Streamlit is a nice tool to turn data into viewable web apps rapidly.
Streamlit executes a single Python file and performs reloads and reruns of the Python file on change.
MapReduce represents a pattern that had a huge impact on the data analysis and big data community. Apache Hadoop allows to scatter and scale data processing with the number of nodes and cores.
One of the many corner points in this full framework is that code is shipped and executed on-site where the data resides. Next, only a pre-processed transformed version (map) of the data is then shuffled and sorted to the aggregators on different executors via the network.
MapReduce is hard to use on its own, so it usually is deployed with
Apache Hadoop or Apache Spark. To play around with it without either one of those large frameworks, I created one in Python – MapReduceSlim. It emulates all core features of the MapReduce. It has one difference, it loads each line of the files separately into the map function. In the case of Apache Hadoop, it would be block-wise. This provides a nice solution to understand the behavior and the pattern of MapReduce and how to implement a mapper and reducer.
# Hint: in MapReduce with Hadoop Streaming the # input comes from standard input STDIN def wc_mapper(key: str, values: str): # remove leading and trailing whitespaces line = values.strip() # split the line into words words = line.split() for word in words: # write the results to standard # output STDOUT yield word, 1
def wc_reducer(key: str, values: list): current_count = 0 word = key for value in values: current_count += value yield word, current_count
Finally, call the function with the MapReduceSlim framework
# Import the slim framework from map_reduce_slim import MapReduceSlim, wc_mapper, wc_reduce ### One input file version # Read the content from one file and use the # content as input for the run. MapReduceSlim('davinci.txt', 'davinci_wc_result_one_file.txt', wc_mapper, wc_reducer) ### Directory input version # Read all files in the given directory and # use the content as input for the run. MapReduceSlim('davinci_split', 'davinci_wc_result_multiple_file.txt', wc_mapper, wc_reducer)
Further information @ Github: https://github.com/2er0/MapReduceSlim
Screenshot of Linus Tech Vessel channel, © Linus Tech, Vessel
A little over a year ago, Vessel launched as an alternative video platform, trying to demonopolize YouTube as the quasi only option to upload videos professionally (i.e. earn money with your videos).
To get a foot into the market, they made deals with a lot of Youtube personalities to upload to vessel a week in advance and instead of showing users advertisements, either in the form of prerolls or sponsor spots in the middle of the video, they collect a small monthly subscription fee. Vessel boldly announced their launch in the form of sponsored YouTube videos giving away a full year of premium subscriptions to everyone signing up in the first month or so.
In March 2016 the free premium accounts expired, and I wanted to know, how many people are still watching on vessel now, compared to when they had a free premium account.
Sadly, Vessel doesn’t disclose a view count, or how many people have purchased a premium account after the first month. They do however have a ‚like-button‘ below every video and a comment counter. I decided to at least get a rough idea about their view count compared to last year.