H2O makes Hadoop do math! H2O scales statistics, machine learning and math over BigData. H2O is extensible and users can build blocks using simple math legos in the core. H2O keeps familiar interfaces like R, Excel & JSON so that BigData enthusiasts & experts can explore, munge, model and score datasets using a range of simple to advanced algorithms. Data collection is easy. Decision making is hard. H2O makes it fast and easy to derive insights from your data through faster and better predictive modeling. H2O has a vision of online scoring and modeling in a single platform.
H2O product, the Analytics Engine will scale Classification and Regression.
- RandomForest, Gradient Boosted Methods (GBM), Generalized Linear Modeling (GLM), Deep Learning and K-Means available over R / REST / JSON-API
- Basic Linear Algebra as building blocks for custom algorithms
- High predictive power of the models
- High speed and scale for modeling and scoring over BigData
Data Sources
- We read and write from/to HDFS, S3, NoSQL, SQL
- We ingest data in CSV format from local and distributed filesystems (nfs)
- A JDBC driver for SQL and DataAdapters for NoSQL datasources is in the roadmap. (v2)
Console provides Adhoc Data Analytics at scale via R-like Parser on BigData
- Able to pass and evaluate R-like expressions, slicing and filters make this the most powerful web calculator on BigData
Primary users are Data Analysts looking to wield a powerful tool for Data Modeling in the Real-Time. Microsoft Excel, R, SAS wielding Data Analysts and Statisticians. Hadoop users with data in HDFS will have a first class citizen for doing Math in Hadoop ecosystem. Java and Math engineers can extend core functionality by using and extending legos in a simple java that reads like math. See package hex. Extensibility can also come from writing R expressions that capture your domain.
We use the best execution framework for the algorithm at hand. For first cut parallel algorithms: Map Reduce over distributed fork/join framework brings fine grain parallelism to distributed algorithms. Our algorithms are cache oblivious and fit into the heterogeneous datacenter and laptops to bring best performance. Distributed Arraylets & Data Partitioning to preserve locality. Move code, not data, not people.
One of our first powerful extension will be a small tool belt of stats and math legos for Fraud Detection. Dealing with Unbalanced Datasets is a key focus for this. Users will use JSON/REST-api via H2O.R through connects the Analytics Engine into R-IDE/RStudio.
Find H2O Dev on Maven Central via http://search.maven.org/#search%7Cga%7C1%7Cai.h2o
Getting started with H2O development requires JDK 1.7, Node.js, and Gradle. We use the Gradle wrapper (called gradlew) to ensure an up-to-date local version of Gradle and other dependencies are installed in your development directory.
(possibly sudo)
pip install grip
Install Java 1.7 and add the appropriate directory C:\Program Files\Java\jdk1.7.0_65\bin with java.exe to PATH in Environment Variables. Check to make sure the command prompt is detecting the correct Java version by running:
javac -version
Install Node.js and add installed directory C:\Program Files\nodejs that should include node.exe and npm.cmd to PATH if it isn't already prepended. After you have installed Node.js, install bower using
npm install -g bower
Install R and add the preferred bin\i386 or bin\x64 directory to your PATH.
Note: Acceptable versions of R are >= 2.13 && <= 3.0.0 && >= 3.1.1.
Install the following R packages: bitops, devtools, digest, Rcpp, RCurl, rjson, roxygen2, statmod, stringr, and testthat.
cd Downloads
R CMD INSTALL bitops_x.x-x.zip
R CMD INSTALL RCurl_x.xx-x.x.zip
R CMD INSTALL rjson_x.x.xx.zip
R CMD INSTALL statmod_x.x.xx.zip
R CMD INSTALL Rcpp_x.xx.x.zip
R CMD INSTALL digest_x.x.x.zip
R CMD INSTALL testthat_x.x.x.zip
R CMD INSTALL stringr_x.x.x.zip
R CMD INSTALL roxygen2_x.x.x.zip
R CMD INSTALL devtools_x.x.x.zip
You may alternatively install these packages from within an R session:
R> install.packages("RCurl")
R> install.packages("rjson")
R> install.packages("statmod")
R> install.packages(c("devtools", "roxygen2", "testthat"))
Finally, install Rtools, which are a collection of command line tools to facilitate R development on Windows.
Step 4. Git Clone h2o-dev
If you don't already have a Git client, please install one. The default one can be found here http://git-scm.com/downloads . Make sure that during the install command prompt support is turned on.
Download and update h2o-dev source codes:
git clone https://github.com/h2oai/h2o-dev
cd h2o-dev
gradlew.bat build
If you encounter errors run again with --stacktrace for more instructions on missing dependencies.
If you don't have Homebrew install, please consider it. It makes package management for OS X easy.
Install Java 1.7. Check to make sure the command prompt is detecting the correct Java version by running:
javac -version
Using Homebrew:
brew install node
Otherwise install from the NodeJS website. After you have installed Node.js, install bower using
npm install -g bower
Install R and add the bin directory to your PATH if not already included.
Install the following R packages: RCurl, rjson, statmod, devtools, roxygen2 and testthat.
cd Downloads
R CMD INSTALL bitops_x.x-x.tgz
R CMD INSTALL RCurl_x.xx-x.x.tgz
R CMD INSTALL rjson_x.x.xx.tgz
R CMD INSTALL statmod_x.x.xx.tgz
R CMD INSTALL Rcpp_x.xx.x.tgz
R CMD INSTALL digest_x.x.x.tgz
R CMD INSTALL testthat_x.x.x.tgz
R CMD INSTALL stringr_x.x.x.tgz
R CMD INSTALL roxygen2_x.x.x.tgz
R CMD INSTALL devtools_x.x.x.tgz
You may alternatively install these packages from within an R session:
R> install.packages("RCurl")
R> install.packages("rjson")
R> install.packages("statmod")
R> install.packages(c("devtools", "roxygen2", "testthat"))
Step 4. Git Clone h2o-dev
OS X should have come with Git installed, so just download and update h2o-dev source codes:
git clone https://github.com/h2oai/h2o-dev
cd h2o-dev
./gradlew build
If you encounter errors run again with --stacktrace for more instructions on missing dependencies.
sudo apt-get install npm
sudo ln -s /usr/bin/nodejs /usr/bin/node
npm install -g bower
Install Java 1.7. Installation instructions can be found here JDK installation. Check to make sure the command prompt is detecting the correct Java version by running:
javac -version
Step 3. Git Clone h2o-dev
If you don't already have a Git client,
sudo apt-get install git
Download and update h2o-dev source codes:
git clone https://github.com/h2oai/h2o-dev
cd h2o-dev
./gradlew build
If you encounter errors run again with --stacktrace for more instructions on missing dependencies.
Make sure that you are not running as root since
bowerwill reject such a run
On Ubuntu 13.10, the default Node.js (v0.10.15) is sufficient, but the default npm (v1.2.18) is too old, so we use a fresh install from the npm website.
sudo apt-get install node
sudo ln -s /usr/bin/nodejs /usr/bin/node
wget http://npmjs.org/install.sh
sudo apt-get install curl
sudo sh install.sh
npm install -g bower
For users of Intellij's IDEA, project files can be generated with:
./gradlew idea
For users of Eclipse, project files can be generated with:
./gradlew eclipse
Note: This capability is new to h2o-dev and still under development!
Right now, h2o-dev can only build for one version of Hadoop at a time. This is specified in h2o-dev/build.gradle by the line below.
hadoopVersion = '2.5.0-cdh5.2.0'
The default version is set to CDH5.2 but this also works for HDP2.1 (the example below is launching on hdp2.1).
(In top-level h2o-dev directory)
./gradlew build
After building, you now have the pieces to run H2O on Hadoop. Copy the h2o-hadoop.jar and h2o.jar files to an appropriate Hadoop enabled host. The following command shows an example of running in place, assuming you did your build on a Hadoop enabled host.
(In top-level h2o-dev directory)
hadoop jar h2o-hadoop/build/libs/h2o-hadoop.jar water.hadoop.h2odriver -libjars build/h2o.jar -n 3 -mapperXmx 4g -output hdfsOutputDirectory
This example creates a cluster with three nodes where each node has a 4g Java heap.
Note: For best results, we recommend you give your cluster enough memory to avoid swapping data to disk. (Of course, this depends on the size of your data!)
Output from the above command:
Determining driver host interface for mapper->driver callback...
[Possible callback IP address: 172.16.2.181]
[Possible callback IP address: 127.0.0.1]
Using mapper->driver callback IP address and port: 172.16.2.181:44948
(You can override these with -driverif and -driverport.)
14/10/26 22:59:07 INFO Configuration.deprecation: mapred.map.child.java.opts is deprecated. Instead, use mapreduce.map.java.opts
Memory Settings:
mapred.child.java.opts: -Xms4g -Xmx4g -Dlog4j.defaultInitOverride=true
mapred.map.child.java.opts: -Xms4g -Xmx4g -Dlog4j.defaultInitOverride=true
Extra memory percent: 10
mapreduce.map.memory.mb: 4505
14/10/26 22:59:07 INFO Configuration.deprecation: mapred.used.genericoptionsparser is deprecated. Instead, use mapreduce.client.genericoptionsparser.used
14/10/26 22:59:07 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
14/10/26 22:59:07 INFO Configuration.deprecation: mapred.map.max.attempts is deprecated. Instead, use mapreduce.map.maxattempts
14/10/26 22:59:07 INFO Configuration.deprecation: mapred.job.reuse.jvm.num.tasks is deprecated. Instead, use mapreduce.job.jvm.numtasks
14/10/26 22:59:08 INFO client.RMProxy: Connecting to ResourceManager at mr-0xd7.0xdata.loc/172.16.2.187:8050
14/10/26 22:59:09 INFO mapreduce.JobSubmitter: number of splits:3
14/10/26 22:59:09 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1413598290344_0035
14/10/26 22:59:09 INFO impl.YarnClientImpl: Submitted application application_1413598290344_0035
14/10/26 22:59:09 INFO mapreduce.Job: The url to track the job: http://mr-0xd7.0xdata.loc:8088/proxy/application_1413598290344_0035/
Job name 'H2O_3581' submitted
JobTracker job ID is 'job_1413598290344_0035'
For YARN users, logs command is 'yarn logs -applicationId application_1413598290344_0035'
Waiting for H2O cluster to come up...
H2O node 172.16.2.184:54321 reports H2O cluster size 1
H2O node 172.16.2.185:54321 reports H2O cluster size 1
H2O node 172.16.2.181:54321 reports H2O cluster size 1
H2O node 172.16.2.181:54321 reports H2O cluster size 3
H2O cluster (3 nodes) is up
(Note: Use the -disown option to exit the driver after cluster formation)
(Press Ctrl-C to kill the cluster)
Blocking until the H2O cluster shuts down...
H2O node 172.16.2.185:54321 reports H2O cluster size 3
H2O node 172.16.2.184:54321 reports H2O cluster size 3
We will breathe & sustain a vibrant community with the focus of taking software engineering approach to data science and empower everyone interested in data to be able to hack data using math and algorithms. Join us on google groups h2ostream.
Team & Committers
SriSatish Ambati
Cliff Click
Tom Kraljevic
Tomas Nykodym
Michal Malohlava
Kevin Normoyle
Spencer Aiello
Anqi Fu
Nidhi Mehta
Arno Candel
Josephine Wang
Amy Wang
Max Schloemer
Ray Peck
Prithvi Prabhu
Patrick Aboyoun
Brandon Hill
Radu Munteanu
Jeff Gambera
Ariel Rao
Viraj Parmar
Kendall Harris
Anna Chavez
Anand Avati
Joel Horwitz
Jessica Lanford
Yan Zou
Scientific Advisory Council
Stephen Boyd
Rob Tibshirani
Trevor Hastie
Systems, Data, FileSystems and Hadoop
Doug Lea
Chris Pouliot
Dhruba Borthakur
Charles Zedlewski
Jishnu Bhattacharjee, Nexus Venture Partners
Anand Babu Periasamy
Anand Rajaraman
Ash Bhardwaj
Rakesh Mathur
Michael Marks