The datascientists workplace of the future, IBM developerDays 2014, Vienna by Romeo Kienzler

© 2013 IBM Corporation1
The Data Scientists Workplace of the Future - Workshop
SwissRE, 11.6.14
Romeo Kienzler
IBM Center of Excellence for Data Science, Cognitive Systems and BigData
(A joint-venture between IBM Research Zurich and IBM Innovation Center DACH)
Source: http://www.kdnuggets.com/2012/04/data-science-history.jpg

The Data Scientists Workplace of the Future -
* * C R E D I T S * *
Romeo Kienzler
IBM Innovation Center
●
Parts of these slides have been copied from and/or revised by
●
Dr. Anand Ranganathan, IBM Watson Research Lab
●
Dr. Stefan Mück, IBM BigData Leader Europe
●
Dr. Berthold Rheinwald, IBM Almaden Research Lab
●
Dr. Diego Kuonen, Statoo Consulting
●
Dr. Abdel Labbi, IBM Zurich Research Lab
●
Brandon MacKenzie, IBM Software Group

What is DataScience?
Source: Statoo.com http://slidesha.re/1kmNiX0

DataScience at present
●
Tools (http://blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html)
●
SQL (42%)
●
R (33%)
●
Python (26%)
●
Excel (25%)
●
Java, Ruby, C++ (17%)
●
SPSS, SAS (9%)
●
Limitations (Single Node usage)
●
Main Memory
●
CPU <> Main Memory Bandwidth
●
CPU
●
Storage <> Main Memory Bandwidth (either Single node or SAN)

DataScience at present - Demo
●
Assume 1 TB file on Hard Drive
●
Spit into 16 files
●
split -d -n 16 output.json
●
Distribute on 4 Nodes
●
for node in `seq 1 16`; do scp x$node id@node$i:~/; done
●
Perform calculation in paralell
●
for node in `seq 1 16`; do
ssh id@node$i 'cat $file
|awk -F":" '{print $6}'
|grep -i samsung
|grep breathtaking |wc -l';
done > result
●
Merge Result
●
cat result |sum
Source: http://sergeytihon.wordpress.com/2013/03/20/the-data-science-venn-diagram/

What is BIG data?

What is BIG data?
Big Data
Hadoop

What is BIG data?
Business Intelligence
Data Warehouse

BigData == Hadoop?
Hadoop BigData
Hadoop

What is beyond “Data Warehouse”?
Data Lake
Data Warehouse

First “BigData” UseCase ?
●
Google Index
●
40 X 10^9 = 40.000.000.000 => 40 billion pages indexed
●
Will break 100 PB barrier soon
●
Derived from MapReduce
●
now “caffeine” based on “percolator”
●
Incremental vs. batch
●
In-Memory vs. disk
●

Map-Reduce → Hadoop → BigInsights

BigData UseCases
●
CERN LHC
●
25 petabytes per year
●
Facebook
●
Hive Datawarehouse
●
300 PB, Growing 600 TB / d
●
> 100 k servers
●
Genomics
●
Enterprises
●
Data center analytics (Logflies, OS/NW monitors, ...)
●
Predictive Maintenance, Cybersecurity
●
Social Media Analytics
●
DWH offload
●
Call Detail Record (CDR) data preservation
http://www.balthasar-glaettli.ch/vorratsdaten/

Why is Big Data important?

BigData Analytics
Source: http://www.strategy-at-risk.com/2008/01/01/what-we-do/

BigData Analytics – Predictive Analytics
"sometimes it's not
who has the best
algorithm that wins;
it's who has the most
data."
(C) Google Inc.
The Unreasonable Effectiveness of Data¹
¹http://www.csee.wvu.edu/~gidoretto/courses/2011-fall-cp/reading/TheUnreasonable%20EffectivenessofData_IEEE_IS2009.pdf
No Sampling => Work with full dataset => No p-Value/z-Scores anymore

We need Data Parallelism

Aggregated Bandwith between CPU, Main
Memory and Hard Drive
1 TB (at 10 GByte/s)
- 1 Node - 100 sec
- 10 Nodes - 10 sec
- 100 Nodes - 1 sec
- 1000 Nodes - 100 msec

Fault Tolerance / Commodity Hardware
AMD Turion II Neo N40L (2x 1,5GHz / 2MB / 15W), 8 GB RAM,
3TB SEAGATE Barracuda 7200.14
< CHF 500
 100 K => 200 X (2, 4, 3) => 400 Cores, 1,6 TB RAM, 200 TB HD
 MTBF ~ 365 d > 1,5 d
Source: http://www.cloudcomputingpatterns.org/Watchdog

NoSQL Databases
 Column Store
– Hadoop / HBASE
– Cassandra
– Amazon Simple DB
 JSON / Document Store
– MongoDB
– CouchDB
 Key / Value Store
– Amazon DynamoDB
– Voldemort
 Graph DBs
– DB2 SPARQL Extension
– Neo4J
 MP RDBMS
– DB2 DPF, DB2 pureScale, PureData for Operational Analytics
– Oracle RAC
– Greenplum

http://nosql-database.org/ > 150

CAP Theorem / Brewers Theorem¹
 impossible for a distributed computer system simultaneously guarantee all 3 properties
– Consistency (all nodes see the same data at the same time)
– Availability (guarantee that every request knows whether it was successful or failed)
– Partition tolerance (continues to operate despite failure of part of the system)
 What about ACID?
– Atomicity
– Consistency
– Isolation
– Durability
 BASE, the new ACID
– Basically Available
– Soft state
– Eventual consistency
• Monotonic Read Consistency
• Monotonic Write Consistency
• Read Your Own Writes
–


What role is the cloud playing here?

“Elastic” Scale-Out
Source: http://www.cloudcomputingpatterns.org/Continuously_Changing_Workload

of

of
CPU Cores

of
CPU Cores Storage

of
CPU Cores Storage Memory

linear
Source: http://www.cloudcomputingpatterns.org/Elastic_Platform

How do Databases Scale-Out?
Shared Disk Architectures

How do Databases Scale-Out?
Shared Nothing Architectures

Hadoop?
Shared Nothing Architecture?
Shared Disk Architecture?

Data Science on Hadoop
SQL (42%)
R (33%)
Python (26%)
Excel (25%)
Java, Ruby, C++ (17%)
SPSS, SAS (9%)
Data Science Hadoop

Large Scale Data Ingestion
●
Traditionally
●
Crawl to local file system (e.g. wget http://www.heise.de/newsticker/)
●
Export RDBMS data to CSV (local file system)
●
Batched FTP Servers uploads
●
Then: Copy to HDFS
●
BigInsights
●
Use one of built-in importers
●
Imports directly info HDFS
●
Use Eclipse-Tooling to deploy custom importers easily

Large Scale Data Ingestion (ETL on M/R)
●
Modern ETL (Extract, Transform, Load) tools support Hadoop as
●
Source, Sink (HDFS)
●
Engine (MapReduce)
●
Example: InfoSphere DataStage

Real-Time/ In-Memory Data Ingestion
●
If volume can be reduced dramatically during first processing steps
●
Feature Extraction of
●
Video
●
Audio
●
Semistructured Text (e.g. Logfiles)
●
Structured Text
●
Filtering
●
Compression
●
Recommendation: Usage of Streaming Engines
●
IBM InfoSphere Streams
●
Twitter Storm (now Apache incubator)
●
Apache Spark Streaming

Real-Time/ In-Memory Data Ingestion
●
If volume can be reduced dramatically during first processing steps
●
Feature Extraction of
●
Video
●
Audio
●
Semistructured Text (e.g. Logfiles)
●
Structured Text
●
Filtering
●
Compression

SQL on Hadoop
●
IBM BigSQL (ANSI 92 compliant)
●
HIVE (SQL dialect)
●
Cloudera Impala
●
Lingual
●
...
SQL Hadoop

BigSQL V3.0 – ANSI SQL 92 compliant
IBM BigInsights v3.0, with Big SQL 3.0, is the only Hadoop distribution to
successfully run ALL 99 TPC-DS queries and ALL 22 TPC-H queries without
modification. Source: http://www.ibmbigdatahub.com/blog/big-deal-about-infosphere-biginsights-v30-big-sql

BigSQL V3.0 – Architecture

BigSQL V3.0 – Demo (small)
●
32 GB Data, ~650.000.000 rows (small, Innovation Center Zurich)
●
3 TB Data, ~ 60.937.500.000 rows (middle, Innovation Center Zurich)
●
0.7 PB Data, ~ 1.421875×10¹³ rows (large, Innovation Center Hursley)
●
●
●

CREATE EXTERNAL TABLE trace (
hour integer, employeeid integer,
departmentid integer, clientid integer,
date string, timestamp string)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LINES
TERMINATED BY 'n' STORED AS TEXTFILE LOCATION
'/user/biadmin/32Gtest';
select count(hour), hour from trace group by hour order by hour
-- This command runs on 32 GB / ~650.000.000 rows in HDFS

R on Hadoop
●
IBM BigR (based on SystemML Almadan Research project)
●
Rhadoop
●
RHIPE
●
...
“R” Hadoop

BigR (based on SystemML)
Example: Gaussian Non-negative Matrix Factorization
package gnmf;
import java.io.IOException;
import java.net.URISyntaxException;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapred.JobConf;
public class MatrixGNMF
{
public static void main(String[] args) throws IOException, URISyntaxException
{
if(args.length < 10)
{
System.out.println("missing parameters");
System.out.println("expected parameters: [directory of v] [directory of w] [directory
of h] " +
"[k] [num mappers] [num reducers] [replication] [working directory] " +
"[final directory of w] [final directory of h]");
System.exit(1);
}
String vDir = args[0];
String wDir = args[1];
String hDir = args[2];
int k = Integer.parseInt(args[3]);
int numMappers = Integer.parseInt(args[4]);
int numReducers = Integer.parseInt(args[5]);
int replication = Integer.parseInt(args[6]);
String outputDir = args[7];
String wFinalDir = args[8];
String hFinalDir = args[9];
JobConf mainJob = new JobConf(MatrixGNMF.class);
String vDirectory;
String wDirectory;
String hDirectory;
FileSystem.get(mainJob).delete(new Path(outputDir));
vDirectory = vDir;
hDirectory = hDir;
wDirectory = wDir;
String workingDirectory;
String resultDirectoryX;
String resultDirectoryY;
long start = System.currentTimeMillis();
System.gc();
System.out.println("starting calculation");
System.out.print("calculating X = WT * V... ");
workingDirectory = UpdateWHStep1.runJob(numMappers, numReducers, replication,
UpdateWHStep1.UPDATE_TYPE_H, vDirectory, wDirectory, outputDir, k);
resultDirectoryX = UpdateWHStep2.runJob(numMappers, numReducers, replication,
workingDirectory, outputDir);
FileSystem.get(mainJob).delete(new Path(workingDirectory));
System.out.println("done");
System.out.print("calculating Y = WT * W * H... ");
wDirectory, outputDir);
resultDirectoryY = UpdateWHStep4.runJob(numMappers, replication, workingDirectory,
UpdateWHStep4.UPDATE_TYPE_H, hDirectory, outputDir);
System.out.print("calculating H = H .* X ./ Y... ");
hDirectory, resultDirectoryX, resultDirectoryY, hFinalDir, k);
FileSystem.get(mainJob).delete(new Path(resultDirectoryX));
FileSystem.get(mainJob).delete(new Path(resultDirectoryY));
System.out.print("storing back H... ");
FileSystem.get(mainJob).delete(new Path(hDirectory));
hDirectory = workingDirectory;
System.out.print("calculating X = V * HT... ");
UpdateWHStep1.UPDATE_TYPE_W, vDirectory, hDirectory, outputDir, k);
resultDirectoryX = UpdateWHStep2.runJob(numMappers, numReducers, replication,
workingDirectory, outputDir);
System.out.print("calculating Y = W * H * HT... ");
hDirectory, outputDir);
resultDirectoryY = UpdateWHStep4.runJob(numMappers, replication, workingDirectory,
UpdateWHStep4.UPDATE_TYPE_W, wDirectory, outputDir);
System.out.print("calculating W = W .* X ./ Y... ");
wDirectory, resultDirectoryX, resultDirectoryY, wFinalDir, k);
FileSystem.get(mainJob).delete(new Path(resultDirectoryX));
FileSystem.get(mainJob).delete(new Path(resultDirectoryY));
System.out.print("storing back W... ");
FileSystem.get(mainJob).delete(new Path(wDirectory));
package gnmf;
import gnmf.io.MatrixObject;
import gnmf.io.MatrixVector;
import gnmf.io.TaggedIndex;
import java.util.Iterator;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.SequenceFileInputFormat;
import org.apache.hadoop.mapred.SequenceFileOutputFormat;
public class UpdateWHStep2
{
static class UpdateWHStep2Mapper extends MapReduceBase
implements Mapper<TaggedIndex, MatrixVector, TaggedIndex,
MatrixVector>
{
@Override
public void map(TaggedIndex key, MatrixVector value,
OutputCollector<TaggedIndex, MatrixVector> out,
Reporter reporter) throws IOException
{
out.collect(key, value);
}
}
static class UpdateWHStep2Reducer extends MapReduceBase
implements Reducer<TaggedIndex, MatrixVector, TaggedIndex,
MatrixObject>
{
@Override
public void reduce(TaggedIndex key, Iterator<MatrixVector> values,
OutputCollector<TaggedIndex, MatrixObject> out, Reporter
reporter)
throws IOException
{
MatrixVector result = null;
while(values.hasNext())
{
MatrixVector current = values.next();
if(result == null)
{
result = current.getCopy();
} else
{
result.addVector(current);
}
}
if(result != null)
{
out.collect(new TaggedIndex(key.getIndex(),
TaggedIndex.TYPE_VECTOR_X),
new MatrixObject(result));
}
}
}
public static String runJob(int numMappers, int numReducers, int
replication,
String inputDir, String outputDir) throws IOException
{
String workingDirectory = outputDir + System.currentTimeMillis() +
"-UpdateWHStep2/";
JobConf job = new JobConf(UpdateWHStep2.class);
job.setJobName("MatrixGNMFUpdateWHStep2");
job.setInputFormat(SequenceFileInputFormat.class);
FileInputFormat.setInputPaths(job, new Path(inputDir));
package gnmf;
import gnmf.io.MatrixCell;
import gnmf.io.MatrixFormats;
import gnmf.io.MatrixObject;
import gnmf.io.MatrixVector;
import gnmf.io.TaggedIndex;
import java.util.Iterator;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.SequenceFileInputFormat;
import org.apache.hadoop.mapred.SequenceFileOutputFormat;
public class UpdateWHStep1
{
public static final int UPDATE_TYPE_H = 0;
public static final int UPDATE_TYPE_W = 1;
static class UpdateWHStep1Mapper extends MapReduceBase
implements Mapper<TaggedIndex, MatrixObject, TaggedIndex, MatrixObject>
{
private int updateType;
@Override
public void map(TaggedIndex key, MatrixObject value,
OutputCollector<TaggedIndex, MatrixObject> out,
Reporter reporter) throws IOException
{
if(updateType == UPDATE_TYPE_W && key.getType() == TaggedIndex.TYPE_CELL)
{
MatrixCell current = (MatrixCell) value.getObject();
out.collect(new TaggedIndex(current.getColumn(), TaggedIndex.TYPE_CELL),
new MatrixObject(new MatrixCell(key.getIndex(), current.getValue())));
} else
{
out.collect(key, value);
}
}
@Override
public void configure(JobConf job)
{
updateType = job.getInt("gnmf.updateType", 0);
}
}
static class UpdateWHStep1Reducer extends MapReduceBase
implements Reducer<TaggedIndex, MatrixObject, TaggedIndex, MatrixVector>
{
private double[] baseVector = null;
private int vectorSizeK;
@Override
public void reduce(TaggedIndex key, Iterator<MatrixObject> values,
OutputCollector<TaggedIndex, MatrixVector> out, Reporter reporter)
throws IOException
{
if(key.getType() == TaggedIndex.TYPE_VECTOR)
{
if(!values.hasNext())
throw new RuntimeException("expected vector");
MatrixFormats current = values.next().getObject();
if(!(current instanceof MatrixVector))
throw new RuntimeException("expected vector");
baseVector = ((MatrixVector) current).getValues();
} else
{
while(values.hasNext())
{
MatrixCell current = (MatrixCell) values.next().getObject();
if(baseVector == null)
{
out.collect(new TaggedIndex(current.getColumn(), TaggedIndex.TYPE_VECTOR),
new MatrixVector(vectorSizeK));
} else
{
if(baseVector.length == 0)
throw new RuntimeException("base vector is corrupted");
MatrixVector resultingVector = new MatrixVector(baseVector);
resultingVector.multiplyWithScalar(current.getValue());
if(resultingVector.getValues().length == 0)
throw new RuntimeException("multiplying with scalar failed");
out.collect(new TaggedIndex(current.getColumn(), TaggedIndex.TYPE_VECTOR),
resultingVector);
}
}
baseVector = null;
}
}
@Override
public void configure(JobConf job)
{
vectorSizeK = job.getInt("dml.matrix.gnmf.k", 0);
Java Implementation
(>1500 lines of code)
Equivalent SystemML Implementation
(10 lines of code)
Experimenting with multiple variants!
W = W*max(V%*%t(H) – alphaW JW, 0)/(W%*%H%*%t(H))
H = H*max(t(W)%*%V – alphaH JH, 0)/(t(W)%*%W%*%H)
W = W*((S*V)%*%t(H))/((S*(W%*%H))%*%t(H))
H = H*(t(W)%*%(S*V))/(t(W)%*%(S*(W%*%H)))
W = W*(V/(W%*%H) %*% t(H))/(E%*%t(H))
H = H*(t(W)%*%(V/(W%*%H)))/(t(W)%*%E)

BigR (based on SystemML)
SystemML compiles hybrid runtime plans ranging from in-
memory, single machine (CP) to large-scale, cluster (MR)
compute
●
Challenge
●
Guaranteed hard memory constraints
(budget of JVM size)
●
for arbitrary complex ML programs
●
Key Technical Innovations
●
CP & MR Runtime: Single machine & MR operations, integrated runtime
●
Caching: Reuse and eviction of in-memory objects
●
Cost Model: Accurate time and worst-case memory estimates
●
Optimizer: Cost-based runtime plan generation
●
Dyn. Recompiler: Re-optimization for initial unknowns
Data size
Runtime
CP CP/MR MR
Gradually exploit
MR parallelism
High performance
computing for
small data sizes.
Scalable
computing for
large data sizes.
Hybrid Plans

R Clients
SystemML
Statistics
Engine
Data Sources
Embedded R
Execution
IBM R Packages
IBM R Packages
Pull data
(summaries) to
R client
Or, push R
functions
right on the
data
1
2
3
© 2014 IBM Corporation17 IBM Internal Use Only
BigR Architecture

BigR Demo (small)
●
●
●

BigR Demo (small)
library(bigr)
bigr.connect(host="bigdata",
port=7052, database="default",
user="biadmin", password="xxx")
is.bigr.connected()
tbr <- bigr.frame(dataSource="DEL", coltypes =
c("numeric","numeric","numeric","numeric","character","character"),
dataPath="/user/biadmin/32Gtest", delimiter=",",
header=F, useMapReduce=T)
h <- bigr.histogram.stats(tbr$V1, nbins=24)

BigR Demo (small)
class bins counts centroids
1 ALL 0 18289280 1.583333
2 ALL 1 15360 2.750000
3 ALL 2 55040 3.916667
4 ALL 3 189440 5.083333
5 ALL 4 579840 6.250000
6 ALL 5 5292160 7.416667
7 ALL 6 8074880 8.583333
8 ALL 7 15653120 9.750000
...

BigR Demo (small)

BigR Demo (small)
jpeg('hist.jpg')
bigr.histogram(tbr$V1, nbins=24)
# This command runs on 32 GB / ~650.000.000 rows in HDFS
dev.off()

BigR Demo (small)
Sampling, Resampling, Bootstrapping
vs
Whole Dataset Processing
What is your experience?

Python on Hadoop
python Hadoop

SPSS on Hadoop

BigSheets Demo (small)
●
●
●
●
●
●

This command runs on 32 GB /
~650.000.000 rows in HDFS

Text Extraction (SystemT, AQL)

If this is not enough? → BigData AppStore

BigData AppStore, Eclipse Tooling
●
Write your apps in
●
Java (MapReduce)
●
PigLatin,Jaql
●
BigSQL/Hive/BigR
●
Deploy it to BigInsights via Eclipse
●
Automatically
●
Schedule
●
Update
●
hdfs files
●
BigSQL tables
●
BigSheets collections

Questions?
http://www.ibm.com/software/data/bigdata/
Twitter: @RomeoKienzler, @IBMEcosystem_DE, @IBM_ISV_Alps

DFT/Audio Analytics (as promised)
library(tuneR)
a <- readWave("whitenoisesine.wav")
f<- fft(a@left)
jpeg('rplot_wnsine.jpg')
plot(Re(f)^2)
dev.off()
a <- readWave("whitenoise.wav")
f<- fft(a@left)
jpeg('rplot_wn.jpg')
plot(Re(f)^2)
dev.off()
a <- readWave("whitenoisesine.wav")
brv <- as.bigr.vector(a@left)
al <- as.list(a@left)

Backup Slides

Map-Reduce
Source: http://www.cloudcomputingpatterns.org/Map_Reduce

The datascientists workplace of the future, IBM developerDays 2014, Vienna by Romeo Kienzler

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The datascientists workplace of the future, IBM developerDays 2014, Vienna by Romeo Kienzler