Incremental Log File Parsing

Many applications generate log files that can grow very large. Processing these files entirely from scratch every time can be inefficient. This challenge asks you to implement an incremental parsing mechanism in Python that can process a log file in chunks, maintaining state between processing calls. This allows for efficient handling of large log files or streams where you only need to process newly added lines.

Problem Description

Your task is to create a Python class, IncrementalLogParser, that can parse lines from a log file incrementally. The parser should maintain its position within the log file and be able to resume parsing from where it left off. Each log line is expected to follow a specific format: a timestamp followed by a message.

Key Requirements:

1. Initialization: The IncrementalLogParser should be initialized with the path to a log file.
2. Parsing Method: A method (e.g., parse_chunk) should accept an integer chunk_size and return a list of parsed log entries. Each entry should be a dictionary containing timestamp and message.
3. State Management: The parser must keep track of the current position (line number or byte offset) in the log file so that subsequent calls to parse_chunk start from where the previous one ended.
4. Log Entry Format: Each log line will be in the format: YYYY-MM-DD HH:MM:SS - Message.
5. Error Handling: Gracefully handle cases where a line might not conform to the expected format (e.g., malformed timestamp, missing hyphen). Malformed lines should be skipped.

Expected Behavior:

When parse_chunk(n) is called, the parser should read up to n valid log entries from the file, starting from its current position. It should then update its internal position to point to the beginning of the next line after the last successfully parsed entry.

Edge Cases:

• An empty log file.
• A log file with only malformed lines.
• chunk_size being larger than the remaining number of lines in the file.
• chunk_size being zero or negative.

Examples

Example 1:

Let's assume a log file named app.log with the following content:

2023-10-27 10:00:00 - Application started.
2023-10-27 10:01:15 - User 'alice' logged in.
2023-10-27 10:02:30 - Processing request ID 123.
2023-10-27 10:03:45 - Request ID 123 completed.
Malformed line here.
2023-10-27 10:05:00 - System shutting down.

Scenario:

1. Initialize parser = IncrementalLogParser('app.log')
2. Call entries1 = parser.parse_chunk(2)

Output:

[
  {"timestamp": "2023-10-27 10:00:00", "message": "Application started."},
  {"timestamp": "2023-10-27 10:01:15", "message": "User 'alice' logged in."}
]

Explanation: The parser read the first two valid log entries. The malformed line and the subsequent valid lines are not processed in this call.

Scenario (continued):

3. Call entries2 = parser.parse_chunk(3)

Output:

[
  {"timestamp": "2023-10-27 10:02:30", "message": "Processing request ID 123."},
  {"timestamp": "2023-10-27 10:03:45", "message": "Request ID 123 completed."},
  {"timestamp": "2023-10-27 10:05:00", "message": "System shutting down."}
]

Explanation: The parser resumes from where it left off, skipping the malformed line and successfully parsing the next three valid entries.

Example 2:

Log file events.log:

2023-10-27 11:00:00 - Event A
2023-10-27 11:01:00 - Event B
Invalid line format
2023-10-27 11:02:00 - Event C

Scenario:

1. Initialize parser = IncrementalLogParser('events.log')
2. Call entries = parser.parse_chunk(5)

Output:

[
  {"timestamp": "2023-10-27 11:00:00", "message": "Event A"},
  {"timestamp": "2023-10-27 11:01:00", "message": "Event B"},
  {"timestamp": "2023-10-27 11:02:00", "message": "Event C"}
]

Explanation: The parser attempts to read 5 entries but only finds 3 valid ones. The invalid line is skipped.

Example 3 (Edge Case: Empty File)

Log file empty.log (empty content).

Scenario:

1. Initialize parser = IncrementalLogParser('empty.log')
2. Call entries = parser.parse_chunk(3)

Output:

[]

Explanation: The file is empty, so no entries can be parsed.

Constraints

• The log file path will be a valid string representing a file that exists.
• Log file lines will not exceed 1024 characters.
• chunk_size will be an integer. Handle chunk_size <= 0 by returning an empty list.
• The parsing should be reasonably efficient. Aim for a solution that doesn't re-read the entire file for each parse_chunk call.

Notes

• Consider using Python's datetime module to validate timestamps if strict validation is desired, though for this challenge, a string-based check for the format might suffice.
• Think about how to efficiently manage the file pointer or cursor to resume parsing.
• The definition of a "malformed line" is any line that does not start with YYYY-MM-DD HH:MM:SS - or where the timestamp part itself is invalid (e.g., non-numeric characters in date/time parts). However, for simplicity, focus on the initial pattern match.