Implement a Token Bucket Rate Limiter in Python

Rate limiting is a crucial technique for managing the flow of requests to a service, preventing overload, and ensuring fair usage. The token bucket algorithm is a popular and effective method for implementing rate limiting. Your challenge is to implement this algorithm in Python.

Problem Description

You need to create a Python class, TokenBucket, that simulates the token bucket rate limiting algorithm. This class should manage a bucket of tokens that represents the allowed rate of operations. Tokens are added to the bucket at a fixed rate, and operations consume tokens. If an operation attempts to consume a token when the bucket is empty, it is considered to have exceeded the rate limit.

Key Requirements:

Initialization: The TokenBucket should be initialized with a capacity (maximum number of tokens the bucket can hold) and a fill_rate (number of tokens added to the bucket per second).
Adding Tokens: Tokens should be added to the bucket over time. The fill_rate determines how many tokens are added per second. The bucket should not exceed its capacity.
Consuming Tokens: A method should exist to attempt to consume a specified number of tokens. This method should return True if successful (enough tokens were available) and False otherwise.
Time Management: The implementation must correctly account for the passage of time to refill the bucket.

Expected Behavior:

When consume(n) is called, if there are at least n tokens in the bucket, n tokens are removed, and the method returns True.
If there are fewer than n tokens, no tokens are removed, and the method returns False.
The TokenBucket should maintain the current number of tokens and the timestamp of the last refill.

Edge Cases:

What happens if consume(n) is called with n greater than the bucket's capacity? (The algorithm generally handles this by checking available tokens).
How to handle the initial state of the bucket (full or empty)?

Examples

Example 1:

# Initialize a bucket with capacity 5 and fill rate of 2 tokens/second
bucket = TokenBucket(capacity=5, fill_rate=2)

# Simulate some operations
print(bucket.consume(1)) # Output: True (Bucket has 5 tokens, consumes 1, remaining 4)
print(bucket.consume(3)) # Output: True (Bucket has 4 tokens, consumes 3, remaining 1)
print(bucket.consume(2)) # Output: False (Bucket has 1 token, cannot consume 2)

# Let time pass for 1.5 seconds
# After 1.5 seconds, 1.5 * 2 = 3 tokens should be added (capped at capacity 5)
# Bucket should now have min(1 + 3, 5) = 4 tokens
# This simulation part requires external time passing.
# If we assume time has passed:
# Assume bucket.last_refill_time is now 1.5 seconds in the past.
# bucket.refill() # (Internal call, or simulated)
# Now bucket.tokens is effectively 4

print(bucket.consume(3)) # Output: True (Bucket has 4 tokens, consumes 3, remaining 1)

Explanation: Initially, the bucket is full (5 tokens). The first two consume calls succeed. The third consume call fails because only 1 token is left. After some time, more tokens are added, allowing the next consume call to succeed.

Example 2:

# Initialize a bucket with capacity 10 and fill rate of 5 tokens/second
bucket = TokenBucket(capacity=10, fill_rate=5)

# Consume a large amount immediately
print(bucket.consume(8)) # Output: True (Bucket has 10 tokens, consumes 8, remaining 2)

# Try to consume more than available
print(bucket.consume(3)) # Output: False (Bucket has 2 tokens, cannot consume 3)

# Let time pass for 2 seconds.
# 2 * 5 = 10 tokens added.
# Bucket should now have min(2 + 10, 10) = 10 tokens
# Assume bucket.refill() has been implicitly called due to time passing.

print(bucket.consume(10)) # Output: True (Bucket has 10 tokens, consumes 10, remaining 0)
print(bucket.consume(1))  # Output: False (Bucket has 0 tokens, cannot consume 1)

Explanation: The bucket starts full. Consuming 8 tokens leaves 2. The next attempt to consume 3 fails. After 2 seconds, the bucket refills to its capacity (10 tokens), allowing a subsequent consumption of 10 tokens.

Constraints

capacity: An integer, 1 <= capacity <= 1000.
fill_rate: An integer, 1 <= fill_rate <= 100. Represents tokens per second.
The consume method should not raise exceptions for insufficient tokens; it should return False.
The implementation should be efficient and handle time passing reasonably. Avoid busy-waiting for refills.

Notes

You will need to use Python's time module, specifically time.time(), to track the passage of time.
The refill logic should be triggered implicitly when consume is called, based on the time elapsed since the last refill.
Consider how to handle floating-point arithmetic for time and token calculations carefully. The number of tokens added might not always be a whole number, but tokens themselves are discrete. You might need to decide how to handle fractional tokens (e.g., truncate, round). A common approach is to maintain the number of tokens as a float internally and only consider them as consumed in whole units.

Problem Description

Key Requirements:

Initialization: The TokenBucket should be initialized with a capacity (maximum number of tokens the bucket can hold) and a fill_rate (number of tokens added to the bucket per second).

Adding Tokens: Tokens should be added to the bucket over time. The fill_rate determines how many tokens are added per second. The bucket should not exceed its capacity.

Consuming Tokens: A method should exist to attempt to consume a specified number of tokens. This method should return True if successful (enough tokens were available) and False otherwise.

Time Management: The implementation must correctly account for the passage of time to refill the bucket.

Expected Behavior:

When consume(n) is called, if there are at least n tokens in the bucket, n tokens are removed, and the method returns True.

If there are fewer than n tokens, no tokens are removed, and the method returns False.

The TokenBucket should maintain the current number of tokens and the timestamp of the last refill.

Edge Cases:

What happens if consume(n) is called with n greater than the bucket's capacity? (The algorithm generally handles this by checking available tokens).

How to handle the initial state of the bucket (full or empty)?

Examples

Example 1:

# Initialize a bucket with capacity 5 and fill rate of 2 tokens/second bucket = TokenBucket(capacity=5, fill_rate=2) # Simulate some operations print(bucket.consume(1)) # Output: True (Bucket has 5 tokens, consumes 1, remaining 4) print(bucket.consume(3)) # Output: True (Bucket has 4 tokens, consumes 3, remaining 1) print(bucket.consume(2)) # Output: False (Bucket has 1 token, cannot consume 2) # Let time pass for 1.5 seconds # After 1.5 seconds, 1.5 * 2 = 3 tokens should be added (capped at capacity 5) # Bucket should now have min(1 + 3, 5) = 4 tokens # This simulation part requires external time passing. # If we assume time has passed: # Assume bucket.last_refill_time is now 1.5 seconds in the past. # bucket.refill() # (Internal call, or simulated) # Now bucket.tokens is effectively 4 print(bucket.consume(3)) # Output: True (Bucket has 4 tokens, consumes 3, remaining 1)

Example 2:

# Initialize a bucket with capacity 10 and fill rate of 5 tokens/second bucket = TokenBucket(capacity=10, fill_rate=5) # Consume a large amount immediately print(bucket.consume(8)) # Output: True (Bucket has 10 tokens, consumes 8, remaining 2) # Try to consume more than available print(bucket.consume(3)) # Output: False (Bucket has 2 tokens, cannot consume 3) # Let time pass for 2 seconds. # 2 * 5 = 10 tokens added. # Bucket should now have min(2 + 10, 10) = 10 tokens # Assume bucket.refill() has been implicitly called due to time passing. print(bucket.consume(10)) # Output: True (Bucket has 10 tokens, consumes 10, remaining 0) print(bucket.consume(1)) # Output: False (Bucket has 0 tokens, cannot consume 1)

Constraints

capacity: An integer, 1 <= capacity <= 1000.

fill_rate: An integer, 1 <= fill_rate <= 100. Represents tokens per second.

The consume method should not raise exceptions for insufficient tokens; it should return False.

The implementation should be efficient and handle time passing reasonably. Avoid busy-waiting for refills.

Notes

You will need to use Python's time module, specifically time.time(), to track the passage of time.

The refill logic should be triggered implicitly when consume is called, based on the time elapsed since the last refill.

Consider how to handle floating-point arithmetic for time and token calculations carefully. The number of tokens added might not always be a whole number, but tokens themselves are discrete. You might need to decide how to handle fractional tokens (e.g., truncate, round). A common approach is to maintain the number of tokens as a float internally and only consider them as consumed in whole units.