Draft:Polypheny

Polypheny (/poʊlɪfɛnɪ/ ) is a multi-model database management system (DBMS) intended to process and manage heterogeneous data and mixed workloads.^[2] Based on the concept of a PolyDBMS (Polymorphic Database Management System), Polypheny provides a framework for managing diverse data through a unified platform.^[2] It can execute queries over heterogeneous datasets by using third-party database systems as execution engines, with different engines used for different workloads.^{[3] [4]} It supports structured, semi-structured, and unstructured data and can be used for different workloads.^[5]

Polypheny natively supports the relational, document and labeled-property graph data models.^[6] It includes a browser-based user interface for system monitoring as well as data, schema, and query management. The interface also supports computational notebooks for workflows that combine data integration and analysis.

Polypheny supports multiple query languages, including SQL, openCypher, the Contextual Query Language, and the MongoDB Query Language.^[6] It supports cross-model queries, in which data from different data models can be combined within a single query through a unified interface.^[6]

Polypheny started as a research project at the University of Basel in 2017.^{[7] [8]} Initial funding was provided by the Swiss National Science Foundation (SNF).^[8] Early publications described Polypheny-DB as a polystore system intended to support heterogeneous data and workloads.^[9]

Polypheny was accepted as a mentoring organization for the Google Summer of Code (GSoC) in 2021,^[10] and again in 2022^[11] and 2024.^[12]

The first public release was published in February 2022.^[13]

The establishment of Polypheny GmbH followed in December 2022,^[14] adopting an open-core model ^[15] The Polypheny GmbH offers support and additional functionalities through enterprise editions.

Polypheny is developed in Java and can run on systems with a Java Runtime Environment (JRE). Releases are available for Windows, macOS, and Linux.^[7] For Linux, support is limited to distributions using either RPM or DEB packages.

Polypheny's schema model accommodates multiple data models, including the relational, document, and Labeled Property Graph (LPG) models, with each model treated as a first-class citizen within the system.^[6] Rather than converting all data into a single model, Polypheny supports these models in their original forms.^[6] This approach is intended to maintain the unique advantages and characteristics of each data model, aiming to provide users with the respective benefits and capabilities of each model.^[2] No model is prioritized or considered primary; they coexist, providing an environment for managing diverse data types and structures.

Polypheny uses namespaces to organize data models within a logical schema. A namespace groups objects that follow the rules of a particular data model (for example, a relational or document namespace).^[6] Each namespace in Polypheny acts as a logical container, segregating different data models within the logical schema and identified by a unique name corresponding to a specific data model. The definition of a schema occurs within a namespace, and this namespace determines the rules and semantics for that schema, adhering to the principles of the corresponding data model.^[6] For instance, a namespace associated with a relational model adheres to its schema rules, while a namespace related to a document model follows its schema-less structure.

Polypheny supports cross-model queries and automated mapping between different data models.^[6] This capability allows data, organized and structured according to different data models, to be combined in a single query using one query language and interface, without the need for manual data transformation or migration.^[16]

Polypheny distinguishes between data stores and data sources as part of its storage model.^[6]

Data stores are components used for persistent storage and query execution.^{[2] [4]} Polypheny can use external database systems as data stores, and supports replication and partitioning across multiple data stores.^{[3] [17]}

Data sources connect Polypheny to external systems without storing the data inside Polypheny.^[6] They can be used to query external databases or files and combine results with data stored in Polypheny's data stores.^[6]

Polypheny includes an internal execution engine used for query planning and optimization across multiple data stores and data sources.^[6] Where an underlying store or source does not support a required operation, the internal engine can execute parts of a query plan itself, including across different supported data models.^[6]

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