The document outlines a series of five webinars focusing on the Denodo platform, particularly addressing performance and scalability. It covers advanced query optimization techniques, architecture modules, and features of Denodo Express, a free data virtualization platform. Key topics include query plan generation, execution engine responsibilities, and mechanisms for performance optimization and caching.

1. Five In-depth Technology and Architecture Sessions
on Data Virtualization
Session 2: Performance

2. Today’s Speaker
■ Paul Moxon
Senior Director, Product Management

3. Architect-to-Architect Series
■ Series of five webinars over next 2 months
■ Deeper look into Denodo Platform
■ Architectural Overview
■ Performance (today’s session)
■ Scalability
■ Data Discovery and Governance
■ Security

4. Denodo Express
■ Denodo Express
■ Free to Download
■ Fully functioning Data Virtualization Platform
■ Single user, supports common data sources
■ Many of the same capabilities of Denodo
Platform
■ Performance, Data Discovery, Governance,
internal Security, Publishing, …

5. Performance – Architecture Modules

6. Performance – Architecture Modules

7. Performance – Optimizer, etc.
■ Optimizer
■ The Optimizer applies state-of-the-art
optimization techniques to relational and non-
relational sources.
■ Query Plan Generator
■ The Plan Generator is in charge of generating
possible execution plans for the query and
selecting the optimum one.
■ Execution Engine
■ Responsible for executing the selected query
plan, executing the necessary sub-queries on
the sources (or collecting data from cache as
appropriate) and integrating the results to
generate the global response.

8. Performance Optimization
■ Advanced Query Optimization
■ Cost and Source Constraint Based Query Plans
■ Query Delegation
■ Automatic Query Rewriting
■ Join Optimizations
■ Data Movement
■ Asynchronous Multi-threaded Processing
■ Server Throttling Mechanisms
■ Scalability
■ Caching
■ Multiple configuration modes – full or partial

9. Static vs. Dynamic Optimization
■ Static optimization
■ Takes place before query is executed
■ Rewrite query in more optimal way
■ Push-down delegation
■ Optimize query by – where possible – pushing down
sub-trees to underlying data source
■ Delegate functions to underlying data source
■ Dynamic optimization
■ Use statistics and indices to estimate costs of
alternative execution plans
■ Select Join methods and Join ordering

10. Cost-based Optimization
■ Objective – select best execution method
for each operation
■ Estimate query costs based on:
■ View statistics
■ No. of rows, row size, for each field: max value, min
value, no. of different values, …
■ View indices
■ Available indices, type of indices (clustered, hash, …)
■ Data source I/O information
■ Block size, blocks/read operation, data transfer rate, …

11. Source Constraint Optimization
■ Denodo Platform optimization has to work
across multiple diverse data source types
■ Not just relational databases
■ Not all data sources have same capabilities
■ Recognize and optimize for constraints in
underlying data sources
■ e.g. MySQL can be ordered for Merge join…but a
delimited file cannot

12. Statistics Gathering

13. Query Delegation
■ Objective – Push the processing to the data
■ Utilize power and optimizations of underlying
data sources
■ Especially relational databases and data warehouses
■ Minimize expensive data movement
■ Delegation mechanisms
■ Vendor specific SQL dialect
■ Function delegation
■ Configurable by data source
■ Delegate SQL operations
■ e.g. Join, Union, Group By, Order By, etc.

14. Automatic Query Rewriting
■ Objective – Rewrite query in a more optimal
manner before the query is executed
■ Static optimization technique
■ Typical optimizations:
■ Simplify partitioned unions
■ Remove redundant sub-views
■ Transform outer joins to inner joins
■ Static join reordering to maximize delegation

15. Simplify Partitioned Unions
Select * from Sales_Product where region=‘NA'
North
America
EMEA
Sales_NA Product_EMEA
North
America
Product_NA
EMEA
Sales_EMEA
U U
|><|
S S S S
region=‘NA' region=‘NA'region=‘EMEA' region=‘EMEA'
Join cannot be delegated

16. Simplify Partitioned Unions (Cont’d)
Select * from Sales_Product where region=‘NA'
North
America
Sales_NA
North
America
Product_NA
U U
|><|
S S
region=‘NA' region=‘NA'
Join can be delegated

17. Transform Outer Joins to Inner Joins
As a.iinc_id = c.pinc_id ∴ c.pinc_id cannot be null
DS2
internet_inc
DS3
phone_inc
DS1
Internet_inc
||><|
|><|
S
b c
a.iinc_id = c.pinc_id
a

18. Transform Outer Joins to Inner Joins
As a.iinc_id = c.pinc_id ∴ c.pinc_id cannot be null
DS2
internet_inc
DS3
phone_inc
DS1
Internet_inc
||><|
|><|
S
b c
a.iinc_id = c.pinc_id
a
The left outer is equivalent to an inner join
|><|

19. Join Optimizations
■ Multiple Join options:
■ Merge
■ Nested
■ Nested Parallel
■ Hash
■ Optimizer automatically selects based on
statistics and source capabilities
■ e.g. when using databases joining two large
datasets, Merge Join is preferred
■ e.g. if one dataset is significantly larger, use
Nested Join

20. Join Optimizations (Cont’d)
■ You can override the optimizer

21. Data Movement
■ Typically used when one dataset is significantly
smaller and aggregations performed on joined
data
1. Execute query in DS1
and fetch its data
2. Create a temporary table in DS2
and insert data from step 1
3. When step 2 is completed, execute
the JOIN in DS2 and return the results
to the DV layer
DS1
DS2

22. Query Plans
■ Optimizer calculates cost of multiple plans and
selects ‘best’ plan
■ Cost estimates:
1. Traverse query tree top-down looking for
‘interesting’ patterns
• e.g. ‘GROUP BY region’ can execute faster if rows arrive
ordered by ‘region’
2. Estimate costs of sub-queries on data sources
• Use source statistics and constraints
3. Traverse tree bottom-up to calculate costs for each
node
• Choose execution with minimum cost
• Remember ‘interesting’ patterns (overall cost vs. node cost)

23. Other Optimization Techniques
■ Asynchronous Multi-threaded Processing
■ Execute multiple queries in parallel
■ Server Throttling Mechanisms
■ Controls to limit concurrency
■ Waiting queues for inbound connections
■ Connection pools for data sources
■ Swapping data to disk to handle large datasets

24. Caching
■ Caching – for slow sources and protect
operational data sources
■ Caching enabled at view level
■ Enables mixed mode query plans
■ Caching modes
■ Full – all data in cache
■ Partial – query-by-query
■ Manual refresh or automated refresh

25. Q&A

26. Data Virtualization – Next Steps
Move forward at your own pace
 Download Denodo Express –
The fastest way to Data Virtualization
 Denodo Community: Documents, Videos, Tutorials, more.
 Attend Architect-to-Architect Series
 Performance
 Scalability
Move forward with one of our Data
Virtualization experts
 Phone: (+1) 877-556-2531 (NA)
 Phone: (+44) (0)20 7869 8053 (EMEA)
 Email: info@denodo.com | www.denodo.com
 Data Discovery and Governance
 Security

27. Five In-depth Technology and Architecture Sessions
on Data Virtualization
Thank You!
Next Session
Session 3
Denodo Platform: Scalability