Adventures in Machine Learning

Efficient Data Storage in Python: Pickle SQLite3 and SqliteDict

When working with Python, it is common to encounter scenarios where we need to store data for future use or to share it with others. Luckily, Python provides us with several options for storing data that are simple and effective.

In this article, we will explore two popular methods: using Pickle to store Python objects and using SQLite3 for persistent database storage. These methods vary in their use cases, advantages, and limitations.

1) Storing data using Pickle

Pickling is the process of converting a Python object into a byte stream and unpickling is the process of converting that byte stream back into an object. Pythons Pickle library provides an easy way to serialize and deserialize complex Python objects.

This means that we can save our Python objects to a file and reload them whenever we need them.

Using Pickle to store Python objects

By using the Pickle library, we can store almost any Python object in a file. This makes Pickle a great option for storing complex data structures that cannot be easily stored as text.

To use Pickle, we first need to import the pickle library. Then, we can use the `pickle.dump()` method to convert our Python object to a byte stream and write it to a file.

Usage of pickle.dump() and pickle.load()

Once we have saved the pickled object to a file, we can retrieve it later using `pickle.load()`. This method reads in the byte stream from the file, converts it back to a Python object, and returns the object.

It is important to note that the order in which you pickle and unpickle objects is crucial. You must unpickle the objects in the same order that you pickled them, or else you will not get the same result.

Advantages of using Pickle

– Cross-platform support: Pickle is a built-in library in Python and therefore works on any platform that Python supports. – Easy to use: Using Pickle is easy and straightforward.

You only need to import the library and use two methods. – Can pickle most Python objects: Pickle is able to pickle most Python objects, including user-defined classes.

– Fast: Pickling and unpickling are relatively fast processes that can be executed in just a few lines of code.

Limitations of using Pickle

– Security issues: Pickling and unpickling can pose security risks since the byte stream can potentially execute malicious code. It is not recommended to pickle and unpickle data from untrusted sources.

– Data versioning: If the Python objects code changes in the future, the pickled data may no longer be compatible with the current code. You must ensure to always use the same codebase when unpickling data.

2) Using SQLite3 for persistent database storage

Sometimes, we need a more robust way to store data than just a file. Enter SQLite3, a lightweight and self-contained database engine that provides us with a way to store and retrieve data in a structured Sqlite3 library for persistent storage

SQLite3 is a versatile library that allows us to create and manage a database engine with SQL.

It is a serverless library, meaning that we dont need to install and configure a dedicated database server. Instead, the entire database engine is saved to a single file, making it perfect for small projects.

Serialization and de-serialization of Python objects for database storage

To save and retrieve our Python objects in SQLite3, we need to serialize and deserialize them. In other words, we need to convert our Python objects to a format that can be stored in the database.

The most common way to do this is to convert Python objects to JSON and back. JSON is a lightweight and readable format that is easy to work with.

Advantages of using SQLite3

– Lightweight: SQLite3 is a lightweight and self-contained database engine that doesnt require a dedicated server. – Easy to use: Using SQLite3 is easy and straightforward.

You can create and manage a database engine in just a few lines of code. – Portable: SQLite3 works on any platform that supports Python.

– Structured: SQLite3 allows us to store data in a structured manner and retrieve it using SQL queries.

Limitations of using SQLite3

– Limited scalability: Since SQLite3 is not a client-server database engine, it may not be able to handle large-scale applications. – Concurrent access: SQLite3 doesnt support concurrent write access.

If two processes attempt to write to the database at the same time, it can cause issues. – Bigger databases: As the database size grows, it may slow down database operations.


In conclusion, there are several techniques we can use to store data in Python. Pickle is a simple and efficient way to save and load Python objects.

SQLite3 is an excellent option for more complex data structures because it offers structured storage and retrieval of data using SQL queries. Understanding the advantages and limitations of each technique can help us choose the right method for our use case.

3) Using SqliteDict for persistent cachingto SqliteDict for persistent caching

When working with large datasets or performing computationally expensive operations, it is common to use memory caching to speed up our code. Memory caching involves temporarily storing data in memory for fast access, but sometimes this strategy does not work, or it is not feasible to use memory caching.

This is where SqliteDict comes in – SqliteDict is a persistent cache that allows us to store data on disk and retrieve it efficiently for future use. Usage of key:value mapping to store/retrieve data

SqliteDict works by mapping key-value pairs to corresponding SQLite database entries.

That is, every key we add to the cache is mapped to a corresponding value, which we can access at a later time. The key-value pairs in the cache act like a dictionary, hence the name SqliteDict.

Using SqliteDict is very simple. First, you need to install the SqliteDict package.

Then, you can create a new instance of SqliteDict as shown below:


from sqlitedict import SqliteDict

my_cache = SqliteDict(‘./my_cache.sqlite’)


Once you have created an instance of SqliteDict, you can start storing key-value pairs. This is done using the same syntax you would use for a dictionary:


my_cache[‘key1’] = ‘value1’

my_cache[‘key2’] = ‘value2’

my_cache[‘key3’] = [1, 2, 3]


You can retrieve the values later using the same keys:


print(my_cache[‘key1’]) # Output: value1

print(my_cache[‘key3’]) # Output: [1, 2, 3]


Advantages of using SqliteDict

– Persistence: One of the main advantages of using SqliteDict is that it provides persistence. Persistent caching ensures that we don’t lose the cached data even if our program crashes or is stopped.

This saves us the time and resources needed to recompute the data from scratch. – Efficiency: Since SqliteDict is stored on disk, it can handle large datasets and still provide fast access times.

This makes it ideal for use in applications where memory caching may not be practical, such as when working with large datasets. – Easy to use: SqliteDict is simple to use and can be easily integrated into existing applications.

It is designed to be used like a dictionary, meaning that you can use the same syntax you are used to for working with dictionaries. – Thread-safe: SqliteDict is thread-safe, meaning that multiple threads can safely write to the cache at the same time.

This makes it a good option for use in threaded programs.

Limitations of using SqliteDict

– Database size: As with any database, the size of the SqliteDict database can become a limiting factor. If you are working with very large datasets, you may need to consider a more robust database solution.

– Speed: Although SqliteDict is efficient, it may not be as fast as memory caching for smaller datasets. However, for large datasets, it is still faster than recomputing the data from scratch.

– Disk IO: SqliteDict relies on disk IO to store and retrieve data, which can be slower than memory-based caching. However, for large datasets, this is still the best option.


In conclusion, SqliteDict is a powerful caching option for Python applications. Its ability to persist data on disk makes it ideal for use in situations where memory caching is not practical, and its simple interface and compatibility with existing Python dictionary functions make it easy to use in code.

While it may not be as fast as memory caching for smaller datasets, it is still much faster than recomputing data from scratch, making it an excellent option for caching in Python applications. In this article, we explored three popular methods for storing data in Python: using Pickle to store Python objects, using SQLite3 for persistent database storage, and using SqliteDict for persistent caching.

Pickle is an easy way to save and load complex Python objects, SQLite3 allows for structured storage and retrieval of data using SQL queries, and SqliteDict provides persistent caching for large datasets. Each method has its own advantages and limitations, and understanding these can help us choose the right method for our use case.

Overall, the article emphasizes the importance of data storage and provides practical takeaways for Python developers to store and retrieve data efficiently and effectively.

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