Full time SQL Server DBA since 2008
Database Developer since 1997
SQL Server MCSE Data Platform
Twitter: @SQLJudo
Blog:
Plural Sight Author
2 |
1/2/2019 |
@SQLJudo - Russ Thomas DBA

3. In Memory OLTP (“Hekaton”)
SQL Server 2014 oooh aaaah technology

4. Drawbacks of Memory Expensive Volatile Precious Local
Not a file system

5. Benefits of Memory
Fast

6. In Memory OLTP (“hekaton”)
MAGIC UNICORN VERSION WAS ALREADY TAKEN BY APPLE

7. 2014 Limitations (just a few)
< 250GB (durable tables -> log must also be durable)
< 8060 byte rows
No unique indexes
No calculated columns
No foreign keys
No partitioning
No check constraints
No alter statements
No table locks
No truncate
Non integer indexes must be binary collation
No adding indexes after creation

8. 2016 Limitations
< 250GB (durable tables -> log must also be durable)
< 8060 byte rows
No unique indexes
No calculated columns
No foreign keys
No partitioning
No check constraints
No alter statements
No table locks (who cares)
No truncate
Non integer indexes must be binary collation (still faster though)
No adding indexes after creation
(list of all TSQL constructs not supported by 2016)

9. Why OLTP Size Concurrency Speed < 250GB No Locks (row versioning)
Reads and Writes

10. Why not ETL ??? Staging Tables Size ( > 250GB schema only tables )
Heaps
No constraints
No foreign keys
No indexes
No alters

11. LIVE DEMO

12. Variables to Consider Memory Limit Storage Sub-structure
Multi-threading / CPU cores
# of Transformations
Memory Limit
Running out of memory is bad
Storage Sub-structure
PCIe vs Spinning Disks vs SSD – benefit might not be worth the hassle
Multi-threading / CPU cores
Serial workloads to stay under memory limit may or may not be faster than parallel work loads from multiple spinning disks