Large-scale data analytics using statistical machine learning (ML), popularly called advanced analytics, underpins many modern data-driven applications. The data management community has been working for over a decade on tackling data management-related challenges that arise in ML workloads, and has built several systems for advanced analytics. This tutorial provides a comprehensive review of such systems and analyzes key data management challenges and techniques. We focus on three complementary lines of work: (1) integrating ML algorithms and languages with existing data systems such as RDBMSs, (2) adapting data management-inspired techniques such as query optimization, partitioning, and compression to new systems that target ML workloads, and (3) combining data management and ML ideas to build systems that improve ML lifecycle-related tasks. Finally, we identify key open data management challenges for future research in this important area.

Data Management in Machine Learning: Challenges, Techniques, and Systems

Data replication is used in distributed systems to maintain up-to-date copies of shared data across multiple computers in a network. However, despite decades of research, algorithms for achieving consistency in replicated systems are still poorly understood. Indeed, many published algorithms have later been shown to be incorrect, even some that were accompanied by supposed mechanised proofs of correctness. In this work, we focus on the correctness of Conflict-free Replicated Data Types (CRDTs), a class of algorithm that provides strong eventual consistency guarantees for replicated data. We develop a modular and reusable framework in the Isabelle/HOL interactive proof assistant for verifying the correctness of CRDT algorithms. We avoid correctness issues that have dogged previous mechanised proofs in this area by including a network model in our formalisation, and proving that our theorems hold in all possible network behaviours. Our axiomatic network model is a standard abstraction that accurately reflects the behaviour of real-world computer networks. Moreover, we identify an abstract convergence theorem, a property of order relations, which provides a formal definition of strong eventual consistency. We then obtain the first machine-checked correctness theorems for three concrete CRDTs: the Replicated Growable Array, the Observed-Remove Set, and an Increment-Decrement Counter. We find that our framework is highly reusable, developing proofs of correctness for the latter two CRDTs in a few hours and with relatively little CRDT-specific code.

https://dl.acm.org/doi/pdf/10.1145/3133933

Verifying strong eventual consistency in distributed systems

The concept of state and its applications vary widely across big data processing systems. This is evident in both the research literature and existing systems, such as Apache Flink, Apache Heron, Apache Samza, Apache Spark, and Apache Storm. Given the pivotal role that state management plays, particularly, for iterative batch and stream processing, in this survey, we present examples of state as an enabler, discuss the alternative approaches used to handle and implement state, capture the many facets of state management, and highlight new research directions. Our aim is to provide insight into disparate state management techniques, motivate others to pursue research in this area, and draw attention to open problems.

A survey of state management in big data processing systems

Many machine learning (ML) systems allow the specification of ML algorithms by means of linear algebra programs, and automatically generate efficient execution plans. The opportunities for fused operators---in terms of fused chains of basic operators---are ubiquitous, and include fewer materialized intermediates, fewer scans of inputs, and sparsity exploitation across operators. However, existing fusion heuristics struggle to find good plans for complex operator DAGs or hybrid plans of local and distributed operations. In this paper, we introduce an exact yet practical cost-based optimization framework for fusion plans and describe its end-to-end integration into Apache SystemML. We present techniques for candidate exploration and selection of fusion plans, as well as code generation of local and distributed operations over dense, sparse, and compressed data. Our experiments in SystemML show end-to-end performance improvements of up to 22x, with negligible compilation overhead.

/pdf/on-optimizing-operator-fusion-plans-for-large-scale-machine-3160ftb2df.pdf

On optimizing operator fusion plans for large-scale machine learning in systemML

We introduce a new sublinear space data structure--the count-min sketch--for summarizing data streams. Our sketch allows fundamental queries in data stream summarization such as point, range, and inner product queries to be approximately answered very quickly; in addition, it can be applied to solve several important problems in data streams such as finding quantiles, frequent items, etc. The time and space bounds we show for using the CM sketch to solve these problems significantly improve those previously known--typically from 1/e2 to 1/e in factor.

An improved data stream summary: The Count-Min Sketch and its applications

The appeal of MapReduce has spawned a family of systems that implement or extend it. In order to enable parallel collection processing with User-Defined Functions (UDFs), these systems expose extensions of the MapReduce programming model as library-based dataflow APIs that are tightly coupled to their underlying runtime engine. Expressing data analysis algorithms with complex data and control flow structure using such APIs reveals a number of limitations that impede programmer's productivity. In this paper we show that the design of data analysis languages and APIs from a runtime engine point of view bloats the APIs with low-level primitives and affects programmer's productivity. Instead, we argue that an approach based on deeply embedding the APIs in a host language can address the shortcomings of current data analysis languages. To demonstrate this, we propose a language for complex data analysis embedded in Scala, which (i) allows for declarative specification of dataflows and (ii) hides the notion of data-parallelism and distributed runtime behind a suitable intermediate representation. We describe a compiler pipeline that facilitates efficient data-parallel processing without imposing runtime engine-bound syntactic or semantic restrictions on the structure of the input programs. We present a series of experiments with two state-of-the-art systems that demonstrate the optimization potential of our approach.

/pdf/implicit-parallelism-through-deep-language-embedding-2718bmz63z.pdf

Implicit Parallelism through Deep Language Embedding

Collaborative work on shared documents was revolutionized by web services like Google Docs or Etherpad. Multiple users can work on the same document in a comfortable and distributed way. For the synchronization of the changes a replication system named Operational Transformation is used. Such a system consists of a control algorithm and a transformation function. In essence, a transformation function solves the conflicts that arise when multiple users change the document at the same time. In this work we investigate on the correctness of such transformation functions. We introduce transformation functions n-ary trees that we designed especially for the purpose of synchronization changes on JSON objects. We provide a detailed proof of the necessary property: the Transformation Property 1.

TP1-valid Transformation Functions for Operations on ordered n-ary Trees

This paper describes Aura, a parallel dataflow engine for analysis of large-scale datasets on commodity clusters. Aura allows to compose program plans from relational operators and second-order functions, provides automatic program parallelization and optimization, and is a scalable and efficient runtime. Furthermore, Aura provides dedicated support for control flow, allowing advanced analysis programs to be executed as a single dataflow job. This way, it is not necessary to express, for example, data preprocessing, iterative algorithms, or even logic that depends on the outcome of a preceding dataflow as multiple separate jobs. The entire dataflow program is instead handled as one job by the engine, allowing to keep intermediate results in-memory and to consider the entire program during plan optimization to, for example, re-use partitions.

Aura: A Flexible Dataflow Engine for Scalable Data Processing

Many machine learning (ML) models are of a stochastic nature. We aim to combine the principles of weak consistency with large scale distributed machine learning. We see interesting opportunities in this domain in (1) perceiving parallel ML algorithms based on model replication as a "collaborative task" where local progress on models is instantaneously exchanged and by (2) making this exchange more efficient by exploiting the underlying stochastic nature. Based on this motivation, we extend the notion of consistency for replicated objects with intrinsic stochastic structure and introduce harmonization as the reconciliation principle to enable efficient consistency maintenance of these objects. We present as a concrete application the harmonization of replicated ML models.

Weak consistency and stochastic environments: harmonization of replicated machine learning models

Managing replicated data in distributed systems that is concurrently accessed by multiple sites is a complex task, because consistency must be ensured. In this paper, we present the Replicated Convergent Data Containers (RCDCs) - a set of distributed data structures that coordinate replicated data and allow for optimistic inserts, updates and deletes in a lock-free, non-blocking fashion. It is crucial that continuous data harmonization among containers takes place over time. This is achieved by a synchronization mechanism that is based on a technique called Operational Transformation (OT) which continously reconciles diverging containers. A generic architecture is placed on top of this underlying synchronization mechanism that allows to realize a multitude of different RCDCs. Two container specializations are presented: (a) the linear container that organizes data in an ordered sequence, (b) the hierarchical container that organizes the data in an n-ary tree.

Replicated Convergent Data Containers

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