Introduction
This guide covers the basics of the KNIME Analytics Plaform usage, guiding you in the first steps with the platform but also providing more advanced information about the most important concepts together with indication on how to configure the platform.
Workspaces
When you start KNIME Analytics Platform, the KNIME Analytics Platform launcher window appears and you are asked to define the KNIME workspace, as shown in Figure 2.
The KNIME workspace is a folder on the local computer to store KNIME workflows, node settings, and data produced by the workflow. |
The workflows, components and data stored in the workspace are available through the space explorer in the side panel navigation.
You can switch the workspace in a later moment under Menu, in the top right corner of the user interface, and select Switch workspace.
User interface
After selecting a workspace for the current project, click Launch. The KNIME Analytics Platform user interface - the KNIME Workbench - opens.
The active workflow of the KNIME Analytics Platform will be displayed after switching from an opened workflow. If you have open multiple workflows before you switch the perspective, only the active workflow and all loaded workflow tabs of the current KNIME Analytics Platform will be displayed in the KNIME Modern UI. For each workflow you will see a workflow tab after switching. After clicking the first tab (with the KNIME logo) you end up at the entry page.
In the next few sections we explain the functionality of these components of the user interface:
Entry page
The entry page is displayed by clicking the Home tab.
Here you will find:
-
Three example workflows to help you get started
-
Your local workspace - navigate your local workspace to find the workflow you want to work on
-
Create a new workflow by clicking the yellow plus button
-
Access one of the available mount points. Click Sign in, provide your credentials and start navigating the available spaces. By default only the local workspace, and the link to connect to your personal KNIME Community Hub space are visible. To add a new mount point follow the instructions in the Connect to KNIME Hub section.
With KNIME Analytics Platform version 5.2 you can also add a KNIME Server mount point. |
Workflow editor & nodes
The workflow editor is where workflows are assembled. Workflows are made up of individual tasks, represented by nodes.
One way to create a new workflow is to go to the space explorer, click the three dots and select Create workflow from the menu. Give the workflow a name and click Create.
In the new empty workflow editor, create a workflow by dragging nodes from the node repository to the workflow editor, then connecting, configuring, and executing them.
Nodes
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.
Facts about nodes
-
Each node is displayed as a colored box with input and output ports, as well as a status, as shown in Figure 6
-
The input port(s) hold the data that the node processes, and the output port(s) hold the resulting datasets of the operation
-
The data is transferred over a connection from the output port of one to the input port of another node.
For simplicity we refer to data when we refer to node input and output ports, but nodes can also have input and output ports that hold a model, a database query, or another type explained in Node Ports. |
A node can be in different status as shown in the Figure 7.
Changing the status of a node
The status of a node can be changed, either configuring, executing, or resetting it.
All these options can be found:
-
In the node action bar - click the different icons to configure, execute, cancel, reset and when available open the view.
Figure 8. Action bar of a node -
In the context menu of a node - open the context menu by right clicking a node.
Identifying the node status
The traffic light below each node shows the status of the node. When a node is configured, the traffic light changes from red to yellow, i.e. from "not configured" to "configured".
When a new node is first added to the workflow editor, its status is "not configured" - shown by the red traffic light below the node.
Configuring the node
The node can be configured by adjusting the settings in its configuration dialog.
Open the configuration dialog of a node by either:
-
Double clicking the node
-
Clicking the Configure button in the node action bar
-
Right clicking a node and selecting Configure in the context menu
-
Or, selecting the node and pressing F6
Executing the node
Some nodes have the status "configured" already when they are created. These nodes are executable without adjusting any of the default settings.
Execute a node by either:
-
Clicking the Execute button in the node action bar
-
Right clicking the node and selecting Execute
-
Or, selecting the node and pressing F7
If execution is successful, the node status becomes "executed", which corresponds to a green traffic light. If the execution fails, an error sign will be shown on the traffic light, and the node settings and inputs will have to be adjusted as necessary.
Canceling execution of the node
To cancel the execution of a node click the Cancel button in the node action bar, or right click it and select Cancel or select it and press F9.
Resetting the node
To reset a node click the Reset button in the node action bar, or right click it and select Reset or select it and press F8.
Resetting a node also resets all of its subsequent nodes in the workflow. Now, the status of the node(s) turns from "executed" into "configured", the nodes' outputs are cleared. |
Node ports
A node may have multiple input ports and multiple output ports. A collection of interconnected nodes, using the input ports on the left and output ports on the right, constitutes a workflow. The input ports consume the data from the output ports of the predecessor nodes, and the output ports provide data to the successor nodes in the workflow.
Besides data tables, input and output ports can provide other types of inputs and outputs. For each type the pair of input and output port looks different, as shown in Figure 10.
An output port can only be connected to an input port of the same type - data to data, model to model, and so on.
Some input ports can be empty, like the data input port of the Decision Tree View node in Figure 10. This means that the input is optional, and the node can be executed without the input. The mandatory inputs, shown by filled input ports, have to be provided to execute the node.
A tooltip gives a short explanation of the input and output ports. If the node is executed, the dimensions of the outcoming data are shown in its data output port. A more detailed explanation of the input and output ports is in the node description.
Adding nodes to the canvas
Currently, there are three ways of adding nodes to your canvas to build your workflow:
-
Drag and drop a node from the node repository,
-
double-click on a node inside the node repository, or
-
drop a connection into an empty area inside the workflow canvas to display the quick nodes adding panel. Up to 12 recommended nodes are displayed inside this panel. Also, you can search in the panel for all compatible nodes.Click the desired node to add it.
To use quick nodes adding you need to allow us to receive anonymous usage data. This is possible at the startup of the KNIME Analytics Platform or after switching to a new workspace by selecting Yes in the “Help improve KNIME” dialog.
You can also activate it, via the Open Preference button that is displayed in the quick nodes adding panel.
Click here to find out what is being transmitted. If you don’t want to do this anymore, you can deactivate it at any time in the KNIME Workflow Coach Preferences.
To open the preferences follow these steps:
-
Click Preferences in the top right corner of the user interface
-
Go to KNIME → Workflow Coach
-
Deactivate the setting Node Recommendations by the Community
How to select, move, copy, and replace nodes in a workflow
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, 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 14. Releasing the mouse replaces the node.
Connect to KNIME Hub
By default you can connect to your account on KNIME Community Hub from the Home tab.
It is possible to add a new KNIME Hub instance mount point by clicking Preferences, in the top right corner of the user interface.
Go to KNIME Explorer section and click New…. In the window that opens select KNIME Hub and add your Hub URL. Then click Apply.
Now the new mount point will show up in the Home tab.
Sign in and select the space you want to work on. The content of the space and the related operations you can do on the items are visible in the space explorer.
Switch back to KNIME classic user interface
You can switch back to the classic KNIME Analytics Platform user interface under Menu, in the top right corner of the user interface, and select Switch to classic user interface.
You can switch back to KNIME Modern UI at any time by pressing the button Open KNIME Modern UI in the classic user interface, at the top right corner.
Really, really, really important disclaimer Workflow elements such as connectors or annotations are visualized in a new way and may not look exactly like in the current KNIME Analytics Platform. Changes will therefore not look 100% the same. |
Space explorer
The space explorer is where you can manage workflows, folders, components and files in a space, either local or remote on a KNIME Hub instance. A space can be:
-
Your local workspace you selected at the start up of the KNIME Analytics Platform
-
One of your user’s spaces on KNIME Community Hub
-
One of your team’s spaces on KNIME Business Hub
You can switch to other spaces by:
-
Going to the Home tab and selecting one of the available spaces
-
On the top of the space explorer you can sign in to any of the Hub or Server mount points and select a space. You will see the spaces grouped by owner when on KNIME Hub.
Figure 16. Select a space to explore
In the space explorer you can see:
-
Workflows
-
Folders
-
Data files
-
Components
-
Metanodes
Double click a new empty workflow to open it in the workflow canvas and start adding nodes to the canvas from the node repository.
An overview on components and metanodes is available in the KNIME Components Guide. |
Here you can click the three dots to select one of the following actions within the current space:
-
Create a new folder or a new workflow
-
Import a workflow
-
Add a file
You can also drop files to the canvas. KNIME will create the appropriate file reading node automatically and preconfigure it. Finally you can drop a component to the canvas to use the component in the current workflow.
Select an item from the current space and right click on it to access the item context menu. From here you can Rename, Delete, Export (only available for workflows), Upload (if you are already connected to one of the available Hub mount points) or Connect.
Building workflows
When you create a new workflow, the canvas will be empty.
To build the workflow you will need to add nodes to it by dragging them from the node repository and connecting them. Alternatively you can drag an output port of a node to show the workflow coach which will suggest you the compatible nodes and directly connect them.
Once two nodes are added to the workflow editor, they can be connected by clicking the output port of the first node and release the mouse at the input
port of the second node. Now, the nodes are connected.
For some nodes you might have the ability to add specific ports. When hovering over these nodes you will see a +
sign appearing. Click it to add a port.
If the nodes supports different types of these dynamic ports a list will appear for you to scroll down to select the type of port you want to add.
You can also add a node between two nodes in a workflow. To do so drag the node from the node repository, and release it at its place in the workflow.
Node repository
Currently installed nodes are available in the node repository. You can add a node from the node repository into the workflow editor by drag and drop, as explained in the section Building Workflows.
Search for a node by typing a search term in the search field on top of the node repository, as shown in Figure 18.
By default a specific set of nodes to help you get started with the KNIME Analytics Platform will be shown. You can expand the search results by changing the filter settings of the node repository. Click the icon in the node repository to go to the KNIME Modern UI preferences page and change the default of the nodes included in the node repository search results.
You can view your node repository as icon or list view. To switch between the two states click (icon view) or (list view).
Node description
You can access the node description, with information about the node function, the node configuration and the different ports available for the node in the following ways:
-
Select a node you added in the canvas, go to the side panel navigation and select the first option
-
Hover over a node in the node repository and click the info icon that appears. This will open the node description panel.
Workflow description
The description panel on the left of the KNIME Analytics Platform provides a description of the currently active workflow, or a selected component.
Click the pen icon to change the workflow description, add links to external resources and add tags.
KNIME AI Assistant
KNIME features an AI assistant designed to efficiently answer your queries about the KNIME platform and assist in constructing customized workflows, simplifying your data analysis tasks.
Installation
To install the AI assistant, first locate and open the AI assistant side panel within the KNIME interface, as shown in Figure 20. Then, click the Install AI Assistant button and carefully follow the prompts in the installation menu to complete the setup process.
If you can not find the AI Assistant side panel, the AI Assistant might be deactivated by your administrator.
Deactivation
If you want to deactivate the AI assistant and hide it from the side panel, you can do so by adding this entry to the knime.ini file:
-Dorg.knime.ui.feature.ai_assistant=false
Usage
To access the AI assistant, please log in to KNIME Hub.
If you have access to a KNIME Business Hub instance that is equipped with AI assistant support, you can select the specific instance to use via the AI Assistant preference page and then log in via the AI assistant side panel.
To use the AI assistant, it is mandatory to first accept the terms outlined in the disclaimer. Please note that to deliver our services, KNIME shares data with OpenAI or Microsoft Azure. This includes all user queries, and for the Build mode, it includes the table specifications of selected nodes, such as column names and data types, but not the data itself.
The KNIME AI Assistant offers two modes:
-
A Q&A mode, and
-
A Build mode.
These can be selected using the toggle button located at the top of the side panel.
Q&A mode
In the Q&A mode, you can inquire about KNIME functionalities, including how to execute specific tasks, and receive informative answers.
These answers may feature recommendations for nodes effective in achieving the tasks at hand. If the suggested nodes are already installed, they can be directly dragged into the workflow. For nodes not yet installed, a link to the KNIME Hub is provided. You can then install these nodes via drag-and-drop from KNIME Hub.
By clicking the question mark located at the top of the answer, you will be provided with links to the sources that were used to generate the response.
Build mode
The Build mode is engineered to extend workflows in response to a query. The set of nodes available to the Build mode is currently limited, but many more nodes will be added in the future. It is important to note that in Build mode, workflows cannot currently be initiated from scratch. You are required to select pre-existing nodes that already supply data, and from there, the workflow is dynamically expanded according to the your query.
Node monitor
The Node Monitor tab is located on the bottom part of the user interface shown in Figure 28. It is especially useful to inspect intermediate output tables in the workflow.
Here you can choose to show the flow variables or a preview of the output data at any port of a selected node in the active workflow.
Switch to Statistics in order to see some basic statistics of the data.
You can also detach the table or the statistics view and open it in a new window. To do so click the icon in the respective view (Table or Statistics). This allows you to open multiple table or statistics for multiple nodes in your workflow.
Read more about the data table shown in the node monitor in the KNIME tables section.
Help
By clicking the Help button in the top right corner of the user interface you can see multiple useful links like:
-
Some learning resources like cheat sheets, getting started guide and documentation
-
The KNIME Forum to ask the community about workflow building, tips and tricks
-
About page for KNIME Analytics Platform - also with currently installed version and access to Installation Details
-
Additional credits about open source software components
Customizing the Analytics Platform
Reset and logging
When a node is reset, the node status changes from "executed" to "configured" and the output of the node is not available anymore. When saving a workflow in an executed state, the data used in the workflow are saved as well. That is, the larger the dataset, the larger the file size. Therefore, resetting workflows before saving them is recommended in case the dataset can be accessed without any restrictions.
A reset workflow only saves the node configurations, and not any results.
However, resetting a node does not undo the operation executed before. All
operations done during creation, configuration, and execution of a workflow are
reported in the knime.log
file.
Click Menu > Show KNIME log in File Explorer in the top right corner of the user interface to see the knime.log
location folder.
The knime.log
file has a limited size, and after reaching it the rows will be overwritten from the top.
Configuring KNIME Analytics Platform
Preferences
With the release of KNIME Analytics Platform version 5.1 the preferences have been rearranged. They can be open, from the Modern User Interface by clicking Preferences in the top right corner of the Analytics Platform.
Here, a list of subcategories is displayed in the dialog that opens. Each category contains a separate dialog for specific settings like database drivers, available update sites, and appearance.
KNIME
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 in the quick node adding panel.
KNIME Modern UI
In the KNIME Modern UI category you can:
-
Select which nodes to include in the node repository and node recommendations
-
Select which action is associated with the mouse wheel.
-
Under AI Assistant you can also select which KNIME Hub the AI assistant connects to.
KNIME classic user interface
The KNIME category, contains a subcategory KNIME classic user interface. In this dialog, you can define the console view log level. By default it is set to "WARN", because more detailed information is only useful for diagnosis purposes.
Further below, you can select which confirmation dialogs are shown when using KNIME Analytics Platform. Choose from the following:
-
Confirmation after resetting a node
-
Deleting a node or connection
-
Replacing a connection
-
Saving and executing workflow
-
Loading workflows created with a nightly build
In the same dialog, you can define what happens if an operation requires executing the previous nodes in the workflow. You have these three options:
-
Execute the nodes automatically
-
Always reject the node execution
-
Show a dialog to execute or not
The following options allow you to define whether workflows should be saved automatically and after what time interval, also whether linked components and metanodes should be automatically updated. You can also define visual properties such as the border width of workflow annotations.
Table backend
Starting with KNIME Analytics Platform version 4.3 a new Columnar Backend is introduced, in order to optimize the use of main memory in KNIME Analytics Platform, where cell elements in a table are represented by Java objects by reviewing the underlying data representation.
The KNIME Columnar Table Backend extension addresses these issues by using a different underlying data layer (backed by Apache Arrow), which is based on a columnar representation.
The type of table backend used can be defined:
-
At the workflow level. Open a workflow and select the Open the Description tab from the side panel navigation. Click the icon in the top right corner of the description panel. A workflow configuration dialog will open. Here, in the tab Table Backend you can select the desired backend for this specific workflow from the menu.
Figure 30. Configure a workflow to use Columnar Backend -
As default for all new workflows created. Open the KNIME Preferences and select Table Backend under KNIME in the left pane of the preferences window. Here you can select Columnar Backend as Table backend for new workflows, as shown in Figure 31.
Figure 31. The Table Backend preference page.
The parameters relative to memory usage of the Columnar Backend can also be configured. Go to File → Preferences and select Table Backend → Columnar Backend under KNIME in the left pane of the preferences window, as shown in Figure 32.
Note that the caches of the Columnar Backend that reside in the off-heap memory region require an amount
of memory in addition to whatever memory you have allotted to the heap space of
your KNIME’s Java Virtual Machine via the -Xmx
parameter in the knime.ini
.
When altering the sizes of these cache via the preference page, make sure not to
exceed your system’s physical memory size as otherwise you might encounter system
instability or even crashes.
For a more detailed explanation of the Columnar Backend technical background please refer to this post on KNIME Blog. |
High memory usage on Linux: On some Linux systems KNIME Analytics Platform can allocate more system memory than expected when using the Columnar Backend. This is caused by an unfavorable interaction between the JVM and the glibc native memory allocator. There are multiple options to circumvent this issue.
-
Option 1: Reduce the number of allowed malloc areas
-
Run KNIME Analytics platform with the environment variable
MALLOC_ARENA_MAX
set to 1.
-
-
Option 2: Use jemalloc
-
Install jemalloc on your OS. For ubuntu:
apt install libjemalloc2
(link to package). -
Find the path to jemalloc:
$ ldconfig -p | grep jemalloc
. On Ubuntu 22.04 it is/lib/x86_64-linux-gnu/libjemalloc.so.2
. -
Start KNIME Analytics Platform with the environment variable
LD_PRELOAD=<path to libjemalloc.so from step 2>
.
-
-
Option 3: Use tcmalloc
-
Install tcmalloc on your OS. For ubuntu:
apt install google-preftools
. -
Find the path to tcmalloc:
$ ldconfig -p | grep tcmalloc
. On Ubuntu 22.04 it is/lib/x86_64-linux-gnu/libtcmalloc.so.4
. -
Start KNIME Analytics Platform with the environment variable
LD_PRELOAD=<path to libtcmalloc.so from step 2>
.
-
Setting up knime.ini
When installing KNIME Analytics Platform, configuration options are set to their defaults. The configuration options, i.e. options used by KNIME Analytics Platform, range from memory settings to system properties required by some extensions.
You can change the default settings in the knime.ini
file. The knime.ini
file
is located in the installation folder of KNIME Analytics Platform.
To locate the |
Edit the knime.ini
file with any plaintext editor, such as Notepad
(Windows), TextEdit (MacOS) or gedit (Linux).
The entry -Xmx1024m
in the knime.ini
file specifies how much memory KNIME
Analytics Platform is allowed to use. The setting for this value will depend on
how much memory is available in the running machine. We recommend setting it to
approximately one half of the available memory, but this value can be modified and
personalized.
For example, if the computer has 16GB of memory, the entry might be set to -Xmx8G
.
Besides the memory available, you can define many other settings in the
knime.ini
file. Find an overview of some of the most common settings in
Table 1 or in this
complete list
of the configuration options.
Setting | Explanation |
---|---|
|
Sets the maximum amount of memory available for KNIME Analytics Platform. |
|
Determines which compression algorithm (if any) to use when writing temporary tables to disk. |
|
This setting defines the size of a "small table". Small tables are attempted to be kept in memory, independent of the Table Caching strategy. By increasing the size of a small table, the number of swaps to the disk can be limited, which comes at the cost of reducing memory space available for other operations. |
|
This setting defines which browser should be used to display the layout editor. |
|
Determines whether to attempt to cache large tables (i.e., tables that are not considered to be "small"; see setting |
|
When trying to connect or read data from an URL, this value defines a timeout for the request. Increase the value if a reader node fails. A too high timeout value may lead to slow websites blocking dialogs in KNIME Analytics Platform. |
|
This configuration setting when set to |
|
This configuration setting when set to |
KNIME runtime options
KNIME’s runtime behavior can be configured in various ways by passing options on the command line during startup. Since KNIME is based on Eclipse, all Eclipse runtime options also apply to KNIME.
KNIME also adds additional options, which are described below.
Command line arguments
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.
|
Java system properties
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
.
General properties
|
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Plug-in dependent properties
These properties only affect some plug-ins and are only applicable if they are installed.
|
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KNIME tables
Data table
Very common input and output ports of nodes are data input ports and data output ports, which correspond to the black triangles in Figure 33.
A data table is organized by columns and rows, and it contains a number of equal-length rows. Elements in each column must have the same data type.
The data table shown in Figure 34 is produced by a CSV Reader node, which is one of the many nodes with a black triangle output port for data output. To open the table, click the node. Execute the node if it is not yet execute. The table will show up in the node monitor.
The output table has row numbers, unique RowIDs and column headers. The RowIDs are automatically created by the reader node, but they can also be defined manually. The RowIDs and the column headers can therefore be used to identify each data cell in the table. Missing values in the data are shown by a red question mark in a circle.
At the top of the node monitor you can select which output port you want to view via the tabs and the flow variable tab, which shows the available flow variables in the node output and their current values. Next row will indicate the table dimensions, meaning how many rows and how many columns there are in the table at that specific output port. Here you can also use the toggle to switch to Statistics. This tab shows the meta information of the table, like the columns names, columns types and some other statistics data.
Column types
The basic data types in KNIME Analytics Platform are Integer
, Double
, and
String
, along with other supported data types such as Long
, Boolean
value, JSON
, URI
, Document
, Date&Time
, Bit vector
, Image
, and
Blob
. KNIME Analytics Platform also supports customized data types, for
example, a representation of a molecule.
Switch to the Statistics view in an output table, to see the data types of the columns in the data table, as shown in Figure 35. For numerical values, only the range of the values in the data is shown. For string values, the different values appearing in the data are shown.
The reader nodes in KNIME Analytics Platform assign a data type to each column based on their interpretation of the content. If the correct data type of a column is not recognized by the reader node, the data type can be corrected afterwards. There are nodes available to convert data types. For example: String to Number, Number to String, Double to Int, String to Date&Time, String to JSON, and String to URI.
Many of the special data types are recognized as String
by the reader nodes.
To convert these String
columns to their correct data types, use the
Column Type Auto Cast node.
When you use the File Reader node to read a file you can convert the column types directly via the node configuration dialog. To do so go to the Transformation tab in the configuration dialog and change the type of the desired column, as shown in Figure 36.
Sorting
Rows in the table view output can be sorted by values in one column by clicking the up (ascending) and down (descending) arrow that appears hovering over the column name in the header. Note that this sorting only affects the current output view and has no effect on the node output.
To sort rows in an output table permanently, use the Sorter node. Use the Column Resorter node to reorder columns.
Column rendering
In a table view output, you can also change the way in which numeric values are displayed in a data table. For example, it is possible to display numeric values as percentages, with full precision, or replace digits by a grey scale or bars. To see these and other rendering options for a column, click the carat icon in the column header, and select the desired available renderer, as shown in Figure 37. Note that these changes are temporary and have no effect on the node output.
Table storage
When executed, many KNIME nodes generate and provide access to tabular data at their output ports. These tables might be small or large and, therefore, might fit into the main memory of the executing machine or not. Several options are available for configuring which tables to hold in memory as well as when and how to write tables to disk. These options are outlined in this section.
In-memory caching
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.
Disk storage
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.
Columnar Backend
Starting with KNIME Analytics Platform version 4.3 a new Columnar 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.
Shortcuts
The listed shortcuts are available for the KNIME Modern UI. They cannot be changed at the moment. Eclipse preferences have no impact on them. |
General actions
Action | Mac | Windows & Linux |
---|---|---|
Create workflow |
⌘ N |
Ctrl + N |
Open workflow |
⌘ O |
Ctrl + O |
Close workflow |
⌘ W |
Ctrl + W |
Save workflow |
⌘ S |
Ctrl + S |
Copy |
⌘ C |
Ctrl + C |
Cut |
⌘ X |
Ctrl + X |
Paste |
⌘ V |
Ctrl + V |
Undo |
⌘ Z |
Ctrl + Z |
Redo |
⌘ ⇧ Z |
Ctrl + Shift + Z |
Delete |
⌫ |
Delete |
Copy selected table cells |
⌘ C |
Ctrl + C |
Copy selected table cells and corresponding header |
⌘ ⇧ C |
Ctrl + Shift + C |
Workflow editor modes
Action | Mac | Windows & Linux |
---|---|---|
Selection mode |
V |
V |
Annotation mode |
T |
T |
Pan mode |
P |
P |
Leave Annotation mode and reset to Selection mode |
ESC |
ESC |
Leave Pan mode and reset to Selection mode |
ESC |
ESC |
Execution
Action | Mac | Windows & Linux |
---|---|---|
Execute |
F7 |
F7 |
Cancel |
F9 |
F9 |
Reset |
F8 |
F8 |
Execute all nodes |
⇧ F7 |
Shift + F7 |
Cancel all nodes |
⇧ F9 |
Shift + F9 |
Reset all nodes |
⇧ F8 |
Shift + F8 |
Start step loop execution |
⌘ ⌥ F6 |
Ctrl + Alt + F6 |
Pause step loop execution |
⌘ ⌥ F7 |
Ctrl + Alt + F7 |
Resume step loop execution |
⌘ ⌥ F8 |
Ctrl + Alt + F8 |
Close modern UI configuration dialog without saving the changes (Cancel button) |
ESC |
ESC |
Close modern UI configuration dialog saving the changes (Ok button) |
↵ |
↵ |
Close modern UI configuration dialog and execute node |
⌘ ↵ |
Ctrl + ↵ |
Zooming and panning
Action | Mac | Windows & Linux |
---|---|---|
Fit to screen |
⌘ 1 |
Ctrl + 1 |
Fill entire screen |
⌘ 2 |
Ctrl + 2 |
100% |
⌘ 0 |
Ctrl 0 |
Zoom in |
⌘ \+ |
Ctrl \+ |
Zoom out |
⌘ - |
Ctrl - |
Panning workflow canvas |
hold “SPACE” — while pressing left mouse button canvas can be moved around right or middle click and move |
hold “SPACE” — while pressing left mouse button canvas can be moved around right or middle click and move |
Component and metanode building
Action | Mac | Windows & Linux |
---|---|---|
Create component |
⌘ J |
Ctrl + J |
Expand component |
⌘ ⇧ J |
Ctrl + Shift + J |
Create metanode |
⌘ G |
Ctrl + G |
Expand metanode |
⌘ ⇧ G |
Ctrl + Shift + G |
Rename component or metanode |
⇧ F2 |
Shift F2 |
Open component layout editor |
⌘ D |
Ctrl + D |
Open component layout editor of selected component |
⌘ ⇧ D |
Ctrl + Shift + D |
Enter component or metanode |
⌘ ⌥ ↩ |
Ctrl + Alt + ↵ |
Leave component or metanode |
⌘ ⌥ ⇧ ↩ |
Ctrl + Alt + Shift + ↵ |
Node labels
Action | Mac | Windows & Linux |
---|---|---|
Edit node label |
F2 |
F2 |
Apply changes and leave edit mode |
⌘ ↩ |
Ctrl + ↵ |
Workflow annotations
Action | Mac | Windows & Linux |
---|---|---|
Normal text |
⌘ ⌥ 0 |
Ctrl + Alt + 0 |
Headline 1 |
⌘ ⌥ 1 |
Ctrl + Alt + 1 |
Headline 2 |
⌘ ⌥ 2 |
Ctrl + Alt + 2 |
Headline 3 |
⌘ ⌥ 3 |
Ctrl + Alt + 3 |
Headline 4 |
⌘ ⌥ 4 |
Ctrl + Alt + 4 |
Headline 5 |
⌘ ⌥ 5 |
Ctrl + Alt + 5 |
Headline 6 |
⌘ ⌥ 6 |
Ctrl + Alt + 6 |
Bold |
⌘ B |
Ctrl + B |
Italic |
⌘ I |
Ctrl + I |
Underline |
⌘ U |
Ctrl + U |
Strikethrough |
⌘ ⇧ X |
Ctrl + Shift + X |
Ordered list |
⌘ ⇧ 7 |
Ctrl + Shift + 7 |
Bullet list |
⌘ ⇧ 8 |
Ctrl + Shift + 8 |
Add or edit link |
⌘ K |
Ctrl + K |
Bring workflow annotation to front |
⌘ ⇧ ↑ |
Ctrl + Shift + ↑ |
Bring workflow annotation forwards |
⌘ ↑ |
Ctrl + ↑ |
Send workflow annotation to the back |
⌘ ↓ |
Ctrl + ↓ |
Send workflow annotation backwards |
⌘ ⇧ ↓ |
Ctrl + Shift + ↓ |
Quick nodes adding
Action | Mac | Windows & Linux |
---|---|---|
Open quick nodes panel and switch through ports |
⌘ . |
Ctrl + . |
Select node |
via ↑ ← ↓ → |
via ↑ ← ↓ → |
Enter selected node |
↩ |
↵ |
Comments and annotations
You have two options in the workflow editor to document a workflow:
Node label - Add a comment to an individual node by double clicking the text field below the node and editing the text
Workflow annotation - Add a general comment to the workflow, right click the workflow editor and select New workflow annotation in the menu. Now a text box will appear in the workflow editor.
T
to enter annotation mode.Double click the workflow annotation to add text and format the text and change the color of the annotation outline. To change the format you can use the annotation bar or use the following syntax:
To create a heading, add number signs (
#
), followed by a space, in front of a word or phrase. The number of number signs you use should correspond to the heading level (<h1>
to<h6>
).To create a bullet list, add a star sign (
*
) followed by a space.To create a numbered list, add a number followed by a point (
1.
), followed by a space.To make a text bold, italic, or underlined, select the text and press CTRL+b, CTRL+i, CTRL+u.
Finally you can click outside the annotation and click the annotation once again to move it around the canvas or to change its dimensions.