esProc SPL Community

esProc SPL is an open-source and JVM-based analyzing and computing engine for structured data and semi-structured data, and good at solving data problems, including hard to write, slow to run and difficult to operate and maintain. esProc SPL adopts self-created SPL (Structured Process Language) syntax, boasting the characteristics of low code, high performance, lightweight and versatility. Compared with SQL, SPL has more abundant data types and calculation features, which greatly enhance its computing and description abilities; SPL provides more agile syntax and advocates step-wise coding, which allow you to implement complex calculation logic according to natural thinking, and debug and correct the error easily, thereby greatly reducing the development cost. SPL encapsulates a lot of high-performance algorithms (and storage) and needs less hardware to achieve the same performance, so the hardware cost is effectively reduced. SPL is more open in computing ability, enabling you to calculate directly on various data sources, and supports independent or integrated use, making framework lighter; in addition, SPL offers comprehensive functions, making it easy to implement complex calculation, and accomplish most tasks without other technologies, thus making the technology stack simpler and the O&M cost lower. Moreover, SPL can replace some data analysis and statistical technologies such as Python, Scala, Java, Kotlin. esProc SPL provides a simple and easy-to-use development environment with complete debugging functions, enabling you to code step by step, and view the running result of each step in real time. SPL is a specially designed syntax system, and naturally supports step-wise calculation, and complex procedural calculation in particular. SPL has built-in rich data computing library, including the string, date and time, mathematical calculations, file and database read/write, JSON/XML multi-layer data parsing, and AI modeling and prediction. esProc SPL has very high integration, and can run independently, or be seamlessly integrated in applications to serve as an in-application computing engine to play an important role in scenarios such as micro-service, edge computing, and report data preparation. esProc SPL supports diverse data sources, including dozens of data sources like MongoDB, Elasticsearch, Hbase, HDFS and Influxdb. Such data can be calculated directly or in a mixed way without loading them into database. In addition, SPL provides its own efficient data file storage, these private data formats not only make performance higher, but allow you to store data based on business category in file system tree directory. In addition to off-line batch job and on-line query, esProc SPL also supports more application scenarios: data-driven micro-service, replacing stored procedures, eliminating intermediate tables from databases, handling endless report development requirements, programmable data routing to implement front-end calculation, mixed computation to implement real-time HTAP, and performing computation on files to implement Lakehouse. For full text:http://c.raqsoft.com/article/167..., and for more information, visit: http://www.scudata.com

Usually, the streaming data sources are dynamic and unbounded, and appear quite different from the static and bounded batch data source. For framework reasons, it is difficult for traditional database technologies to directly process streaming data source, so programmers have to resort to later technologies. The computing frameworks such as heron\samza\storm\spark\flink were the first to make breakthroughs and gained first-mover advantage in stream computing technology. These frameworks are so successful that as soon as a stream computing is involved, the application programmers will naturally turn to one of them. On the contrary, for those computing technologies that do not claim to be a certain framework, they are generally considered unsuitable for implementing stream computing. Refer to http://c.raqsoft.com/article/169...