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Ideas and goals behind the Go Kafka Client

December 10, 2014 Leave a comment

I think a bunch of folks have heard already that B.D.O.S.S. was working on a new Apache Kafka Client For Go. Go Kafka Client was open sourced last Friday. Today we are starting the release of Minotaur which is our lab environment for Apache Zookeeper, Apache Mesos, Apache CassandraApache Kafka, Apache Hadoop and our new Go Kafka Client.

To get started using the consumer client check out our example code and its property file.

Ideas and goals behind the Go Kafka Client:

 

1) Partition Ownership

We decided on implementing multiple strategies for this including static assignment. The concept of re-balancing is preserved but now there are a few different strategies to re-balancing and they can run at different times depending on what is going on (like a blue/green deploy is happening). For more on blue/green deployments check out this video with Jim Plush and Sean Berry from 2014 AWS re:Invent.
 

2) Fetch Management

This is what “fills up the reservoir” as I like to call it so the processing (either sequential or in batch) will always have data if there is data for it to have without making a network hop. The fetcher has to stay ahead here keeping the processing tap full (or if empty that is controlled) pulling the data for the Kafka partition(s) it is owning.
 

3) Work Management

For the Go consumer we currently only support “fan out” using go routines and channels. If you have ever used go this will be familiar to you if not you should drop everything and learn Go.
 

4) Offset Management

Our offset management is based on a per batch basis with each highest offset from the batch committed on a per partition basis.
 
func main() {
	config, consumerIdPattern, topic, numConsumers, graphiteConnect, graphiteFlushInterval := resolveConfig()
	startMetrics(graphiteConnect, graphiteFlushInterval)

	ctrlc := make(chan os.Signal, 1)
	signal.Notify(ctrlc, os.Interrupt)

	consumers := make([]*kafkaClient.Consumer, numConsumers)
	for i := 0; i < numConsumers; i++ {
		consumers[i] = startNewConsumer(*config, topic, consumerIdPattern, i)
		time.Sleep(10 * time.Second)
	}

	<-ctrlc
	fmt.Println("Shutdown triggered, closing all alive consumers")
	for _, consumer := range consumers {
		<-consumer.Close()
	}
	fmt.Println("Successfully shut down all consumers")
}

func startMetrics(graphiteConnect string, graphiteFlushInterval time.Duration) {
	addr, err := net.ResolveTCPAddr("tcp", graphiteConnect)
	if err != nil {
		panic(err)
	}
	go metrics.GraphiteWithConfig(metrics.GraphiteConfig{
		Addr:          addr,
		Registry:      metrics.DefaultRegistry,
		FlushInterval: graphiteFlushInterval,
		DurationUnit:  time.Second,
		Prefix:        "metrics",
		Percentiles:   []float64{0.5, 0.75, 0.95, 0.99, 0.999},
	})
}

func startNewConsumer(config kafkaClient.ConsumerConfig, topic string, consumerIdPattern string, consumerIndex int) *kafkaClient.Consumer {
	config.Consumerid = fmt.Sprintf(consumerIdPattern, consumerIndex)
	config.Strategy = GetStrategy(config.Consumerid)
	config.WorkerFailureCallback = FailedCallback
	config.WorkerFailedAttemptCallback = FailedAttemptCallback
	consumer := kafkaClient.NewConsumer(&config)
	topics := map[string]int {topic : config.NumConsumerFetchers}
	go func() {
		consumer.StartStatic(topics)
	}()
	return consumer
}

func GetStrategy(consumerId string) func(*kafkaClient.Worker, *kafkaClient.Message, kafkaClient.TaskId) kafkaClient.WorkerResult {
	consumeRate := metrics.NewRegisteredMeter(fmt.Sprintf("%s-ConsumeRate", consumerId), metrics.DefaultRegistry)
	return func(_ *kafkaClient.Worker, msg *kafkaClient.Message, id kafkaClient.TaskId) kafkaClient.WorkerResult {
		kafkaClient.Tracef("main", "Got a message: %s", string(msg.Value))
		consumeRate.Mark(1)

		return kafkaClient.NewSuccessfulResult(id)
	}
}

func FailedCallback(wm *kafkaClient.WorkerManager) kafkaClient.FailedDecision {
	kafkaClient.Info("main", "Failed callback")

	return kafkaClient.DoNotCommitOffsetAndStop
}

func FailedAttemptCallback(task *kafkaClient.Task, result kafkaClient.WorkerResult) kafkaClient.FailedDecision {
	kafkaClient.Info("main", "Failed attempt")

	return kafkaClient.CommitOffsetAndContinue
}
We set our mailing list to be kafka-clients@googlegroups.com this is the central place for client library discussions for the Apache Kafka community.
 

Plans moving forward with the Go Kafka Client:

1) Build up suite of integration tests.
2) Stabilize the API nomenclature with the JVM Kafka Client for consistency.
3) Run integration tests and create baseline regression points.
4) Re-work changes from feedback of issues found and from our own customer support.
5) Push to production and continue on going support.
 
 

Ideas and goals behind Minotaur:

Provide a controlled and isolated environment through scripts for regression of versions and benchmarks against instance sizing. This comes in 3 parts: 1) Supervisor 2) Infrastructure 3) Labs. The Labs portion is broker down with both chef cook books and python wrappers for cloud formation templates for each of the following systems:
 

Plans moving forward with Minotaur:

1) Upstream our Cloud Formation Scripts (this is going to take some more work detangling Dexter (the name for our private repo of Minotaur).
2) Move the work we do with Apache Cassandra into the project.
3) Start support of Puppet in addition to the work we did with Chef.
4) Get the Docker supervisor console pushed upstream through Bastion hosts.
5) Cut our labs over to using Minotaur.
 
/*******************************************
 Joe Stein
 Founder, Principal Consultant
 Big Data Open Source Security LLC
 Twitter: @allthingshadoop
********************************************/

Hadoop Streaming Made Simple using Joins and Keys with Python

December 16, 2011 10 comments

There are a lot of different ways to write MapReduce jobs!!!

Sample code for this post https://github.com/joestein/amaunet

I find streaming scripts a good way to interrogate data sets (especially when I have not worked with them yet or are creating new ones) and enjoy the lifecycle when the initial elaboration of the data sets lead to the construction of the finalized scripts for an entire job (or series of jobs as is often the case).

When doing streaming with Hadoop you do have a few library options.  If you are a Ruby programmer then wukong is awesome! For Python programmers you can use dumbo and more recently released mrjob.

I like working under the hood myself and getting down and dirty with the data and here is how you can too.

Lets start first with defining two simple sample data sets.

Data set 1:  countries.dat

name|key

United States|US
Canada|CA
United Kingdom|UK
Italy|IT

Data set 2: customers.dat

name|type|country

Alice Bob|not bad|US
Sam Sneed|valued|CA
Jon Sneed|valued|CA
Arnold Wesise|not so good|UK
Henry Bob|not bad|US
Yo Yo Ma|not so good|CA
Jon York|valued|CA
Alex Ball|valued|UK
Jim Davis|not so bad|JA

The requirements: you need to find out grouped by type of customer how many of each type are in each country with the name of the country listed in the countries.dat in the final result (and not the 2 digit country name).

To-do this you need to:

1) Join the data sets
2) Key on country
3) Count type of customer per country
4) Output the results

So first lets code up a quick mapper called smplMapper.py (you can decide if smpl is short for simple or sample).

Now in coding the mapper and reducer in Python the basics are explained nicely here http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/ but I am going to dive a bit deeper to tackle our example with some more tactics.

#!/usr/bin/env python

import sys

# input comes from STDIN (standard input)
for line in sys.stdin:
	try: #sometimes bad data can cause errors use this how you like to deal with lint and bad data
        
		personName = "-1" #default sorted as first
		personType = "-1" #default sorted as first
		countryName = "-1" #default sorted as first
		country2digit = "-1" #default sorted as first
		
		# remove leading and trailing whitespace
		line = line.strip()
	 	
		splits = line.split("|")
		
		if len(splits) == 2: #country data
			countryName = splits[0]
			country2digit = splits[1]
		else: #people data
			personName = splits[0]
			personType = splits[1]
			country2digit = splits[2]			
		
		print '%s^%s^%s^%s' % (country2digit,personType,personName,countryName)
	except: #errors are going to make your job fail which you may or may not want
		pass

Don’t forget:

chmod a+x smplMapper.py

Great! We just took care of #1 but time to test and see what is going to the reducer.

From the command line run:

cat customers.dat countries.dat|./smplMapper.py|sort

Which will result in:

CA^-1^-1^Canada
CA^not so good^Yo Yo Ma^-1
CA^valued^Jon Sneed^-1
CA^valued^Jon York^-1
CA^valued^Sam Sneed^-1
IT^-1^-1^Italy
JA^not so bad^Jim Davis^-1
UK^-1^-1^United Kingdom
UK^not so good^Arnold Wesise^-1
UK^valued^Alex Ball^-1
US^-1^-1^United States
US^not bad^Alice Bob^-1
US^not bad^Henry Bob^-1

Notice how this is sorted so the country is first and the people in that country after it (so we can grab the correct country name as we loop) and with the type of customer also sorted (but within country) so we can properly count the types within the country. =8^)

Let us hold off on #2 for a moment (just hang in there it will all come together soon I promise) and get smplReducer.py working first.

#!/usr/bin/env python
 
import sys
 
# maps words to their counts
foundKey = ""
foundValue = ""
isFirst = 1
currentCount = 0
currentCountry2digit = "-1"
currentCountryName = "-1"
isCountryMappingLine = False

# input comes from STDIN
for line in sys.stdin:
	# remove leading and trailing whitespace
	line = line.strip()
	
	try:
		# parse the input we got from mapper.py
		country2digit,personType,personName,countryName = line.split('^')
		
		#the first line should be a mapping line, otherwise we need to set the currentCountryName to not known
		if personName == "-1": #this is a new country which may or may not have people in it
			currentCountryName = countryName
			currentCountry2digit = country2digit
			isCountryMappingLine = True
		else:
			isCountryMappingLine = False # this is a person we want to count
		
		if not isCountryMappingLine: #we only want to count people but use the country line to get the right name 

			#first check to see if the 2digit country info matches up, might be unkown country
			if currentCountry2digit != country2digit:
				currentCountry2digit = country2digit
				currentCountryName = '%s - Unkown Country' % currentCountry2digit
			
			currentKey = '%s\t%s' % (currentCountryName,personType) 
			
			if foundKey != currentKey: #new combo of keys to count
				if isFirst == 0:
					print '%s\t%s' % (foundKey,currentCount)
					currentCount = 0 #reset the count
				else:
					isFirst = 0
			
				foundKey = currentKey #make the found key what we see so when we loop again can see if we increment or print out
			
			currentCount += 1 # we increment anything not in the map list
	except:
		pass

try:
	print '%s\t%s' % (foundKey,currentCount)
except:
	pass

Don’t forget:

chmod a+x smplReducer.py

And then run:

cat customers.dat countries.dat|./smplMapper.py|sort|./smplReducer.py

And voila!

Canada	not so good	1
Canada	valued	3
JA - Unkown Country	not so bad	1
United Kingdom	not so good	1
United Kingdom	valued	1
United States	not bad	2

So now #3 and #4 are done but what about #2? 

First put the files into Hadoop:

hadoop fs -put ~/mayo/customers.dat .
hadoop fs -put ~/mayo/countries.dat .

And now run it like this (assuming you are running as hadoop in the bin directory):

hadoop jar ../contrib/streaming/hadoop-0.20.1+169.89-streaming.jar -D mapred.reduce.tasks=4 -file ~/mayo/smplMapper.py -mapper smplMapper.py -file ~/mayo/smplReducer.py -reducer smplReducer.py -input customers.dat -input countries.dat -output mayo -partitioner org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner -jobconf stream.map.output.field.separator=^ -jobconf stream.num.map.output.key.fields=4 -jobconf map.output.key.field.separator=^ -jobconf num.key.fields.for.partition=1

Let us look at what we did:

hadoop fs -cat mayo/part*

Which results in:

Canada	not so good	1
Canada	valued	3
United Kingdom	not so good	1
United Kingdom	valued	1
United States	not bad	2
JA - Unkown Country	not so bad	1

So #2 is the partioner KeyFieldBasedPartitioner explained here further Hadoop Wiki On Streaming which allows the key to be whatever set of columns you output (in our case by country) configurable by the command line options and the rest of the values are sorted within that key and sent to the reducer together by key.

And there you go … Simple Python Scripting Implementing Streaming in Hadoop.  

Grab the tar here and give it a spin.

/*
Joe Stein
Twitter: @allthingshadoop
Connect: On Linked In
*/

Categories: Hadoop, MapReduce, Python
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