Скачать или смотреть How to Solve pickle unserializable Python Objects: A Guide to Handling BufferedReader Errors

Discover how to effectively manage `pickle` serialization errors with BufferedReader objects in Python. Learn techniques to serialize your data and avoid common pitfalls!
---
This video is based on the question https://stackoverflow.com/q/63372768/ asked by the user 'rkm19' ( https://stackoverflow.com/u/13580544/ ) and on the answer https://stackoverflow.com/a/63372864/ provided by the user 'Avery' ( https://stackoverflow.com/u/1067211/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: pickle unserializable python objects

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding Pickle and Serialization in Python

Serialization is the process of converting an object into a byte stream, which can then be stored or transmitted. In Python, the pickle module is a popular way to perform serialization. However, not all objects can be easily pickled. For example, you may encounter issues when attempting to pickle certain objects like BufferedReader. This guide discusses common errors you might face and offers solutions for serializing objects that include components that are often non-pickleable.

The Problem: Pickling BufferedReader Objects

Let's dive into the scenario. A user has a custom object that is part of a module for quantifying chemical compounds. The output from one of its functions includes a BufferedReader instance, which leads to the error:

[[See Video to Reveal this Text or Code Snippet]]

The user even tried using dill, another serialization library, but experienced the same issue. So, why does this happen?

Why BufferedReader Can't Be Pickled

The root of the problem lies in what BufferedReader actually represents. A BufferedReader is a way to read data from a source (like a file or a stream), but it doesn't itself contain the data. Instead, it merely acts as a pointer to where that data is located. Since pickle needs actual data to serialize and not a reference to a source, it can't handle BufferedReader objects.

Solution: Serializing the Underlying Data

Now that we understand the issue, let's explore how to circumvent this problem effectively. Here are a couple of approaches you can take:

1. Read and Serialize All Data

If the data being read by the BufferedReader can be fully obtained, you should consider reading the entire data into a variable and then serializing that variable instead of the BufferedReader.

Steps to Serialize Data:

Open the file or data source.

Read the data into a string or bytes object.

Use pickle to serialize the string/bytes object.

Example Code:

[[See Video to Reveal this Text or Code Snippet]]

2. Save Instructions Instead of Data

In cases where the data source represents an unending stream or is too large to entirely read into memory, another solution is to save only the instructions needed to recreate the desired state later.

Possible Approaches:

Save the file path and method calls required to generate the data.

Consider using a configuration object that contains parameters for the calculations instead.

Example Instructions:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

While serializing Python objects using pickle can be a straightforward process, encountering types that cannot be serialized, such as BufferedReader, can be frustrating. However, with the strategies outlined in this post, you can effectively handle these challenges by either extracting and serializing the underlying data or saving instructions to reconstruct the desired data later.

Feel free to tailor your approach based on your specific use case and remember that sometimes, avoiding serialization altogether may be the simplest solution!