• Igor Davydenko
• Python & React.js developer at EVO Company
• From Kyiv, Ukraine
• Love Python 3, asyncio & aio stack
• Use Python 3.6 in production
• Personal Site
• @ GitHub
• @ Twitter

name = 'Igor'
country = 'Belarus'
f"Hi, {country}! I'm {name}"

async def coro():
    for item in range(10):
        yield item


async def main():
    async for item in coro():
        ...

data = [item async for item in fetch_data()]
data_gen = (item async for item in fetch_data())

from typing import Any, Dict, List

data: List[Dict[str, Any]] = []

key: str  # No initial value

class Validator(object):

    data: Dict[str, Any] = {}

• number = 1_000_000 aka underscores in numeric literals
• New secrets module
• os.PathLike aka file system path protocol
• PYTHONMALLOC var & DTrace probing support
• Simpler metaclasses & enhancements to descriptors
• Local Time Disambiguation
• Change Windows filesystem/console encoding to UTF-8
• New dict implementation
• And even more…

• Why to switch to Python 3 if it slower than Python 2?
• Plus there is a PyPy

• We understand that Python 3 is slow
• We need a tool to compare Python 2 / Python 3 performance
• We don't need to enforce benchmark-driven-developmen

• perf.readthedocs.io
• Toolkit to write, run, analyze & modify benchmarks
• Store results in JSON
• Has tool to display, compare, analyze and modify benchmark results
• Includes statistical tools to analyze the distribution of results

$ python3 -m perf compare_to 2016-11-03_15-36-2.7-91f024fc9b3a.json.gz
2016-11-03_15-38-3.6-c4319c0d0131.json.gz -G --min-speed=5
Slower (40):
- python_startup: 7.74 ms +- 0.28 ms -> 26.9 ms +- 0.6 ms: 3.47x slower
- python_startup_no_site: 4.43 ms +- 0.08 ms -> 10.4 ms +- 0.4 ms: 2.36x slower
- ...

Faster (15):
- telco: 707 ms +- 22 ms -> 22.1 ms +- 0.4 ms: 32.04x faster
- unpickle_list: 15.0 us +- 0.3 us -> 7.86 us +- 0.16 us: 1.90x faster
- ...

Benchmark hidden because not significant (8): 2to3, dulwich_log,
nbody, pidigits, regex_dna, tornado_http, unpack_sequence, unpickle
Ignored benchmarks (3) of 2016-11-03_15-36-2.7-91f024fc9b3a.json:
hg_startup, pyflate, spambayes

• speed.python.org
• Authirative source of benchmarks for all Python implementations
• Focus on real-world benchmarks, rather than synthetic benchmarks
• pyperformance will run Student's two-tailed T test on the benchmark results at the 95% confidence level to indicate whether the observed difference is statistically significant

Examples

• django_template: use the Django template system to build a 150x150-cell HTML table
• dulwich_log: Iterate on commits of the asyncio Git repository using the Dulwich module
• hexiom: Solver of Hexiom board game (level 25 by default)
• sqlalchemy_declarative: SQLAlchemy Declarative benchmark using SQLite
• telco: Benchmark the decimal module
• tornado_http: Benchmark HTTP server of the tornado module

• Python 2.7 is still fastest Python
• Python 3.5 much faster Python 3.4
• Python 3.6 faster Python 3.5
• Python 3.7 has some significant improvements already

• asyncio.Future now has an optimized C implementation
• asyncio.Task now has an optimized C implementation

Python Future & Task   |   C Future & Py Task   |   C Future & C Task
      23K req/s        |           26K          |          30K
                       |      ~10-15% boost     |         ~15%

After optimizations uvloop start working faster up to 5% as well

• ASCII decoder for surrogateescape, ignore and replace up to 60 times faster
• ASCII & Latin-1 encoder for surrogateescape up to 3 times faster
• UTF-8 decoder for error handlers up to 75 times faster
• UTF-8 encoder for error handlers up to 15 times faster
• bytes % args / bytearray % args up to 5 times faster
• More optimizations for bytes.fromhex(), bytearray.fromhex() & bytes.replace(...), bytearray.replace(...) methods

• Optimized glob() and iglob() functions; up to 6 times faster
• Optimized globbing in pathlib; up to 4 times faster
• Optimization gained by using os.scandir() introduced in Python 3.5

• dict type now using "compact" representation
• First implemented in PyPy
• The memory usage of the new dict() is between 20% and 25% smaller compared to 3.5

• Preserving class attribute definition order
• Preserving keyword argument order

data = {'a': 1, 'b': 2}
data['c'] = 3
assert str(data) == "{'a': 1, 'b': 2, 'c': 3}"

curr_size = 24 * t
new_size = 24 * n + sizeof(index) * t

data = {'one': 'один', 'two': 'два', 'three': 'три'}
# Before
entries = [['--', '--', '--'],
           [-8522787127447073495, 'two', 'два'],
           ['--', '--', '--'],
           ['--', '--', '--'],
           ['--', '--', '--'],
           [-9092791511155847987, 'one', 'один'],
           ['--', '--', '--'],
           [-6480567542315338377, 'three', 'три']]

data = {'one': 'один', 'two': 'два', 'three': 'три'}
# After
indices =  [None, 1, None, None, None, 0, None, 2]
entries =  [[-9092791511155847987, 'one', 'один'],
            [-8522787127447073495, 'two', 'два'],
            [-6480567542315338377, 'three', 'три']]

• Optimization of passing keyword arguments, comparing to passing positional arguments
• picke.load() & picke.loads() up to 10% faster on many small objects
• Various implementation improvements in typing module
• xml.etree.ElementTree parsing, iteration and deepcopy improvements
• Creation of fractions.Fraction from floats and decimals up to 3 times faster