Introducing SPL: An Open‑Source Structured Data Processing Language with Full SQL‑92 Capabilities

SPL (Structure Process Language) is an open‑source computation engine that provides full SQL‑92 capabilities without requiring a relational database, allowing users to run SQL‑like queries directly on a wide variety of data sources such as CSV, Excel, JSON, XML, NoSQL stores, HDFS, Hive, Spark, Kafka and more.

Typical SQL operations – filtering, computed columns, SELECT, ORDER BY, DISTINCT, GROUP BY … HAVING, JOIN types (left, right, full, inner), sub‑queries, AS aliasing, set operations (UNION, INTERSECT, MINUS) and INTO for exporting results – are all supported, with examples such as:

$select * from d:/Orders.csv where Amount>=100 and Client like '%bro%'

$select round(Amount,2), price*quantity from d:/Orders.csv

$select year(OrderDate) from d:/Orders.csv

$select distinct Client, Sellerid from d:/Orders.csv

$select Client,sum(Amount) from d:/Orders.csv group by Client having sum(Amount)<=100

$select o.OrderId,o.Client,e.Name from d:/Orders.txt o left join d:/Employees.txt e on o.SellerId=e.Eid

SPL also offers extended functions to import non‑standard sources, e.g., custom delimiters, header‑less files, HTTP‑downloaded CSV/JSON, Excel sheets, and to parse complex JSON or XML structures into two‑dimensional tables.

Beyond single‑step SQL, SPL enables multi‑step calculations by chaining statements, making complex analytics such as longest consecutive stock rise days or cumulative sales ranking much shorter and easier to read than equivalent window‑function SQL.

For interactive analysis SPL provides a dedicated IDE with step‑by‑step debugging and visual tables, a command‑line interface for any OS, and a JDBC driver for seamless integration into Java applications, allowing SPL scripts to be called as stored procedures or external files.

In summary, SPL gives developers a flexible, database‑agnostic query language that bridges the gap between traditional RDBMS SQL and modern heterogeneous data environments, reducing development effort and improving performance for mixed‑source analytics.