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Schema Evolution Survival Guide for Tables: Avoid Rigid Childhood and You’re En Route to a Quiet Life

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Journal on Data Semantics


In this paper, we study the factors that relate to the survival of a table in the context of schema evolution in open-source software. We study the history of the schema of eight open-source software projects that include relational databases and extract patterns related to the survival or death of their tables. Our study shows that the probability of a table with a wide schema (i.e., a large number of attributes) being removed is systematically lower than average. Activity and duration are related to survival too. Rigid tables, without any change to their schema, are more likely to be removed than tables that sustain changes. Durations of dead and survival tables demonstrate a mirror image: dead tables’ durations are mostly short, whereas survivor tables gravitate toward higher durations. Our findings are mostly summarized by a pattern, which we call electrolysis pattern, due to its diagrammatic representation, stating that dead and survivor tables live quite different lives: tables typically die shortly after birth, with short durations and mostly no updates, whereas survivors mostly live quiet lives with few updates—except for a small group of tables with high update ratios that are characterized by high durations and survival. Equally important is the evidence that schema evolution suffers from the antagonism of gravitation to rigidity, i.e., the tendency to minimize evolution as much as possible in order to minimize the resulting impact to the surrounding code. Several factors contribute to this observation: the absence of long durations in removed tables, the low percentage of tables whose schema size is scaled up or down, and the low numbers of tables with a high rate of updates, contrasted to the high numbers of tables with zero or few updates. We complement our findings with explanations and recommendations to developers.

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  1. Rigidity is used in its software engineering meaning, referring to software that is hard to evolve and maintain.


  3. An acute reader might express the concern whether it would be better to gather all the tables in one single set and average over them. We disagree: each data set comes with its own the requirements, development style and idiosyncrasy and putting all tables in a single data set, not only scandalously favors large data sets, but integrates different things. We average the behavior of schemata, not tables here.


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Correspondence to Panos Vassiliadis.

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Vassiliadis, P., Zarras, A.V. Schema Evolution Survival Guide for Tables: Avoid Rigid Childhood and You’re En Route to a Quiet Life. J Data Semant 6, 221–241 (2017).

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