beam | Unified programming model to create a data processing

Multiple things could cause this behaviour, because the pipeline runs fine with less Data, analysing what has changed could lead us to a resolution.

The third line of the logs you provide might indicate that you're processing unclean data in your bigger pipeline mmap(NULL, could mean that | "Get Content" >> beam.Map(lambda x: x.read_utf8()) is trying to read a null value.

Is there an empty file somewhere ? Are your files utf8 encoded ?

I'm guessing using the fileio.ReadMatches() will try to load into memory the whole file, if your file is bigger than your memory, this could lead to errors. Can you split your data into smaller files ?

If files are too big for your current machine with a DirectRunner you could try to use an on-demand infrastructure using another runner on the Cloud such as DataflowRunner

In a streaming pipeline, Dataflow retries work items running into errors indefinitely.

The code itself does not need to have retry logic.

Because that is not possible (or at least very hard) to do in the current design. Atoms are important part of:

• modules, as module names are atoms
• function names, which also are atoms
• distributed Erlang also extensively use atoms

Especially last point makes it hard. Imagine for second that we would have a GC for atoms. What would happen if there would be a GC cleanup in between the distributed call where we send some atoms over the wire? All of that makes atoms quite essential to how VM works and making them GCed would not only make implementation of VM much more complex, it would also make code much slower as atoms do not need to be copied between processes and as these aren't GCed, these can be completely omitted in GC mark step.

If you are using the Java SDK, you can use a transform Watch to achieve this. I don't see a counterpart in the Python SDK though.

I think it's better to write a program polling the files in the GCS directory. When 8 parts of a file is available, publish a message containing the file name to Pub/Sub or similar product.

Then in your Beam pipeline, use the Pub/Sub topic as the streaming source to do your ETL.

If the condition if(hit.collider.gameObject.tag == "Mirror") is not met, you are doing a lightPoints.Add(hit.point); and upadting the beam, adding points to the LineRenderer component position array also. That does not seem a good idea, as presumably you will get to the point when the ray does not hit anymore. As is, once you get to that point you keep on adding points, leading to the stack overflow.

I would add some safegard condition, where you stop adding points to your lists/arrays if you dont hit a gamobject of interest, maybe a determined ray length, or a condition that avoids the point to be added if the ray does not hit.

I checked the line of the error you are having in the script itself with not very revaling info. But you got the script there in case that helps.

Probably the wrapper-runner script generated by Bazel (you can find path to it by calling bazel build on a target) restrict set of modules available in your script. The proper approach is to fetch PyPI dependencies by Bazel, look at example

OK, so my initial solution for that is to use Stateful DoFn like this (I'm using Scio's Scala SDK as mentioned in the question):

I think, you just need to figure out string concatenation.

You need to check for duplicate model names as well. Because Abaqus replaces the already existing model if you create a model with duplicate name.
To address this issue, you can use dictionary object in following way:

First of all, you shouldn't save it as CSV if you don't actually use CSV's features, or its features would drive you nuts. Instead, you can save as a plain text file with the header prepended into original dataframe.