Query Rewriting for Continuously Evolving NoSQL Databases

Möller, Mark Lukas; Klettke, Meike; Hillenbrand, Andrea; Störl, Uta

doi:10.1007/978-3-030-33223-5_18

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…Then all datasets are kept in these particular versions. In order to access datasets in previous or subsequent versions, backward and forward query rewriting has to be used, respectively, which rewrites each query by distributing them onto all present schema versions [3,10]. This exclusive use of query rewriting, though, leads to a very high latency overhead and migration debt.…”

Section: Discussionmentioning

confidence: 99%

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Self-adapting data migration in the context of schema evolution in NoSQL databases

Hillenbrand

Störl

Nabiyev

et al. 2021

Distrib Parallel Databases

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When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development.

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Section: Discussionmentioning

confidence: 99%

“…This could be resolved through query rewriting [3,10], which rewrites the queries to reach all legacy entities by keeping track of past schema changes. However, over time the exclusive use of query rewriting leads to high latency for data accesses because of the increasing structural entropy of the database.…”

Section: Introductionmentioning

confidence: 99%

Self-adapting data migration in the context of schema evolution in NoSQL databases

Hillenbrand

Störl

Nabiyev

et al. 2021

Distrib Parallel Databases

Self Cite

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“…Recent work considers schema versioning in the context of NoSQL stores. In [36], the authors introduce forward and backward query rewriting for querying data with different versions. Furthermore, it is possible to have heterogeneity within the same database, e.g., different cardinally, and different structures.…”

Section: Schema Heterogeneitymentioning

confidence: 99%

“…Recent research work introduced a transparent querying mechanism to enable querying for heterogeneous documents. In [39], the authors introduced novel querying mechanisms based on query rewriting techniques [36] where they overcome the problem of structural heterogeneity in document stores. Their contribution consists of generating a dictionary with different attributes and their corresponding paths.…”

Section: Schema Heterogeneitymentioning

confidence: 99%

A dataspace-based framework for OLAP analyses in a high-variety multistore

et al. 2021

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The success of NoSQL DBMSs has pushed the adoption of polyglot storage systems that take advantage of the best characteristics of different technologies and data models. While operational applications take great benefit from this choice, analytical applications suffer the absence of schema consistency, not only between different DBMSs but within a single NoSQL system as well. In this context, the discipline of data science is steering analysts away from traditional data warehousing and toward a more flexible and lightweight approach to data analysis. The idea is to perform OLAP analyses in a pay-as-you-go manner across heterogeneous schemas and data models, where the integration is progressively carried out by the user as the available data is explored. In this paper, we propose an approach to support data analysis within a high-variety multistore, with heterogeneous schemas and overlapping records. Our approach supports relational, document, wide-column, and key-value data models by automatically handling both data model and schema heterogeneity through a dataspace layer on top of the underlying DBMSs. The expressiveness we enable corresponds to GPSJ queries, which are the most common class of queries in OLAP applications. We rely on nested relational algebra to define a cross-database execution plan. The system has been prototyped on Apache Spark.

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“…There are two general approaches to capture the semantics mappings: (1) To search for the queries that can transform the old schema to the new schema [9], [25], [30], [51], [61], [66]. (2) To ask the database administrators (DBAs) or application developers to use a domain-specific language (DSL) to describe the schema transformation process [11], [17], [31]- [33], [41], [53], [55], [56], [64].…”

Section: Introductionmentioning

confidence: 99%

Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning

Wang,

Zhou,

Das

et al. 2020

Preprint

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Data is the king in the age of AI. However data integration is often a laborious task that is hard to automate. Schema change is one significant obstacle to the automation of the end-to-end data integration process. Although there exist mechanisms such as query discovery and schema modification language to handle the problem, these approaches can only work with the assumption that the schema is maintained by a database. However, we observe diversified schema changes in heterogeneous data and open data, most of which has no schema defined. In this work, we propose to use deep learning to automatically deal with schema changes through a super cell representation and automatic injection of perturbations to the training data to make the model robust to schema changes. Our experimental results demonstrate that our proposed approach is effective for two realworld data integration scenarios: coronavirus data integration, and machine log integration.

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Query Rewriting for Continuously Evolving NoSQL Databases

Cited by 7 publications

References 5 publications

Self-adapting data migration in the context of schema evolution in NoSQL databases

Self-adapting data migration in the context of schema evolution in NoSQL databases

A dataspace-based framework for OLAP analyses in a high-variety multistore

Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning

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