Planning Your Deployment
It's pretty straightforward to install and configure a single-computer deployment of Tableau Server. This chapter gets you started.
Questions you need to be able to answer
Before you run setup, you must have answers to the following questions:
How will you license your installation?
How will users authenticate to Tableau Server?
How will Tableau Server access data sources?
What hardware will you need?
This chapter will help you answer these questions.
The Tableau Server licensing model
Tableau Server term licenses are available with two different license metrics: User-Based and Core-Based. Term licenses, also known as subscription licenses, allow you to use and update Tableau Server for a specified period of time.
Tableau offers multiple types of User-Based term licenses that grant a range of capabilities at various price points, providing the flexibility for organizations to pay for the data analysis and data visualization capabilities that each type of user in their organization needs.
User-based licenses specify exactly how many named users of each type (Creator, Explorer or Viewer) you can have for Tableau Server. With these licenses you can deploy Tableau Server on a single computer or on multiple computers in a cluster, as long as the total number of users doesn't exceed what the license allows.
Each user who interacts with Tableau Server content—publishes, views, downloads, etc.—must sign in to the server. (We discuss later how you can create user identities on Tableau Server and options for how users can sign in.) A single user can work on multiple sites and projects, and can even have different permissions on different sites. From the licensing perspective, a user is simply a user identity on Tableau Server.
With a core-based license you can run Tableau Server on a specific number of CPU cores(Link opens in a new window). For core-based licensing, you can install Tableau Server on a single-node or multi-node cluster, as long as the total number of cores for all of the nodes does not exceed the number of cores that you have licensed. Core-based licensing imposes no constraints on the number of user accounts in the system. This can include the Guest users who are allowed to interact with embedded views, but who don't have to sign in to Tableau Server in order to do so.
An important consideration when using a core-based license model will be performance, because a set number of cores can only support so many users without having an impact on server responsiveness. Depending on the complexity of the workbooks on the server, extract usage, user concurrency, and the depth of interaction, you can support 10 and 100 users per core and still expect reasonable performance.
Note that if you intend to install Tableau Server on a virtual machine (VM), check the specifications for the VM, which might be listed using vCPUs.
Choose a license
The type of license you choose depends on how your users will work with Tableau Server. Here are a couple of scenarios:
You have a small workgroup where only a handful of users will publish and view workbooks. In this case, you might start with a user-based license for 10 users (or more if you have more users).
You have a small workgroup of users who will publish and manage workbooks, but who will make views available to hundreds or thousands of people in the company. For this scenario, you might start with a core-based license that allows unlimited users.
You can change the license metric used—for example, you can move from a user-based to a core-based license if the number of users you need to support grows.
If you're still deciding what type of license to get, define the scenario you anticipate and contact Tableau(Link opens in a new window) to discuss what license and metric will best accommodate your needs. You can also learn more in the Tableau Server online help.
Identity storage: use an external or use local identity store?
You must choose one of these models during the installation process; you can't change the identity store type later unless you reinstall Tableau Server. If you are working with your IT department, you'll want to connect with the identity management folks to help plan and implement your identity store model.
Does your organization run Active Directory or another LDAP directory service? These are considered "external identity stores." If your organization uses an external identity store, then you probably want to use it with Tableau Server as well. If your organization doesn't use Active Directory or another LDAP directory service, then you'll configure Tableau Server to use local identity store.
The identity store method you choose determines how you plan for user provisioning, site and server management, and data and client access models. Mixed-mode functionality—where some users are managed in an external directory and some are managed by the local Tableau server computer—is not supported. If you have some users who are not part of your corporate directory service and need access, then you must provision and manage all users locally.
This section describes both options and how to plan for either identity store model. How you plan to authenticate users will inform how you manage identities. We cover the basics of what authentication means and how Tableau Server can integrate with other authentication technologies like Kerberos, OpenID, and SAML.
What is authentication?
Authentication confirms a user's identity: who the user is. Any time you sign in to a server or a website, the credentials you provide (typically a user name and password) authenticate you.
Tableau Server has its own user identity and authentication system that lets you determine who can sign in to Tableau Server. Every user who accesses the server must be represented as a user identity—an account—on the Tableau server. (Actually, the Guest user feature we've mentioned allows anonymous user access to the server, but for now, let's not include that in the discussion.)
As an administrator, you determine how you want to create these user accounts in Tableau. The process of creating users and assigning permissions is called provisioning. Provisioning users is the first of several steps where the question of using an external directory vs. local identity store comes in.
Your IT department might be happy to know that they can also provision users with the Tableau command line tool (tabcmd) or with the REST API.
Local identity store
If you're installing Tableau Server in an organization that doesn't run an external directory, or connecting to the external directory is not available for you, you must configure Tableau Server for local identity store.
When you configure Tableau Server with a local identity store, then Tableau Server will authenticate the users. This means that when users want to access Tableau Server, Tableau Server prompts them for a user name and password and determines whether they're authenticated.
When you configure Tableau Server with local identity store, you can provision users either by creating them in the server web admin tool one at a time, or by importing user names and passwords via a CSV file.
Single sign-on: OpenID, SAML, and Kerberos authentication
After installation, you can configure Tableau Server with a single sign-on (SSO) provider. With SSO, users don't have to explicitly sign in to Tableau Server. Instead, the credentials they've used to authenticate already (for example, by signing in to your corporate network) are reused to authenticate them into Tableau Server, and they can skip the step of entering a user name and password in Tableau Server.
Tableau Server supports several types of SSO solutions: OpenID, SAML, and Kerberos. We don't include explicit instructions for how to configure any of these SSO solutions in this guide. But it's important to understand how the decision about whether to use Active Directory, LDAP directory, or local authentication affects SSO:
OpenID requires a local identity store.
Kerberos requires Windows Active Directory.
SAML works with either an external directory or an internal identity store.
For more information about these options, see the links at the end of this chapter.
As you plan your Tableau Server installation, you need to consider how your users access data and how Tableau Server will interact with those data sources. This is mostly relevant at this stage for the purposes of server sizing and hardware planning.
Where is your data?
Tableau is designed with the assumption that you have data in many places and that the data sources can be of various different types—spreadsheets, databases, cloud-based storage, etc. If your organization has data in only one place, you can simplify your Tableau Server deployment by optimizing for that single data source.
However, if your users will connect to multiple disparate sources of data, you'll need to determine how Tableau Server will sign in to the various data sources and how "fresh" the data served by a given source needs to be for your users.
Data "freshness" and performance
All workbooks that your users create in Tableau Desktop start with data. Unless they are accessing a local file on their computer, they connect to a data source—such as a relational database, a file on a network share, or data in the cloud. A primary goal of self-service analytics is to provide an experience where users can get into the creative flow of asking and answering questions in real time. To enable flow, your users need fast and uninterrupted access to the most relevant data.
If data is incomplete, outdated, or if users must wait for it to load, your organization will not realize the full potential of Tableau self-service analytics. Balancing data freshness and performance relies in large part on whether users are interacting with live data or if they are working with extracts.
Understand the difference between extracts and live connections
Let's take a moment to describe the difference between extracts and live connections, then we'll explore their trade-offs and benefits.
A Tableau Server extract is a snapshot of data that has been copied from a data source. Extracts provide great performance because the extract contains all the data that the workbook needs. Think of an extract as a cache of data loaded into Tableau Server for quick querying, analysis, and visualization.
The other option is a live connection. When a Tableau data source is configured for a live connection, Tableau Server runs a query against the data source and caches the data. This means fresh data is always available as users request it. You can configure how long this cache is kept or whether it should be refreshed each time a user loads a view that uses live data.
When users publish a workbook to Tableau Server, they can choose how they want that workbook to access the data source:
Extract the data and package it with the workbook as a
.twbxfile, and then publish the packaged workbook. When other users view the published workbook on Tableau Server, Tableau Server renders views using the embedded extract. In this case, each workbook has its own extract, even if different workbooks began by connecting to the same database or other source. The extract can be refreshed, either manually (by the user) or automatically (on a schedule).
Extract the data and publish the extract to the server as a saved data source. When other users view the file on Tableau Server, the server renders the view with the extract that is hosted and managed on the server. In this case, you can configure Tableau Server to refresh the extract from the underlying data source, either manually or on a schedule. Hosting data as an extract on Tableau Server reduces duplication and reduces traffic to the underlying source database. A single reused extract will be cached by server and will load much faster for subsequent viewers.
Use a live data connection. Publishing a workbook that uses a live connection creates a Tableau Server data source. The data source configuration includes a pointer to the data source and can include the author's embedded (and encrypted) credentials to the data source. Alternatively, workbook authors can leave their credentials out of the workbook. In this case, other users must enter credentials when they open a workbook that then connects to the data source, or the data source can use the Tableau Server account (the Run As service account).
In the context of data freshness, the freshest data will be served by a live connection to the data source. However, if there's a lot of data, if the data requires complex queries, if the database is slow, or if the data doesn't change frequently, performance is often better with an extract. If users do work with extracts, we recommend that you create a schedule for refreshing the extracts.
When to use extracts
Users need to do deep analysis of huge amounts of data stored on traditional databases, or on data resources that have high latency or are overtaxed.
Users need offline access to the data, such as when they're traveling or presenting off-site.
Users are making analytic decisions that don't rely on real-time data.
User need to work with data that's consolidated from multiple sources.
Users are prototyping an analysis using a small set of data. This keeps development fast and can reduce the load on the network and on databases. (When they finish developing, they can switch to a live connection.)
As the Tableau Server administrator, you can create a refresh schedule for extracts. During a refresh, Tableau Server queries the live data source and updates the extract with the latest version of the data. The only practical limitation on extract refresh frequency is the performance of your underlying data source—that is, how quickly it can run the queries needed to update your extract. (In general, we recommend that you schedule extract refresh jobs for off hours, because a refresh job can be CPU intensive.)
When to use live connections
Your users require up-to-the-minute or real-time data to make business decisions.
You have database hardware dedicated to servicing Tableau Server analysis. The query load on a database is primarily a function of the complexity of the workbooks. For complex workbooks, the query load on traditional relational databases can be significant, because calculations are offloaded to the database.
You host your data in a database that is optimized for real-time analysis. Most big data and cloud database solutions are designed for real-time, ad hoc analysis. Others, such as Hadoop, can be latent and have different performance results depending on factors like the size of the data, the connection method, and the configuration.
Operating system requirements
The following distributions of Linux are supported:
Red Hat Enterprise Linux (RHEL) 7.3 and higher (not 8.x), and Amazon Linux 2
CentOS 7.3 and higher (not 8.x)
Oracle Linux 7.3 and higher (not 8.x)
The latest versions of Ubuntu 16.04 LTS and 18.04 LTS (not 17.04)
Additional notes on Linux distributions:
Red Hat Enterprise Linux (RHEL), CentOS, Oracle Linux, and Amazon Linux distributions are collectively referred to in this documentation as RHEL-like.
RHEL 8 is not supported.
Non-LTS releases of Ubuntu are not supported.
Ubuntu version 17.04 is not supported.
Previous versions of CentOS and Ubuntu are not supported because Tableau Server requires
systemdfor process management.
The version of the installer with the file suffix, .
deb, installs on both Ubuntu and Debian distributions.
Custom kernels are not supported.
What kind of server hardware will you need? To install Tableau Server, you must have a computer that meets the minimum hardware requirements(Link opens in a new window). Setup won't run if the computer you are installing onto doesn't meet these requirements.
The minimum hardware requirements specified in the above link are really just recommended for trial and feasibility testing purposes. We don't suggest running Tableau Server in a production environment with the minimum requirements. Instead, we have a minimum recommendation for hardware:
Free Disk Space
8-core, 2.0 GHz or higher
|If you are adding Tableau Prep Conductor to your Tableau Server installation, we recommend you add a second node and dedicate this to running Tableau Server Prep Conductor. This node should have a minimum of 4 cores (8 vCPUs), and 16 GB of RAM.|
Multi-node and enterprise deployments
Contact Tableau for technical guidance.
Nodes must meet or exceed the minimum hardware recommendations, except:
Important: The disk space requirement cannot be checked until you initialize TSM.
50 GB disk space available, with a minimum of 15 GB allocated to the
/opt directory, and the remainder allocated to the
/var directory for data storage.
Free disk space is calculated after the Tableau Server Setup program is unzipped. The Setup program uses about 1 GB of space. You may need to allocate additional disk space depending on various factors like whether you will be using extracts.
The core Tableau Server bits must be installed in a directory with at least 15 GB of free disk space. If you attempt to install Tableau Server on a computer that does not have enough space, the Tableau Server package will install, but you will be unable to continue with setup. By default the install location is the
/optdirectory. You can change the installation path for Tableau Server on RHEL distros.
If you plan to make heavy use of extracts then you may need to allocate additional disk space.You can specify a different directory for data (extract) storage during installation.
Core count is based on "physical" cores. Physical cores can represent actual server hardware or cores on a virtual machine (VM). Hyper-threading is ignored for the purposes of counting cores.
RAM shown is the minimum recommended for a single-node installation. Your installation may function better with more RAM, depending on activity, number of users, and background jobs, for example.
Ideally, you can dedicate a computer to only host Tableau Server. For example, to achieve the best performance, the computer hosting Tableau Server should not also be running other applications or running a full antivirus scanning solution. We also discourage running other databases on the same computer. If your server computer needs to run other applications as well, you need to account for their load on the shared resources as you plan your server sizing.
To determine if the recommended minimum hardware will work for your goals, consider how your users will interact with Tableau Server. This guide assumes you are installing Tableau Server for a user base of up to 100 users. However, hardware requirements will depend more on active simultaneous users, also referred to as concurrent users. The requirements also depend on how frequently Tableau Server is called on to refresh extracts that those users rely on to make business decisions.
Our minimum hardware recommendation should be sufficient for single-server installations where up to 10 active users simultaneously interact with content on Tableau Server. The recommendation also assumes a low frequency of extract refreshes that are all scheduled during off hours.
If this sounds like your scenario, then skip the rest of this section, set up your hardware, and continue to Running Setup.
If you're not sure whether the minimum hardware recommendation meets your needs, read the rest of this section for guidance on how to determine the correct hardware specifications for your deployment.
This section focuses on where you might consider increasing essential hardware resources based on a handful of critical variables to optimize for specific usage profiles.
Heavy workbook processing
If you expect to have more than 10 active simultaneous users interacting with content on the server, or if those users are all interacting with live connections, consider increasing the server RAM to 64 GB. Also consider converting popular data sources to extracts, in which case an installation with 64 GB of RAM typically can service up to 60 active simultaneous users.
Frequent extract refresh
As discussed in a previous section, users accessing Tableau content frequently interact with data that is extracted and managed on the server. How often Tableau Server refreshes these extracts is configurable for each data source. When possible, we recommend running scheduled extracts during non-work hours, but for mission-critical data, this is not always feasible.
Each extract refresh process consumes an entire processor thread and is RAM intensive. The more frequently extracts are refreshed, the more cores and RAM you should add and dedicate to the extract refresh process. Particularly, in the default server configuration, if you expect to schedule multiple extract refreshes simultaneously, they will run serially and queue until a core and Backgrounder process is free. If you need to extract multiple refreshes simultaneously, then you should configure Tableau Server to use two or more Backgrounder processes. For more about this, see the links at the end of this chapter.
Our minimum recommended hardware assumes that you are refreshing the majority of your extracts during non-work hours. This approach is considered a low refresh data use profile.
A moderate data refresh use profile is when you refresh extracts hourly. In this case, we recommend at least 16 cores and 128 GB of RAM.
If you have more than 500 extracts, or if you refresh extracts to support live data analysis, this is considered a high data refresh use profile. In this case, you are exceeding the scope of this guide and you should work with a Tableau consultant to design your deployment.
The more extracts you host on Tableau Server, the more physical hard disk space your computer will require. Centrally managed extracts reduce duplication that is common with workbooks that have packaged data.
Continue to Running Setup.
OpenID Connect(Link opens in a new window). Information in the Tableau Server Help about letting users sign in to the server using an OpenID Connect provider such as Google.
Kerberos(Link opens in a new window). A section in the Tableau Server Help that describes how to let users sign in using Kerberos, as configured on the local network for your organization.
SAML(Link opens in a new window). Information in the Tableau Server Help about using SAML (Security Assertion Markup Language) for single-sign.
Improve Server Performance(Link opens in a new window). Suggestions in the Tableau Server Help for how to help tune Tableau Server performance, including how to balance the demands of user responsiveness and data freshness.