Lets say we have spark cluster with below configuration.

I encountered a strange error. All of the value of a column become completed same after toPandas().

I have a dataframe having 3 columns, two boolean type and one column as string.

I am learning about Spark and came across the following thing. I create a simple dataframe using range.

I have a big parquet table stored in multiple partitions, and each partition having multiple files inside of it, like:

I have a container running Spark on port 7077, and got the container IPAddress through Docker inspect command, and I’m trying to create a SparkSession through my host machine (I’m using Jupyter on my host) on the container’s cluster.

I am trying to read the following json data which is inside a data.json file:

I am writing an iterative algorithm and I am using localCheckpoint to break the dataframe lineage as well as to persist the data for next iteration. The problem is unpersist doesn’t seem to work on local checkpointed dataframe, and thus the local checkpointed dataframe is never cleared (the storage UI also confirms that).

I have a dataframe with metadata df_meta that has the uri of text files with the content that I require:
| id | s3_uri |
| ——– | ————– |
| 1 | s3://bucket1/prefix |
| 2 | s3://bucket2/another_prefix |
| 3 | s3://bucket3/a_different_prefix |

Thank you for making time to answer this question.