# Troubleshooting Build Memory Issues

> Troubleshooting Memory Issues

*Source: https://docs.sisense.com/main/SisenseLinux/troubleshooting-memory-issues.htm*

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Last updated: June 11, 2026

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## Out-of-Memory Build Issues

When building an ElastiCube with connectors that use the JVM framework, you might receive an "out of memory" error. This could be a result of your computer not having enough memory allocated to run all the necessary processes, or your computer might need more RAM. For more information about minimum requirements, see [Minimum Requirements for Sisense in Linux Environments](https://docs.sisense.com/main/SisenseLinux/linux-minimum-requirements.md).

Information about other build-related memory issues can be found on the Community site, such as [Managing Linux Build Service Settings for Optimal Performance and Stability](https://community.sisense.com/t5/knowledge/managing-linux-build-service-settings-for-optimal-performance/ta-p/4876).

### Allocating Memory According to Connector Deployment Type

The process for changing the memory allocations for your connectors depends on the connector deployment type as found in the Data Group settings.

To see which connector deployment type your system is using:

1. Open the **Admin** tab and search for and select **Data Groups** (located under **Server & Hardware**) or go to `http://<IP address:Port>/app/settings/datagroups`.
2. Click the Edit icon of the Data Group. The Edit Data Group window is displayed.

   ![Data group connector running mode](https://docs.sisense.com/main/Resources/Images/data-group-connector-running-mode.png)
3. Note the Connector Running Mode (i.e., the connector deployment type) to determine which instructions to follow below.

Connector deployment types (“Connector Running Mode”):

- Internal - This connector deployment type supports **both**:

  - Connectors running under the new connectors framework. See [New Connectors Framework](https://docs.sisense.com/main/SisenseLinux/new-connectors-framework.md) for more information. The new connectors framework (and its connectors) are the default beginning with release 8.2.4.
  - Older JVM connectors that are deployed in the Kubernetes `connectors` pod.)
- System - This connector deployment type only supports the older JVM connectors that are deployed in the Kubernetes `connectors` pod.

#### Memory allocation for JVM connectors deployed in Kubernetes

To allocate more memory for the (older) JVM connectors you must increase the amount of memory allocated for **both** the:

- JVM of the connectors
- Kubernetes connectors pod where the `connectors` JVM is running

**JVM settings:**

1. Open **Admin**, search for and select **System Management** which is located under **Server & Hardware**, and click **File Management** or go to `http://<IP address:Port>/app/explore`.
2. Navigate to the `connectors` folder and open the `configuration.json` file.

   **Note:**

   The `configuration.json` file **will not be found when** you are using the new connectors framework **and** no JVM connectors are deployed. (Which should only be possible for the Internal connector deployment type.)
3. In the line that starts with "jvmParameters:", look for a parameter that starts with '-Xmx', such as '-Xmx2G' or '-Xmx500M'. This parameter indicates the maximal heap size of the JVM.

   For example:

   `"jvmParameters": ["-Xmx8g", "-server", "-Dfile.encoding=UTF-8"],`

   When this parameter does not exist, the heap size is set by default to one-third of the computer's RAM. To modify the default size, add the parameter from this example into your `configuration.json` file and modify the `-Xmx` value according to your needs.

   For additional information about the -Xmx parameter, see [What are the -Xms and -Xmx parameters when starting JVM?](https://stackoverflow.com/questions/14763079/what-are-the-xms-and-xmx-parameters-when-starting-jvm).

**Pod settings:**

1. The JVM runs inside a pod, so the pod's memory limit should be updated to match or exceed the updated JVM Xmx memory limit. To change the pod's memory limit:

   Edit the deployment settings of the `connectors` pod:

   `kubectl -n sisense edit deployment connectors`

   In the **Limits** section, set the **Memory** value to at least the same size as the JVM memory.
2. Restart the JVM service.

   `kubectl -n sisense delete pod -l app=connectors`
3. Verify that the JVM parameters you specified were accepted. The JVM parameter values are in the Command Line column for the Java processes.

Use these Kubernetes CLI commands to check your memory allocation configuration changes:

1. Run this command to print the metadata for the connector processes in the `connectors` pod:

   `kubectl exec $(kubectl -n sisense get pod -l app=connectors -o custom-columns=":.metadata.name")-n sisense -- ps -eo args --no-headers | grep ContainerLauncherApp | awk '{print $10 " " $2}'`
2. Run this command to check the older JVM connectors in the Kubernetes `connectors` pod (container) during a build:

   `kubectl exec $(kubectl get po -l mode=build -o custom-columns=":.metadata.name,:.spec.containers[1].name" --no-headers -n sisense| awk '{print $1" -c " $2}') -n sisense -- ps -eo args --no-headers | grep ContainerLauncherApp | awk '{print $10 " " $2}'`
3. Check that all the connectors are running and that the JVM value is correct.

## High Memory Consumption Due to Many Active Connectors

If you have many active JVM connectors, (and you are not using the new connectors framework) you might experience memory consumption issues. To reduce the number of active JVM connectors, disable the connectors you are not using.

Note:

This issue was resolved by the new connectors framework. See [New Connectors Framework](https://docs.sisense.com/main/SisenseLinux/new-connectors-framework.md) for more information.

**To disable unused connectors:**

1. Open the `usedConnectors.json` file in the `connectors` folder.

   Note:

   The `usedConnectors.json` file **will not be found when** you are using the new connectors framework **and** no JVM connectors are deployed. This should only be possible for the Internal connector deployment type. (See [Allocating Memory According to Connector Deployment Type](#Allocati) for more information.)
2. Add or remove connectors to the `usedConnectors.json` file.
3. Set the Custom connectors field to **TRUE**:

- TRUE - Only connectors in the "displayConnectors" are active. (Only active connectors consume memory.)
- FALSE - All connectors are active.

4. Restart the `connectors` pod:

   `kubectl -n sisense delete pod -l app=connectors`