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❔ Interview Question

What are optimistic and pessimistic locking in the context of databases?

Answer: These are two approaches to managing concurrent data access.

Pessimistic locking assumes conflicts are likely. Therefore, data is locked immediately upon reading or writing and remains locked until the end of the transaction. This prevents concurrent modifications but reduces scalability and can lead to deadlocks.

Optimistic locking assumes conflicts are rare. Data is read without locking, and before committing changes, a version check is performed to see if someone else has modified the data. If so, the transaction is rolled back and retried. This approach offers better performance under low contention.

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❔ Interview question

What is ORM and what is SQLAlchemy used for?

Answer: ORM (Object-Relational Mapping) allows interacting with the database through Python classes and objects instead of writing SQL queries manually.

With SQLAlchemy, you can describe tables as classes, rows as objects, and perform SELECT, INSERT, UPDATE, DELETE operations through Python methods.

This simplifies working with databases, makes the code more readable, reduces the risk of SQL injections, and facilitates maintenance and migrations.

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❔ Interview Question

What is the difference between pass, continue, and break?

Answer:

▶️ pass — this is a no-op, which does nothing. It is used as a placeholder when syntax requires the presence of code (for example, inside a function, class, or condition), but the logic is not yet implemented

▶️ continue — terminates the current iteration of the loop and moves to the next, skipping the remaining code in the loop body

▶️ break — completely terminates the execution of the loop, exiting it prematurely, regardless of the condition

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❔ Interview Question

How does the map() function work?

Answer:map() takes a function and an iterable object, and returns an iterator that sequentially yields the result of applying this function to each element. The map() itself does not create a list — it lazily forms values on demand. If a list is needed, the result can be wrapped in list().

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Checking Memory Usage in Python

psutil is imported, then through psutil.virtual_memory() memory data is obtained.

The function convert_bytes converts bytes to gigabytes.

Then the code calculates:

- total RAM
- available RAM
- used RAM
- percentage usage

And outputs this to the console.

import psutil

memory = psutil.virtual_memory()

def convert_bytes(size):
    # Convert bytes to GB
    gb = size / (1024 ** 3)
    return gb

total_gb = convert_bytes(memory.total)
available_gb = convert_bytes(memory.available)
used_gb = convert_bytes(memory.used)

print(f"Total RAM: {total_gb:.3f} GB")
print(f"Available RAM: {available_gb:.3f} GB")
print(f"Used RAM: {used_gb:.3f} GB")
print(f"RAM Usage: {memory.percent}%")

Or simply press CTRL + ALT + DELETE and open Task Manager. It has worked since the days of Windows 95.

The RAM usage percentage loses its meaning if you have Chrome open — it will consume everything on its own 😄

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❔ Interview Question

What can be a key in a dictionary?

Answer: Dictionaries in Python are hash tables. Instead of keys in the dictionary, hashes are used. Consequently, any hashable data type can be a key in the dictionary, which includes all immutable data types.

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❔ Question from the interview

Why does isinstance(True, int) return True?

Answer: In Python, bool is a subclass of int. True and False are instances of int with values 1 and 0, respectively.

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❔ Python Quiz

❔ Question from the interview

What is Big O notation?

Answer: Big O notation is a way of describing how quickly the running time or memory consumption of an algorithm increases as the size of the input data increases. It shows the asymptotic complexity: the upper bound of the algorithm's behavior, without taking into account constants and minor details.

For example, O(n) grows linearly, O(n²) - quadratically, O(1) - does not depend on the size of the input.

Big O does not give exact figures, but allows you to compare algorithms in terms of their scalability.

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❔ Interview question

What is __slots__?

Answer: By default, class instances store their attributes in the internal dictiondictct__. This is flexible, but it requires more memory and makes access to fields a bit slower, because the search is done in the dictionarslotsts__ allows you to fix a set of allowed attributes and abandon the usedictct__. Instead of a dictionary, Python allocates compact slots — thereby reducing the memory consumption for each object and speeding up access to attributes. This is especially important when millions of class instances are created in a program or the performance of data access is critical.

There is one restriction: it is not possible to add an attribute that is notslotsts__. To retain the ability to dynamically create fields, you can dictct__ to the list of slots.

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