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Implement a Parallel Image Processor | Anthropic Coding Question

Implement a Parallel Image Processor

Company: Anthropic

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

You are building a batch image-processing utility. Implement a function that receives a list of jobs. Each job contains: - `input_path`: the source image file - `output_path`: where to write the processed image - `operations`: an ordered list of image transformations Support these operations: 1. `grayscale` — convert the image to grayscale. 2. `scale(f)` — multiply both width and height by a positive scaling factor `f`. 3. `resize(w, h)` — resize the image to exactly `w x h` pixels. For each job, load the image, apply the operations in order, and save the final result to `output_path`. Follow-up: 1. First, implement a correct solution for a small number of images. 2. Then improve the implementation for a very large batch. Assume image transformations are CPU-intensive and that jobs are independent. Use process-based parallelism to increase throughput, while still returning one result record per input job and handling per-file failures without stopping the entire batch. Explain any important design choices, such as worker-count selection, memory considerations, and how you would preserve correctness while improving performance.

Quick Answer: This question evaluates understanding of parallel and process-based concurrency, batch image-processing pipelines, transform correctness, and per-job fault isolation within the Coding & Algorithms domain.

Part 1: Sequential Virtual Image Processor

You are given a virtual file system of images. Each image is stored only as metadata `(width, height, mode)`, where `mode` is either `'RGB'` or `'GRAY'`. Implement a correct sequential batch processor for a small number of jobs. Each job contains: - `input_path`: the path to read - `output_path`: the path to write - `operations`: an ordered list of transformations Supported operations: - `('grayscale',)` -> change the mode to `'GRAY'` - `('scale', f)` -> set `width = int(width * f)` and `height = int(height * f)` - `('resize', w, h)` -> set the size to exactly `(w, h)` Jobs run strictly in order. A successful job writes its output into the file system, so later jobs may use earlier outputs as inputs. If a job fails, it must not modify the file system, and processing continues with the next job. A job fails with: - `'missing_input'` if `input_path` does not exist at the time the job starts - `'invalid_operation'` if an operation is unknown or malformed - `'invalid_dimensions'` if scaling/resizing would produce a width or height less than 1, or if the scale factor is not positive

Constraints

`0 <= len(jobs) <= 1000`
`0 <= len(operations) <= 20` per job
All input images use only `'RGB'` or `'GRAY'` modes
For `('scale', f)`, use `int(width * f)` and `int(height * f)` exactly
A failed job must not write `output_path`

Examples

Input: ({'a': (10, 20, 'RGB')}, [{'input_path': 'a', 'output_path': 'b', 'operations': [('grayscale',), ('scale', 0.5)]}, {'input_path': 'b', 'output_path': 'c', 'operations': [('resize', 4, 6)]}])

Expected Output: ([('ok', 'b', (5, 10, 'GRAY')), ('ok', 'c', (4, 6, 'GRAY'))], {'a': (10, 20, 'RGB'), 'b': (5, 10, 'GRAY'), 'c': (4, 6, 'GRAY')})

Explanation: The second job reads the file produced by the first job.

Input: ({}, [{'input_path': 'missing', 'output_path': 'out', 'operations': [('grayscale',)]}])

Expected Output: ([('error', 'missing_input')], {})

Explanation: The input file does not exist.

Input: ({'x': (3, 3, 'RGB')}, [{'input_path': 'x', 'output_path': 'y', 'operations': [('scale', 2), ('rotate', 90)]}])

Expected Output: ([('error', 'invalid_operation')], {'x': (3, 3, 'RGB')})

Explanation: An unknown operation causes the whole job to fail, and `y` is not written.

Input: ({'p': (1, 1, 'RGB')}, [{'input_path': 'p', 'output_path': 'q', 'operations': [('scale', 0.4)]}, {'input_path': 'p', 'output_path': 'r', 'operations': [('resize', 2, 2)]}])

Expected Output: ([('error', 'invalid_dimensions'), ('ok', 'r', (2, 2, 'RGB'))], {'p': (1, 1, 'RGB'), 'r': (2, 2, 'RGB')})

Explanation: Scaling by `0.4` would make the image `0 x 0`, so the first job fails. The second job still succeeds.

Input: ({'solo': (7, 8, 'GRAY')}, [])

Expected Output: ([], {'solo': (7, 8, 'GRAY')})

Explanation: No jobs means the file system is unchanged.

Hints

Keep a mutable copy of the file system and update it only after a job fully succeeds.
Since jobs are sequential, an output from one successful job can become the input to a later job.

Part 2: Parallel Virtual Image Processor with Failure Isolation

You now need to handle a very large batch of independent image-processing jobs. Each image is still represented only by metadata `(width, height, mode)`, where `mode` is `'RGB'` or `'GRAY'`. Unlike Part 1, every job must read from the original `files` snapshot only. No job may depend on the output of another job. Implement a process-based batch processor that: - uses process parallelism for throughput - returns one result record per input job - preserves the original job order in the returned results - isolates failures so one bad job does not stop the rest - returns a deterministic final file map Transformations are the same: - `('grayscale',)` - `('scale', f)` -> `width = int(width * f)`, `height = int(height * f)` - `('resize', w, h)` Failure rules are also the same: - `'missing_input'` - `'invalid_operation'` - `'invalid_dimensions'` To keep the final file map deterministic, successful outputs are committed in input order after processing completes. If multiple successful jobs write the same `output_path`, the later job in the input list wins.

Constraints

`0 <= len(jobs) <= 100000`
`0 <= len(operations) <= 20` per job
Jobs are independent and must read only from the original `files` map
If `max_workers` is `None`, a typical choice is `min(cpu_count, number_of_jobs)`
If two successful jobs write the same `output_path`, the later job in input order wins in `final_files`

Examples

Input: ({'a': (10, 20, 'RGB')}, [{'input_path': 'a', 'output_path': 'b', 'operations': [('grayscale',)]}, {'input_path': 'b', 'output_path': 'c', 'operations': [('resize', 4, 4)]}], 2)

Expected Output: ([('ok', 'b', (10, 20, 'GRAY')), ('error', 'missing_input')], {'a': (10, 20, 'RGB'), 'b': (10, 20, 'GRAY')})

Input: ({'x': (4, 4, 'RGB'), 'y': (8, 2, 'RGB')}, [{'input_path': 'x', 'output_path': 'z', 'operations': [('scale', 2)]}, {'input_path': 'y', 'output_path': 'z', 'operations': [('grayscale',)]}], 2)

Expected Output: ([('ok', 'z', (8, 8, 'RGB')), ('ok', 'z', (8, 2, 'GRAY'))], {'x': (4, 4, 'RGB'), 'y': (8, 2, 'RGB'), 'z': (8, 2, 'GRAY')})

Input: ({'p': (1, 1, 'RGB'), 'q': (2, 3, 'RGB')}, [{'input_path': 'p', 'output_path': 'bad', 'operations': [('scale', 0.4)]}, {'input_path': 'q', 'output_path': 'good', 'operations': [('resize', 5, 1), ('grayscale',)]}], 3)

Expected Output: ([('error', 'invalid_dimensions'), ('ok', 'good', (5, 1, 'GRAY'))], {'p': (1, 1, 'RGB'), 'q': (2, 3, 'RGB'), 'good': (5, 1, 'GRAY')})

Input: ({'img': (2, 2, 'GRAY')}, [], 4)

Expected Output: ([], {'img': (2, 2, 'GRAY')})

Input: ({}, [], None)

Expected Output: ([], {})

Explanation: Both files and jobs empty: empty results, empty final map.

Input: ({'p': (8, 8, 'RGB')}, [{'input_path': 'missing', 'output_path': 'a', 'operations': []}, {'input_path': 'p', 'output_path': 'b', 'operations': [('rotate', 90)]}, {'input_path': 'p', 'output_path': 'c', 'operations': [('resize', 0, 5)]}, {'input_path': 'p', 'output_path': 'e', 'operations': [('grayscale',), ('scale', 2)]}], 1)

Expected Output: ([('error', 'missing_input'), ('error', 'invalid_operation'), ('error', 'invalid_dimensions'), ('ok', 'e', (16, 16, 'GRAY'))], {'p': (8, 8, 'RGB'), 'e': (16, 16, 'GRAY')})

Explanation: missing_input, invalid_operation, invalid_dimensions errors are isolated; the valid job still commits.

Input: ({'x': (10, 10, 'RGB'), 'y': (4, 6, 'GRAY')}, [{'input_path': 'x', 'output_path': 'out', 'operations': [('scale', 2)]}, {'input_path': 'y', 'output_path': 'out', 'operations': [('resize', 3, 3)]}], None)

Expected Output: ([('ok', 'out', (20, 20, 'RGB')), ('ok', 'out', (3, 3, 'GRAY'))], {'x': (10, 10, 'RGB'), 'y': (4, 6, 'GRAY'), 'out': (3, 3, 'GRAY')})

Explanation: Two successes write the same output_path; the later job in input order wins in final_files.

Input: ({'base': (7, 5, 'RGB')}, [{'input_path': 'base', 'output_path': 'base', 'operations': [('scale', 1.5)]}], 2)

Expected Output: ([('ok', 'base', (10, 7, 'RGB'))], {'base': (10, 7, 'RGB')})

Explanation: scale floors dims via int(); a job may overwrite its own input_path in final_files.

Hints

Carry each job's original index so you can rebuild results in input order even if workers finish out of order.
Avoid shared mutable state across processes; let workers compute results independently, then merge successful writes in the parent process.

Implement a Parallel Image Processor

Company: Anthropic

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

Part 1: Sequential Virtual Image Processor

Constraints

`0 <= len(jobs) <= 1000`
`0 <= len(operations) <= 20` per job
All input images use only `'RGB'` or `'GRAY'` modes
For `('scale', f)`, use `int(width * f)` and `int(height * f)` exactly
A failed job must not write `output_path`

Examples

Input: ({'a': (10, 20, 'RGB')}, [{'input_path': 'a', 'output_path': 'b', 'operations': [('grayscale',), ('scale', 0.5)]}, {'input_path': 'b', 'output_path': 'c', 'operations': [('resize', 4, 6)]}])

Expected Output: ([('ok', 'b', (5, 10, 'GRAY')), ('ok', 'c', (4, 6, 'GRAY'))], {'a': (10, 20, 'RGB'), 'b': (5, 10, 'GRAY'), 'c': (4, 6, 'GRAY')})

Explanation: The second job reads the file produced by the first job.

Input: ({}, [{'input_path': 'missing', 'output_path': 'out', 'operations': [('grayscale',)]}])

Expected Output: ([('error', 'missing_input')], {})

Explanation: The input file does not exist.

Input: ({'x': (3, 3, 'RGB')}, [{'input_path': 'x', 'output_path': 'y', 'operations': [('scale', 2), ('rotate', 90)]}])

Expected Output: ([('error', 'invalid_operation')], {'x': (3, 3, 'RGB')})

Explanation: An unknown operation causes the whole job to fail, and `y` is not written.

Input: ({'p': (1, 1, 'RGB')}, [{'input_path': 'p', 'output_path': 'q', 'operations': [('scale', 0.4)]}, {'input_path': 'p', 'output_path': 'r', 'operations': [('resize', 2, 2)]}])

Expected Output: ([('error', 'invalid_dimensions'), ('ok', 'r', (2, 2, 'RGB'))], {'p': (1, 1, 'RGB'), 'r': (2, 2, 'RGB')})

Explanation: Scaling by `0.4` would make the image `0 x 0`, so the first job fails. The second job still succeeds.

Input: ({'solo': (7, 8, 'GRAY')}, [])

Expected Output: ([], {'solo': (7, 8, 'GRAY')})

Explanation: No jobs means the file system is unchanged.

Hints

Keep a mutable copy of the file system and update it only after a job fully succeeds.
Since jobs are sequential, an output from one successful job can become the input to a later job.

Part 2: Parallel Virtual Image Processor with Failure Isolation

Constraints

`0 <= len(jobs) <= 100000`
`0 <= len(operations) <= 20` per job
Jobs are independent and must read only from the original `files` map
If `max_workers` is `None`, a typical choice is `min(cpu_count, number_of_jobs)`
If two successful jobs write the same `output_path`, the later job in input order wins in `final_files`