Apache Hadoop India Summit 2011 talk "Hadoop Map-Reduce Programming & Best Practices" by Basant Verma

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Map-Reduce Programming & Best Practices
Apache Hadoop India Summit 2011
Basant Verma
Yahoo! India R&D
February 16, 2011

Hadoop Components
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Client 1
Client 2
Processing Framework
DFS
HDFS (Hadoop Distributed File System)
Modeled on GFS
Reliable, High Bandwidth file system that can store TB' and PB's data.
Map-Reduce
Using Map/Reduce metaphor from Lisp language
A distributed processing framework paradigm that process the data stored onto HDFS in key-value.

Word Count DataFlow

Word Count
$ cat ~/wikipedia.txt |
sed -e 's/ /n/g' | grep . |
sort |
uniq -c >
~/frequencies.txt
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MR for Word-Count
mapper (filename, file-contents):
for each word in file-contents:
emit (word, 1)
reducer (word, values[]):
sum = 0
for each value in values:
sum = sum + value
emit (word, sum)
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MR Dataflow
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MapReduce Pipeline
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Pipeline Details
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Available tweaks and optimizations!
Input to Maps
Map only jobs
Combiner
Compression
Speculation
Fault Tolerance
Buffer Size
Parallelism (threads)
Partitioner
Reporter
DistributedCache
Task child environment settings

Input to Map
Maps should process significant amount of data to minimize the effect of overhead.
Process multiple-files per map for jobs with very large number of small input files.
Process large chunk of data for large scale processing
Use as fewer maps to process data in parallel, as few as possible without having bad failure recovery cases.
Unless the application's maps are heavily CPU bound, there is almost no reason to ever require more than 60,000-70,000 maps for a single application.
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Map only jobs
Run map only job once for generating data
Run multiple jobs with different reduce implementations
Map only jobs will write directly to HDFS

Combiner
Provides map-side aggregation of data
Each and every record emitted by the Mapper need not be shipped to the reducers.
Reduce code can be used as combiner. Example : Word count!
Helps reduce network traffic for the shuffle. Results in lesser disk space usage.
However, it is important to ensure that
Ensure they really work
the Combiner does provide sufficient aggregation.

Compression
Map and Reduce outputs can be compressed
Compressing intermediate data will help reduce the amount of disk usage and network I/O.
Compression helps reduce the total data size on the DFS.

Shuffle
Shuffle Phase performance depends on the crossbar between the map tasks and the reduce tasks, which must be minimized.
Compression of intermediate output
Use of Combiner
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Reduces
Configure appropriate number of reduces
Too few hurt the nodes
Too many hurt the cross-bar
All reduces must be complete in single wave.
Each reduce should process at least 1-2 GB of data, and at most 5-10GB of data, in most scenarios.

Partitioner
Distribute data evenly across reduces
Uneven distribution will hurt the whole job runtime.
Default is hash partitioner
hash(key)%num-reducers
Why is a custom partitioner needed?
Sort
WordCount

Output
Outputs to a few large files, with each file spanning multiple HDFS blocks and appropriately compressed.
Number of output artifacts is linearly proportionate to the number of configured reduces
Compress Outputs
Use appropriate file-formats for output
E.g. compressed text file is not a great idea if not using splittable codec.
Consider using Hadoop ARchive (HAR) to reduce namespace usage.
Think of the consumers of your data-set

Speculation
Slow running tasks can be speculated
Slowness is determined by the expected time the task will take to complete.
Speculation will kick-in only when there are no pending tasks.
Total number of tasks that can be speculated for a job is capped to reduce wastage.

Fault Tolerance
Data is stored as blocks on separate nodes
Nodes are composed of cheap commodity hardware
Tasks are independent of each other
New tasks can be scheduled on new nodes
The JobTracker tries 4 times (default) before giving up.
Job can be configured to tolerate task failures up to N% of the total tasks.

Reporter
Used to report progress to the parent processes.
Commonly used when the tasks try to
- Connect to a remote application like web-service, database
- Do some disk intensive computation
- Get blocked on some event
One can also spawn a thread and make it report the progress periodically

Distributed Cache
Efficient distribution of read-only files for applications
Localized automatically once the task is scheduled on the slave node
Cleaned up once no task running on the slave needs the cache files
Designed for small number of mid-size files.
Artifacts in the distributed-cache should not require more i/o than the actual input to the application tasks.

Few tips for better performance
Increase the memory/buffer allocated to the tasks (io.sort.mb)?
Increase the number of tasks that can be run in parallel
Increase the number of threads that serve the map outputs
Disable unnecessary logging
Find the optimal value of dfs block size
Share the cluster between the DFS and MR for data locality
Turn on speculation
Run reducers in one wave as they can be really costly
Make proper use of DistributedCache

Anti-Patterns
Processing thousands of small files (sized less than 1 HDFS block, typically 128MB) with one map processing a single small file.
Processing very large data-sets with small HDFS block size i.e. 128MB resulting in tens of thousands of maps.
Applications with a large number (thousands) of maps with a very small runtime (e.g. 5s).
Straight-forward aggregations without the use of the Combiner.
Applications with greater than 60,000-70,000 maps.
Applications processing large data-sets with very few reduces (such as1).
Applications using a single reduce for total-order amount the output records.
Pig scripts processing large data-sets without using the PARALLEL keyword

Anti-Patterns (Cont…)
Applications processing data with very large number of reduces, such that each reduce processes less than 1-2GB of data.
Applications writing out multiple, small, output files from each reduce.
Applications using the DistributedCache to distribute a large number of artifacts and/or very large artifacts (hundreds of MBs each).
Applications using more than 25 counters per task.
Applications performing metadata operations (e.g. listStatus) on the file-system from the map/reduce tasks.
Applications doing screen-scraping of JobTracker web-ui for status of queues/jobs or worse, job-history of completed jobs.
Workflows comprising of hundreds of of small jobs processing small amounts of data with a very high job submission rate.

Debugging
Side effect files : Write to external files from M/R code
Web UI : Web UI shows stdout/stderr
Isolation Runner : Run the task on the tracker where the task failed. Switch to the workspace of the task and run IsolationRunner.
Debug Scripts : Upload the script to the DFS, create a symlink and pass this script in the conf file. One common use is to filter out exceptions from the logs/stderr/stdout
LocalJobRunner is used to run a MapReduce job on local node. It can be used for faster debugging and proof-of-concept.

Task child environment settings
The child-task inherits the environment of the parent TaskTracker. The user can specify additional options to the child JVM via the mapred.child.java.opts
An example showing multiple arguments and substitutions
showing jvm GC logging
start of a passwordless JVM JMX agent so that it can connect with jconsole
get the thread dumps
sets the maximum heap-size of the child jvm to 512MB
add an additional path to the java.library.path of the child-jvm.
<property>
<name>mapred.child.java.opts</name>
<value>-Xmx512M -Djava.library.path=/home/mycompany/lib -verbose:gc
-Xloggc:/tmp/@taskid@.gc
-Dcom.sun.management.jmxremote.authenticate=false
-Dcom.sun.management.jmxremote.ssl=false
</value>
</property>

Checklist..
Are your partitions uniform?
Can you combine records at the map side?
Are maps reading off a DFS block worth of data?
Are you running a single reduce wave (unless the data size per reducers is too big) ?
Have you tried compressing intermediate data & final data?
Are your buffer sizes large enough to minimize spills but small enough to stay clear of swapping?
Do you see unexplained “long tails” ? (can be mitigated via speculative execution)
Are you keeping your cores busy? (via slot configuration)
Is at least one system resource being loaded?

Apache Hadoop India Summit 2011 talk "Hadoop Map-Reduce Programming & Best Practices" by Basant Verma

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