Advanced Topics

Unlock maximum performance and flexibility with sluggi’s advanced features for batch, async, parallel, and custom mapping.

Batch and Async Performance

• Use parallel=True for large batches.
• mode="process" for CPU-bound work, mode="thread" for I/O-bound or cached inputs.
• Tune workers and chunk_size for your workload.
• Async batch uses a semaphore to avoid event loop starvation.

Custom Emoji Mapping

You can extend or override emoji mappings:

from sluggi import slugify
emoji_map = {"😀": "smile", "🔥": "fire"}
slug = slugify("Hello 😀🔥", custom_map=emoji_map)
print(slug)  # hello-smile-fire

Batch Slugification: Custom Chunk Size & Error Handling

Handle errors and tune chunk size for large jobs:

from sluggi import batch_slugify
try:
    slugs = batch_slugify(["foo", "bar", None], chunk_size=2)
except TypeError as e:
    print(f"Error: {e}")

Async Slugification in FastAPI

Integrate async batch slugification into a web API:

from fastapi import FastAPI
from sluggi import async_batch_slugify

app = FastAPI()

@app.post("/bulk-slugify")
async def bulk_slugify(payload: list[str]):
    return await async_batch_slugify(
        payload,
        parallel=True,
        workers=8,
    )

Parallel Processing Benchmarks

Compare thread vs. process mode for large batches:

import time
from sluggi import batch_slugify

texts = ["foo"] * 10000

start = time.time()
batch_slugify(
    texts,
    parallel=True,
    mode="thread",
    workers=4,
)
print("Threaded:", time.time() - start)

start = time.time()
batch_slugify(
    texts,
    parallel=True,
    mode="process",
    workers=4,
)
print("Process:", time.time() - start)

Caching

• Thread mode: Enables result caching for repeated inputs.
• Process mode: Disables cache (each process is isolated).

When to Use Which Mode

• Serial: Small batches, low latency.
• Parallel: Large batches, heavy CPU work.
• Async: For async web frameworks or high concurrency.