the Hadooper in me: Platform base: HDFS + MR

Wednesday, November 17, 2010

Platform base: HDFS + MR

1:57 AM | Publicado por Cristian |

| Edit Post

Let's explore the core of the platform
Here are some relevant points about the filesystem

Scales linearly over a set of low-budget comodity machines, doubled the amount of machines = reduced the processing time to half
Tolerates faults at different levels, from network, switches, disks, nodes, readdressing data traffic to other nodes, accomplishing a replica factor
Flexible scalability, for maintenance tasks, you only have to dis/connect computers into the rack
Lets you allocate any kind of files and formats, although it has better performance on files bigger than 128mb
The results of MapReduce jobs are stored in the filesystem
There's a unique namespace, with an automatic replication schema administered by the master, it's not possible to impact on a certain node in the cluster, whether to allocate files or execute jobs
The master itself balances the workload over execution plans and status reports from the slave nodes

The picture shows a more practical brief over HDFS responsibilities & execution flows
(Click to enlarge)

Has a mechanism of continuous replication per file, rack-aware, to extend data reliability and data availability warranties
Has an automatic file checksum with inmediate correction
Works in a master-slave schema where slaves share nothing between them, they only respond to master requests
The master coordinates all types of transactions, read/write, replication, restore, manages the tx log and the filesystem namespace
The slaves only take charge on low level operations over data, read, write, deletion, transport to-from client

Let's pass now to the processing model
Main characteristics of Map Reduce

It's a processing model about dividing and distributing information, in two chained phases: map first, then reduce
Both phases have as input and output, a key-value pair list,
The schema allows to define method parameters and own logic for both phases, as well as their own partitioning system and intermediate storage between phases.
The transactions are handled by a JobTracker daemon, that runs the initial data partitioning and the intermediate data combination, by posting tasks of type Map and type Reduce over the TaskTracker daemons (1-n x computer) of the nodes involved in the cluster, according the data being processed
the Reduce phase only starts when finished the Map, cause after the Map the resulting keys are combined, to distribute a sorted list of key-value pairs between the Reducers, that can be matched at the end of them.
The process is transactional, those map or reduce tasks not executed, (for data availability issues) will be reattempted a number of times, and then redistributed to other nodes.

The picture shows how these methods will interact in phases.

(Click to enlarge)

Detail:

- First the files are partitioned in parts that will be distributed to process across the cluster nodes
- Each part is parsed in pairs of Key(sorteable object) - Value(object), that will be the input parameters for the tasks implementing the Map function
- These user defined tasks (map), will read the value object, do something with it, and then build a new key-value list that will be stored by the framework, in intermediate files.
- Once all the map tasks are finished, it means that the whole data to process was completely read, and reordered into this mapreduce model of key-value paris.
- These intermediate key-value results are combined, resulting a new paris of key-value that will be the input for the next reduce tasks
- These user defined tasks (reduce), will read the value object, do something with it, and then produce the 3rd and last list of key-value pairs, that the framework will combine, and regroup into a final result.

Let's see a sample job with a reverted-index function, for analyzing the webcrawler's output files (just for instance)
(Click to enlarge)

MapReduce something, is about iterate a huge record collection, extract something good, mix and regroup intermediate results, that's all, it may look more complex than what it is.

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Python Code: (Double-click to select all)
1
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