This document provides an overview of key concepts in Hadoop including:
- Hadoop was tested on a 4000 node cluster with 32,000 cores and 16 petabytes of storage.
- MapReduce is Hadoop's programming model and consists of mappers that process input splits in parallel, and reducers that combine the outputs of the mappers.
- The JobTracker manages jobs, TaskTrackers run tasks on slave nodes, and Tasks are individual mappers or reducers. Data is distributed to nodes implicitly based on the HDFS file distribution. Configurations are set using a JobConf object.
2. Announcements
My office hours: M 2:30—3:30 in CSE 212
Cluster is operational; instructions in
assignment 1 heavily rewritten
Eclipse plugin is “deprecated”
Students who already created accounts:
let me know if you have trouble
3. Breaking news!
Hadoop tested on 4,000 node cluster
32K cores (8 / node)
16 PB raw storage (4 x 1 TB disk / node)
(about 5 PB usable storage)
http://developer.yahoo.com/blogs/hadoop/2008/09/
scaling_hadoop_to_4000_nodes_a.html
4. You Say, “tomato…”
Google calls it: Hadoop equivalent:
MapReduce Hadoop
GFS HDFS
Bigtable HBase
Chubby Zookeeper
5. Some MapReduce Terminology
Job – A “full program” - an execution of a
Mapper and Reducer across a data set
Task – An execution of a Mapper or a
Reducer on a slice of data
a.k.a. Task-In-Progress (TIP)
Task Attempt – A particular instance of an
attempt to execute a task on a machine
6. Terminology Example
Running “Word Count” across 20 files is
one job
20 files to be mapped imply 20 map tasks
+ some number of reduce tasks
At least 20 map task attempts will be
performed… more if a machine crashes,
etc.
7. Task Attempts
A particular task will be attempted at least once,
possibly more times if it crashes
If the same input causes crashes over and over, that
input will eventually be abandoned
Multiple attempts at one task may occur in
parallel with speculative execution turned on
Task ID from TaskInProgress is not a unique identifier;
don’t use it that way
9. Node-to-Node Communication
Hadoop uses its own RPC protocol
All communication begins in slave nodes
Prevents circular-wait deadlock
Slaves periodically poll for “status” message
Classes must provide explicit serialization
10. Nodes, Trackers, Tasks
Master node runs JobTracker instance,
which accepts Job requests from clients
TaskTracker instances run on slave nodes
TaskTracker forks separate Java process
for task instances
11. Job Distribution
MapReduce programs are contained in a Java
“jar” file + an XML file containing serialized
program configuration options
Running a MapReduce job places these files
into the HDFS and notifies TaskTrackers where
to retrieve the relevant program code
… Where’s the data distribution?
12. Data Distribution
Implicit in design of MapReduce!
All mappers are equivalent; so map whatever
data is local to a particular node in HDFS
If lots of data does happen to pile up on
the same node, nearby nodes will map
instead
Data transfer is handled implicitly by HDFS
13. Configuring With JobConf
MR Programs have many configurable options
JobConf objects hold (key, value) components
mapping String ’a
e.g., “mapred.map.tasks” 20
JobConf is serialized and distributed before running
the job
Objects implementing JobConfigurable can
retrieve elements from a JobConf
15. Job Launch Process: Client
Client program creates a JobConf
Identify classes implementing Mapper and
Reducer interfaces
JobConf.setMapperClass(), setReducerClass()
Specify inputs, outputs
FileInputFormat.addInputPath(),
FileOutputFormat.setOutputPath()
Optionally, other options too:
JobConf.setNumReduceTasks(),
JobConf.setOutputFormat()…
16. Job Launch Process: JobClient
Pass JobConf to JobClient.runJob() or
submitJob()
runJob() blocks, submitJob() does not
JobClient:
Determines proper division of input into
InputSplits
Sends job data to master JobTracker server
17. Job Launch Process: JobTracker
JobTracker:
Inserts jar and JobConf (serialized to XML) in
shared location
Posts a JobInProgress to its run queue
18. Job Launch Process: TaskTracker
TaskTrackers running on slave nodes
periodically query JobTracker for work
Retrieve job-specific jar and config
Launch task in separate instance of Java
main() is provided by Hadoop
19. Job Launch Process: Task
TaskTracker.Child.main():
Sets up the child TaskInProgress attempt
Reads XML configuration
Connects back to necessary MapReduce
components via RPC
Uses TaskRunner to launch user process
20. Job Launch Process: TaskRunner
TaskRunner, MapTaskRunner,
MapRunner work in a daisy-chain to
launch your Mapper
Task knows ahead of time which InputSplits it
should be mapping
Calls Mapper once for each record retrieved
from the InputSplit
Running the Reducer is much the same
21. Creating the Mapper
You provide the instance of Mapper
Should extend MapReduceBase
One instance of your Mapper is initialized
by the MapTaskRunner for a
TaskInProgress
Exists in separate process from all other
instances of Mapper – no data sharing!
23. What is Writable?
Hadoop defines its own “box” classes for
strings (Text), integers (IntWritable), etc.
All values are instances of Writable
All keys are instances of
WritableComparable
24. Getting Data To The Mapper
Input file
InputSplit InputSplit InputSplit InputSplit
Input file
RecordReader RecordReader RecordReader RecordReader
Mapper
(intermediates)
Mapper
(intermediates)
Mapper
(intermediates)
Mapper
(intermediates)
InputFormat
25. Reading Data
Data sets are specified by InputFormats
Defines input data (e.g., a directory)
Identifies partitions of the data that form an
InputSplit
Factory for RecordReader objects to extract
(k, v) records from the input source
26. FileInputFormat and Friends
TextInputFormat – Treats each ‘n’-
terminated line of a file as a value
KeyValueTextInputFormat – Maps ‘n’-
terminated text lines of “k SEP v”
SequenceFileInputFormat – Binary file of
(k, v) pairs with some add’l metadata
SequenceFileAsTextInputFormat – Same,
but maps (k.toString(), v.toString())
27. Filtering File Inputs
FileInputFormat will read all files out of a
specified directory and send them to the
mapper
Delegates filtering this file list to a method
subclasses may override
e.g., Create your own “xyzFileInputFormat” to
read *.xyz from directory list
28. Record Readers
Each InputFormat provides its own
RecordReader implementation
Provides (unused?) capability multiplexing
LineRecordReader – Reads a line from a
text file
KeyValueRecordReader – Used by
KeyValueTextInputFormat
29. Input Split Size
FileInputFormat will divide large files into
chunks
Exact size controlled by mapred.min.split.size
RecordReaders receive file, offset, and
length of chunk
Custom InputFormat implementations may
override split size – e.g., “NeverChunkFile”
30. Sending Data To Reducers
Map function receives OutputCollector
object
OutputCollector.collect() takes (k, v) elements
Any (WritableComparable, Writable) can
be used
By default, mapper output type assumed
to be same as reducer output type
32. Sending Data To The Client
Reporter object sent to Mapper allows
simple asynchronous feedback
incrCounter(Enum key, long amount)
setStatus(String msg)
Allows self-identification of input
InputSplit getInputSplit()
34. Partitioner
int getPartition(key, val, numPartitions)
Outputs the partition number for a given key
One partition == values sent to one Reduce
task
HashPartitioner used by default
Uses key.hashCode() to return partition num
JobConf sets Partitioner implementation
35. Reduction
reduce( K2 key,
Iterator<V2> values,
OutputCollector<K3, V3> output,
Reporter reporter)
Keys & values sent to one partition all go
to the same reduce task
Calls are sorted by key – “earlier” keys are
reduced and output before “later” keys