Adding the Known Issues post.

Adjusting the author id.

Author: 7homasSutter

_data/authors.yml

@@ -6,9 +6,9 @@
 #   url: {homepage_of_author}
 # -------------------------------------
 
-cotes:
+Tom:
   name: Thomas Sutter
-  twitter: Me7e0r
+  twitter: @Me7e0r
   url: https://github.com/7homasSutter/
 
 

_posts/2024-04-02-exploring-the-api.md

@@ -4,6 +4,7 @@ author: tom
 date: 2024-04-02 13:37:00 +0800
 categories: [Tutorial, API]
 tags: [getting started, api, tutorial]
+toc: true
 ---
 
 All the examples in this tutorial use the graphql API available under 

_posts/2024-07-08-adding-a-static-analyzer.md

@@ -4,6 +4,7 @@ author: Tom
 date: 2024-07-08 13:37:00 +0800
 categories: [Tutorial, Static-Analysis]
 tags: [getting started, static-analysis]
+toc: true
 ---
 
 In this tutorial, we will show you how to add a new static analyzer (python based) to FMD and scan a couple of apk 

_posts/2024-07-11-Known-Issues.md

@@ -0,0 +1,28 @@
+---
+title: Known Issues
+author: Tom
+date: 2024-07-11 13:37:00 +0800
+description: Short summary of the post.
+categories: [Issues, Troubleshooting]
+tags: [Issues, Troubleshooting, Problems, Bugs]
+toc: true
+comments: false
+---
+
+### Work-horse terminated unexpectedly
+**Issue**: The worker container is terminated unexpectedly with the following error message:
+```
+extractor-worker-high-1  | 06:32:32 Moving job to FailedJobRegistry (Work-horse terminated unexpectedly; waitpid returned 9 (signal 9); )
+extractor-worker-high-1  | 06:32:32 Cleaning registries for queue: high-python
+```
+- **Solution**: This issue is most likely caused by the worker container running out of memory. 
+You can increase the memory limit for the worker container by setting the `DOCKER_MEMORY_LIMIT` environment variable in 
+the `.env` file. For example, to set the memory limit to 20GB, add the following line to the `.env` file:
+  ```
+  DOCKER_MEMORY_LIMIT=20GB
+  ```
+After setting the memory limit, restart the worker container to apply the changes. Depending on your operating system
+you might need to adjust the memory limit of the docker daemon as well.
+
+- **Workaround**: If increasing the memory limit does not solve the issue, you can try to reduce the number of workers
+or the number of elements in the queue to process.