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NEW QUESTION: 1
To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory. What is the best way to accomplish this?
A. Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper.
B. Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper.
C. Place the data file in the DistributedCache and read the data into memory in the map method of the mapper.
D. Place the data file in the DataCache and read the data into memory in the configure method of the mapper.
Answer: C
Explanation:
Hadoop has a distributed cache mechanism to make available file locally that may be needed by Map/Reduce jobs
Use Case
Lets understand our Use Case a bit more in details so that we can follow-up the code snippets. We have a Key-Value file that we need to use in our Map jobs. For simplicity, lets say we need to replace all keywords that we encounter during parsing, with some other value.
So what we need is
A key-values files (Lets use a Properties files) The Mapper code that uses the code
Write the Mapper code that uses it
view sourceprint?
01.
public class DistributedCacheMapper extends Mapper<LongWritable, Text, Text, Text> {
02.
03.
Properties cache;
04.
05.
@Override
06.
protected void setup(Context context) throws IOException, InterruptedException {
07.
super.setup(context);
08.
Path[] localCacheFiles = DistributedCache.getLocalCacheFiles(context.getConfiguration());
09.
10.
if(localCacheFiles != null) {
11.
// expecting only single file here
12.
for (int i = 0; i < localCacheFiles.length; i++) {
13.
Path localCacheFile = localCacheFiles[i];
14.
cache = new Properties();
15.
cache.load(new FileReader(localCacheFile.toString()));
16.
}
17.
} else {
18.
// do your error handling here
19.
}
20.
21.
}
22.
23.
@Override
24.
public void map(LongWritable key, Text value, Context context) throws IOException,
InterruptedException {
25.
// use the cache here
26.
// if value contains some attribute, cache.get(<value>)
27.
// do some action or replace with something else
28.
}
29.
30.
}
Note:
* Distribute application-specific large, read-only files efficiently.
DistributedCache is a facility provided by the Map-Reduce framework to cache files (text, archives, jars etc.) needed by applications.
Applications specify the files, via urls (hdfs:// or http://) to be cached via the JobConf. The DistributedCache assumes that the files specified via hdfs:// urls are already present on the FileSystem at the path specified by the url.
Reference: Using Hadoop Distributed Cache

NEW QUESTION: 2
Which two statements about the monitored traffic in a SPAN session are true? (Choose two )
A. Ingress SPAN monitors packets received by the source interface before any QoS modifications
B. By default, all monitored packets include the IEEE 802.1Q tag that they had on the source port
C. Egress SPAN monitors packets sent by the source interface before any QoS modifications
D. You cannot configure two separate SPAN or RSPAN source sessions with separate or overlapping sets of SPAN source ports and VLANs.
E. Sources can be ports or VLANs or any combination in the same session
F. By default, all monitored packets are captured without the IEEE 802.1Q tag
Answer: C,E

NEW QUESTION: 3
The output of the risk management process is an input for making:
A. software design decisions.
B. business plans.
C. audit charters.
D. security policy decisions.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The risk management process is about making specific, security-related decisions, such as the level of acceptable risk. Choices A, B and D are not ultimate goals of the risk management process.