Based on the text by Jimmy Lin and Chris Dryer;
and on the yahoo tutorial on mapreduce at
http://developer.yahoo.com/hadoop/tutorial/
index.html
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Namenode responsibilities:
Namespace management: file name,
locations, access privileges etc.
Coordinating client operations: Directs
clients to datanodes, garbage collection etc.
Maintaining the overall health of the system:
replication factor, replica balancing etc.
Namenode does not take part in any
computation
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A MapReduce job use individual files as a basic unit for
splitting input data.
Workloads are batch-oriented, dominated by long
streaming reads and large sequential writes.
Applications are aware of the distributed file system.
File system can be implemented in an environment of
cooperative users.
See figure 2.6 and understand
Operations: (mapper, reducer) {combiner} [partitioner,
shuffle and sort] : these operations have specific meaning
in the MR context. You must understand it fully before
using them.
Finally study the job configuration: items you can specify
declaratively and how to specify these attributes.
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Module 4 in yahoo tutorial
Read every line of: Functional programming
section
Understand the mapper, reducer and most
importantly the driver method (job config)
Module 5: Read the details about partitioner
Metrics
Monitoring: web monitoring possible
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Figure 2.1 map and fold
Map is a “transformation” function that can be carried
out in parallel: can work on the elements of list in
parallel
Fold is an “aggregation” function that has restrictions
on data locality: requires elements of the list to be
brought together before the operation
For operations that are associative and commutative,
significant performance can be achieved by local
aggregation and sorting.
User specifies the map&reduce operations and the
execution framework coordinates the execution of
the programs and data movement.
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imposes <key, value> structure to data
◦ Example 1: <URL, content at this URL>
◦ Example 2: <docid, doc>
map: (k1, v1) → [(k2, v2)]
reduce: (k2, [v2]) → [(k3, v3)]
Map generates intermediate values, and they are
implicitly operated using “group by” operator and are
in order within a given reducer.
Each reducer output is written into a external file.
Reduce method is called once for each key value in
the data space to be processed by reduce.
Mapper with identity reducer is essentially a sorter.
Typical Mapreduce processes data in distributed file
system and writes back to the same file system.
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Data Storage: output from MR could go into a
sparse multi-dimensional table called BigTable in
Google’s system.
The Apache open source version is HBASE.
HABSE is a column based table.
Rows, column families each with many columns.
Data is stored normalized in a relational schema.
Data in Hbase is not normalized by choice and by
design.
Column families are stored together and storage
methods optimized for this.
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Very interesting since there are many tasks to
manage.
Transparent, policy-driven, predictable multiuser scheduling
Speculative scheduling: Due to the barrier
between M and R, the map is only as fast as
the slowest Map; managing stragglers
But how to handle skew in the data: better
local aggregation
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Data/operation co-location
Synchronization: copying into reduce as the
map is going on; existence of barrier between
map and reduce
Error and fault-tolerance: hardware as well as
software
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Partitioners: Partitioners divide the
intermediate key space and assign the parts
to the reducers.
Combiners are optimization means by which
local aggregation can be done before sort
and shuffle.
Thus a complete MR job consists of mapper,
reducer, combiner, partitioner and job
configuration; rest is taken care of by the
execution framework.