Icarus: Towards a multistore database system

Vogt, Marco; Stiemer, Alexander; Schuldt, Heiko

doi:10.1109/bigdata.2017.8258207

Cited by 13 publications

(6 citation statements)

References 15 publications

(11 reference statements)

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“…The focus of existing polystores on long-running queries is fully justified because this is the field where a polystore can massively accelerate query performance as shown in [4,10,14]. Due to the nature of a polystore, it is hardly possible to accelerate simple transactional queries.…”

Section: Polypheny-dbmentioning

confidence: 99%

“…This second implementation is based on the approach introduced in [14]. The basic assumption of this approach is that queries generated by applications are usually derived from templates.…”

Section: Query Routingmentioning

confidence: 99%

“…Because queries derived from the same template are similar regarding execution time on a specific underlying data store, it is possible to route queries based on structural similarity of queries. While the approach introduced in [14] determines the structural similarity based on the SQL query, the implementation in Polypheny-DB determines the similarity based on the logical query plan.…”

Section: Query Routingmentioning

confidence: 99%

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Polypheny-DB: Towards Bridging the Gap Between Polystores and HTAP Systems

Vogt

Hansen

Schönholz

et al. 2021

Lecture Notes in Computer Science

Self Cite

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Polystore databases allow to store data in different formats and data models and offer several query languages. While such polystore systems are highly beneficial for various analytical workloads, they provide limited support for transactional and for mixed OLTP and OLAP workloads, the latter in contrast to hybrid transactional and analytical processing (HTAP) systems. In this paper, we present Polypheny-DB, a modular polystore that jointly provides support for analytical and transactional workloads including update operations and that thus takes one step towards bridging the gap between polystore and HTAP systems.

show abstract

Section: Polypheny-dbmentioning

confidence: 99%

“…This second implementation is based on the approach introduced in [14]. The basic assumption of this approach is that queries generated by applications are usually derived from templates.…”

Section: Query Routingmentioning

confidence: 99%

Section: Query Routingmentioning

confidence: 99%

See 1 more Smart Citation

Polypheny-DB: Towards Bridging the Gap Between Polystores and HTAP Systems

Vogt

Hansen

Schönholz

et al. 2021

Lecture Notes in Computer Science

Self Cite

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“…Related Work. An example for a multistore system is Icarus [19]. Compared to other multistore systems, Icarus always stores all data on all underlying stores and executes incoming queries on the store with the best characteristics for this type of query.…”

Section: Polystore Databasesmentioning

confidence: 99%

PolyMigrate: Dynamic Schema Evolution and Data Migration in a Distributed Polystore

Stiemer

Vogt

Schuldt

et al. 2021

Heterogeneous Data Management, Polystores, and Analytics for Healthcare

Self Cite

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In the last years, polystore databases have been proposed to cope with the challenges stemming from increasingly dynamic and heterogeneous workloads. A polystore database provides a logical schema to the application, but materializes data in different data stores, different data models, and different physical schemas. When the access pattern to data changes, the polystore can decide to migrate data from one store to the other or from one data model to another. This necessitates a schema evolution in one or several data stores and the subsequent migration of data. Similarly, when applications change, the global schema might have to be changed as well, with similar consequences on local data stores in terms of schema evolution and data migration. However, the aspect of schema evolution in a polystore database has so far largely been neglected. In this paper, we present the challenges imposed by schema evolution and data migration in Polypheny-DB, a distributed polystore database. With our work-in-progress approach called PolyMigrate, we show how schema evolution and data migration affect the different layers of a distributed polystore and we identify different approaches to effectively and efficiently propagate these changes to the underlying stores.

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“…A CBR based workload characterization model is presented in [17], which considered the classification as binary classification and characterized the workload into OLTP and DSS. Authors in [34], presented a multistore database system ICARUS to handle the storage issues of different data types. It considered three types of workload viz.…”

Section: Database and Dwh Multi-class Prediction And Classificationmentioning

confidence: 99%

A Novel Optimized Case-Based Reasoning Approach With K-Means Clustering and Genetic Algorithm for Predicting Multi-Class Workload Characterization in Autonomic Database and Data Warehouse System

2020

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Data management systems are essential elements for any organization which is dealing with large volume of data now a days. Due to increase in data volume, and its complexities, it has become more challenging job for workload management system to maintain its performance. So, there is a need of such a system that can autonomically deal with such complexities with less or without human involvement. Performance of these systems can be improved by making the systems well-aware about the workload entering into the system. The workload of a prevalent typical database and data warehouse system can be characterized into three types that is Online Transaction Processing (OLTP), Decision Support Systems (DSS) and Mixed type of workload. Currently, autonomic characterization of workload into a binary class such as OLTP and DSS is being carried out as reported in the literature, however, characterizing the workload into three types that refers to a multi-class classification problem is relatively a more challenging task. In this study, we propose a novel optimized Case-based Reasoning (CBR) approach based on clustering for autonomically characterizing the workload into multi-class types before entering into the system. We implement four phases of CBR along with case-base generation and map it to the elements of autonomic MAPE-K model. In Retrieve phase, k-means clustering is used for enhancing retrieval efficiency and workload types predictions are made in Reuse phase. Genetic Algorithm is used in Revise and Adapt phase of CBR. Few autonomic self_* characteristics are incorporated to make it autonomic. We performed various experiments and results show that the proposed model outperforms in prediction as compared to existing approaches. We performed posthoc test for the validation of results in comparison with other machine learning classifiers using the Friedman test that show that the proposed model stands out as the best classifier. INDEX TERMS Workload management system, workload characterization, case-based reasoning, autonomic systems, Genetic Algorithm.

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Icarus: Towards a multistore database system

Cited by 13 publications

References 15 publications

Polypheny-DB: Towards Bridging the Gap Between Polystores and HTAP Systems

Polypheny-DB: Towards Bridging the Gap Between Polystores and HTAP Systems

PolyMigrate: Dynamic Schema Evolution and Data Migration in a Distributed Polystore

A Novel Optimized Case-Based Reasoning Approach With K-Means Clustering and Genetic Algorithm for Predicting Multi-Class Workload Characterization in Autonomic Database and Data Warehouse System

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