1 - MADlib General
Q1-1 What is MADlib?
MADlib is an open source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical, graph and machine learning methods for structured and unstructured data.
MADlib is a Top Level Project in the Apache Software Foundation.
MADlib home page http://madlib.apache.org/
Q1-2 What database platforms does MADlib support and what is the upgrade matrix?
- MADlib runs on Greenplum database (GPDB) and and PostgreSQL. The last three major versions of these databases are directly tested with each MADlib release. Older versions of these databases may work fine as well, but we don't test them explicitly.
Apache HAWQ (incubating)
Upgrade Path from Previous
|1.9 and all earlier versions|
* If you are on an older version of MADlib you may need to do multiple hops to update to the latest version.
OS support generally follows that of the underlying databases:
RHEL 5.5-5.7, 6.1, 6.2, 6.4, 6.5
CentOS 5.5-5.7, 6.1, 6.2
- GPDB 5+ does not support RHEL5 or CentOS5.
RHEL 6.1, 6.2, 6.4, 6.5, 7.x
CentOS 6.1, 6.2, 7.x
- Ubuntu 16.04
RHEL 6.1, 6.2, 6.4, 6.5
CentOS 6.1, 6.2
RHEL 5.x, 6.x, 7.x
CentOS 5.x, 6.x, 7.x
Mac OSX 10.6+
- Ubuntu 16.04
Q1-3 What are the main advantages of MADlib?
Use MPP architecture’s full compute power
Use MPP architecture’s entire memory to process large data sets, so don’t need to sample
Familiar SQL interface
Q1-4 Who uses MADlib?
Data scientists mostly
Wide range of verticals including financial services, healthcare, retail, energy, manufacturing and government
Q1-5 What are the benefits of MADlib compared with products like R and scikit-learn?
MADlib is a fully parallelized implementation on GPBD and HAWQ for large data sets, so it is far more performant than R or Python libraries.
Add more nodes to achieve higher performance as your data scales. R and Python libraries are limited by the amount of data you can load into memory on a single node.
Using all data, not a sample, can improve accuracy
Familiar, user friendly SQL interface
Ease of data preparation
Supports commonly used database data formats
Q1-6 Where can I find the details of how existing MADlib algorithms have been implemented?
- Please refer to the algorithm technical design document
Q1-7 What is the MADlib security model?
- MADlib security model is the same as that of the underlying database. It runs in-database and does not do file level operations.
Regarding specific roles and privileges, MADlib users need object privileges in the schema that they are working in. This is because MADlib creates temporary tables and output tables as part of regular execution of MADlib functions.
For Greenplum database, see Table 2 for a list of required object privileges: http://gpdb.docs.pivotal.io/latest/admin_guide/roles_privs.html .
- For PostgreSQL, see the GRANT commandL https://www.postgresql.org/docs/current/static/sql-grant.html .
2 - MADlib Usage
Q2-1 What are my options if MADlib does not have the algorithm that I need?
In this case, you might think about building separate models for different chunks of a larger dataset (partition by state, range of user IDs, product category, etc.)
This is referred to data parallelism: break up the problem into a number of parallel tasks, if you can ensure there is no dependency (or communication) between those parallel tasks.
Then you can use a procedural languages such as PL/Python or PL/R on each chunk and combine the results downstream.
And of course, you can build and contribute the module to MADlib for the benefit of the community
Q2-2 What are the differences in functionality between the GPDB and HDB/HAWQ versions of MADlib?
There are very few differences and they are listed below.
Can specify a user defined distance function for GPDB & PostgreSQL. For HAWQ, does not support UDFs for distance, so restricted to the built-in distance functions provided.
“Deprecated modules” quartile and profile have some minor differences and limitations between HAWQ and GPDB. See the documentation for details.
Q2-3 Can I export models from MADlib to PMML?
Yes. MADlib models can be exported in PMML format for use in scoring by a PMML evaluator.
The following MADlib algorithms can be exported in PMML format:
Q2-4 What is PMML?
The Predictive Model Markup Language (PMML) is an XML-based file format that provides a way for applications to describe and exchange models produced by data mining and machine learning algorithms.
For more information, please see http://www.dmg.org/
Q2-5 What is JPMML?
JPMML is an open source PMML evaluator available under GPL license.
Q2-6 Can I import models from PMML to MADlib?
Not currently. You can only export from MADlib into PMML as described above.
Q2-7 Can I call MADlib from Python?
There is a nascent Python wrapper called PyMADlib. It is based on an older version of MADlib and supports a very limited number of algorithms.
We want to build a full Python API for MADlib, so let us know if you would like to participate in this effort.
Q2-8 What are some examples of connecting MADlib with 3rd party products?
3 - PivotalR
Q3-1 What is PivotalR?
PivotalR is a package that enables users of R, the most popular open source statistical programming language and environment, to interact with the GPDB, HAWQ and the open source database PostgreSQL on large data sets. It does so by providing an interface to the operations on tables/views in the database.
Q3-2 Where can I learn more about PivotalR?
Please refer to this PivotalR wiki page.
4 - Other
Q4-1 Your question here
Let us know if you have a question and we will add it here.