distributed computations mapreduce/dryad m/r slides adapted from those of jeff dean’s dryad slides...
Post on 14-Jan-2016
220 Views
Preview:
TRANSCRIPT
Distributed ComputationsMapReduce/Dryad
M/R slides adapted from those of Jeff Dean’s
Dryad slides adapted from those of Michael Isard
What we’ve learnt so far
• Basic distributed systems concepts– Consistency (sequential, eventual)
– Concurrency
– Fault tolerance (recoverability, availability)
• What are distributed systems good for?– Better fault tolerance
• Better security?
– Increased storage/serving capacity • Storage systems, email clusters
– Parallel (distributed) computation (Today’s topic)
Why distributed computations?
• How long to sort 1 TB on one computer?– One computer can read ~60MB from disk– Takes ~1 days!!
• Google indexes 100 billion+ web pages – 100 * 10^9 pages * 20KB/page = 2 PB
• Large Hadron Collider is expected to produce 15 PB every year!
Solution: use many nodes!
• Cluster computing– Hundreds or thousands of PCs connected by high
speed LANs
• Grid computing– Hundreds of supercomputers connected by high
speed net
• 1000 nodes potentially give 1000X speedup
Distributed computations are difficult to program
• Sending data to/from nodes
• Coordinating among nodes
• Recovering from node failure
• Optimizing for locality
• Debugging
Same for all problems
MapReduce• A programming model for large-scale computations
– Process large amounts of input, produce output– No side-effects or persistent state (unlike file system)
• MapReduce is implemented as a runtime library:– automatic parallelization– load balancing– locality optimization– handling of machine failures
MapReduce design
• Input data is partitioned into M splits• Map: extract information on each split
– Each Map produces R partitions
• Shuffle and sort– Bring M partitions to the same reducer
• Reduce: aggregate, summarize, filter or transform• Output is in R result files
More specifically…• Programmer specifies two methods:
– map(k, v) → <k', v'>*– reduce(k', <v'>*) → <k', v'>*
• All v' with same k' are reduced together, in order.
• Usually also specify:– partition(k’, total partitions) -> partition for k’
• often a simple hash of the key• allows reduce operations for different k’ to be
parallelized
Example: Count word frequencies in web pages
• Input is files with one doc per record
• Map parses documents into words– key = document URL– value = document contents
• Output of map:
“doc1”, “to be or not to be”
“to”, “1”“be”, “1”“or”, “1”…
Example: word frequencies• Reduce: computes sum for a key
• Output of reduce saved
“be”, “2”“not”, “1”“or”, “1”“to”, “2”
key = “or”values = “1”
“1”
key = “be”values = “1”, “1”
“2”
key = “to”values = “1”, “1”
“2”
key = “not”values = “1”
“1”
Example: Pseudo-codeMap(String input_key, String input_value): //input_key: document name //input_value: document contents for each word w in input_values: EmitIntermediate(w, "1");
Reduce(String key, Iterator intermediate_values): //key: a word, same for input and output //intermediate_values: a list of counts int result = 0; for each v in intermediate_values: result += ParseInt(v); Emit(AsString(result));
MapReduce is widely applicable
• Distributed grep
• Document clustering
• Web link graph reversal
• Detecting duplicate web pages
• …
MapReduce implementation
• Input data is partitioned into M splits• Map: extract information on each split
– Each Map produces R partitions
• Shuffle and sort– Bring M partitions to the same reducer
• Reduce: aggregate, summarize, filter or transform• Output is in R result files, stored in a replicated,
distributed file system (GFS).
MapReduce scheduling
• One master, many workers – Input data split into M map tasks (e.g. 64 MB)– R reduce tasks– Tasks are assigned to workers dynamically– E.g. M=200,000; R=4,000; workers=2,000
MapReduce scheduling• Master assigns a map task to a free worker
– Prefers “close-by” workers when assigning task– Worker reads task input (often from local disk!)– Worker produces R local files containing intermediate
k/v pairs
• Master assigns a reduce task to a free worker – Worker reads intermediate k/v pairs from map workers– Worker sorts & applies user’s Reduce op to produce
the output
Parallel MapReduce
Map Map Map Map
Inputdata
Inputdata
Reduce
Shuffle
Reduce
Shuffle
Reduce
Shuffle
Partitioned output
Partitioned output
Master
WordCount Internals• Input data is split into M map jobs
• Each map job generates in R local partitions
“doc1”, “to be or not to be”
“to”, “1”“be”, “1”“or”, “1”“not”, “1“to”, “1”
“be”,“1”
“not”,“1”“or”, “1”
R localpartitions
“doc234”, “do not be silly”
“do”, “1”“not”, “1”“be”, “1”“silly”, “1 “be”,“1”
R localpartitions
“not”,“1”
“do”,“1”
“to”,“1”,”1”Hash(“to”) %
R
WordCount Internals• Shuffle brings same partitions to same reducer
“to”,“1”,”1”
“be”,“1”
“not”,“1”“or”, “1”
“be”,“1”
R localpartitions
R localpartitions
“not”,“1”
“do”,“1”
“to”,“1”,”1”
“do”,“1”
“be”,“1”,”1”
“not”,“1”,”1”“or”, “1”
WordCount Internals• Reduce aggregates sorted key values pairs
“to”,“1”,”1”
“do”,“1”
“not”,“1”,”1”
“or”, “1”
“do”,“1”“to”, “2”
“be”,“2”
“not”,“2”“or”, “1”
“be”,“1”,”1”
The importance of partition function
• partition(k’, total partitions) -> partition for k’– e.g. hash(k’) % R
• What is the partition function for sort?
Load Balance and Pipelining• Fine granularity tasks: many more map
tasks than machines– Minimizes time for fault recovery– Can pipeline shuffling with map execution– Better dynamic load balancing
• Often use 200,000 map/5000 reduce tasks w/ 2000 machines
Fault tolerance via re-execution
On worker failure:• Re-execute completed and in-progress map
tasks• Re-execute in progress reduce tasks• Task completion committed through masterOn master failure:• State is checkpointed to GFS: new master
recovers & continues
Avoid straggler using backup tasks• Slow workers drastically increase completion time
– Other jobs consuming resources on machine– Bad disks with soft errors transfer data very slowly– Weird things: processor caches disabled (!!)– An unusually large reduce partition
• Solution: Near end of phase, spawn backup copies of tasks– Whichever one finishes first "wins"
• Effect: Dramatically shortens job completion time
MapReduce Sort Performance
• 1TB (100-byte record) data to be sorted
• 1700 machines
• M=15000 R=4000
MapReduce Sort Performance
When can shuffle start?
When can reduce start?
Dryad
Slides adapted from those of Yuan Yu and Michael Isard
Dryad• Similar goals as MapReduce
– focus on throughput, not latency– Automatic management of scheduling,
distribution, fault tolerance
• Computations expressed as a graph– Vertices are computations– Edges are communication channels– Each vertex has several input and output edges
WordCount in Dryad
CountWord:n
MergeSortWord:n
CountWord:n
DistributeWord:n
Why using a dataflow graph?
• Many programs can be represented as a distributed dataflow graph– The programmer may not have to know this
• “SQL-like” queries: LINQ
• Dryad will run them for you
Job = Directed Acyclic Graph
Processingvertices Channels
(file, pipe, shared memory)
Inputs
Outputs
Scheduling at JM
• General scheduling rules: – Vertex can run anywhere once all its inputs are
ready• Prefer executing a vertex near its inputs
– Fault tolerance• If A fails, run it again• If A’s inputs are gone, run upstream vertices again
(recursively)• If A is slow, run another copy elsewhere and use output
from whichever finishes first
Advantages of DAG over MapReduce
• Big jobs more efficient with Dryad– MapReduce: big job runs >=1 MR stages
• reducers of each stage write to replicated storage• Output of reduce: 2 network copies, 3 disks
– Dryad: each job is represented with a DAG• intermediate vertices write to local file
Advantages of DAG over MapReduce
• Dryad provides explicit join– MapReduce: mapper (or reducer) needs to read from
shared table(s) as a substitute for join– Dryad: explicit join combines inputs of different types– E.g. Most expensive product bought by a customer,
PageRank computation
DAG optimizations: merge tree
DAG optimizations: merge tree
Dryad Optimizations: data-dependent re-partitioning
Distribute to equal-sized ranges
Sample to estimate histogram
Randomly partitioned inputs
Dryad example:the usefulness of join
• SkyServer Query: 3-way join to find gravitational lens effect
• Table U: (objId, color) 11.8GB• Table N: (objId, neighborId) 41.8GB• Find neighboring stars with similar colors:
– Join U+N to findT = N.neighborID where U.objID = N.objID, U.color
– Join U+T to findU.objID where U.objID = T.neighborID
and U.color ≈ T.color
D D
MM 4n
SS 4n
YY
H
n
n
X Xn
U UN N
U U
SkyServer query
u: objid, color
n: objid, neighborobjid
[partition by objid]
select
u.color,n.neighborobjid
from u join n
where
u.objid = n.objid
D D
MM 4n
SS 4n
YY
H
n
n
X Xn
U UN N
U U
(u.color,n.neighborobjid)
[re-partition by n.neighborobjid]
[order by n.neighborobjid]
[distinct]
[merge outputs]
select
u.objid
from u join <temp>
where
u.objid = <temp>.neighborobjid and
|u.color - <temp>.color| < d
Another example: how Dryad optimizes DAG automatically
• Example Application: compute query histogram
• Input: log file (n partitions)
• Extract queries from log partitions
• Re-partition by hash of query (k buckets)
• Compute histogram within each bucket
Naïve histogram topology
Q Q
R
Q
R k
k
k
n
n
is:Each
R
is:
Each
MS
C
P
C
S
C
S
D
P parse lines
D hash distribute
S quicksort
C count occurrences
MS merge sort
Efficient histogram topologyP parse lines
D hash distribute
S quicksort
C count occurrences
MS merge sort
M non-deterministic merge
Q' is:Each
R
is:
Each
MS
C
M
P
C
S
Q'
RR k
T
k
n
T
is:
Each
MS
D
C
RR
T
Q’
MS►C►D
M►P►S►C
MS►C
P parse lines D hash distribute
S quicksort MS merge sort
C count occurrences M non-deterministic merge
R
MS►C►D
M►P►S►C
MS►C
P parse lines D hash distribute
S quicksort MS merge sort
C count occurrences M non-deterministic merge
RR
T
R
Q’Q ’Q ’Q ’
MS►C►D
M►P►S►C
MS►C
P parse lines D hash distribute
S quicksort MS merge sort
C count occurrences M non-deterministic merge
RR
T
R
Q’Q ’Q ’Q ’
T
MS►C►D
M►P►S►C
MS►C
P parse lines D hash distribute
S quicksort MS merge sort
C count occurrences M non-deterministic merge
RR
T
R
Q’Q ’Q ’Q ’
T
P parse lines D hash distribute
S quicksort MS merge sort
C count occurrences M non-deterministic merge
MS►C►D
M►P►S►C
MS►C RR
T
R
Q’Q ’Q ’Q ’
T
P parse lines D hash distribute
S quicksort MS merge sort
C count occurrences M non-deterministic merge
MS►C►D
M►P►S►C
MS►C RR
T
R
Q’Q ’Q ’Q ’
T
Final histogram refinement
Q' Q'
RR 450
TT 217
450
10,405
99,713
33.4 GB
118 GB
154 GB
10.2 TB
1,800 computers
43,171 vertices
11,072 processes
11.5 minutes
top related