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bigfish.
bigdata lake.
MapReduce Framework
<key, value>
(input) <k1, v1> -> map -> <k2, v2> -> combine-> <k2, v2> -> reduce-> <k3, v3> (output)
// Somedata
private int counter;
privatelong timestamp;
public void write(DataOutput out)
throws IOException {
out.writeInt(counter);
out.writeLong(timestamp);
{
{
_
_
Plugins
input of first map:
output of first map:
< Hello, 1>
< World, 1>
< Bye, 1>
< World, 1>
_
< Bye, 1>
< Hello, 1>
< World, 2>
_
conf configuration_file
Dproperty=value
fs host:port or local
_
_
files_
archives
lessthan
0.5%
of that data
will be analyzed
andused
murkywater
1.7 MB 
of newinfo
will be created
everysecondfor
everyhumanbeing
onthe planet
by2020
flexingyour gills
ina bigdata lake
Collect
void collect(K key,
V value)
throws IOException
_ Parameters:
key - the key to collect.
value - to value to collect.
_
Throws:
IOException
_
#!/ usr/bin/python
import sys;
def generateLongCountToken(id):
return"LongValueSum:" + id+ "t" + "1"
def main(argv):
line= sys.stdin.readline();
try:
whileline:
line=line[:-1];
fields = line.split("t");
print generateLongCountToken(fields[0]);
line= sys.stdin.readline();
except "endof file":
returnNone
if __name__=="__main__":
main(sys.argv)
swimfast-to-wow
$ bin/ hadoop dfs -ls /usr/joe/wordcount/input/
/usr/joe/wordcount/input/file01
/usr/joe/wordcount/input/file02
$ bin/ hadoop dfs -cat / usr/joe/wordcount/input/
file01
HelloWorld ByeWorld
$ bin/ hadoop dfs -cat / usr/joe/wordcount/input/
file02
Hello Hadoop Goodbye Hadoop
$ mkdir wordcount_classes
$ javac -classpath ${HADOOP_HOME}/ hadoop-${HADOOP_VERSION}-core.jar -d wordcount_classes
WordCount.java
$ jar -cvf /usr/joe/wordcount.jar -Cwordcount_classes//
scale for digitization
get reel protective
bin/ hadoop pipes 
[-input inputDir] 
[-output outputDir] 
[-jar applicationJarFile] 
[-inputformat class] 
[-mapclass] 
[-partitioner class] 
[-reduceclass] 
[-writer class] 
[-programprogram url] 
[-conf configurationfile] 
[-Dproperty=value] 
[-fs local|namenode:port ] 
[-jt local|jobtracker:port ] 
[-files commaseparatedlist of files] 
[-libjars commaseparatedlist of jars] 
[-archives commaseparatedlist of archives]
incrCounter
void incrCounter(String group,
String counter ,
long amount)
incrCounter
void incrCounter(String group,
String counter ,
long amount)
off-the-hook
competitive advantage
a 10%
increase in
data accessibility
will result inmore than
$65 million
additional
net income
{
BufferedReader fis =new BufferedReader (new
FileReader(patternsFile.toString()));
String pattern=null;
while((pattern= fis.readLine()) !=null) {
patternsToSkip.add(pattern);
}
} catch(IOException ioe) {
System.err.println("Caught exceptionwhileparsing thecachedfile'" +
patternsFile + "' : " + StringUtils.stringifyException(ioe));
}
}
public voidmap(LongWritable key, Text value, OutputCollector
<Text, IntWritable> output, Reporter reporter) throws IOException {
String line=(caseSensitive) ?value.toString() : value.toString()
.toLowerCase(); for (String pattern: patternsToSkip) {
line=line.replaceAll(pattern, "");
}
StringTokenizer tokenizer =new StringTokenizer(line);
}

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BMC Keynote @ 2016 Hadoop Summit

  • 1. bigfish. bigdata lake. MapReduce Framework <key, value> (input) <k1, v1> -> map -> <k2, v2> -> combine-> <k2, v2> -> reduce-> <k3, v3> (output) // Somedata private int counter; privatelong timestamp; public void write(DataOutput out) throws IOException { out.writeInt(counter); out.writeLong(timestamp); { {
  • 2. _ _ Plugins input of first map: output of first map: < Hello, 1> < World, 1> < Bye, 1> < World, 1> _ < Bye, 1> < Hello, 1> < World, 2> _ conf configuration_file Dproperty=value fs host:port or local _ _ files_ archives lessthan 0.5% of that data will be analyzed andused murkywater 1.7 MB  of newinfo will be created everysecondfor everyhumanbeing onthe planet by2020
  • 3. flexingyour gills ina bigdata lake Collect void collect(K key, V value) throws IOException _ Parameters: key - the key to collect. value - to value to collect. _ Throws: IOException _
  • 4. #!/ usr/bin/python import sys; def generateLongCountToken(id): return"LongValueSum:" + id+ "t" + "1" def main(argv): line= sys.stdin.readline(); try: whileline: line=line[:-1]; fields = line.split("t"); print generateLongCountToken(fields[0]); line= sys.stdin.readline(); except "endof file": returnNone if __name__=="__main__": main(sys.argv) swimfast-to-wow
  • 5. $ bin/ hadoop dfs -ls /usr/joe/wordcount/input/ /usr/joe/wordcount/input/file01 /usr/joe/wordcount/input/file02 $ bin/ hadoop dfs -cat / usr/joe/wordcount/input/ file01 HelloWorld ByeWorld $ bin/ hadoop dfs -cat / usr/joe/wordcount/input/ file02 Hello Hadoop Goodbye Hadoop $ mkdir wordcount_classes $ javac -classpath ${HADOOP_HOME}/ hadoop-${HADOOP_VERSION}-core.jar -d wordcount_classes WordCount.java $ jar -cvf /usr/joe/wordcount.jar -Cwordcount_classes// scale for digitization
  • 6. get reel protective bin/ hadoop pipes [-input inputDir] [-output outputDir] [-jar applicationJarFile] [-inputformat class] [-mapclass] [-partitioner class] [-reduceclass] [-writer class] [-programprogram url] [-conf configurationfile] [-Dproperty=value] [-fs local|namenode:port ] [-jt local|jobtracker:port ] [-files commaseparatedlist of files] [-libjars commaseparatedlist of jars] [-archives commaseparatedlist of archives] incrCounter void incrCounter(String group, String counter , long amount)
  • 7. incrCounter void incrCounter(String group, String counter , long amount) off-the-hook competitive advantage a 10% increase in data accessibility will result inmore than $65 million additional net income { BufferedReader fis =new BufferedReader (new FileReader(patternsFile.toString())); String pattern=null; while((pattern= fis.readLine()) !=null) { patternsToSkip.add(pattern); } } catch(IOException ioe) { System.err.println("Caught exceptionwhileparsing thecachedfile'" + patternsFile + "' : " + StringUtils.stringifyException(ioe)); } } public voidmap(LongWritable key, Text value, OutputCollector <Text, IntWritable> output, Reporter reporter) throws IOException { String line=(caseSensitive) ?value.toString() : value.toString() .toLowerCase(); for (String pattern: patternsToSkip) { line=line.replaceAll(pattern, ""); } StringTokenizer tokenizer =new StringTokenizer(line); }

Hinweis der Redaktion

  1. Businesses are struggling to keep pace with the massive glut of data from digitization -- and it’s about to get even murkier with the digital universe doubling every 2 years. By the year 2020, about 1.7 MB of new info will be created every second for every human being on the planet.   The data lake pays no attention to how or when this gluttony of info will be used, governed, defined or secured. This is where you can play a pivotal role in helping your businesses get a competitive edge.
  2. Becoming the strongest fish in the big data lake takes more than just brains and brawn. You need a little automated help along the way that allows you to focus on what you do best – innovating – and let software that understands the future state now manage the process of getting your applications to market fast.   Here are 3 ways automation ensures you can flex your gills often and with great success in the data lake.
  3. Control-M’s enterprise integration provides views beyond the data lake needed to tackle issues before they slow you down. Automating Hadoop batch workflows from development, to test, to production – working the way you want while adhering to organizational standards – enables you to build and release your applications faster.   Gone are the days of time consuming, manual and error-prone scripting. You now have full power at your fingertips to take ownership of the complex scheduling and integration requirements to drive your Hadoop projects forward.
  4. Make infrastructure growth in your Hadoop clusters predictable, so your apps are optimized for performance – and your big data investment is as well.   Scale up or down, in deep or shallow data lakes, with the ability to easily add, remove, or adjust compute, storage, network, and other infrastructure resources to meet changing workload demands.   With the ability to easily visualize resource utilization you can focus more on driving measurable business value.
  5. Protect your apps and the business data inside them with BMC Discovery. Automate application dependency mapping and get an up-to-date view of everything in your applications’ environments. Once you have maps, you can merge them with patterns that indicate where problems may be and do predictive analytics to look for patterns of activity around a certain item so you can prevent application failures.
  6. In this brave new digital world, getting the right insights at the right time across every scrap of data matters.   BMC Big Data Solutions empower you to innovate in the murkiest of waters -- harnessing the power of data to automate Hadoop batch processing across development, test and production environments, optimize the utilization of Hadoop clusters and connect the Hadoop ecosystem with the enterprise ecosystem to get a holistic view into infrastructure, performance, workflows and services.   It’s time to make data your new weapon for achieving off-the-hook competitive advantage.