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The MADlib Analytics Library or MAD Skills, the SQL
Joe Hellerstein, Christopher Ré, Florian Schoppmann, Daisy Zhe Wang, Eugene Fratkin, Aleksander Gorajek, Kee Siong Ng, Caleb Welton, Xixuan Feng, Kun Li, Arun Kumar
TL;DR
MADlib addresses the need for scalable statistical analytics within SQL database systems, despite SQL’s limitations for iterative algorithms and schema-flexible queries. The paper presents its in-database architecture and design patterns, reports linear speedup for a Greenplum example, and describes its growing method library and academic collaborations.
Problem
MADlib targets the need to push statistical methods into SQL database systems, where scalable analytics are increasingly valuable but standard SQL limits iterative algorithms and templated queries.
Method
The paper presents MADlib’s SQL-based in-database architecture, using user-defined aggregates, driver functions, and convex optimization to implement scalable analytic methods.
Results
MADlib v0.3 provides a relatively large number of widely-used statistical and analytic methods and is already used at research universities and customer sites.
Takeaways & Limitations
MADlib offers a library and design-pattern approach for scalable analytics in parallel database systems, while remaining open to new methods, academic partnerships, and ports across DBMSs.
Takeaways & Limitations
The paper does not compare MADlib’s performance with Hadoop-based analytics projects such as Mahout.
Abstract
from arXiv · showhide
MADlib is a free, open source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that run at scale within a database engine, with no need for data import/export to other tools. The goal is for MADlib to eventually serve a role for scalable database systems that is similar to the CRAN library for R: a community repository of statistical methods, this time written with scale and parallelism in mind. In this paper we introduce the MADlib project, including the background that led to its beginnings, and the motivation for its open source nature. We provide an overview of the library's architecture and design patterns, and provide a description of various statistical methods in that context. We include performance and speedup results of a core design pattern from one of those methods over the Greenplum parallel DBMS on a modest-sized test cluster. We then report on two initial efforts at incorporating academic research into MADlib, which is one of the project's goals. MADlib is freely available at http://madlib.net, and the project is open for contributions of both new methods, and ports to additional database platforms.
Introducing MADlib
MADlib is an extensible-SQL library of analytic methods that executes inside relational database engines, supporting scale-out computation near stored data. Its methods and database ports are intended to expand through public contributions.
- MADlib provides analytic methods that install and execute within relational database engines supporting extensible SQL.
- Table 1 lists MADlib v0.3 methods tested on PostgreSQL and Greenplum Database.
- Its methods support in- or out-of-core execution and shared-nothing scale-out parallelism, keeping computation close to the data.Declarative SQL orchestrates movement across disk and networked machines, while single-node inner loops can use high-performance math libraries.
- MADlib is publicly hosted, combines industry and academic contributions, and welcomes new methods and ports to additional platforms.
2. GOALS OF THE PROJECT
MADlib aims to fill a gap in scalable SQL analytics by providing an open-source community library that connects research, industry, and end users. Its design responds to the growing feasibility and value of full-dataset analysis.
- MADlib’s primary goal is to accelerate Data Science innovation and technology transfer through a shared library of scalable in-database analytics.The project is intended to play a role analogous to CRAN for the R community while remaining grounded in standard SQL.
- 2.1 Why Databases?: Cheap storage and computation make full-dataset processing on computer clusters increasingly feasible, weakening the case for sample-first analytics.The paper links sample-based optimization to simplistic decisions that may not translate well to small populations in long-tail distributions.
- 2.1 Why Databases?: MADlib targets SQL systems because valuable data is expected to continue entering them and parallel databases provide a platform for sophisticated analytics.
- 2.2 Why Open Source?: Open source supports customization, shared modifications, and connections among academic researchers, vendors, and analytics users.
- 2.2 Why Open Source?: MADlib also seeks to level the playing field by bringing standard statistical capabilities to database users and focusing community innovation.
- 2.3 A Model for Open-Source Collaboration: Corporate software-engineering time, quality assurance, and support are used to bootstrap an open-source environment for academic research and technology transfer.
3. MADLIB ARCHITECTURE
MADlib combines SQL-based macro-programming for partitioning, data movement, aggregation, and iteration with lower-level numerical components for efficient in-database analytics. Driver functions and generated queries address limitations in standard SQL while preserving scalability.
- 3.1 Macro-Programming (Orchestration): MADlib organizes scalable linear algebra through divide-and-conquer partitioning, piecewise processing, and merging across database machines.
- 3.1.1 User-Defined Aggregation: User-defined aggregates provide a natural SQL building block for data-parallel mathematical functions over arbitrary numbers of rows.Their transition, optional merge, and final functions support parallel execution when the aggregation state combines associatively.
- 3.1.2 Driver Functions for Multipass Iteration: Python driver UDFs control complex iterative methods and pass state across iterations because SQL workarounds lack both generality and portability.The paper identifies multipass, data-dependent algorithms such as Gradient Descent and MCMC as the motivating case.
- 3.1.3 Templated Queries: Templated queries use Python UDFs to inspect table schemas and synthesize customized SQL for modules whose outputs depend on arbitrary inputs.This approach supports modules such as profile, which summarizes each column of an input table with a nonfixed schema.
- 3.1.3 Templated Queries: Templated SQL requires upfront validation and error handling because syntax errors in generated statements are discovered only at execution time.The paper proposes a future Python library to provide programmer APIs and better user feedback.
- 3.3 A C++ Abstraction Layer for UDFs: A C++ abstraction layer makes high-performance UDFs more maintainable and portable by encapsulating type bridging, resource management, and math-library integration.
4. EXAMPLES
MADlib illustrates its SQL-based analytics patterns through linear regression, logistic regression, and k-means, emphasizing parallel aggregation, iterative control flow, and performance tuning. Linear regression achieves perfect linear speedup in the reported Greenplum example, while implementation choices materially affect runtime.
- Examples: MADlib examines OLS linear regression, binary logistic regression, and k-means clustering as examples of single-pass, iterative, and large-state distributed algorithms.OLS uses a single-pass aggregation; logistic regression requires iterations; k-means maintains large intermediate states across machines.
- Linear Regression: OLS regression computes local sums of transformed data points, merges them across processes, then performs a comparatively cheap matrix inversion and multiplication.The final operations are cheap when the number of independent variables is small.
- Linear Regression: The linear-regression implementation exposes transition, merge, and final aggregate stages over tuple inputs, returning coefficients and diagnostic statistics.An example output reports coefficients, R^2, standard errors, t-statistics, p-values, and condition number.
- Logistic Regression: The logistic-regression implementation cannot use grouping constructs because its logregr UDF is not an aggregate function.Multiple regressions therefore require a join construct, and the authors identify interface non-uniformity as an issue for future refinement.
5. UNIVERSITY RESEARCH AND MADLIB
University collaborations extended MADlib by reducing the effort needed to implement convex models and by bringing statistical text analytics into the database. These efforts used MADlib abstractions and database-native tools to support scalable analysis close to the data.
- Convex Optimization: MADlib implementation required developers to specify, optimize, test, and robustly validate each model and algorithm, creating a time-consuming development burden.
- Convex Optimization: A mathematical abstraction based on convex optimization decouples model specification from the algorithm used to solve it and implements all models in Table 2.
- Convex Optimization: Gradient methods iteratively move in the steepest-descent direction, with a decreasing stepsize and a provable convergence rate for convex functions.
- Convex Optimization: Stochastic gradient descent approximates the gradient using one convex component at a time, and this approximation is guaranteed to converge to an optimal solution.
- Convex Optimization: MADlib maps input tuples to vector representations and averages per-tuple gradient vectors, while initial experiments reported higher performance than prior data-mining tools on some datasets.
- Statistical Text Analytics: Statistical text analytics in MADlib combines CRFs, feature extraction, Viterbi inference, MCMC inference, and approximate string matching for POS tagging, NER, and ER.
- Statistical Text Analytics: The integrated text-analysis engine uses database extension features and MADlib modules, achieving comparable raw performance to off-the-shelf implementations while running natively in a DBMS.
6. RELATED WORK
Related work divides analytics systems into language-based approaches and framework-based approaches. MADlib belongs to the framework-based category, alongside systems that provide reusable primitives or templates over a processing substrate.
- Analytics systems can either bring a statistical language to a data-processing substrate or provide a framework for expressing statistical techniques on that substrate.
- Top-Down, Language-based Approaches: Top-down language-based approaches may be imperative, requiring analysts to express parallelization and data partitioning, or declarative, delegating those responsibilities to the system.
- Framework-based Approaches: Framework-based approaches provide low-level and coordination primitives, often through templates that automate common aspects of deploying analytic tasks.
- Framework-based Approaches: MADlib is framework-based because it provides a library of functions over an RDBMS, with macro- and micro-programming serving as design templates.
- Spark provides a Scala machine-learning DSL over partitioned, fault-tolerant in-memory datasets, while ScalOps translates a Scala DSL to Datalog.
- Other Data Processing Systems: Pregel uses a graph abstraction in which functions communicate with neighboring nodes, and it distributes computation while providing fault tolerance.
7. CONCLUSION AND DISCUSSION
MADlib addresses demand for scalable analytics while remaining an early-stage project with substantial portability and infrastructure work ahead. It already offers many widely used methods and is used in research and customer settings.
- MADlib fills a vacuum for scalable analytics in SQL DBMSs and connects database research to market needs.
- Performance comparisons with Hadoop-based analytics projects such as Mahout had not yet been undertaken.
- Porting MADlib across DBMSs is mechanical but non-trivial because macro infrastructure, testing, inner loops, and portability interfaces require revision.
- MADlib v0.3 provides a relatively large number of widely-used statistical and analytic methods compared with most highly scalable analytics packages.
- MADlib is still in its early stages of development but is already used at research universities and customer sites.