A graph-based framework for analyzing SQL query logs
Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data …, 2018•dl.acm.org
Analytical SQL queries are a valuable source of information. Query log analysis can provide
insight into the usage of datasets and uncover knowledge that cannot be inferred from
source schemas or content alone. To unlock this potential, flexible mechanisms for meta-
querying are required. Syntactic and semantic aspects of queries must be considered along
with contextual information. We present an extensible framework for analyzing SQL query
logs. Query logs are mapped to a multi-relational graph model and queried using domain …
insight into the usage of datasets and uncover knowledge that cannot be inferred from
source schemas or content alone. To unlock this potential, flexible mechanisms for meta-
querying are required. Syntactic and semantic aspects of queries must be considered along
with contextual information. We present an extensible framework for analyzing SQL query
logs. Query logs are mapped to a multi-relational graph model and queried using domain …
Analytical SQL queries are a valuable source of information. Query log analysis can provide insight into the usage of datasets and uncover knowledge that cannot be inferred from source schemas or content alone. To unlock this potential, flexible mechanisms for meta-querying are required. Syntactic and semantic aspects of queries must be considered along with contextual information.
We present an extensible framework for analyzing SQL query logs. Query logs are mapped to a multi-relational graph model and queried using domain-specific traversal expressions. To enable concise and expressive meta-querying, semantic analyses are conducted on normalized relational algebra trees with accompanying schema lineage graphs. Syntactic analyses can be conducted on corresponding query texts and abstract syntax trees. Additional metadata allows to inspect the temporal and social context of each query.
In this demonstration, we show how query log analysis with our framework can support data source discovery and facilitate collaborative data science. The audience can explore an exemplary query log to locate queries relevant to a data analysis scenario, conduct graph analyses on the log and assemble a customized logmonitoring dashboard.
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