Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without knowing schema details or query languages. These systems take the keywords from the input query, locate the elements of the target database that correspond to these keywords, and look for ways to "connect" these elements using the information on key/foreign key pairs. Although several such systems have been proposed, most of them only support queries whose keywords refer to the contents of the target database and only a few support queries in which keywords may also refer to elements of the database schema. We showcase PyLatheDB, a Python library for Relational Keyword Search with Support to Schema References. PyLatheDB is based on Lathe, an R-KwS framework that generalizes the well-known concepts of Query Matches (QMs) and Candidate Joining Networks (CJNs) to handle keywords referring to schema elements and introduces new algorithms to generate them. Lathe also introduced a novel approach to automatically select the CJNs that are more likely to represent the user intent when issuing a keyword query. This approach includes two major innovations: a ranking algorithm for selecting better QMs, yielding the generation of fewer but better CJNs, and an eager evaluation strategy for pruning useless CJNs. We demonstrate through a Jupyter1notebook the functioning of PyLatheDB for two representative application scenarios, showing each step of the keyword query processing. The users can interact with the notebook by running keyword queries and experimenting with configuration parameters to see how they affect the results. The notebook, a video, and the code of PyLatheDB are available at https://github.com/pr3martins/PyLatheDB.

The adoption of Continuous Deployment (CD) aims at allowing software systems to quickly evolve to accommodate new features. However, structural changes to the database schema are frequent and may incur in systems' services downtime. This encompasses the proper maintenance of both schema and source code, including rewrites of all outdated queries that use the same database. Previous solutions try to mitigate the burdening task of manually rewriting outdated queries. Unfortunately, a software team must still interact with some tools to properly fix the affected queries. Moreover, the team still has to locate and modify all the impacted code, which are often error-prone tasks. Thus, a project may not experience CD benefits when changes impact various code regions. In this paper we present an alternative approach, called LESSQL, whose goal is to improve queries' stability in the presence of structural schema changes over time. LESSQL supports queries that are less dependent on the database schema since they do not include the FROM clause. An underlying framework intercepts each LESSQL query and generates a corresponding SQL query for the current schema. It also locates the query attributes in the current schema and generates proper expressions to join the required tables. LESSQL supports unsupervised, supervised and hybrid configurations to process mappings of attributes to a newer schema version. We conducted experiments in the context of a popular open-source project, which experienced many diverse structural schema changes. Experiments outcomes indicate that our approach is effective in significantly reducing the modifications required for applying schema changes, allowing to better reap the benefits of CD. While supervised and hybrid configurations achieved a success rate higher than 95% with a minor query generation overhead, the unsupervised configuration was also successful for certain types of structural schema changes. These results show that LESSQL effectively favours CD and keeps queries running after database schema changes without services interruption.