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Python's pickle module provides a simple way to serialize and deserialize complex objects such as lists, dictionaries, and class instances, allowing them to be saved to or loaded from files or byte streams, with examples of pickling, unpickling, using dumps/loads, protocol versions, and security considerations.
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This article introduces Python's built-in pickle module, explains its four core functions (dumps, loads, dump, load), details their parameters and usage, and provides code examples for serializing and deserializing objects to memory and files, while noting performance limitations and alternatives such as ZODB.
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This article explains why storing data in a container layer is problematic and details Docker's three persistent storage options—volumes, bind mounts, and tmpfs—along with their usage commands, scenarios, and advanced features such as bind propagation, SELinux labels, and volume drivers.
The article explains the various storage methods for container cloud platforms, highlights why storage is critical for data safety and business continuity, and outlines key factors such as persistent data needs, performance, scalability, and product selection for cloud‑native environments.
This article explains the Linux kernel writeback path, detailing how data travels from user space through page cache, kernel buffers, and the disk controller, and shows how to tune dirty page thresholds and writeback threads for optimal performance.