Designing dashboards for performance shridhar wip 040613

PRESENTED BY
Name Here
Designing dashboards for performance
Mrunal Shridhar
Product Consulting, EMEA

©2012 Tableau Software Inc. All rights reserved.
Basic principles
• Everything in moderation
• If it isn‟t fast in database, it won‟t be fast in Tableau
• If it isn‟t fast in desktop, it won‟t be fast in server

Extracts
• Aggregated extracts
• Optimizing extracts

Aggregated extracts
• Helps improve performance
• Aggregate to summary level
• Filter unnecessary values
• Hide all unused fields
• Multiple level of detail across extracts
• Querying from higher aggregation to detail

Optimizing extracts
• Optimize deterministic calculations
• String manipulations and concatenations
• Groups and sets
• Non-deterministic calculations not stored
• Cheaper to store data than recalculate

Filtering
• Filtering categorical dimensions
• Filtering dates
• Context filters
• Quick filters

Filtering categorical dimensions
• Discrete filters can be slower
• Keep only and exclude perform poor
• Complex WHERE clause
• Join on a temp table
• Ranged filters can be faster
• Faster than large itemised list of discrete values
• Faster when increasing dimensions cardinality
• Indexes impact efficiency of filters

Filtering dates
• Discrete dates or date levels
• Can result in poor query execution
• Tables not partitioned on DATEPART
• Data extract can optimise performance
• DATEPART materialised in extract
• Range of contiguous dates
• Very efficient for query optimisers
• Leverage indexes and partitions
• Relative to a specific date
• Uses ranged date filter

Context filters
• All filters are computed independently
• Set one or more filters as context filters
• Any other filters are defined as dependent
filters
• Writes filter result set to temp table
• Subsequent filters and queries on smaller
dataset
• Creation of temp table expensive activity
• Context filters not frequently changed by user
• Custom SQL statements can be optimised

Quick filters
• Too many quick filters will slow you down
• „Only Relevant Values‟
• Lots of discrete lists
• Try guided analytics over many quick filters
• Multiple dashboards with different levels
• Action filters within a dashboard

Quick filter alternatives
• Create a parameter and filter based on users‟
selection
• PROS
• Do not require a query before rendering
• Parameters + calculated fields = complex logic
• Can be used to filter across data sources
• CONS
• Single-value selection only
• Selection list cannot be dynamic

Quick filter alternatives
• Use filter actions between views
• PROS
• Supports multi-value selection
• Evaluated at run-time to show a dynamic list
• Can be used to filter across data sources
• CONS
• Filter actions are more complex to set up
• Not the same UI as parameters or quick filters
• Source sheet still needs to query the data source

Calculations
• Basic and aggregate vs. table calculations
• Calculations vs. native features
• Performance techniques

Basic and aggregate calculations
• Basic and aggregate calculations
• Expressed as part of the query sent to data
source
• Calculated by the database
• Basic calculations scale very well
• Tuning techniques can improve performance
• Table calculations
• Calculated locally on query results returned
• Generally done over a smaller set of records
• If performance is slow…
• Then push calculations to data source layer
• Consider aggregated data extracts

Calculations vs. native features
• Native features often more efficient than a
manual calculation
• Grouping dimension members together
• Consider using groups
• Grouping measure values together into „bins‟
• Consider using bins
• Changing displayed values for dimension
members
• Consider using aliases

Performance techniques
• Data type used has a significant impact on
performance
• Integers are faster than Booleans
• Both are faster than Strings
• Use Booleans for basic logic calculations
• Bad
• IF [DATE]= TODAY() THEN “TODAY” ELSE “NOT TODAY” END
• Good
• [DATE]=TODAY()
• String searches
• FIND() slower than CONTAINS()
• CONTAINS() slower than wildcard match quick
filter

• Parameters for conditional calculations
• Take advantage of „display as‟
• Integer values for calculation logic
VALUE DISPLAY AS
YEAR YEAR
QUARTER QUARTER
MONTH MONTH
WEEK WEEK
DAY DAY
VALUE DISPLAY AS
1 YEAR
2 QUARTER
3 MONTH
4 WEEK
5 DAY

Dashboard
• Desktop design considerations
• Server optimizations

Desktop considerations
• Less views, less quick filters
• Each view requires at least one query
• Each quick filter requires at least one query
• Can result into a lot of I/O before rendering
• Dashboards process views from same data
source in a serial fashion
• “Exclude all values”
• Avoids expensive query of asking for all data

Server optimizations
• Turn off tabs
• Must process every view in every tab
• Need to understand structure of tabs for actions
or filters
• Reducing tabs improves performance
• Try „:tabs=no‟
• Fixed size dashboards
• Different window sizes mean views are drawn
differently
• VizQL server must render view separately for
each user
• “Automatic (Fit To Window)” has low cache hit
rate

Analyze performance
• Tableau desktop messages

Tableau desktop messages
• Executing query
• Process
• Execute a query to return records for the view
• Investigate
• Review log file to see queries taking long time
• Possible solution
• Consider calculation, query and filter techniques

• Computing View Layout
• Process
• Tableau rendering display on all data received
• Investigate
• Slow-running table calculations
• Very large crosstab
• Lots of marks rendered
• Review techniques for calculation optimisation
and view design

• Computing quick filters
• Process
• Tableau is processing quick filters for view(s)
• Investigate
• Long time rendering and refreshing quick filters
• Consider not using „show relevant values‟
• Consider not using enumerated quick filters
• Review techniques for filter performance and
view design

FIN

Additional Topics

Data sources
• File-based data sources
• Relational data sources
• OLAP data sources
• Web-based data sources

File-based data sources
• Import into Tableau‟s fast data engine
• Microsoft JET driver
• 255 columns, 255 characters
• No COUNT DISTINCT or MEDIAN
• Files greater than 4GB in size
• Tableau text parser
• Used when no data modeled

Relational data sources
• Indexes
• On all columns part of table JOINs
• On any column used in FILTER
• Referential integrity
• Primary and foreign key explicitly defined
• Helps bypass integrity checks
• Join culling
• Partitioning
• Split larger table into smaller, individual tables
• Partition across dimension

Relational data sources
• NULLs
• Define dimension as NOT NULL
• Increase effectiveness of indexes
• Calculations
• Very complex calculations in a view or function
within DB
• Create a custom SQL
• Summary tables
• Summarize data to higher level of aggregation

Other data sources
• OLAP data sources
• Underlying language differences
• Metadata definition
• Filtering
• Totals and aggregations
• Data blending
• Web-based data sources
• “Connect live” not an option
• Extracts can be refreshed
• Automated and scheduled

Queries
• Understanding the query
• Multiple tables vs. custom SQL
• Blending vs. joining

Understanding the query
• Slow-running visualisation
• Time it takes to query
• Time to stream records back
• Number of records
• Large number vs. smaller number of
aggregated records
• Desktop log files

Multiple tables vs. custom SQL
• Multiple tables
• Dynamically create SQL based on fields
• Join culling to drop unused dimension tables
• Referential integrity
• Custom SQL
• Never deconstructed, and executed atomically
• Use in conjunction with Tableau‟s data engine
• Context filters materialise results in a temp
table
• Include parameters in SQL statements

Blending vs. joining
• More than one data source
• Blend data or federated database system
• Join is better on same data source
• Improve performance
• Improve filtering control
• Blend is better across data sources
• Too many records for a join to be practical
• Display summary and details at the same time

Extracts
• Creating extracts
• Extracts vs. live connections

Creating extracts
• Factors
• Database technology
• Network speed
• Data volumes
• Workstation specs
• Fast CPU with multiple cores
• Lots of RAM
• Fast I/O
• Creation requires temp disk space
• Up to square of the size of resulting extract file
• Create extract on workstation, populate on
server

Filtering
• Quick filters
• User filters

Quick filters
• Enumerated quick filters can be slow
• Requires a query for all potential field values
• Multiple value list
• Single value list
• Compact list
• Slider
• Measure filters
• Ranged date filters
• Non-enumerated quick filters can be helpful
• Do not require field values
• Custom value list
• Wildcard match
• Relative date filters
• Browse period date filters
• Performance at the expense of visual context for end user

Quick filters
• Show potential values in 3 different ways
• All values in database
• No need to re-query when other filters changed
• All values in context
• Temp table regardless of other filters
• Only relevant values
• Other filters are considered

User filters
• More data source I/O
• Need to ask exact same query again
• More cache space required
• Each user session creates own query results
and model cache
• Caches being cleared can result in more I/O

Calculations
• Performance techniques

• Date conversion
• Numeric field to a string to a date is inefficient
• Bad
• DATE(LEFT(STR([YYYYMMDD]),4) + “-“ + MID(STR([YYYYMMDD]),4,2) + “-“ +
RIGHT(STR([YYYYMMDD]),2))
• Keep numeric field and use DATEADD()
• Good
• DATEADD(„DAY‟, [YYYYMMDD]%100-1, DATEADD(„MONTH‟,
INT(([YYYYMMDD]%10000)/100)-1, DATEADD(„YEAR‟, INT([YYYYMMDD]/10000)-1900,
#1900-01-01#)))
• Date functions
• NOW() on for time stamp
• TODAY() for date level

• Logic statements
• ELSEIF != ELSE IF
IF [REGION] = "EAST" AND [CUSTOMER SEGMENT] = "CONSUMER"
THEN "EAST-CONSUMER"
ELSE IF [REGION] = "EAST" AND CUSTOMER SEGMENT] <>"CONSUMER"
THEN "EAST-ALL OTHERS"
END
END
• would run much faster as:
IF [REGION] = "EAST" AND [CUSTOMER SEGMENT] = "CONSUMER"
THEN "EAST-CONSUMER"
ELSEIF [REGION] = "EAST" AND [CUSTOMER SEGMENT] <>"CONSUMER"
THEN "EAST-ALL OTHERS"
END
• but this is faster still:
IF [REGION] = "EAST" THEN
IF [CUSTOMER SEGMENT] = "CONSUMER" THEN
"EAST-CONSUMER"
ELSE "EAST-ALL OTHERS"
END
END

• Separate basic and aggregate calculations
• When using extracts and custom aggregations
• Divide calculations into multiple parts
• Row level calculations on one calculated field
• Aggregated calculations on second calculated
field

Dashboards and views
• Views

Views
• Only fetch and draw what you need
• Remove unnecessary fields from level of detail
• Charts vs. crosstabs
• Marks display faster than a tabular report
• Rendering a text table consumes more memory
• Tableau is not a back door data extract process
• Leverage aggregate to detail action filters
• Removing unnecessary geographic roles
• Save time to lookup generated latitudes &
longitudes

Views
• Blending vs. custom geographic roles
• Custom geocoding embeds entire
GEOCODING.FDB
• Significant increase in TWBX file size
• Joining or blending geographic data is smaller
size
• Save only relevant custom geographic data

Tools to analyze performance
• Tableau 8 performance metrics
• Log files
• Database performance monitors

Performance metrics V8
• Interpret performance recording
• Timeline
• Workbook, dashboard, worksheet
• Events
• Event nature and duration
• Query
• Executing query in either timeline or events

Performance metrics events
• Computing layouts
• If layouts are taking too long, consider simplifying your workbook.
• Connecting to data source
• Slow connections could be due to network issues or issues with the
database server.
• Executing query
• If queries are taking too long, consult your database server‟s
documentation.
• Generating extract
• To speed up extract generation, consider only importing some data from
the original data source.
• Geocoding
• To speed up geocoding performance, try using less data or filtering out
data.
• Blending data
• To speed up data blending, try using less data or filtering out data.
• Server rendering
• You can speed up server rendering by running additional VizQL Server
processes on additional machines.

Log files
• Understand where bottlenecks are occurring
• Information on what Tableau is doing
• Tableau communication with the data source
• Time taken by each Tableau step
• For Tableau Desktop
• C:UsersusernameDocumentsMy Tableau RepositoryLogs
• For Tableau Server, the VizQL log file:
• C:ProgramDataTableauTableau ServerdatatabsvcvizqlserverLogs

Database performance monitors
• Database performance monitors
• Insight on queries hitting your DB
• Understand how database processes them
• Advice on additional tuning
• Tableau 8 performance metrics
• Turn on to record metrics

Tableau server
• General performance guidelines
• Caching

General guidelines
• General guidelines
• Use a 64-bit operating system
• Ensures 64-bit version of Tableau data engine
• 32-bit processes have access to more memory
• Add more cores and memory
• Configuration
• Schedule extract refreshes for off-peak hours
• Check the VizQL session timeout limit
• Default is 30 minutes
• Idle session still consumes memory
• Change using tabadmin
• vizqlserver.session.expiry.timeout.setting
• Assess your process configuration

Caching
• Maximise cache use
• Reuse image tile and model caches
• Set dashboard size rule to „exact size‟
• Tuning caches
• Tableau server configuration utility
• Minimise queries
• Balanced
• Most up-to-date
• “:refresh=yes” to view URL
• Tabadmin
• Model cache
• vizqlserver.modelcachesize:30
• Query cache
• vizqlserver.querycachesize:64

Designing dashboards for performance shridhar wip 040613

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