Commit 76434f24 authored by Omran Saleh's avatar Omran Saleh
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A wide range of (near) real-time applications process stream-based data including
financial data analysis, traffic management, telecommunication monitoring, environmental monitoring, the smart grid, weather forecasting, and social media analysis, etc. These applications focus mainly on finding useful information and patterns on-the-fly as well as deriving valuable higher-level information from lower-level ones from continuously incoming data stream to report and monitor the progress of some activities. In the last few years, several systems for processing streams of information, where each offering their own processing solution, have been proposed. It is pioneered by academic systems such as Aurora and Borealis~\cite{Abadi:2003:ANM:950481.950485} and commercial systems like IBM InfoSphere Streams or StreamBase. Recently, some novel distributed stream computing platforms have been developed based on data parallelization approaches, which try to support scalable operation in cluster environments for processing massive data streams. Examples of these platforms Storm~\cite{storm}, Spark ~\cite{spark}, and Flink~\cite{flink}. Though, these engines (SPE) provide abstractions for processing (possibly) infinite streams of data, they lack support for higher-level declarative languages. Some of these engines provide only a programming interface where operators and topologies have to be implemented in a programming language like Java or Scala. Moreover, to build a particular program (i.e., query) in these systems, the users should be expert and should have a deeper knowledge of the syntax and programming constructs of the language, especially, if the system supports multiple languages. Therefore, no time and effort savings can be achieved as the user needs to proceed by writing each programming statements correctly. To make the life much easier, the current trend in data analytics should be the adopting of the "Write once, run anywhere" slogan. This is a slogan first-mentioned by Sun Microsystems to illustrate that the Java code can be developed on any platform and be expected to run on any platform equipped with a java virtual machine (JVM). In general, the development of various stream processing engines raises the question whether we can provide an unified programming model or a standard language where the user can write one steam-processing script and he/she expects to execute this script on any stream-processing engines. By bringing all these things together, we provide a demonstration of our solution called \PipeFlow. In our \PipeFlow system, we address the following issues:
\begin{itemize}
\item Developing a scripting language that provides most of the features of stream-processing scripting languages, e.g., Storm and Spark. Therefore, we have chosen a dataflow language called \PipeFlow. At the beginning, this language was intended to be used in conjunction with a stream processing engine called PipeFabric \cite{DBIS:SalBetSat14year2014,DBIS:SalSat14}. Later, it is extended to be used with other engines. The source script written in \PipeFlow language is parsed and compiled and then a target program (i.e., for Spark and Storm as well as PipeFabric) is generated based on the user selection. This target script is equivalent in its functionalities to the original \PipeFlow program. Once the target program is generated, the user can execute this program in the specific engine.
\item Developing a scripting language that provides most of the features of stream-processing scripting languages, e.g., Storm and Spark. Therefore, we have chosen a dataflow language called \PipeFlow. At the beginning, this language was intended to be used in conjunction with a stream processing engine called PipeFabric \cite{DBIS:SalBetSat14year2014,DBIS:SalSat14}. Later, it is extended to be used with other engines. The source script written in the \PipeFlow language is parsed and compiled and then a target program (i.e., for Spark, Storm, or PipeFabric) is generated based upon user's selection. This target script is equivalent in its functionalities to the original \PipeFlow program. Once the target program is generated, the user can execute this program in the specific engine.
\item Mapping or translating a \PipeFlow script into other scripts necessitates the existing of each operator in \PipeFlow to be implemented in the target engine. Since \PipeFlow contains a set of pref-defined operators, all of these operators have been implemented directly or indirectly in that engine.
\item Providing a flexible architecture for users for extending the system by supporting more engines as well as new operators. These extensions should be integrated in the system smoothly.
\item Developing a web application as a front-end to enable users who have less experience in \PipeFlow to express the script or program and its associated processing algorithm and data pipeline graphically.
\item Developing a front-end web application to enable users who have little experience in the \PipeFlow language to express the script or the program and its associated processing algorithm and data pipeline graphically.
\end{itemize}
One of the useful applications for this approach is helping the developers to evaluate various stream processing systems. Instead of writing several scripts, which should perform the same task, for different systems manually, writing a singular script in our approach will help to get the same result faster and more efficiently.
One of the useful applications for this approach is helping the developers to evaluate various stream processing systems. Instead of writing several scripts, which should perform the same task, for different systems manually, writing a single script in our approach will help to get the same result faster and more efficiently.
The remainder of the paper is structured as follows: In Sect.~\ref{sec:pipeflow}, we introduce the \PipeFlow language, the system architecture, and an example for the translation between scripts. Next, in Sect.~\ref{sec:app}, we describe our front-end application and give details about its design and provided functionalities. Finally, a description of the planned demonstration is discussed in Sect.~\ref{sec:demo}.
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\maketitle
\begin{abstract}
Recently, some distributed stream computing platforms have been developed for processing massive data streams such as Storm and Spark. In general, these platforms lack support for higher-level declarative languages and provide only a programming interface. Moreover, the users should be well versed of the syntax and programming constructs of each language in these platforms. In this paper, we are going to demonstrate our \PipeFlow system. In \PipeFlow system, the user can write a stream-processing script (i.e., query) using a higher-level dataflow language. This script can be translated to different stream-processing scripts written in different languages that run in the corresponding engines. In this case, the user is only willing to know a singular language, thus, he/she can write one steam-processing script and expects to execute this script on different engines.
Recently, some distributed stream computing platforms have been developed for processing massive data streams such as Storm and Spark. However, these platforms lack support for higher-level declarative languages and provide only a programming interface. Moreover, the users should be well versed of the syntax and programming constructs of each language in these platforms. In this paper, we are going to demonstrate our \PipeFlow system. In \PipeFlow system, the user can write a stream-processing script (i.e., query) using a higher-level dataflow language. This script can be translated to different stream-processing scripts written in different languages that run in the corresponding engines. In this case, the user is only willing to know a single language, thus, he/she can write one steam-processing script and expects to execute this script on different engines.
\end{abstract}
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