1. Azure SQL Data Warehouse Performance Tuning
Simon Facer
Microsoft PFE

2. Simon Facer Microsoft PFE since 2011
SQL Server since 1995 – version 4.21
(Sybase System 12)
APS since 2013
ADW since 2016

3. Out of SCope Data Bricks Polybase Optimized for Elasticity
What we aren’t going to talk about (in detail) …
Data Bricks
Polybase
Optimized for Elasticity
Adaptive Caching
HDInsight
Azure Data Lake
Azure Data Factory
Azure Analytics
Power BI
Optimized for Compute / Gen 2

4. In Scope (aka the Agenda)
Azure SQL DW Overview
Table Basics
Capturing Query Data
Common Design and Performance Issues

5. What is MPP? Control Compute Compute Compute Compute Compute Compute
Massively Parallel Processing
Control
Compute
Compute
Compute
Compute
Compute
Compute

6. What is Azure Data Warehouse?
Massively Parallel Processing
Storage
Compute
Compute
Compute
Compute
Compute
Compute
Compute
Compute
Compute
Compute
(60)

7. Table use cases Fact Tables Dimension Tables Stage Tables
Goals
Fact Tables
Millions / Billions of rows
Aggregatable data
Dimension Tables
Attribute data
Stage Tables
Data sink
Fast scan of M or B of rows
SCAN
Fast read of specific rows
SEEK
Fast write

8. Table geometries Hash Distributed Tables
Data is distributed based on the hash of the distribution key value
60 Distributions
Fact Tables
Round-Robin Distributed Tables
Data is distributed evenly across all distributions
Staging Tables
Replicated Tables
Copy on each compute node *
Dimension tables
< 2GB
Hash Distributed:
Hashing algorithm is deterministic – value ‘x’ will always go to the same distribution. All tables use the same hashing algorithm, so value ‘x’ in the hash key for table tbl_SalesHdr will be in the same distribution as value ‘x’ in the hash key for table tbl_SalesDtl.
The Hashing algorithm is data-type agnostic - it is based on the bytes of the field, not the value. The results are counter-intuitive – value 1 in an INT field will hash differently to value 1 in a BIGINT field, because the BIGINT field has more bytes than the INT field.
May be a good candidate for tables with frequent DML operations.
Round-Robin Distributed
Rows are distributed evenly across all 60 distributions. Data is not assigned to distributions in any deterministic pattern.
Queries against RR tables will almost always incur data movement.
Can be a good candidate when …
There is no obvious JOIN key
There is no good candidate HASH key,
The table does not share a common join key with other tables
When the table is a temporary staging table
Replicated Tables
Data for Replicated tables is copied on each Compute node
Should be < 2GB is size
After scaling the DW, needs to be re-initialized on Compute nodes
After updates, needs to be re-initialized on Compute Nodes
Query DMV sys.pdw_replicated_table_cache_state to identify tables that need to be re-initialized
-- Code Start:
SELECT [ReplicatedTable] = t.[name]
FROM sys.tables t
JOIN sys.pdw_replicated_table_cache_state c
ON c.object_id = t.object_id
JOIN sys.pdw_table_distribution_properties p
ON p.object_id = t.object_id
WHERE c.[state] = 'NotReady'
AND p.[distribution_policy_desc] = 'REPLICATE’ --
Re-initialize table with:
SELECT TOP 1 * FROM [ReplicatedTable]
Re-initialization incurs a table-level EXCLUSIVE lock
Re-initialization also rebuild all indexes on the table

9. Storage Options Rowstore – Clustered Index Indexed / ordered data
Indexes get Fragmented over time
Data insert ordered on Cluster Key - new rows appended to the end of the table – fast load performance
Data insert not ordered on Cluster Key - new rows inserted into existing pages results in Page Splits – poor load performance
Index maintenance on DML – overhead on data load
Good lookup performance
Ideal for limited range scans & singleton selects (Seeks)
Slower for table scans / partition scans / loading

10. Storage Options Rowstore – Heap No clustered index / unordered data
New rows appended to the end of the table – fast load performance
Whole table is / may be read for lookups (Seeks)
Whole table is read for Scans
Bad read performance

11. Storage Options Clustered ColumnStore Index (CCI)
Highly compressed – IO efficient
Compression up to 15x (vs. RowStore up to 3.5x)
Load performance dependent on Batch Size
Lookup (seek) queries perform badly
Scan queries – optimized!
Query performance depends on CCI quality / health

12. How to capture Query MetaData
EXPLAIN
Equivalent of SQL Server’s ‘Estimated Execution Plan’

13. How to capture Query MetaData
DMVs
Equivalent of SQL Servers ‘Actual Execution Plan’

14. How to capture Query MetaData
XML Output shows D-SQL operations:

15. How to capture Query MetaData
Demo …
See the ‘Resources’ slide at the end for scripts used in this session.

16. Common Design and Perf. Issues
Data Movement
Data Skew
Statistics
CCI Health
Locking
Resource Contention

17. Common Issues – Data Movement
Why does data move?
fact_OrderHeader
OrderID
1 …
2 …
3 …
4 …
7 …
8 …
fact_OrderDetail
SKU (Order ID)
(7) …
(3) …
(1) …
(7) …
Compute #1
Compute # 2
Compute # 3

18. Common Issues – Data Movement
Why does data move?
Distribution incompatible JOINs
Distribution incompatible AGGREGATIONs
Store_Sales HASH([ProductKey])
[ProductKey] INT NULL
Web_Sales HASH([ProductKey])
[ProductKey] BIGINT NULL

19. Common Issues – Data Movement
Why does data move?
Distribution incompatible JOINs
Distribution incompatible AGGREGATIONs
SELECT COUNT_BIG(*)
FROM [dbo].[FactOnlineSales]
GROUP BY [StoreKey];
Incompatiblity:
FactOnlineSales distributed by ProductKey
Query groups by Store
Resolution:
Re-distribute data on ProductKey

20. Common Issues – Data Skew
Causes of Data Skew
Natural Skew
NULL hash key values
Default hash key value
Bad hash key choice
Resolution:
Pick a different hash key
Split default values into a secondary table

21. Common Issues - Statistics
The MPP Query Optimizer heavily relies on statistics to evaluate plans
Out-of-Date or Non-Existent Statistics is the most common reason for MPP performance issues!
Avoid issues with statistics by creating them on all recommended columns and updating them after every load

22. Common Issues - Statistics
It is recommended Statistics are created on all columns used in:
Joins
Predicates
Aggregations
Group By’s
Order By’s
Computations
Don’t forget about multi-column statistics …

23. Common Issues - Statistics
It is recommended Statistics are created on all columns used in:
Joins
Predicates
Aggregations
Group By’s
Order By’s
Computations
Don’t forget about multi-column statistics …

24. Common Issues - Statistics
Azure SQL DW now supports automatic creation of column level statistics
Auto Update not supported
Multi-column stats not auto created
Stats Creation is Synchronous
Stats Creation is triggered by:
, yet
SELECT
INSERT-SELECT
CTAS
UPDATE
DELETE
EXPLAIN
May 10th release

25. Common Issues – CCI Health
Clustered ColumnStore Indexes
Better Performance with > 100K rows / Compressed Row Group
Best Performance with 1,048,576 rows / Compressed Row Group
Deleted rows impact performance
Open Row Groups (Delta Store) – HEAPs
Loading Batches –
> 100K rows / distribution – direct to Compressed format
Small Resource Class – memory pressure can limit Compressed RGs size
Compressing a Row Group requires Memory:
72MB + (r * c * 8) + (r * short str col * 32) + (long str col * 16MB)
Distributed tables have 60 sets of Row Groups
Recommended ≥ 60M rows (1M / distribution)
Each distribution has its own Delta Store
Partitions add CCIs / distribution
Short str ≤ 32 bytes, Long str > 32 bytes

26. Common Issues – Resource ConteNtion
Queries occupy Concurrency Slots, based on Resource Class
# of concurrent queries depends on DWU service objective
Allocated RAM / Query allocated depends on Resource Class and DWU

27. Common Issues – Resource ConteNtion
Memory:
Gen 1
Performance level
Compute nodes
Memory per data warehouse (GB)
DW100 1 24
DW200 2 48
DW300 3 72
DW400 4 96
DW500 5
120
DW600 6
144
DW1000 10
240
DW1200 12
288
DW1500 15
360
DW2000 20
480
DW3000 30
720
DW6000 60
1440
Gen 2
Performance level
Compute nodes
Memory per data warehouse (GB)
DW1000c 2
600
DW1500c 3
900
DW2000c 4
1200
DW2500c 5
1500
DW3000c 6
1800
DW5000c 10
3000
DW6000c 12
3600
DW7500c 15
4500
DW10000c 20
6000
DW15000c 30
9000
DW30000c 60
18000

28. Common Issues – Resource ConteNtion
Gen 1 (Static Resource Classes):
Concurrency Slots Used
Service level
Maximum concurrent queries
Maximum concurrency slots
staticrc10
staticrc20
staticrc30
staticrc40
staticrc50
staticrc60
staticrc70
staticrc80
DW100 4 1 2
DW200 8
DW300 12
DW400 16
DW500 20
DW600 24
DW1000 32 40
DW1200 48
DW1500 60
DW2000 80 64
DW3000
120
DW6000
128
240

29. Common Issues – Resource ConteNtion
Gen 1 (Dynamic Resource Classes):
Concurrency Slots Used
Service level
Maximum concurrent queries
Concurrency slots available
smallrc
mediumrc
largerc
xlargerc
DW100 4 1 2
DW200 8
DW300 12
DW400 16
DW500 20
DW600 24
DW1000 32 40
DW1200 48
DW1500 60
DW2000 80 64
DW3000
120
DW6000
128
240

30. Common Issues – Resource ConteNtion
Gen 2 (Static Resource Classes):
Concurrency Slots Used
Service Level
Maximum concurrent queries
Concurrency slots available
staticrc10
staticrc20
staticrc30
staticrc40
staticrc50
staticrc60
staticrc70
staticrc80
DW1000c 32 40 1 2 4 8 16
DW1500c 60
DW2000c 48 80 64
DW2500c
100
DW3000c
120
128
DW5000c
200
DW6000c
240
DW7500c
300
DW10000c
400
DW15000c
600
DW30000c
1200

31. Common Issues – Resource ConteNtion
Gen 2 (Dynamic Resource Classes):
Concurrency Slots Used
Service Level
Maximum concurrent queries
Concurrency slots available
smallrc
mediumrc
largerc
xlargerc
DW1000c 32 40 1 4 8 28
DW1500c 60 6 13 42
DW2000c 80 2 17 56
DW2500c
100 3 10 22 70
DW3000c
120 12 26 84
DW5000c
200 20 44
140
DW6000c
240 7 24 52
168
DW7500c
300 9 30 66
210
DW10000c
400 88
280
DW15000c
600 18
132
420
DW30000c
1200 36
264
840

32. Resources Microsoft Azure SQL Data Warehouse
SQL Data Warehouse Documentation

33. Questions …