NodePit Runner

Run Your Workflows and Scripts Around The Clock!

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Frequently Asked Questions

This page contains answers to our most frequently asked questions. If you cannot find what you are looking for, please get in touch with us.

In which way are you affiliated with the KNIME AG?

We are not affiliated with the KNIME AG. We deliberately are not a “KNIME Partner”, but an independent company. We strongly believe that only this way we can provide you with the best and most innovative solutions and services and avoid any conflicts of interest which would stem from a coupling with the KNIME AG.

My runs fail with workflows containing visualization nodes. What shall I do?

If you are using KNIME nodes from the KNIME Views or KNIME JavaScript Views extensions such as the Line Plot or Line Plot (JavaScript) nodes in your workflow, runs may fail with one of the following errors:

  • ERROR:elf_dynamic_array_reader.h(64) tag not found
  • Execute failed: No more handles [gtk_init_check() failed]

This is caused by a bug in the underlying Chromium browser embedded in the KNIME Views and KNIME JavaScript Views extensions and can be solved as follows:

  1. Open the “Environment” menu and select the environment you'd like to use to run your workflow.

  2. Use the “Extension Search” to add the extension KNIME Bundled Chromium Browser (legacy).

  3. Under “Preference Entries”, manually add the following preferences:

    • /instance/org.knime.js.core/js.core.headlessBrowser=org.knime.ext.seleniumdrivers.multios.ChromiumImageGenerator
    • /instance/org.knime.js.core/js.core.viewBrowser=org.knime.ext.seleniumdrivers.multios.ChromiumWizardNodeView

  4. Save your environment and re-run your schedule successfully.

My runs fail because of insufficient Java heap space. How can I increase it?

KNIME runs with a fairly low heap space limit by default. If you're getting java.lang.OutOfMemoryError: Java heap space errors during runs, you need to increase the Java heap space in your environment. This can easily be done as follows:

  1. Open the “Environment” menu and select the environment for which you'd like to increase the heap space.

  2. Go to section “Additional vmargs” and add a new parameter -Xmx4g (e.g. here you set the available heap space to 4 GB). Make sure that the value corresponds to the available memory on your host machine.

  3. Save your environment and re-run your schedule successfully.

Attention: Especially if you’re using Workflows or Nodes which make use of the “Columnar Backend” do not assign all your memory as heap via the -Xmx parameter, otherwise there’ll not be enough space for the columnar backend which can lead to crashes when executing the workflow. Also keep your eyes open for a warning message in the execution log which says “Configured heap memory limit does not leave enough free memory for the columnar backend off-heap memory.”

How do I add R to my environment?

If you need the R programming language in your environment, for example because your KNIME workflow contains nodes from KNIME Interactive R Statistics Integration, you can easily install R as follows:

  1. Open the “Environment” menu and select the environment for which you'd like to install R for.

  2. Go to section “Additional Dockerfile Instructions” and add the below snippet. This will install r-base and Rserve into your environment. R is then available via /usr/lib/R and should be automatically discovered by the KNIME Interactive R Statistics nodes.

    RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -yq r-base && rm -rf /var/lib/apt/lists/* && R -e "install.packages('Rserve',,'http://rforge.net/',type='source')"

  3. Save your environment and run your schedule.

How do I run the Selenium Nodes?

To run Selenium Nodes workflows for web scraping, task automation, or testing with the NodePit Runner, the following steps are needed:

  1. Make sure to select “Chrome for Testing (bundled)” in your workflow’s Start WebDriver or WebDriver Factory node.

  2. Open the “Environment” menu and create a new environment or chose an existing one to edit.

  3. Use the “Extension Search” to add the extensions Selenium Nodes for KNIME and Selenium Nodes for KNIME: Chrome for Testing.

  4. Under “Preference Entries”, manually add a preference entry /instance/com.nodepit.licensing.plugin/ws.palladian.nodes.selenium.plugin= with your license code as value. You’ll need to manually edit the license key before pasting it so that it is on one single line: Replace line breaks with \n and prepend = characters with a \, i.e. \=

  5. Under “Additional vmargs” add the following:

    -Dseleniumnodes.chromium.args='--headless=new --no-sandbox --disable-dev-shm-usage'

  6. Save your environment and run your schedule.

In case of questions, please reach out to mail@nodepit.com — we’re happy to help!

Where do I find the REST API?

NodePit Runner can be fully controlled via its REST API. When running NodePit Runner locally, the OpenAPI specification can be found via http://localhost:8080/api/docs/json, a Swagger UI is accessible via http://localhost:8080/api/docs/.

For authorization, please request the POST /users/login operation to receive a valid JWT token which can be used to access all other operations. You can then provide the JWT token as header Authorization: Bearer <token> in your requests.

We also provide the NodePit Runner Nodes to control NodePit Runner from within the KNIME Analytics Platform. An example workflow including authentication and data collection can be found here.

Can I use the Python scripting nodes within my workflows?

Using Python scripting nodes is generally possible. We strongly recommend to use the new Python nodes which simplify the setup and portability, as they include a bundled Python environment.

In contrast, the “Python (legacy)” nodes require you to setup Python yourself, which is tedious and brittle – while theoretically possible, we advice against this and cannot give any official support for that.

If you have workflows using the “legacy” nodes, you can usually migrate them to the new Python nodes with a few simple modifications. Please see here for more details.

To install the Python nodes within your environment, perform the following steps:

  1. Open the “Environment” menu and create a new environment or chose an existing one to edit.

  2. Use the “Extension Search” to add the extension KNIME Python Integration. Make sure that the feature org.knime.features.python3.scripting.feature.group has been added.

  3. Under “Preference Entries”, manually add a preference entry /instance/org.knime.python3.scripting.nodes/pythonEnvironmentType=bundled.

If the Python environment bundled with KNIME is not suitable for you due to specific version or dependency requirements, follow the steps below to install and set up a fresh Python installation:

  1. Open the “Environment” menu and create a new environment or select an existing one to edit.

  2. Use the “Extension Search” to add the extension KNIME Python Integration. Make sure that the feature org.knime.features.python3.scripting.feature.group has been added.

  3. Go to the “Additional Dockerfile Instructions” section and add the below snippet. This will install Python 3.9 and add some dependencies required by KNIME (here: py4j, pyarrow, numpy, and pandas). Please adapt the script according to your needs (e.g. Python version and dependencies). You are free to add additional dependencies to the final RUN pip3.9 install command.

     ENV DEBIAN_FRONTEND=noninteractive
     RUN apt update && apt install -y software-properties-common
     RUN add-apt-repository ppa:deadsnakes/ppa
     RUN apt update && apt install -y python3.9 python3.9-distutils python3.9-venv
     RUN python3.9 -m ensurepip
     RUN pip3.9 install py4j pyarrow numpy pandas

  4. Under “Preference Entries”, manually add the preference entries /instance/org.knime.python3.scripting.nodes/pythonEnvironmentType=manual and /instance/org.knime.python3.scripting.nodes/python3Path=python3.9. Please make sure that the Python 3 path matches your snippet in step 3 (e.g python3.9).

In case of questions, please reach out to mail@nodepit.com — we’re happy to help!

I’m running my Executor on an ARM Mac and my runs fail with “Errors were encountered while processing: ca-certificates”

This seems to be a bug with Rosetta on M2 and M3 processors. For now, please disable the option Use Rosetta for x86_64/amd64 emulation on Apple Silicon in your Docker Desktop settings (see here).

How can I change the database password for an already running, deployed instance?

Assuming that you use the setup instructions from here, you can perform the following steps if you need to change the MongoDB password of an already running instance:

  1. Stop the API container:

     docker compose stop api

  2. Use the MongoDB shell to change the password:

     docker compose exec mongodb mongosh -u root -p
     # enter your current DB password
     use admin
     db.changeUserPassword('root', passwordPrompt())
     # enter your new DB password
     exit

  3. Update docker-compose.yml → api → DB_URL and replace the existing password with the newly chosen one.

  4. Start the API container with the updated changes:

     docker compose up -d api

How do I add a vendor-specific database driver?

For some databases, KNIME Analytics Platform does not provide JDBC drivers (e.g. OracleDB). For these cases, you need to register a vendor-specific JDBC driver. Follow the following steps to set this up:

  1. Download the JDBC driver from the vendor's website. Usually this is a jar file (e.g ojdbc8.jar).

  2. Register the database driver in your local KNIME Analytics Platform according to the KNIME Database Extension Guide and make sure your workflow is able to connect to your database.

  3. Export your KNIME preferences via KNIME → Export Preferences….

Once you can confirm the database connection works locally, we port this configuration to NodePit Runner:

  1. Open the “Environment” menu and select the environment for which you'd like to set a driver.

  2. Go to section “Additional Files” and upload the driver file. When executing your workflow, the JDBC driver file will become accessible in folder /nodepit (e.g. /nodepit/ojdbc8.jar).

  3. Go to sections “Preference File” or “Preference Entries” and configure your JDBC driver. Make sure the driver path points to your driver file in folder /nodepit (e.g /nodepit/ojdbc8.jar). Please check your local preferences from step 3 for the proper preference keys, e.g. the following example sets the driver path for a driver named OracleDB: /instance/org.knime.database/drivers/OracleDB/paths/0=/nodepit/ojdbc8.jar

  4. Save your environment and run your schedule.

Which KNIME versions can I run with NodePit Runner?

We have successfully tested NodePit Runner with KNIME 3.0 (released in 2015) up to the current KNIME 5.4.3 and “nightly” versions. NodePit Runner is designed to allow you running different KNIME versions in parallel – you can selectively try old or new versions without affecting all your workflows.

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