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Stream processing made easy with riko 8th Light University - Chicago, IL May 30, 2017 by Reuben Cummings @reubano #8LU

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@reubano #8LU Who am I? Managing Director, Nerevu Development Programming in Python since 2011 Author of several popular packages

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streams

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@reubano #8LU basic stream process 1 * 2 > 2 sum 2 3

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@reubano #8LU 2 basic stream process * 2 > 2 sum 2 3

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@reubano #8LU 4 basic stream process * 2 > 2 sum 3

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@reubano #8LU 6 4 basic stream process * 2 > 2 sum

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@reubano #8LU 4 6 basic stream process * 2 > 2 sum

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@reubano #8LU 10 basic stream process * 2 > 2 sum

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@reubano #8LU 10 basic stream process * 2 > 2 sum

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constructing streams

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>>> 'abracadabra'[0] 'a' >>> 'hello 8th Light University'.split(' ')[0] 'hello' >>> range(1, 11)[0] 1 >>> [{'x': x} for x in range(4)][0] {'x': 0} >>> ({'x': x} for x in range(4)) at 0x103c10830> >>> next({'x': x} for x in range(4)) {'x': 0}

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processing streams

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>>> [ord(x) for x in 'abracadabra'] [97, 98, 114, 97, 99, 97, 100, 97, 98, 114, 97] >>> [2 * x for x in range(1, 11)] [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] >>> [x for x in range(1, 11) if x > 5] [6, 7, 8, 9, 10] >>> stream = ({'num': x} for x in range(4)) >>> sum(s['num'] for s in stream) 6

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so what!

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RSS feeds (feedly)

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aggregators (kayak)

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news feeds (linkedin)

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frameworks

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$ cat file.txt What is Lorem Ipsum? Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap...

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hello world Hadoop

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>>> from mrjob.job import MRJob >>> from mrjob.step import MRStep >>> >>> >>> class MRWordCount(MRJob): ... def steps(self): ... kwargs = { ... 'mapper': self.mapper, ... 'combiner': self.combiner, ... 'reducer': self.reducer} ... ... return [MRStep(**kwargs)] ... ... def mapper(self, _, line):

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>>> >>> class MRWordCount(MRJob): ... def steps(self): ... kwargs = { ... 'mapper': self.mapper, ... 'combiner': self.combiner, ... 'reducer': self.reducer} ... ... return [MRStep(**kwargs)] ... ... def mapper(self, _, line): ... for word in line.split(' '): ... yield word.lower(), 1 ... ... def combiner(self, word, counts):

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... return [MRStep(**kwargs)] ... ... def mapper(self, _, line): ... for word in line.split(' '): ... yield word.lower(), 1 ... ... def combiner(self, word, counts): ... yield word, sum(counts) ... ... def reducer(self, word, counts): ... yield word, sum(counts) >>> >>> if __name__ == '__main__': ... MRWordCount.run()

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$ python hadoop_job.py file.txt '1500s' 1 'a' 2 'an' 1 'and' 1 'been' 1 'book' 1 'but' 1 'centuries' 1 'dummy' 1 'ever' 1 'five' 1 'galley' 1

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hello world Spark

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>>> from operator import add >>> from pyspark.sql import SparkSession >>> >>> spark = SparkSession.builder.getOrCreate() >>> fpath = 'hdfs:///file.txt' >>> >>> stream = (spark.read.text(fpath).rdd ... .flatMap(lambda line: line.split(' ')) ... .map(lambda word: (word.lower(), 1)) ... .reduceByKey(add) ... .collect())

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>>> stream[0] ('1500s', 1)

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introducing riko github.com/nerevu/riko

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github.com/nerevu/riko

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@reubano #8LU riko

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@reubano #8LU riko { 'greeting':'hello', 'location':'8th Light', 'enthusiasm': 9 }

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pip install riko

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obtaining data

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8th Light Blog https://8thlight.com/blog/feed/ rss.xml

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8th Light Blog https://8thlight.com/blog/feed/ rss.xml

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>>> from riko.modules.fetch import pipe >>> >>> url = 'https://8thlight.com/blog/feed' >>> url += '/rss.xml' >>> stream = pipe(conf={'url': url}) >>> item = next(stream) >>> item['author'] 'Rabea Gleissner' >>> item['published'] 'Fri, 26 May 2017 00:00:00 -0500' >>> item['title'] 'How to set up a React project without flipping tables'

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transforming data

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>>> from riko.collections import SyncPipe >>> >>> frule = { ... 'field': 'title', ... 'op': 'contains', ... 'value':'erlang'} >>> >>> stream = ( ... SyncPipe('fetch', conf={'url': url}) ... .filter(conf={'rule': frule}) ... .output)

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>>> item = next(stream) >>> item['title'] 'The Core of Erlang' >>> item['tags'][0]['term'] 'Coding' >>> item['link'] 'https://8thlight.com/blog/kofi-gumbs/ 2017/05/02/core-erlang.html'

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hello world riko

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>>> conf ={'url': 'file:///file.txt'} >>> tconf = {'delimiter': ' '} >>> rule = {'transform': 'lower'} >>> cconf = {'count_key': 'strtransform'} >>> >>> stream = (SyncPipe('fetchtext', conf=conf) ... .tokenizer(conf=tconf, emit=True) ... .strtransform(conf={'rule': rule}) ... .count(conf=cconf) ... .output) >>> >>> next(stream) {'1500s': 1}

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parallel processing

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The Core of Erlang https://8thlight.com/blog/kofi- gumbs/2017/05/02/core-erlang.html

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>>> from riko.modules import xpathfetchpage >>> >>> pipe = xpathfetchpage.pipe >>> xpath = '/html/body/section/div/div[1]' >>> xpath += '/div/div/article/div[3]/div' >>> xpath += '/ul[1]/li/a' >>> >>> xconf = { ... 'url': item['link'], 'xpath': xpath} >>> >>> stream = pipe(conf=xconf)

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>>> next(stream) {'content': "Two Design Patterns You're Probably Already Using", 'href': '/blog/becca-nelson/2017/05/22/two- design-patterns-youre-probably-already- using.html'}

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>>> kwargs = {'conf': xconf} >>> parts = [ ... {'value': 'http://8thlight.com'}, ... {'subkey': 'href'}] >>> >>> fconf = { ... 'url': {'subkey': 'strconcat'}, ... 'start': '

', 'end': '

'} >>> >>> stream = ( ... SyncPipe('xpathfetchpage', **kwargs) ... .strconcat(conf={'part': parts}) ... .fetchpage(conf=fconf) ... .output)

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>>> kwargs = {'conf': xconf} >>> parts = [ ... {'value': 'http://8thlight.com'}, ... {'subkey': 'href'}] >>> >>> fconf = { ... 'url': {'subkey': 'strconcat'}, ... 'start': '

', 'end': '

'} >>> >>> stream = ( ... SyncPipe('xpathfetchpage', **kwargs) ... .strconcat(conf={'part': parts}) ... .fetchpage(conf=fconf) ... .output)

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>>> next(stream)['content'].decode('utf-8') 'I came into this field from a very non- technical background. And when I say very non-technical, I mean that I was an elementary fine arts teacher. The most important calculation I performed on a daily basis was counting my kindergarteners when they lined up to leave to make sure I hadn’t lost any since the beginning of the class period.'

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>>> from time import monotonic >>> >>> start = monotonic() >>> count = len(list(stream)) >>> stop = monotonic() - start >>> count, stop (9, 0.4573155799989763)

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>>> kwargs = {'conf': xconf, 'parallel': True} >>> start = monotonic() >>> >>> stream = ( ... SyncPipe('xpathfetchpage', **kwargs) ... .strconcat(conf={'part': parts}) ... .fetchpage(conf=fconf) ... .output) >>> >>> count = len(list(stream)) >>> stop = monotonic() - start >>> count, stop (10, 0.2804829629985761)

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async processing

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pip install riko[async]

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>>> from riko.bado import coroutine, react >>> from riko.collections import AsyncPipe >>> >>> @coroutine >>> def run(reactor): ... start = monotonic() ... flow = AsyncPipe( ... 'xpathfetchpage', **kwargs) ... ... stream = yield (flow ... .strconcat(conf={'part': parts}) ... .fetchpage(conf=fconf) ... .output) ...

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>>> @coroutine >>> def run(reactor): ... start = monotonic() ... flow = AsyncPipe( ... 'xpathfetchpage', **kwargs) ... ... stream = yield (flow ... .strconcat(conf={'part': parts}) ... .fetchpage(conf=fconf) ... .output) ... ... count = len(list(stream)) ... stop = monotonic() - start ... print((count, stop))

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>>> react(run) (10, 0.2462857809982961)

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@reubano #8LU Head to Head Spark, etc. Huginn riko installation complex moderate simple push/pull push push pull native ingestors few many many parallel ✔ ✔ ✔ async ✔ json serializable ✔ ✔ distributed ✔

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@reubano #8LU Reuben Cummings [email protected] https://www.reubano.xyz Thanks!