“The temptation to form premature theories upon insufficient data is the bane of our profession.”
Sherlock Holmes in the Valley of fear by Arthur Conan Doyle
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”
Jim Barksdale, former CEO Netscape
In this post I use Apache NiFi Dataflow Pipeline along with my Python package yorkpy to crunch through cricket data from Cricsheet. The Data Pipelne flows all the way from the source to target analytics output. Apache NiFi was created to automate the flow of data between systems. NiFi dataflows enable the automated and managed flow of information between systems. This post automates the flow of data from Cricsheet, from where the zip file it is downloaded, unpacked, processed, transformed and finally T20 players are ranked.
While this is a straight forward example of what can be done, this pattern can be applied to real Big Data systems. For example hypothetically, we could consider that we get several parallel streams of cricket data or for that matter any sports related data. There could be parallel Data flow pipelines that get the data from the sources. This would then be followed by data transformation modules and finally a module for generating analytics. At the other end a UI based on AngularJS or ReactJS could display the results in a cool and awesome way.
Incidentally, the NiFi pipeline that I discuss in this post, is a simplistic example, and does not use the Big Data stack like HDFS, Hive, Spark etc. Nevertheless, the pattern used, has all the modules for a Big Data pipeline namely ingestion, unpacking, transformation and finally analytics. This NiF pipeline demonstrates the flow using the regular file system of Mac and my python based package yorkpy. The concepts mentioned could be used in a real Big Data scenario which has much fatter pipes of data coming. If this was the case the NiFi pipeline would utilize HDFS/Hive for storing the ingested data and Pyspark/Scala for the transformation and analytics and other related technologies.
A pictorial representation is given below
In the diagram above each of the vertical boxes could be any technology from the ever proliferating Big Data stack namely HDFS, Hive, Spark, Sqoop, Kafka, Impala and so on. Such a dataflow automation could be created when any big sporting event happens, as long as the data generated large, and there is a need for dynamic and automated reporting. The UI could be based on AngularJS/ReactJS and could display analytical tables and charts.
This post demonstrates one such scenario in which IPL T20 data is downloaded from Cricsheet site, unpacked and stored in a specific directory. This dataflow automation is based on my yorkpy package. To know more about the yorkpy package see Pitching yorkpy … short of good length to IPL – Part 1 and the associated parts. The zip file, from Cricsheet, contains individual IPL T20 matches in YAML format. The convertYaml2DataframeT20() function is used to convert the YAML files into Pandas dataframes before storing them as CSV files. After this done, the function rankIPLT20batting() function is used to perform the overall ranking of the T20 players. My yorkpy Python package has about ~ 50+ functions that perform various analytics on any T20 data for e.g it has the following classes of functions
- analyze T20 matches
- analyze performance of a T20 team in all matches against another T20 team
- analyze performance of a T20 team against all other T20 teams
- analyze performance of T20 batsman and bowlers
- rank T20 batsmen and bowlers
The functions of yorkpy generate tables or charts. While this post demonstrates one scenario, we could use any of the yorkpy T20 functions, generate the output and display on a widget in the UI display, created with cool technologies like AngularJS/ReactJS, possibly in near real time as data keeps coming in.,
To use yorkpy with NiFI the following packages have to be installed in your environment
-pip install yorkpy
-pip install pyyaml
-pip install pandas
-yum install python-devel (equivalent in Windows)
-pip install matplotlib
-pip install seaborn
-pip install sklearn
-pip install datetime
I have created a video of the NiFi Pipeline with the real dataflow fro source to the ranked IPL T20 batsmen. Take a look at RankingT20PlayersWithNiFiYorkpy
You can clone/fork the NiFi template from rankT20withNiFiYorkpy
The NiFi Data Flow Automation is shown below
1. Overall flow
The overall NiFi flow contains 2 Process Groups a) DownloadAnd Unpack. b) Convert and Rank IPL batsmen. While it appears that the Process Groups are disconnected, they are not. The first process group downloads the T20 zip file, unpacks the. zip file and saves the YAML files in a specific folder. The second process group monitors this folder and starts processing as soon the YAML files are available. It processes the YAML converting it into dataframes before storing it as CSV file. The next processor then does the actual ranking of the batsmen before writing the output into IPLrank.txt
1.1 DownloadAndUnpack Process Group
This process group is shown below
1.1.1 GetT20Data
The GetT20Data Processor downloads the zip file given the URL
The ${T20data} variable points to the specific T20 format that needs to be downloaded. I have set this to https://cricsheet.org/downloads/ipl.zip. This could be set any other data set. In fact we could have parallel data flows for different T20/ Sports data sets and generate
1.1.2 SaveUnpackedData
This processor stores the YAML files in a predetermined folder, so that the data can be picked up by the 2nd Process Group for processing
1.2 ProcessAndRankT20Players Process Group
This is the second process group which converts the YAML files to pandas dataframes before storing them as. CSV files. The RankIPLPlayers will then read all the CSV files, stack them and then proceed to rank the IPL players. The Process Group is shown below
1.2.1 ListFile and FetchFile Processors
The left 2 Processors ListFile and FetchFile get all the YAML files from the folder and pass it to the next processor
1.2.2 convertYaml2DataFrame Processor
The convertYaml2DataFrame Processor uses the ExecuteStreamCommand which call a python script. The Python script invoked the yorkpy function convertYaml2Dataframe() as shown below
The ${convertYaml2Dataframe} variable points to the python file below which invoked the yorkpy function yka.convertYaml2PandasDataframeT20()
import yorkpy.analytics as yka
import argparse
parser = argparse.ArgumentParser(description='convert')
parser.add_argument("yamlFile",help="YAML File")
args=parser.parse_args()
yamlFile=args.yamlFile
yka.convertYaml2PandasDataframeT20(yamlFile,"/Users/tvganesh/backup/software/nifi/ipl","/Users/tvganesh/backup/software/nifi/ipldata")
This function takes as input $filename which comes from FetchFile processor which is a FlowFile. So I have added a concurrency of 8 to handle upto 8 Flowfiles at a time. The thumb rule as I read on the internet is 2x, 4x the number of cores of your system. Since I have an 8 core Mac, I could possibly have gone ~ 30 concurrent threads. Also the number of concurrent threads is less when the flow is run in a Oracle Box VirtualMachine. Box since a vCore < actual Core
The scheduling tab is as below
Here are the 8 concurrent Python threads on Mac at bottom right… (pretty cool!)
I have not fully tested how latency vs throughput slider changes, affects the performance.
1.2.3 MergeContent Processor
This processor’s only job is to trigger the rankIPLPlayers when all the FlowFiles have merged into 1 file.
1.2.4 RankT20Players
This processor is an ExecuteStreamCommand Processor that executes a Python script which invokes a yorkpy function rankIPLT20Batting()
import yorkpy.analytics as yka
rank=yka.rankIPLT20Batting("/Users/tvganesh/backup/software/nifi/ipldata")
print(rank.head(15))
1.2.5 OutputRankofT20Player Processor
This processor writes the generated rank to an output file.
1.3 Final Ranking of IPL T20 players
The Nodejs based web server picks up this file and displays on the web page the final ranks (the code is based on a good youtube for reading from file)
2. Final thoughts
As I have mentioned above though the above NiFi Cricket Dataflow automation does not use the Hadoop ecosystem, the pattern used is valid and can be used with some customization in Big Data flows as parallel stream. I could have also done this on Oracle VirtualBox but I thought since the code is based on Python and Pandas there is no real advantage of running on the VirtualBox. GIve the NiFi flow a shot. Have fun!!!
Also see
1.My book ‘Deep Learning from first Practical Machine Learning with R and Python – Part 5
Edition’ now on Amazon
2. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
3.De-blurring revisited with Wiener filter using OpenCV
4. Practical Machine Learning with R and Python – Part 5
5. Natural language processing: What would Shakespeare say?
6.Getting started with Tensorflow, Keras in Python and R
7.Revisiting World Bank data analysis with WDI and gVisMotionChart
To see all posts click Index of posts
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