satish srirama - ut€¦ · – copy map code to the allocated nodes • move computation to data,...
TRANSCRIPT
Basics of Cloud Computing – Lecture 4
Introduction to MapReduce
Satish Srirama
Some material adapted from slides by Jimmy Lin, Christophe Bisciglia, Aaron Kimball,
& Sierra Michels-Slettvet, Google Distributed Computing Seminar, 2007 (licensed
under Creation Commons Attribution 3.0 License)
Outline
• MapReduce
• Hadoop
2/28/2017 Satish Srirama 2/40
Economics of Cloud Providers –
Failures
• Cloud Computing providers bring a shift from high reliability/availability servers to commodity servers– At least one failure per day in large datacenter
• Caveat: User software has to adapt to failures
• Solution: Replicate data and computation– MapReduce & Distributed File System
• MapReduce = functional programming meets distributed processing on steroids – Not a new idea… dates back to the 50’s (or even 30’s)
2/28/2017 Satish Srirama 3/40
Functional Programming ->
MapReduce
• Two important concepts in functional
programming
– Map: do something to everything in a list
– Fold: combine results of a list in some way
2/28/2017 Satish Srirama 4/40
Map
• Map is a higher-order function
• How map works:– Function is applied to every element in a list
– Result is a new list
• map f lst: (’a->’b) -> (’a list) -> (’b list)– Creates a new list by applying f to each element of the
input list; returns output in order.
2/28/2017f f f f f f
Satish Srirama 5/40
Fold
• Fold is also a higher-order function
• How fold works:– Accumulator set to initial value
– Function applied to list element and the accumulator
– Result stored in the accumulator
– Repeated for every item in the list
– Result is the final value in the accumulator
2/28/2017 Satish Srirama 6/40
Map/Fold in Action
• Simple map example:
• Fold examples:
• Sum of squares:
(map (lambda (x) (* x x))
'(1 2 3 4 5))
→ '(1 4 9 16 25)
(fold + 0 '(1 2 3 4 5)) → 15
(fold * 1 '(1 2 3 4 5)) → 120
(define (sum-of-squares v)
(fold + 0 (map (lambda (x) (* x x)) v)))
(sum-of-squares '(1 2 3 4 5)) → 55
2/28/2017 Satish Srirama 7/40
Implicit Parallelism In map
• In a purely functional setting, elements of a list being computed by map cannot see the effects of the computations on other elements
• If order of application of f to elements in list is commutative, we can reorder or parallelize execution
• Let’s assume a long list of records: imagine if...– We can parallelize map operations
– We have a mechanism for bringing map results back together in the fold operation
• This is the “secret” that MapReduce exploits
• Observations:– No limit to map parallelization since maps are independent
– We can reorder folding if the fold function is commutative and associative
2/28/2017 Satish Srirama 8/40
Typical Large-Data Problem
• Iterate over a large number of records
• Extract something of interest from each
• Shuffle and sort intermediate results
• Aggregate intermediate results
• Generate final outputKey idea: provide a functional abstraction for
these two operations – MapReduce
2/28/2017 Satish Srirama 9/40
MapReduce
• Programmers specify two functions:
map (k, v) → <k’, v’>*
reduce (k’, [v’]) → <k’’, v’’>*
– All values with the same key are sent to the same reducer
• The execution framework handles everything else…
2/28/2017 Satish Srirama 10/40
“Hello World” Example: Word Count
Map(String docid, String text):
for each word w in text:
Emit(w, 1);
Reduce(String term, Iterator<Int> values):
int sum = 0;
for each v in values:
sum += v;
Emit(term, sum);
2/28/2017 Satish Srirama 11/40
mapmap map map
Shuffle and Sort: aggregate values by keys
reduce reduce reduce
k1 k2 k3 k4 k5 k6v1 v2 v3 v4 v5 v6
ba 1 2 c c3 6 a c5 2 b c7 8
a 1 5 b 2 7 c 2 3 6 8
r1 s1 r2 s2 r3 s3
MapReduce
2/28/2017 Satish Srirama 12
MapReduce
• Programmers specify two functions:
map (k, v) → <k’, v’>*
reduce (k’, [v’]) → <k’’, v’’>*
– All values with the same key are sent to the same reducer
• The execution framework handles everything else…
What’s “everything else”?
2/28/2017 Satish Srirama 13/40
MapReduce “Runtime”• Handles scheduling
– Assigns workers to map and reduce tasks
• Handles “data distribution”– Moves processes to data
• Handles synchronization– Gathers, sorts, and shuffles intermediate data
• Handles errors and faults– Detects worker failures and automatically restarts
• Handles speculative execution– Detects “slow” workers and re-executes work
• Everything happens on top of a distributed FS (later)
Sounds simple, but many challenges!2/28/2017 Satish Srirama 14/40
MapReduce - extended
• Programmers specify two functions:map (k, v) → <k’, v’>*reduce (k’, [v’]) → <k’’, v’’>*– All values with the same key are reduced together
• The execution framework handles everything else…• Not quite…usually, programmers also specify:
partition (k’, number of parRRons) → parRRon for k’– Often a simple hash of the key, e.g., hash(k’) mod n– Divides up key space for parallel reduce operationscombine (k’, v’) → <k’’, v’’>*– Mini-reducers that run in memory after the map phase– Used as an optimization to reduce network traffic
2/28/2017 Satish Srirama 15/40
combinecombine combine combine
ba 1 2 c 9 a c5 2 b c7 8
partition partition partition partition
mapmap map map
k1 k2 k3 k4 k5 k6v1 v2 v3 v4 v5 v6
ba 1 2 c c3 6 a c5 2 b c7 8
Shuffle and Sort: aggregate values by keys
reduce reduce reduce
a 1 5 b 2 7 c 2 9 8
r1 s1 r2 s2 r3 s3
c 2 3 6 8
2/28/2017 Satish Srirama 16
Two more details…
• Barrier between map and reduce phases
– But we can begin copying intermediate data
earlier
• Keys arrive at each reducer in sorted order
– No enforced ordering across reducers
2/28/2017 Satish Srirama 17/40
split 0
split 1
split 2
split 3
split 4
worker
worker
worker
worker
worker
Master
User
Program
output
file 0
output
file 1
(1) submit
(2) schedule map (2) schedule reduce
(3) read(4) local write
(5) remote read (6) write
Input
files
Map
phase
Intermediate files
(on local disk)
Reduce
phase
Output
files
Adapted from (Dean and Ghemawat, OSDI 2004)
MapReduce Overall Architecture
2/28/2017 Satish Srirama 18
MapReduce can refer to…
• The programming model
• The execution framework (aka “runtime”)
• The specific implementation
Usage is usually clear from context!
2/28/2017 Satish Srirama 19/40
MapReduce Implementations
• Google has a proprietary implementation in C++
– Bindings in Java, Python
• Hadoop is an open-source implementation in Java
– Development led by Yahoo, used in production
– Now an Apache project
– Rapidly expanding software ecosystem
• Lots of custom research implementations
– For GPUs, cell processors, etc.
2/28/2017 Satish Srirama 20/40
Cloud Computing Storage, or how do
we get data to the workers?
Compute Nodes
NAS
SAN
What’s the problem here?
2/28/2017 Satish Srirama 21/40
Distributed File System
• Don’t move data to workers… move workers to the data!– Store data on the local disks of nodes in the cluster
– Start up the workers on the node that has the data local
• Why?– Network bisection bandwidth is limited
– Not enough RAM to hold all the data in memory
– Disk access is slow, but disk throughput is reasonable
• A distributed file system is the answer– GFS (Google File System) for Google’s MapReduce
– HDFS (Hadoop Distributed File System) for Hadoop
2/28/2017 Satish Srirama 22/40
GFS: Assumptions
• Choose commodity hardware over “exotic” hardware
– Scale “out”, not “up”
• High component failure rates
– Inexpensive commodity components fail all the time
• “Modest” number of huge files
– Multi-gigabyte files are common, if not encouraged
• Files are write-once, mostly appended to
– Perhaps concurrently
• Large streaming reads over random access
– High sustained throughput over low latency
GFS slides adapted from material by (Ghemawat et al., SOSP 2003)
2/28/2017 Satish Srirama 23/40
GFS: Design Decisions
• Files stored as chunks
– Fixed size (64MB)
• Reliability through replication
– Each chunk replicated across 3+ chunkservers
• Single master to coordinate access, keep metadata
– Simple centralized management
• No data caching
– Little benefit due to large datasets, streaming reads
• Simplify the API
– Push some of the issues onto the client (e.g., data layout)
HDFS = GFS clone (same basic ideas implemented in Java)
2/28/2017 Satish Srirama 24/40
From GFS to HDFS
• Terminology differences:
– GFS master = Hadoop namenode
– GFS chunkservers = Hadoop datanodes
• Functional differences:
– No file appends in HDFS
– HDFS performance is (likely) slower
For the most part, we’ll use the Hadoop terminology…
2/28/2017 Satish Srirama 25/40
Adapted from (Ghemawat et al., SOSP 2003)
(file name, block id)
(block id, block location)
instructions to datanode
datanode state(block id, byte range)
block data
HDFS namenode
HDFS datanode
Linux file system
…
HDFS datanode
Linux file system
…
File namespace/foo/bar
block 3df2
Application
HDFS Client
HDFS Architecture
2/28/2017 Satish Srirama 26
Namenode Responsibilities
• Managing the file system namespace:
– Holds file/directory structure, metadata, file-to-block mapping, access permissions, etc.
• Coordinating file operations:
– Directs clients to datanodes for reads and writes
– No data is moved through the namenode
• Maintaining overall health:
– Periodic communication with the datanodes
– Block re-replication and rebalancing
– Garbage collection
2/28/2017 Satish Srirama 27/40
Hadoop Processing Model
• Create or allocate a cluster
• Put data onto the file system– Data is split into blocks
– Replicated and stored in the cluster
• Run your job– Copy Map code to the allocated nodes
• Move computation to data, not data to computation
– Gather output of Map, sort and partition on key
– Run Reduce tasks
• Results are stored in the HDFS
2/28/2017 Satish Srirama 28/40
Some MapReduce Terminology w.r.t.
Hadoop
• 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 data chunk
– Also known as Task-In-Progress (TIP)
• Task Attempt – A particular instance of an
attempt to execute a task on a machine
2/28/2017 Satish Srirama 29/40
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 or slow, etc.
2/28/2017 Satish Srirama 30/40
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
2/28/2017 Satish Srirama 31/40
MapReduce: High Level
2/28/2017
datanode daemon
Linux file system
…
tasktracker
slave node
datanode daemon
Linux file system
…
tasktracker
slave node
datanode daemon
Linux file system
…
tasktracker
slave node
namenode
namenode daemon
job submission node
jobtracker
MapReduce job
submitted by
client computer
Master node
Task instance Task instance Task instance
Satish Srirama 32/40
MapReduce/GFS Summary
• Simple, but powerful programming model
• Scales to handle petabyte+ workloads
– Google: six hours and two minutes to sort 1PB (10
trillion 100-byte records) on 4,000 computers
– Yahoo!: 16.25 hours to sort 1PB on 3,800 computers
• Incremental performance improvement with
more nodes
• Seamlessly handles failures, but possibly with
performance penalties
2/28/2017 Satish Srirama 33/40
Limitations with MapReduce V1
• Scalability
– Maximum Cluster Size – 4000 Nodes
– Maximum Concurrent Tasks – 40000
• Coarse synchronization in Job Tracker
– Single point of failure
– Failure kills all queued and running jobs
• Jobs need to be resubmitted by users
– Restart is very tricky due to complex state
• Problems with resource utilization
2/28/2017 Satish Srirama 34/40
MapReduce NextGen aka YARN aka
MRv2• New architecture introduced in hadoop-0.23
• Divides two major functions of the JobTracker– Resource management and job life-cycle management are
divided into separate components
• An application is either a single job in the sense of classic MapReduce jobs or a DAG of such jobs
2/28/2017 Satish Srirama 35/40
YARN Architecture
• RescourceManager:– Arbitrates resources among all the
applications in the system
– Has two main components: Scheduler and ApplicationsManager
• NodeManager:– Per-machine slave
– Responsible for launching the applications’ containers, monitoring their resource usage
• ApplicationMaster:– Negotiate appropriate resource containers
from the Scheduler, tracking their status and monitoring for progress
• Container:– Unit of allocation incorporating resource
elements such as memory, cpu, disk, network etc.
– To execute a specific task of the application
– Similar to map/reduce slots in MRv1
2/28/2017 Satish Srirama 36/40
Execution Sequence with YARN
• A client program submits the application
• ResourceManager allocates a specified container to start the ApplicationMaster
• ApplicationMaster, on boot-up, registers with ResourceManager
• ApplicationMaster negotiates with ResourceManager for appropriate resource containers
• On successful container allocations, ApplicationMaster contacts NodeManager to launch the container
• Application code is executed within the container, and then ApplicationMaster is responded with the execution status
• During execution, the client communicates directly with ApplicationMaster or ResourceManager to get status, progress updates etc.
• Once the application is complete, ApplicationMaster unregisters with ResourceManager and shuts down, freeing its own container process
2/28/2017 Satish Srirama 37/40
Lab related to the lecture
• Work with MapReduce jobs
• Try out WordCount example
– MapReduce in hadoop-2.x maintains API
compatibility with previous stable release
(hadoop-1.x)
– MapReduce jobs still run unchanged on top of
YARN with just a recompile
2/28/2017 Satish Srirama 38/40
Next Lecture
• Let us have a look at some MapReduce
algorithms
2/28/2017 Satish Srirama 39/40
References
• Hadoop wiki http://wiki.apache.org/hadoop/
• Hadoop MapReduce tutorial http://hadoop.apache.org/docs/r1.0.4/mapred_tutorial.html
• Data-Intensive Text Processing with MapReduce
Authors: Jimmy Lin and Chris Dyer
http://lintool.github.io/MapReduceAlgorithms/MapReduce-book-final.pdf
• White, Tom. Hadoop: the definitive guide. O'Reilly, 2012.
• J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters”, OSDI'04: Sixth Symposium on Operating System Design and Implementation, Dec, 2004.
• Apache Hadoop YARN -https://hadoop.apache.org/docs/r2.7.2/hadoop-yarn/hadoop-yarn-site/YARN.html
2/28/2017 Satish Srirama 40/40