Replayable BigData for Multicore Processing and Statistically Rigid Sketching

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Hadoop/MapReduce Problems
M.Zhanikeev -- maratishe@gmail.com -- Replayable BigData for Multicore Processing and ... Sketching -- http://bit.do/marat141105 -- 2/23
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2/23

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Hadoop/MapReduce Problems
upper limit on throughput, both in HDFS and MapReduce jobs 06 09
one machine is not so bad anymore: good RAM, multicore 09
MapReduce is key-value hashes only, very restrictive
MapReduce performs badly under heterogeneous workloads
Small file problem is hard to solve in Hadoop 10
...
06 K.Shvachko HDFS Scalability: the Limits to Growth the Magazine of USENIX, vol.35, no.2 (2012)
09 A.Rowstron+4 Nobody ever got fired for using Hadoop on a cluster 1st HTCDP (2012)
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3/23

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Hadoop/MapReduce Upgrades
solutions for geterogeneous jobs 17
R for statistical processing on top of HDFS shards 18
searchable shards -- HBase + Lucene 19
.... not enough
17 A.Rasooli+1 COSHH: A Classification and Optimization based Scheduler for Heterogeneous Hadoop... McMaster (2013)
18 S.Das+5 Ricardo: Integrating R and Hadoop SIGMOD (2010)
19 X.Gao+2 Experimenting with Lucene Index on HBase in an HPC Environment 1st HPCDB (2012)
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4/23

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Example : Superspreaders
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5/23

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Example Problem : Superspreaders
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A Superspreader...
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... is a one-to-many (o2m) traffic artifact where one host tries to contact
many other hosts within short timespan
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reverse of Superspreader is a Flash Crowd -- same algorithm
many-to-many (m2m, groups) is even more complex
a known problem 16
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QUIZ
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.How to detect superspreaders using MapReduce? Any ideas?
16 S.Venkataraman+3 New Streaming Algorithms for Fast Detection of Superspreaders NDSS (2005)
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6/23

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Superspreaders: (1) raw packets
time, sip, sport, dip, dport, psize, protocol -- the usual packet tuple
convert into text for MapReduce jobs
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7/23

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Superspreaders: (2) 1st MapReduce
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Step 1
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....is to extract unique sip-dip pairs
3rd column is count (took word counting for
basis)
ordered by sip
takes time because needs to process all the
data
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8/23

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Superspreaders: (3) 2nd MapReduce
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Step 2
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... is to count unique sips in sip-dip
pairs
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based on the output of the 1st job
faster because data is relatively small
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9/23

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Superspreaders: Problem NOT Solved
no time/sequence in MapReduce, no way to process data in a time
window
do not know what to discard (tail, small counts) midway until all data is
aggregated -- ineffective for large datasets
...
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10/23

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Proposal: TABID + Sketches
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11/23

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TABID: Time-Aware BIg Data
TABID: acronym of Time Aware BIg Data
the grain is larger then key-value store, but better than HDFS shards 19
KV
Store
Hadoop
(HDFS) and
MapReduce
TABID Time-Aware
Big Data
(this demo)
HDFS
+
Lucene
Index
19 X.Gao+2 Experimenting with Lucene Index on HBase in an HPC Environment 1st HPCDB (2012)
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12/23

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Sketches (Data Streaming)
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Data Streaming
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... is a new concept where data is processed in realtime without
using any storage
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a relatively recent but well defined and mathematically/statistically
formulated 11
many known interesting algorithms 12
algorithms for specific problems 14 15 16
main features: space efficiency, statistical rigidity (information
theory), speed
11 S.Muthukrishnan Data Streams: Algorithms and Applications Foundations and Trends... (2005)
12 M.Sung+3 Scalable and Efficient Data Streaming Algorithms for Detecting .... ICDEW (2006)
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13/23

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Hadoop/MapReduce
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Hadoop is...
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...a platform where your software
meets with data shards
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One Physical Machine (1 shard)
file A
file B
file C
…
Hadoop Space
Read/parse Find
Hadoop Job
(your code)
Hadoop Job
(your code)
Hadoop Job
(your code)
Hadoop Job
(your code)
many many
Manager
Name
Server(s)
Client Machine
Hadoop Client
Start Use
Your
Code
You
Deploy
shards are distributed across
the cluster
nameserver is the
bottleneck
fairness problems are
when heavy and light jobs run
together at the same shard
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14/23

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TABID/Sketches
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TABID is...
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...an API that helps your jobs get access to
data in its natural time order
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One Physical Machine (1 shard)
STuimb-eSltinoere
Store
TABID
Node
TABID
Manager
Schedule
Client Machine
TABID Client
Start Use
Your
Sketcher
You
Multicore
Replay
data shards are downloaded
and replayed
jobs run on multicore
jobs can use any data
structure (well beyond
key-value)
jobs are data streaming
sketches -- can be selected
from a library
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15/23

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TABID: BigData Replay
Core X
Core 1
Core 1
Read/prepare
One SketcOhne Sketch One Sketch
Start End End End
TABID
Manager
Now(replay)
….
Time-Aligned Big Data
Cursor
Time
Direction
Shared Memory
Start
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16/23

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TABID: Lockfree Shared Memory
lockfree shared memory is a new branch of parallel
processing
locks are bad for multicore (memory fences 20)
a generic lockfree design in 05 and software implementation in 23
some attempts to apply multicore to MapReduce 21 22
20 M.Aldinucci+2 FastFlow: Efficient Parallel Streaming Applications on Multi-core Universita di Pisa (2009)
23 current MCoreMemory project page https://github.com/maratishe/mcorememory (myself)
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17/23

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Wrapup (Problem→Solution)
1. MapReduce is about counting, no rich datatypes
SOLVED → any datatype, stored as JSON
2. MapReduce has no solution for heterogeneous jobs
SOLVED → TABID optimizes jobs-to-core mapping (bin packing)
3. MapReduce has accountability problem because clients make their
own jobs
SOLVED → a library of sketches based on known data
streaming algorithms
4. ... many smaller solutions
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18/23

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Hadoop vs Tabid DEMO (today)
working Hadoop and TABID platforms
can play with various configurations on the spot
. Replayable BigData for Multicore
Processing and Stat... Rigid Sketching
.Marat Zhanikeev – maratishe@gmail.com – maratishe.github.io – http://bit.do/marat141105 1/1
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19/23

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That’s all, thank you ...
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20/23

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Performance under Distributions
comparative efficiency (right) and job-to-core mapping efficiency
(left)
all under heterogenious (variance) job distributions
Tuples: min lifespan, max lifespan, exponent
10,1000,0.01
10,1000,0.05
10,1000,0.1
10,1000,0.3
10,1000,0.7
0 200 400 600 800 1000
Number of Sketches
5.4
5.2
5
4.8
4.6
4.4
4.2
4
3.8
Log of Sketchbyte Ratio (HADOOP/TABID)
10,1000,0
More longer lifespans
Tuples: min overhead, max overhead, exponent
0 200 400 600 800 1000
Number of Sketches
6.65
5.95
5.25
4.55
3.85
3.15
Log of Max TABID Overhead (per core)
100,10000,0
100,10000,0.7
100,10000,0.3
100,10000,0.1
100,10000,0.05
100,10000,0.01
Mostly
large overhead
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21/23

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Shared Memory : Logic
Shared
Memory
IDLE
All sketches
caught up
IDLE
Cursor
Time
Sketch
time
...
Data
Stream
…
All
sketches Ring buffer Process
Data
Set Sketch
Time
Add
data
Sketch
started
Monitor Sketch Manager
cursor
Cursor
moved
Each
item
Cursor =
sketch
End time
of life
Global
start
Wait for
all sketches
End of
data
coursor written by
manager and read by jobs on
cores
only manager writes, cores only
read -- lockfree
design
zero collissions guaranteed by
API
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22/23

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Related Subjects
rigid statistics in traffic analysis
QoS of user communities 01, dynamic network management, etc.
isn't BigData replay a bottleneck?
not really, circuits for bulk transfer 02 or multisource aggregation 03 can support very
high throughputs
MapReduce has several bottlenecks: namespace lookup, HDFS, etc. -- see 06
for details
Why BigData Replay? -- it's a new ecosystem
bigdata hoarders announce replays, openly collect public jobs until a deadline, open
outcome
one good solution for bigdata → opendata innitiative
01 myself+0 A holistic community-based architecture for measuring end-to-end QoS at data centres IJCSE (2014)
02 myself+0 Circuit Emulation for Big Data Transfers in Clouds Networking for Big Data, Wiley (in print) (2015)
03 myself+0 Multi-Source Stream Aggregation in the Cloud Wiley (2014)
06 K.Shvachko HDFS Scalability: the Limits to Growth the Magazine of USENIX, vol.35, no.2 (2012)
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23/23

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[01] myself+0 (2014)
A holistic community-based architecture for measuring end-to-end QoS at data
centres
IJCSE
[02] myself+0 (2015)
Circuit Emulation for Big Data Transfers in Clouds
Networking for Big Data, Wiley (in print)
[03] myself+0 (2014)
Multi-Source Stream Aggregation in the Cloud
Wiley
[04] myself+0 (2014)
Optimizing Virtual Machine Migration for Energy-Efficient Clouds
IEICEJ
[05] myself+0 (2014)
A lock-free shared memory design for high-throughput multicore packet traffic
capture
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23/23

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IJNM
[06] K.Shvachko (2012)
HDFS Scalability: the Limits to Growth
the Magazine of USENIX, vol.35, no.2
[07] Y.Chen+3 (2011)
The Case for Evaluating MapReduce Performance Using Workload Suites
MASCOTS
[08] Z.Ren+4 (2012)
Workload Characterization on a Production Hadoop Cluster...
IEEE Workload...
[09] A.Rowstron+4 (2012)
Nobody ever got fired for using Hadoop on a cluster
1st HTCDP
[10] (current)
Small File Problem in Hadoop (blog)
http://amilaparanawithana.blogspot.jp/2012
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23/23

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[11] S.Muthukrishnan (2005)
Data Streams: Algorithms and Applications
Foundations and Trends...
[12] M.Sung+3 (2006)
Scalable and Efficient Data Streaming Algorithms for Detecting ....
ICDEW
[13] Z.Bar-Yossef+2 (2002)
...streaming algorithms, with an application to counting triangles in graphs
ACM SODA
[14] M.Charikar+2 (2002)
Finding frequent items in data streams
29th International Colloquium on Automata...
[15] M.Datar+3 (2002)
Maintaining stream statistics over sliding windows
SIAM
[16] S.Venkataraman+3 (2005)
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23/23

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New Streaming Algorithms for Fast Detection of Superspreaders
NDSS
[17] A.Rasooli+1 (2013)
COSHH: A Classification and Optimization based Scheduler for Heterogeneous
Hadoop...
McMaster
[18] S.Das+5 (2010)
Ricardo: Integrating R and Hadoop
SIGMOD
[19] X.Gao+2 (2012)
Experimenting with Lucene Index on HBase in an HPC Environment
1st HPCDB
[20] M.Aldinucci+2 (2009)
FastFlow: Efficient Parallel Streaming Applications on Multi-core
Universita di Pisa
[21] R.Brightwell (2008)
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23/23

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Workshop on Managed Many-Core Systems
1st Workshop ...Many-Core Systems
[22] R.Chen+2 (2010)
Tiled-MapReduce: Optimizing Resource Usages of Data-parallel... with Tiling
19th PACT
[23] current (myself)
MCoreMemory project page
https://github.com/maratishe/mcorememory
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23/23

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