Optimizing JSONiq Execution in Rumble using MLIR

Semi-structured data formats such as JSON offer the advantage of representing arbitrarily complex data in a format that can be read easily by both humans and machines. Due to their simplicity such formats are popular for applications that produce large amounts of data, where it is not clear in advance how, or even whether, the produced data will be used in the future, such that it is not worth investing effort into schema design, data migration, etc. One major downside, however, is that queries executed on semi-structured data perform much slower when compared to fully-structured data. In this bachelor’s thesis we provide an approach for increasing the efficiency of query execution on JSON data sets by developing an Intermediate Representation for JSONiq as an MLIR dialect. We then try to find transformation mechanisms from queries in Rumble to the dialect and vice versa. Having such a round-trip we can find optimization on the Intermediate Representation and translate it back to a query which then can be executed more efficiently