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Description:
riko 0.67.0
Index
Introduction | Requirements | Word Count | Motivation | Usage |
Installation | Design Principles | Scripts | Command-line Interface |
Contributing | Credits | More Info | Project Structure | License
Introduction
riko is a pure Python library for analyzing and processing streams of
structured data. riko has synchronous and asynchronous APIs, supports parallel
execution, and is well suited for processing RSS feeds [1]. riko also supplies
a command-line interface for executing flows, i.e., stream processors aka workflows.
With riko, you can
Read csv/xml/json/html files
Create text and data based flows via modular pipes
Parse, extract, and process RSS/Atom feeds
Create awesome mashups [2], APIs, and maps
Perform parallel processing via cpus/processors or threads
and much more…
Notes
[1]
Really Simple Syndication
[2]
Mashup (web application hybrid)
Requirements
riko has been tested and is known to work on Python 3.7, 3.8, and 3.9; and PyPy3.7.
Optional Dependencies
Feature
Dependency
Installation
Async API
Twisted
pip install riko[async]
Accelerated xml parsing
lxml [3]
pip install riko[xml]
Accelerated feed parsing
speedparser [4]
pip install riko[xml]
Notes
[3]
If lxml isn’t present, riko will default to the builtin Python xml parser
[4]
If speedparser isn’t present, riko will default to feedparser
Word Count
In this example, we use several pipes to count the words on a webpage.
>>> ### Create a SyncPipe flow ###
>>> #
>>> # `SyncPipe` is a convenience class that creates chainable flows
>>> # and allows for parallel processing.
>>> from riko.collections import SyncPipe
>>>
>>> ### Set the pipe configurations ###
>>> #
>>> # Notes:
>>> # 1. the `detag` option will strip all html tags from the result
>>> # 2. fetch the text contained inside the 'body' tag of the hackernews
>>> # homepage
>>> # 3. replace newlines with spaces and assign the result to 'content'
>>> # 4. tokenize the resulting text using whitespace as the delimeter
>>> # 5. count the number of times each token appears
>>> # 6. obtain the raw stream
>>> # 7. extract the first word and its count
>>> # 8. extract the second word and its count
>>> # 9. extract the third word and its count
>>> url = 'https://news.ycombinator.com/'
>>> fetch_conf = {
... 'url': url, 'start': '<body>', 'end': '</body>', 'detag': True} # 1
>>>
>>> replace_conf = {
... 'rule': [
... {'find': '\r\n', 'replace': ' '},
... {'find': '\n', 'replace': ' '}]}
>>>
>>> flow = (
... SyncPipe('fetchpage', conf=fetch_conf) # 2
... .strreplace(conf=replace_conf, assign='content') # 3
... .tokenizer(conf={'delimiter': ' '}, emit=True) # 4
... .count(conf={'count_key': 'content'})) # 5
>>>
>>> stream = flow.output # 6
>>> next(stream) # 7
{"'sad": 1}
>>> next(stream) # 8
{'(': 28}
>>> next(stream) # 9
{'(1999)': 1}
Motivation
Why I built riko
Yahoo! Pipes [5] was a user friendly web application used to
aggregate, manipulate, and mashup content from around the web
Wanting to create custom pipes, I came across pipe2py which translated a
Yahoo! Pipe into python code. pipe2py suited my needs at the time
but was unmaintained and lacked asynchronous or parallel processing.
riko addresses the shortcomings of pipe2py but removed support for
importing Yahoo! Pipes json workflows. riko contains ~ 40 built-in
modules, aka pipes, that allow you to programatically perform most of the
tasks Yahoo! Pipes allowed.
Why you should use riko
riko provides a number of benefits / differences from other stream processing
applications such as Huginn, Flink, Spark, and Storm [6]. Namely:
a small footprint (CPU and memory usage)
native RSS/Atom support
simple installation and usage
a pure python library with pypy support
builtin modular pipes to filter, sort, and modify streams
The subsequent tradeoffs riko makes are:
not distributed (able to run on a cluster of servers)
no GUI for creating flows
doesn’t continually monitor streams for new data
can’t react to specific events
iterator (pull) based so streams only support a single consumer [7]
The following table summarizes these observations:
library
Stream Type
Footprint
RSS
simple [8]
async
parallel
CEP [9]
distributed
riko
pull
small
√
√
√
√
pipe2py
pull
small
√
√
Huginn
push
med
√
[10]
√
√
Others
push
large
[11]
[12]
[13]
√
√
√
For more detailed information, please check-out the FAQ.
Notes
[5]
Yahoo discontinued Yahoo! Pipes in 2015, but you can view what remains
[6]
Huginn, Flink, Spark, and Storm
[7]
You can mitigate this via the split module
[8]
Doesn’t depend on outside services like MySQL, Kafka, YARN, ZooKeeper, or Mesos
[9]
Complex Event Processing
[10]
Huginn doesn’t appear to make async web requests
[11]
Many libraries can’t parse RSS streams without the use of 3rd party libraries
[12]
While most libraries offer a local mode, many require integrating with a data ingestor (e.g., Flume/Kafka) to do anything useful
[13]
I can’t find evidence that these libraries offer an async APIs (and apparently Spark doesn’t)
Usage
riko is intended to be used directly as a Python library.
Usage Index
Fetching feeds
Synchronous processing
Parallel processing
Asynchronous processing
Cookbook
Fetching feeds
riko can fetch rss feeds from both local and remote filepaths via “source”
pipes. Each “source” pipe returns a stream, i.e., an iterator of
dictionaries, aka items.
>>> from riko.modules import fetch, fetchsitefeed
>>>
>>> ### Fetch an RSS feed ###
>>> stream = fetch.pipe(conf={'url': 'https://news.ycombinator.com/rss'})
>>>
>>> ### Fetch the first RSS feed found ###
>>> stream = fetchsitefeed.pipe(conf={'url': 'http://arstechnica.com/rss-feeds/'})
>>>
>>> ### View the fetched RSS feed(s) ###
>>> #
>>> # Note: regardless of how you fetch an RSS feed, it will have the same
>>> # structure
>>> item = next(stream)
>>> item.keys()
dict_keys(['title_detail', 'author.uri', 'tags', 'summary_detail', 'author_detail',
'author.name', 'y:published', 'y:title', 'content', 'title', 'pubDate',
'guidislink', 'id', 'summary', 'dc:creator', 'authors', 'published_parsed',
'links', 'y:id', 'author', 'link', 'published'])
>>> item['title'], item['author'], item['id']
('Gravity doesn’t care about quantum spin',
'Chris Lee',
'http://arstechnica.com/?p=924009')
Please see the FAQ for a complete list of supported file types and
protocols. Please see Fetching data and feeds for more examples.
Synchronous processing
riko can modify streams via the 40 built-in pipes
>>> from riko.collections import SyncPipe
>>>
>>> ### Set the pipe configurations ###
>>> fetch_conf = {'url': 'https://news.ycombinator.com/rss'}
>>> filter_rule = {'field': 'link', 'op': 'contains', 'value': '.com'}
>>> xpath = '/html/body/center/table/tr[3]/td/table[2]/tr[1]/td/table/tr/td[3]/span/span'
>>> xpath_conf = {'url': {'subkey': 'comments'}, 'xpath': xpath}
>>>
>>> ### Create a SyncPipe flow ###
>>> #
>>> # `SyncPipe` is a convenience class that creates chainable flows
>>> # and allows for parallel processing.
>>> #
>>> # The following flow will:
>>> # 1. fetch the hackernews RSS feed
>>> # 2. filter for items with '.com' in the link
>>> # 3. sort the items ascending by title
>>> # 4. fetch the first comment from each item
>>> # 5. flatten the result into one raw stream
>>> # 6. extract the first item's content
>>> #
>>> # Note: sorting is not lazy so take caution when using this pipe
>>>
>>> flow = (
... SyncPipe('fetch', conf=fetch_conf) # 1
... .filter(conf={'rule': filter_rule}) # 2
... .sort(conf={'rule': {'sort_key': 'title'}}) # 3
... .xpathfetchpage(conf=xpath_conf)) # 4
>>>
>>> stream = flow.output # 5
>>> next(stream)['content'] # 6
'Open Artificial Pancreas home:'
Please see alternate workflow creation for an alternative (function based) method for
creating a stream. Please see pipes for a complete list of available pipes.
Parallel processing
An example using riko’s parallel API to spawn a ThreadPool [14]
>>> from riko.collections import SyncPipe
>>>
>>> ### Set the pipe configurations ###
>>> fetch_conf = {'url': 'https://news.ycombinator.com/rss'}
>>> filter_rule = {'field': 'link', 'op': 'contains', 'value': '.com'}
>>> xpath = '/html/body/center/table/tr[3]/td/table[2]/tr[1]/td/table/tr/td[3]/span/span'
>>> xpath_conf = {'url': {'subkey': 'comments'}, 'xpath': xpath}
>>>
>>> ### Create a parallel SyncPipe flow ###
>>> #
>>> # The following flow will:
>>> # 1. fetch the hackernews RSS feed
>>> # 2. filter for items with '.com' in the article link
>>> # 3. fetch the first comment from all items in parallel (using 4 workers)
>>> # 4. flatten the result into one raw stream
>>> # 5. extract the first item's content
>>> #
>>> # Note: no point in sorting after the filter since parallel fetching doesn't guarantee
>>> # order
>>> flow = (
... SyncPipe('fetch', conf=fetch_conf, parallel=True, workers=4) # 1
... .filter(conf={'rule': filter_rule}) # 2
... .xpathfetchpage(conf=xpath_conf)) # 3
>>>
>>> stream = flow.output # 4
>>> next(stream)['content'] # 5
'He uses the following example for when to throw your own errors:'
Asynchronous processing
To enable asynchronous processing, you must install the async module.
pip install riko[async]
An example using riko’s asynchronous API.
>>> from riko.bado import coroutine, react
>>> from riko.collections import AsyncPipe
>>>
>>> ### Set the pipe configurations ###
>>> fetch_conf = {'url': 'https://news.ycombinator.com/rss'}
>>> filter_rule = {'field': 'link', 'op': 'contains', 'value': '.com'}
>>> xpath = '/html/body/center/table/tr[3]/td/table[2]/tr[1]/td/table/tr/td[3]/span/span'
>>> xpath_conf = {'url': {'subkey': 'comments'}, 'xpath': xpath}
>>>
>>> ### Create an AsyncPipe flow ###
>>> #
>>> # The following flow will:
>>> # 1. fetch the hackernews RSS feed
>>> # 2. filter for items with '.com' in the article link
>>> # 3. asynchronously fetch the first comment from each item (using 4 connections)
>>> # 4. flatten the result into one raw stream
>>> # 5. extract the first item's content
>>> #
>>> # Note: no point in sorting after the filter since async fetching doesn't guarantee
>>> # order
>>> @coroutine
... def run(reactor):
... stream = yield (
... AsyncPipe('fetch', conf=fetch_conf, connections=4) # 1
... .filter(conf={'rule': filter_rule}) # 2
... .xpathfetchpage(conf=xpath_conf) # 3
... .output) # 4
...
... print(next(stream)['content']) # 5
>>>
>>> try:
... react(run)
... except SystemExit:
... pass
Here's how iteration works ():
Cookbook
Please see the cookbook or ipython notebook for more examples.
Notes
[14]
You can instead enable a ProcessPool by additionally passing threads=False to SyncPipe, i.e., SyncPipe('fetch', conf={'url': url}, parallel=True, threads=False).
Installation
(You are using a virtualenv, right?)
At the command line, install riko using either pip (recommended)
pip install riko
or easy_install
easy_install riko
Please see the installation doc for more details.
Design Principles
The primary data structures in riko are the item and stream. An item
is just a python dictionary, and a stream is an iterator of items. You can
create a stream manually with something as simple as
[{'content': 'hello world'}]. You manipulate streams in
riko via pipes. A pipe is simply a function that accepts either a
stream or item, and returns a stream. pipes are composable: you
can use the output of one pipe as the input to another pipe.
riko pipes come in two flavors; operators and processors.
operators operate on an entire stream at once and are unable to handle
individual items. Example operators include count, pipefilter,
and reverse.
>>> from riko.modules.reverse import pipe
>>>
>>> stream = [{'title': 'riko pt. 1'}, {'title': 'riko pt. 2'}]
>>> next(pipe(stream))
{'title': 'riko pt. 2'}
processors process individual items and can be parallelized across
threads or processes. Example processors include fetchsitefeed,
hash, pipeitembuilder, and piperegex.
>>> from riko.modules.hash import pipe
>>>
>>> item = {'title': 'riko pt. 1'}
>>> stream = pipe(item, field='title')
>>> next(stream)
{'title': 'riko pt. 1', 'hash': 2853617420}
Some processors, e.g., pipetokenizer, return multiple results.
>>> from riko.modules.tokenizer import pipe
>>>
>>> item = {'title': 'riko pt. 1'}
>>> tokenizer_conf = {'delimiter': ' '}
>>> stream = pipe(item, conf=tokenizer_conf, field='title')
>>> next(stream)
{'tokenizer': [{'content': 'riko'},
{'content': 'pt.'},
{'content': '1'}],
'title': 'riko pt. 1'}
>>> # In this case, if we just want the result, we can `emit` it instead
>>> stream = pipe(item, conf=tokenizer_conf, field='title', emit=True)
>>> next(stream)
{'content': 'riko'}
operators are split into sub-types of aggregators
and composers. aggregators, e.g., count, combine
all items of an input stream into a new stream with a single item;
while composers, e.g., filter, create a new stream containing
some or all items of an input stream.
>>> from riko.modules.count import pipe
>>>
>>> stream = [{'title': 'riko pt. 1'}, {'title': 'riko pt. 2'}]
>>> next(pipe(stream))
{'count': 2}
In case you are confused from the “Word Count” example up top, count can return
multiple items if you pass in the count_key config option.
>>> counted = pipe(stream, conf={'count_key': 'title'})
>>> next(counted)
{'riko pt. 1': 1}
>>> next(counted)
{'riko pt. 2': 1}
processors are split into sub-types of source and transformer.
sources, e.g., itembuilder, can create a stream while
transformers, e.g. hash can only transform items in a stream.
>>> from riko.modules.itembuilder import pipe
>>>
>>> attrs = {'key': 'title', 'value': 'riko pt. 1'}
>>> next(pipe(conf={'attrs': attrs}))
{'title': 'riko pt. 1'}
The following table summaries these observations:
type
sub-type
input
output
parallelizable?
creates streams?
operator
aggregator
stream
stream [15]
composer
stream
stream
processor
source
item
stream
√
√
transformer
item
stream
√
If you are unsure of the type of pipe you have, check its metadata.
>>> from riko.modules import fetchpage, count
>>>
>>> fetchpage.async_pipe.__dict__
{'type': 'processor', 'name': 'fetchpage', 'sub_type': 'source'}
>>> count.pipe.__dict__
{'type': 'operator', 'name': 'count', 'sub_type': 'aggregator'}
The SyncPipe and AsyncPipe classes (among other things) perform this
check for you to allow for convenient method chaining and transparent
parallelization.
>>> from riko.collections import SyncPipe
>>>
>>> attrs = [
... {'key': 'title', 'value': 'riko pt. 1'},
... {'key': 'content', 'value': "Let's talk about riko!"}]
>>> flow = SyncPipe('itembuilder', conf={'attrs': attrs}).hash()
>>> flow.list[0]
{'title': 'riko pt. 1',
'content': "Let's talk about riko!",
'hash': 1346301218}
Please see the cookbook for advanced examples including how to wire in
vales from other pipes or accept user input.
Notes
[15]
the output stream of an aggregator is an iterator of only 1 item.
Command-line Interface
riko provides a command, runpipe, to execute workflows. A
workflow is simply a file containing a function named pipe that creates
a flow and processes the resulting stream.
CLI Usage
usage: runpipe [pipeid]
description: Runs a riko pipe
positional arguments:
pipeid The pipe to run (default: reads from stdin).
optional arguments:
-h, --help
show this help message and exit
-a, --async
Load async pipe.
-t, --test
Run in test mode (uses default inputs).
CLI Setup
flow.py
from __future__ import print_function
from riko.collections import SyncPipe
conf1 = {'attrs': [{'value': 'https://google.com', 'key': 'content'}]}
conf2 = {'rule': [{'find': 'com', 'replace': 'co.uk'}]}
def pipe(test=False):
kwargs = {'conf': conf1, 'test': test}
flow = SyncPipe('itembuilder', **kwargs).strreplace(conf=conf2)
stream = flow.output
for i in stream:
print(i)
CLI Examples
Now to execute flow.py, type the command runpipe flow. You should
then see the following output in your terminal:
https://google.co.uk
runpipe will also search the examples directory for workflows. Type
runpipe demo and you should see the following output:
Deadline to clear up health law eligibility near 682
Scripts
riko comes with a built in task manager manage.
Setup
pip install riko[develop]
Examples
Run python linter and nose tests
manage lint
manage test
Contributing
Please mimic the coding style/conventions used in this repo.
If you add new classes or functions, please add the appropriate doc blocks with
examples. Also, make sure the python linter and nose tests pass.
Please see the contributing doc for more details.
Credits
Shoutout to pipe2py for heavily inspiring riko. riko started out as a fork
of pipe2py, but has since diverged so much that little (if any) of the original
code-base remains.
More Info
FAQ
Cookbook
iPython Notebook
Step-by-Step Intro. Tutorial
Project Structure
┌── benchmarks
│ ├── __init__.py
│ └── parallel.py
├── bin
│ └── run
├── data/*
├── docs
│ ├── AUTHORS.rst
│ ├── CHANGES.rst
│ ├── COOKBOOK.rst
│ ├── FAQ.rst
│ ├── INSTALLATION.rst
│ └── TODO.rst
├── examples/*
├── helpers/*
├── riko
│ ├── __init__.py
│ ├── lib
│ │ ├── __init__.py
│ │ ├── autorss.py
│ │ ├── collections.py
│ │ ├── dotdict.py
│ │ ├── log.py
│ │ ├── tags.py
│ │ └── py
│ ├── modules/*
│ └── twisted
│ ├── __init__.py
│ ├── collections.py
│ └── py
├── tests
│ ├── __init__.py
│ ├── standard.rc
│ └── test_examples.py
├── CONTRIBUTING.rst
├── dev-requirements.txt
├── LICENSE
├── Makefile
├── manage.py
├── MANIFEST.in
├── optional-requirements.txt
├── py2-requirements.txt
├── README.rst
├── requirements.txt
├── setup.cfg
├── setup.py
└── tox.ini
License
riko is distributed under the MIT License.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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