How does the Faker library perform in generating data?
The Faker library is designed to generate large amounts of fake data quickly and efficiently. Its performance can vary based on the complexity of the data being generated.
What factors affect its performance?
Factors include the type of data being generated, the number of records, and the system's resources. More complex data types may take longer to generate.
Can it handle multi-threading for better performance?
Yes, Faker can be used in a multi-threaded environment to improve performance, especially when generating large datasets.
Are there any benchmarks available for its performance?
Yes, there are benchmarks available that compare Faker's performance with other data generation libraries, showing its efficiency in various scenarios.
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