Execution Framework

1. Execution Framework 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

2. Execution Framework: HDFS • 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

3. Execution Framework: MapReduce • AMapReduce 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.

4. MapReduce Algorithm • 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

5. MapReduce Fundamentals: Text • 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.

6. MapReduce • 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.

7. Other Data Models • 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.

8. Scheduling • Very interesting since there are many tasks to manage. • Transparent, policy-driven, predictable multi-user 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

9. Other functions • 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

10. Other Operations • 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.