Modern enterprise applications are currently undergoing a complete paradigm shift away from traditional transactional processing to combined analytical and transactional processing. This challenge of combining two opposing query types in a single database management system results in additional requirements for transaction management as well. In this paper, we discuss our approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries. We present our approach to distributed snapshot isolation and optimized two-phase commit protocols.

/pdf/apache-flink-stream-and-batch-processing-in-a-single-engine-3u2381y8rg.pdf

Apache flink : Stream and batch processing in a single engine

Spark SQL is a new module in Apache Spark that integrates relational processing with Spark's functional programming API. Built on our experience with Shark, Spark SQL lets Spark programmers leverage the benefits of relational processing (e.g. declarative queries and optimized storage), and lets SQL users call complex analytics libraries in Spark (e.g. machine learning). Compared to previous systems, Spark SQL makes two main additions. First, it offers much tighter integration between relational and procedural processing, through a declarative DataFrame API that integrates with procedural Spark code. Second, it includes a highly extensible optimizer, Catalyst, built using features of the Scala programming language, that makes it easy to add composable rules, control code generation, and define extension points. Using Catalyst, we have built a variety of features (e.g. schema inference for JSON, machine learning types, and query federation to external databases) tailored for the complex needs of modern data analysis. We see Spark SQL as an evolution of both SQL-on-Spark and of Spark itself, offering richer APIs and optimizations while keeping the benefits of the Spark programming model.

http://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf

Spark SQL: Relational Data Processing in Spark

Recurrent Neural Networks for Time Series Forecasting: Current status and future directions

Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.

/pdf/applications-of-big-data-to-smart-cities-1i39kcws84.pdf

Applications of big data to smart cities

Social big data

The academic community and industry are currently researching and building next generation data management systems. These systems are designed to analyze data sets of high volume with high data ingest rates and short response times executing complex data analysis algorithms on data that does not adhere to relational data models. As these big data systems differ from standard relational database systems with respect to data and workloads, the traditional benchmarks used by the database community are insufficient. In this paper, we describe initial solutions and challenges with respect to big data generation, methods for creating realistic, privacy-aware, and arbitrarily scalable data sets, workloads, and benchmarks from real world data. We will in particular discuss why we feel that workloads currently discussed in the testing and benchmarking community do not capture the real complexity of big data and highlight several research challenges with respect to massively-parallel data generation and data characterization.

Issues in big data testing and benchmarking

Advanced data analysis typically requires some form of pre-processing in order to extract and transform data before processing it with machine learning and statistical analysis techniques. Pre-processing pipelines are naturally expressed in dataflow APIs (e.g., MapReduce, Flink, etc.), while machine learning is expressed in linear algebra with iterations. Programmers therefore perform end-to-end data analysis utilizing multiple programming paradigms and systems. This impedance mismatch not only hinders productivity but also prevents optimization opportunities, such as sharing of physical data layouts (e.g., partitioning) and data structures among different parts of a data analysis program.The goal of this work is twofold. First, it aims to alleviate the impedance mismatch by allowing programmers to author complete end-to-end programs in one engine-independent language that is automatically parallelized. Second, it aims to enable joint optimizations over both relational and linear algebra. To achieve this goal, we present the design of Lara, a deeply embedded language in Scala which enables authoring scalable programs using two abstract data types (DataBag and Matrix) and control flow constructs. Programs written in Lara are compiled to an intermediate representation (IR) which enables optimizations across linear and relational algebra. The IR is finally used to compile code for different execution engines.

Bridging the gap: towards optimization across linear and relational algebra

The current research focus on Big Data systems calls for a rethinking of data generation methods. The traditional sequential data generation approach is not well suited to large-scale systems as generating a terabyte of data may require days or even weeks depending on the number of constraints imposed on the generated model. We demonstrate Myriad, a new data generation toolkit that enables the specification of semantically rich data generator programs that can scale out linearly in a shared-nothing environment. Data generation programs built on top of Myriad implement an efficient parallel execution strategy leveraged by the extensive use of pseudo-random number generators with random access support.

/pdf/myriad-scalable-and-expressive-data-generation-4dyc4t4dt6.pdf

Myriad: scalable and expressive data generation

Parallel dataflow engines such as Apache Hadoop, Apache Spark, and Apache Flink are an established alternative to relational databases for modern data analysis applications. A characteristic of these systems is a scalable programming model based on distributed collections and parallel transformations expressed by means of second-order functions such as map and reduce. Notable examples are Flink’s DataSet and Spark’s RDD programming abstractions. These programming models are realized as EDSLs—domain specific languages embedded in a general-purpose host language such as Java, Scala, or Python. This approach has several advantages over traditional external DSLs such as SQL or XQuery. First, syntactic constructs from the host language (e.g., anonymous functions syntax, value definitions, and fluent syntax via method chaining) can be reused in the EDSL. This eases the learning curve for developers already familiar with the host language. Second, it allows for seamless integration of library methods written in the host language via the function parameters passed to the parallel dataflow operators. This reduces the effort for developing analytics dataflows that go beyond pure SQL and require domain-specific logic.At the same time, however, state-of-the-art parallel dataflow EDSLs exhibit a number of shortcomings. First, one of the main advantages of an external DSL such as SQL—the high-level, declarative Select-From-Where syntax—is either lost completely or mimicked in a non-standard way. Second, execution aspects such as caching, join order, and partial aggregation have to be decided by the programmer. Optimizing them automatically is very difficult due to the limited program context available in the intermediate representation of the DSL.In this article, we argue that the limitations listed above are a side effect of the adopted type-based embedding approach. As a solution, we propose an alternative EDSL design based on quotations. We present a DSL embedded in Scala and discuss its compiler pipeline, intermediate representation, and some of the enabled optimizations. We promote the algebraic type of bags in union representation as a model for distributed collections and its associated structural recursion scheme and monad as a model for parallel collection processing. At the source code level, Scala’s comprehension syntax over a bag monad can be used to encode Select-From-Where expressions in a standard way. At the intermediate representation level, maintaining comprehensions as a first-class citizen can be used to simplify the design and implementation of holistic dataflow optimizations that accommodate for nesting and control-flow. The proposed DSL design therefore reconciles the benefits of embedded parallel dataflow DSLs with the declarativity and optimization potential of external DSLs like SQL.

/pdf/representations-and-optimizations-for-embedded-parallel-19cd5yio49.pdf

Representations and Optimizations for Embedded Parallel Dataflow Languages

Parallel dataflow APIs based on second-order functions were originally seen as a flexible alternative to SQL. Over time, however, their complexity increased due to the number of physical aspects that had to be exposed by the underlying engines in order to facilitate efficient execution. To retain a sufficient level of abstraction and lower the barrier of entry for data scientists, projects like Spark and Flink currently offer domain-specific APIs on top of their parallel collection abstractions. This demonstration highlights the benefits of an alternative design based on deep language embedding. We showcase Emma - a programming language embedded in Scala. Emma promotes parallel collection processing through native constructs like Scala's for-comprehensions - a declarative syntax akin to SQL. In addition, Emma also advocates quasi-quoting the entire data analysis algorithm rather than its individual dataflow expressions. This allows for decomposing the quoted code into (sequential) control flow and (parallel) dataflow fragments, optimizing the dataflows in context, and transparently offloading them to an engine like Spark or Flink. The proposed design promises increased programmer productivity due to avoiding an impedance mismatch, thereby reducing the lag times and cost of data analysis.

Emma in Action: Declarative Dataflows for Scalable Data Analysis

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