streamable 1.0.0

Stream-like manipulation of iterables

༄ streamable

Stream-like manipulation of iterables: A Stream[T] decorates an Iterable[T] with a fluent interface enabling the chaining of lazy operations.

---

🔗 Fluent	chain methods!
🇹 Typed	type-annotated and mypyable
💤 Lazy	operations are lazily evaluated at iteration time
🔄 Concurrent	thread-based or asyncio-based concurrency
🛡️ Robust	unit-tested for Python 3.7 to 3.12 with 100% coverage
🪶 Minimalist	pip install streamable with no additional dependencies

---

1. install

pip install streamable

2. import

from streamable import Stream

3. init

Instantiate a Stream[T] from an Iterable[T].

integers: Stream[int] = Stream(range(10))

4. operate

• Streams are immutable: applying an operation returns a new stream.

• Operations are lazy: only evaluated at iteration time.

inverses: Stream[float] = (
    integers
    .map(lambda n: round(1 / n, 2))
    .catch(ZeroDivisionError)
)

5. iterate

• Iterate over a Stream[T] as you would over any other Iterable[T].
• Source elements are processed on-the-fly.

collect it

>>> list(inverses)
[1.0, 0.5, 0.33, 0.25, 0.2, 0.17, 0.14, 0.12, 0.11]
>>> set(inverses)
{0.5, 1.0, 0.2, 0.33, 0.25, 0.17, 0.14, 0.12, 0.11}

reduce it

>>> sum(integers)
2.82
>>> max(inverses)
1.0
>>> from functools import reduce
>>> reduce(..., inverses)

loop it

>>> for inverse in inverses:
>>>    ...

next it

>>> inverses_iter = iter(inverses)
>>> next(inverses_iter)
1.0
>>> next(inverses_iter)
0.5

---

📒 Operations

.map

Applies a transformation on elements:

negative_integer_strings: Stream[str] = integers.map(lambda n: -n).map(str)

assert list(integer_strings) == ['0', '-1', '-2', '-3', '-4', '-5', '-6', '-7', '-8', '-9']

thread-based concurrency

Applies the transformation concurrently using a thread pool of size concurrency (preserving the order):

import requests

pokemon_names: Stream[str] = (
    Stream(range(1, 4))
    .map(lambda i: f"https://pokeapi.co/api/v2/pokemon-species/{i + 1}")
    .map(requests.get, concurrency=3)
    .map(requests.Response.json)
    .map(lambda poke: poke["name"])
)
assert list(pokemon_names) == ['bulbasaur', 'ivysaur', 'venusaur']

async-based concurrency

The sibling operation called .amap applies an async transformation (preserving the order):

import httpx
import asyncio

http_async_client = httpx.AsyncClient()

pokemon_names: Stream[str] = (
    Stream(range(1, 4))
    .map(lambda i: f"https://pokeapi.co/api/v2/pokemon-species/{i}")
    .amap(http_async_client.get, concurrency=3)
    .map(httpx.Response.json)
    .map(lambda poke: poke["name"])
)

assert list(pokemon_names) == ['bulbasaur', 'ivysaur', 'venusaur']
asyncio.run(http_async_client.aclose())

.foreach

Applies a side effect on elements:

self_printing_integers: Stream[int] = integers.foreach(print)

assert list(self_printing_integers) == list(integers)  # triggers the printing

thread-based concurrency

Like .map it has an optional concurrency: int parameter.

async-based concurrency

Like .map it has a sibling operation .aforeach for async.

.filter

Keeps only the elements that satisfy a condition:

pair_integers: Stream[int] = integers.filter(lambda n: n % 2 == 0)

assert list(pair_integers) == [0, 2, 4, 6, 8]

.throttle

Limits the rate at which elements are yielded:

slow_integers: Stream[int] = integers.throttle(per_second=5)

assert list(slow_integers) == list(integers)  # takes 10 / 5 = 2 seconds

.group

Groups elements into Lists:

integers_5_by_5: Stream[List[int]] = integers.group(size=5)

assert list(integers_5_by_5) == [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]

integers_by_parity: Stream[List[int]] = integers.group(by=lambda n: n % 2)

assert list(integers_by_parity) == [[0, 2, 4, 6, 8], [1, 3, 5, 7, 9]]

import timedelta from datetime

integers_within_1s: Stream[List[int]] = (
    integers
    .throttle(per_second=2)
    .group(interval=timedelta(seconds=1))
)

assert list(integers_within_1s) == [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]

Mix size/by/interval parameters:

integers_2_by_2_by_parity: Stream[List[int]] = integers.group(by=lambda n: n % 2, size=2)

assert list(integers_2_by_2_by_parity) == [[0, 2], [1, 3], [4, 6], [5, 7], [8], [9]]

.flatten

Ungroups elements assuming that they are Iterables.

pair_then_odd_integers: Stream[int] = integers_by_parity.flatten()

assert pair_then_odd_integers == [0, 2, 4, 6, 8, 1, 3, 5, 7, 9]

thread-based concurrency

Flattens concurrency iterables concurrently:

letters_mix: Stream[str] = Stream(
    [
        Stream(["a"] * 5).throttle(per_second=10),
        Stream(["b"] * 5).throttle(per_second=10),
        Stream(["c"] * 5).throttle(per_second=10),
    ]
).flatten(concurrency=2)
assert list(letters_mix) == ['a', 'b', 'a', 'b', 'a', 'b', 'a', 'b', 'a', 'b', 'c', 'c', 'c', 'c', 'c']

.catch

Catches a given type of exceptions, and optionally yields a replacement value:

inverses: Stream[float] = (
    integers
    .map(lambda n: round(1 / n, 2))
    .catch(ZeroDivisionError, replacement=float("inf"))
)

assert list(inverses) == [float("inf"), 1.0, 0.5, 0.33, 0.25, 0.2, 0.17, 0.14, 0.12, 0.11]

You can specify an additional when condition for the catch:

import requests
from requests.exceptions import ConnectionError

status_codes_ignoring_resolution_errors: Stream[int] = (
    Stream(["https://github.com", "https://foo.bar", "https://github.com/foo/bar"])
    .map(requests.get, concurrency=2)
    .catch(ConnectionError, when=lambda exception: "Failed to resolve" in str(exception))
    .map(lambda response: response.status_code)
)

assert list(status_codes_ignoring_resolution_errors) == [200, 404]

It has an optional finally_raise: bool parameter to raise the first catched exception when iteration ends.

.truncate

Stops the iteration:

• after a given number of yielded elements:

five_first_integers: Stream[int] = integers.truncate(5)

assert list(five_first_integers) == [0, 1, 2, 3, 4]

• as soon as a condition is satisfied:

five_first_integers: Stream[int] = integers.truncate(when=lambda n: n == 5)

assert list(five_first_integers) == [0, 1, 2, 3, 4]

.observe

Logs the progress of iterations over this stream:

If you iterate on

observed_slow_integers: Stream[int] = slow_integers.observe("integers")

you will get these logs:

INFO: [duration=0:00:00.502155 errors=0] 1 integers yielded
INFO: [duration=0:00:01.006336 errors=0] 2 integers yielded
INFO: [duration=0:00:02.011921 errors=0] 4 integers yielded
INFO: [duration=0:00:04.029666 errors=0] 8 integers yielded
INFO: [duration=0:00:05.039571 errors=0] 10 integers yielded

Note that the amount of logs will never be overwhelming because they are produced logarithmically e.g. the 11th log will be produced when the iteration reaches the 1024th element.

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📦 Notes Box

Contribute

Please help me ! Feel very welcome to:

• open issues
• open pull requests
• check CONTRIBUTING.md

Extract-Transform-Load

ETL scripts (i.e. scripts fetching -> processing -> pushing data) can benefit from the expressivity of this library.

Here is an example that you can copy-paste and try (it only requires requests): it creates a CSV file containing all the 67 quadrupeds from the 1st, 2nd and 3rd generations of Pokémons (kudos to PokéAPI)

import csv
from datetime import timedelta
import itertools
import requests
from streamable import Stream

with open("./quadruped_pokemons.csv", mode="w") as file:
    fields = ["id", "name", "is_legendary", "base_happiness", "capture_rate"]
    writer = csv.DictWriter(file, fields, extrasaction='ignore')
    writer.writeheader()
    (
        # Infinite Stream[int] of Pokemon ids starting from Pokémon #1: Bulbasaur
        Stream(itertools.count(1))
        # Limits to 16 requests per second to be friendly to our fellow PokéAPI devs
        .throttle(per_second=16)
        # GETs pokemons concurrently using a pool of 8 threads
        .map(lambda poke_id: f"https://pokeapi.co/api/v2/pokemon-species/{poke_id}")
        .map(requests.get, concurrency=8)
        .foreach(requests.Response.raise_for_status)
        .map(requests.Response.json)
        # Stops the iteration when reaching the 1st pokemon of the 4th generation
        .truncate(when=lambda poke: poke["generation"]["name"] == "generation-iv")
        .observe("pokemons")
        # Keeps only quadruped Pokemons
        .filter(lambda poke: poke["shape"]["name"] == "quadruped")
        .observe("quadruped pokemons")
        # Catches errors due to None "generation" or "shape"
        .catch(
            TypeError,
            when=lambda error: str(error) == "'NoneType' object is not subscriptable"
        )
        # Writes a batch of pokemons every 5 seconds to the CSV file
        .group(interval=timedelta(seconds=5))
        .foreach(writer.writerows)
        .flatten()
        .observe("written pokemons")
        # Catches exceptions and raises the 1st one at the end of the iteration
        .catch(finally_raise=True)
        # Actually triggers an iteration (the lines above define lazy operations)
        .count()
    )

CPU-bound tasks

For CPU-bound tasks, consider using the PyPy interpreter whose Just In Time (JIT) compilation should drastically improve performances ! (Few rough runtime orders of magnitude: CPython vs PyPy vs Java vs C vs Rust.)

change logging level

logging.getLogger("streamable").setLevel(logging.WARNING)  # default is INFO

visitor pattern

The Stream class exposes an .accept method and you can implement a visitor by extending the streamable.visitors.Visitor abstract class:

from streamable.visitors import Visitor

class DepthVisitor(Visitor[int]):
    def visit_stream(self, stream: Stream) -> int:
        if not stream.upstream:
            return 1
        return 1 + stream.upstream.accept(self)

def depth(stream: Stream) -> int:
    return stream.accept(DepthVisitor())

assert depth(Stream(range(10)).map(str).filter()) == 3

as functions

The Stream's methods are also exposed as functions:

from streamable.functions import catch

inverse_integers: Iterator[int] = map(lambda n: 1 / n, range(10))
safe_inverse_integers: Iterator[int] = catch(inverse_integers, ZeroDivisionError)