Revving Tableau Server Performance

Get to the bottom of why you are experiencing Tableau Server performance loss.

During this on-demand webinar, we discuss

  • Most common causes of Tableau Server performance degradation
  • How to get ahead of your performance issues
  • When to scale out and when to scale up
  • Cloud-based options

You may also be interested in another popular on-demand webinar, Four Methods to Secure and Filter Data by User in Tableau.


Todd Schuman

Practice Lead – Installations, Upgrades and Performance Tuning
Senturus, Inc.

Presentation outline

  • Most common causes of Tableau Server performance loss
  • Data extract refresh schedules
  • Undersized hardware/concurrent users
  • Row level security
  • Poor workbook/dashboard design
  • Data extract chaos
  • Long refreshes during peak hours
  • Failing extracts
  • Duplicates and deprecated extracts
  • Undersized hardware/concurrent users
  • On paper, 8 CPU/32GB RAM should support 1000 total users
    • Assumes 10% are active
    • Light workload
  • Row level security in extracts
    • Live vs. extracts
      • Live can apply security for each SQL execution
      • Extract needs to store specific rows for each user/group
    • One-to-one
      • [Column]=USERNAME()
      • Will create a row for each user
      • 1M rows, 1K users = 1B row extract
    • One-to-group
      • IF ISMEMBEROF(“GROUP”) THEN [Column]=”Value”
      • Can help reduce users to multi-user groups
      • 1M rows, 20 groups, 20M row extract
  • Poor dashboard design
  • Slow dashboards in Desktop will not run faster on Server
  • Usual suspects
    • Custom SQL
    • Too many details/marks
    • Too many quick filters
    • Complex calculations
    • Excel/CSV files as data sources
    • Operational databases
    • LOD calculations

Getting ahead of performance issues

  • Restrict schedules – don’t allow peak hour scheduling
  • Clean up extracts
    • Hide unused columns – expose only what is needed
    • Remove lower level detail if only focused on aggregates
      • If you only report on monthly data, you don’t need to store daily row level detail
    • Filter – If you only need one year of data, filter out the historical data
    • Massive reduction in rows and size
  • Data Stewards
    • Restrict non-stewards from scheduling extract refreshes
    • Data steward ensure extracts are only refreshed when needed
    • Duplicates are removed
    • New extracts are validated
  • Row level security
    • CONTAINS()
      • High performing without duplicates
      • Requires creating custom comma delimited fields
      • Note: issues with HYPER in 10.5 and 2018.1.0
    • Display name
      • Fast performance
      • Users can edit
      • Ugly for users
    • Tableau admin tools
      • Out of box reports
    • Internal postgres Tableau reporting
      • Build your own dashboards
      • Have it delivered automatically
        • Extract timelines
        • Daily use
        • Weekly use
        • Monthly use

Scaling up vs. scaling out

  • Up vs. out
    • Up – adding additional hardware/resources to existing servers
    • Out – adding new servers to distribute load
  • When to scale up
    • Tableau Server recommended hardware
    • If Less < 8-16 CPU – scale up before out
    • If on core-based licensing, make sure you are using what you paid for
      • Single gateway nodes don’t count
      • DEV and TEST environments don’t count as long as they are <= prod core count
    • When to scale out
      • Single node >= 8 CPU
      • Need to address failover/high availability (HA)
      • Need to dedicate hardware to Tableau processes
    • Response times scaling out
    • Comparisons
      • Scaling up
        • Lower cost
        • Less maintenance
        • Simple configuration
      • Scaling out
        • Additional cost for servers and licenses
        • Distribute load
        • Dedicated workers
        • Failover/high availability
      • Cloud-based options
        • Tableau Online
          • Now runs on AWS
          • Turnkey
          • Auto upgrade
          • No access to file system
          • Limited on-prem access
          • 100 GB limit
        • DIY cloud
          • AWS, Azure, Google
          • Maintain updates and software
          • Full feature set
          • Need to setup network access to on-prem data and authentication


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