KNIME Workbench Guide

In KNIME Analytics Platform, individual tasks are represented by nodes. Nodes can perform all sorts of tasks, including reading/writing files, transforming data, training models, creating visualizations, and so on.

Nodes can be moved into the workflow editor by dragging and dropping them. To copy nodes between workflows, select the chosen nodes, right click the selection, and select Copy in the menu. In the destination workflow, right click the workflow editor, and select Paste in the menu.

To select a node in the workflow editor, click it once, and it will be surrounded by a border. To select multiple nodes, either press "Ctrl" and select nodes by mouse click, or draw a rectangle over the nodes with the mouse.

Replace a node by dragging a new node onto an existing node. Now the existing node will be covered with a colored box with an arrow and boxes inside as shown in Figure 7. Releasing the mouse replaces the node.

You have two options in the workflow editor to document a workflow (as shown in Figure 8):

You can do the following actions on the workflow annotation box.

Change the visual properties of the workflow editor by clicking the "Workflow Editor Settings" button in the toolbar shown in Figure 9.

In the dialog that opens you can change the size of the grid or remove the grid lines completely. You can also change the connection style from angular to curved, and make the connections thicker or narrower.

The changes will only apply to the currently active workflow editor. To change the default workflow editor settings, go to File → Preferences → KNIME → KNIME GUI → Workflow Editor.

To view a full list of keyboard shortcuts, choose Help → Show Active Keybindings from the toolbar. Here, it is also possible to modify the bindings, and create personalized shortcuts.

Mount points are workflow repositories that are accessible from KNIME Analytics Platform. They can be displayed as root directories in the KNIME Explorer view.

Each mount point consists of the location of the workflow repository, and a mount ID. For a local workflow repository, the location is the path to the folder, and for a server it is the address of the server. The mount ID is used to reference files and workflows under the mount point.

At the top of the KNIME Explorer are several icons arranged in a toolbar shown in Figure 11.

The functions of the icons are explained in Table 1 below:

The types of content that you can see in the KNIME Explorer are described in Table 2.

To create an empty workflow, right click anywhere in the local workspace, and select New KNIME Workflow…​ in the menu, or use one of the options explained in Building Workflows.
Give the workflow a name, and define the destination of the new workflow.
Click Finish, and the new workflow will appear in the selected workflow group in the KNIME Explorer.
To learn how to build a workflow, take a look at the next section Building Workflows, follow the steps in the Quickstart Guide, or check the video Workflows and Workflow Groups.

To create a workflow, you need an empty workflow editor. To create a new empty workflow editor take any of these actions:

A workflow is built by dragging nodes from the node repository to the workflow editor and connecting them. To add a node from the node repository or from the workflow coach to the workflow editor, you have two options as shown in Figure 13:

Once two nodes are added to the workflow editor, they can be connected in any of these three ways:

To add a node between two nodes in a workflow, drag the node from the node repository, and release it at its place in the workflow when the connector has turned red, as shown in Figure 14. The red connection means that it is ready to accept the new node. Release the mouse and the node is put in place.

Multiple workflows can be organized into workflow groups. Workflow groups are folders in the KNIME workspace that can include multiple workflows, as well as associated datafiles, shared components and metanodes, and even other workflow groups.

The workflow groups are in the currently active local workspace under the LOCAL mount point in the KNIME Explorer.

You have three ways to create a new, empty workflow group:

In the dialog that opens, give the folder a name, and define where to save the folder in the local workspace. Click Finish. Now the new folder will appear in the selected destination in the KNIME Explorer.

You have three options to export a workflow or a workflow group:

In the same way, you can import a workflow to your local workspace in the following ways:

The Workflow Comparison feature provides tools and views that compare workflow structures and node settings. Workflow Difference allows you to view changes in different versions of a workflow. The feature allows users to spot insertions, deletions, substitutions or similar/combined changes of nodes. The node settings comparison makes it possible to track changes in the configuration of a node.

A Workflow Comparison can be triggered from every view that shows multiple workflows, e.g. KNIME Explorer and Server History.

In order to compare two workflows, select them in the KNIME Explorer, with "Ctrl"+click, and select Compare from the context menu, as shown in Figure 17.

It is also possible to compare a workflow with itself. With this option, you are given a list of nodes from which you can choose the two nodes you want to compare (see Node comparison section below).

The comparison of two workflows or of a workflow with itself, opens a tab in the KNIME Workbench, as shown in Figure 18.

To make it easier to see which changes have been identified there are three buttons in the upper right corner of the Workflow Comparison view.

These buttons (from left to right) filter the list to:

Additionally you can use the search field, to check a special node or node type for changes. The last icon clears the search field and displays all nodes.

Workflow Comparison is structure based. This means, that not only workflows, but also components, snapshots, metanodes, i.e. all items that act as a workflow can be compared with each other. This basically includes everything that can be seen in KNIME Explorer and Server History (except data).

Workflow comparison focuses on the functional structure of a workflow. When comparing shared components (Figure 19) or metanodes (Figure 20) they are expanded.

Components inside a workflow, when comparing workflows, on the other hand, are treated as normal nodes, and their content does not appear in the view. This is not true instead for metanodes inside a workflow that are expanded when comparing the workflow. In Figure 21, the comparison between two workflows containing a component and a metanode, respectively, is shown.

On the left column the component contained in the first workflow is shown as a node while the metanode contained in the second workflow, on the right column, is expanded.

If a component has been changed, it is highlighted in the Workflow Compare view, and the node comparison will show two additional entries: "Component Content Hash" and "Component Internal Settings Hash". These two numbers change whenever the content of the Component (e.g. node insertion/substitution) or the settings of an internal node change, respectively.

knime:// protocol is a protocol specific to KNIME that allows to specify file paths relatively to the KNIME workspace or even the location of the currently executing workflow.

The first element in the file path after knime:// is the base for the path. It is either the workflow itself, the current mount point or a specific mount point like LOCAL in the following example:

You can explore the example workflows, which includes also some real-world use cases, on the public EXAMPLES server.

Inspect the workflow groups for different categories by expanding the EXAMPLES mount point in the KNIME Explorer, and then double clicking the text below as shown in Figure 24.

You can download an example workflow by drag and drop, or copy and paste of the workflow into the local workspace. Double click the downloaded copy of the example workflow to open and edit it like any other workflow.
Alternatively, double click the example workflow directly on the EXAMPLES server to open it in the workflow editor. Save it to the local workspace via "File" and then "Save As…​".

Customize the node recommendations in the "Workflow Coach" dialog, under File → Preferences → KNIME → Workflow Coach. You have the following three options:

The Favorite Nodes view displays your favorite, most frequently used and last used nodes. To add this go to View > Others and under KNIME Views select Favorite Nodes. A node is added to your favorites by dragging it from the node repository into the personal favorite nodes category. Whenever a node is dragged onto the workflow editor, the last used and most frequently used categories are updated. Refer to Figure 26.

The number of nodes in the most frequently and last used categories is per default restricted to ten nodes. This number can be adjusted in the preferences. Select File > Preferences…​ and under /KNIME/KNIME GUI set different values for the maximum size of frequently used nodes and maximum number of last used nodes.

Selecting KNIME in the list of subcategories, allows you to define the log file log level. By default it is set to DEBUG. This log level helps developers to find reasons for any unexpected behavior.

Directly below, you can define the maximum number of threads for all nodes. Separate branches of the workflow are distributed to several threads to optimize the overall execution time. By default the number of threads is set to twice the number of CPUs on the running machine.

In the same dialog, you can also define the folder for temporary files.

Check the last option Yes, help improve KNIME. to agree to sending us anonymous usage data. This agreement activates the node recommendations by community in the Workflow Coach.

Listed below are the command line arguments processed by KNIME. They can either be specified permanently in the knime.ini in the root of the KNIME installation, or be passed to the KNIME executable. Please note that command line arguments must be specified before the system properties (see below) i.e. before the -vmargs parameter.
Note that headless KNIME applications, such as the batch executor, offer quite a few command line arguments. They are not described here but are printed if you call the application without any arguments.

Listed below are the Java system properties with which KNIME’s behavior can be changed. They can either be specified permanently in the knime.ini in the root of the KNIME installation, or be passed to the KNIME executable. Please note that system properties must be specified after the -vmargs parameter. The required format is -DpropName=propValue.

These properties only affect some plug-ins and are only applicable if they are installed.

KNIME Analytics Platform differentiates between small and large tables. Tables are considered to be small (large) when they are composed of up to (more than) 5000 cells. This threshold of 5000 cells can be adjusted via the -Dorg.knime.container.cellsinmemory parameter in the knime.ini file. KNIME Analytics Platform always attempts to hold small tables in memory, flushing them to disk only when memory becomes scarce.

In addition, KNIME Analytics Platform attempts to keep recently used large tables in memory while sufficient memory is available. However, it writes these tables asynchronously to disk in the background, such that they can be dropped from memory when they have not been accessed for some time or when memory becomes scarce. You can configure the memory consumption of a specific node to never attempt to hold its tables in memory and, instead, write them to disk on execution. This is helpful if you know that a node will generate a table that cannot be held in memory or if you want to reduce the memory footprint of a node.

Alternatively, by putting the line -Dknime.table.cache=SMALL into the knime.ini file, KNIME Analytics Platform can be globally configured to use a less memory-consuming, albeit much slower caching strategy. This strategy only ever keeps small tables in memory.

KNIME Analytics Platform compresses tables written to disk to reduce the amount of occupied disk space. By default, KNIME Analytics Platform uses the Snappy compression algorithm to compress its tables. However, you can configure KNIME Analytics Platform to use GZIP compression or no compression scheme at all via the -Dknime.compress.io parameter in the knime.ini file.

Starting with KNIME Analytics Platform version 4.3 a new Columnar Table Backend is introduced. This extension addresses these issues by using a different underlying data layer (backed by Apache Arrow), which is based on a columnar representation.

For information on how to set up this type of backend please refer to the Table backend section.