Rasdaman

Last updated
rasdaman
Original author(s) Peter Baumann
Developer(s) rasdaman GmbH
Stable release
rasdaman v10.3 / March 13, 2024 (2024-03-13)
Repository
Written in C++ [1]
Operating system Ubuntu
Type Array DBMS
License GPL v3 (server) / LGPL v3 (client) or proprietary [2]
Website rasdaman.org , rasdaman.com

rasdaman ("raster data manager") is an Array DBMS, that is: a Database Management System which adds capabilities for storage and retrieval of massive multi-dimensional arrays, such as sensor, image, simulation, and statistics data. A frequently used synonym to arrays is raster data, such as in 2-D raster graphics; this actually has motivated the name rasdaman. However, rasdaman has no limitation in the number of dimensions - it can serve, for example, 1-D measurement data, 2-D satellite imagery, 3-D x/y/t image time series and x/y/z exploration data, 4-D ocean and climate data, and even beyond spatio-temporal dimensions.

Contents

History

In 1989, Peter Baumann started a research on database support for images, then at Fraunhofer Computer Graphics Institute. Following an in-depth investigation on raster data formalizations in imaging, in particular the AFATL Image Algebra, he established a database model for multi-dimensional arrays, including a data model and declarative query language. [3] pioneering the field of Array Databases. Today, multi-dimensional arrays are also known as Data Cubes.

At TU Munich, in the EU funded basic research project RasDaMan, a first prototype was established, on top of the O2 object-oriented DBMS, and tested in Earth and Life science applications. [4] Over further EU funded projects, this system was completed and extended to support relational DBMSs. A dedicated research spin-off, rasdaman GmbH, [5] was established to give commercial support in addition to the research which subsequently has been continued at Jacobs University. [6] Since then, both entities collaborate on the further development and use of the rasdaman technology.

Concepts

Data model

Based on an array algebra [7] specifically developed for database purposes, rasdaman adds a new attribute type, array, to the relational model. As this array definition is parametrized it constitutes a second-order construct or template; this fact is reflected by the second-order functionals in the algebra and query language.

For historical reasons, tables are called collections, as initial design emphasized an embedding into the object-oriented database standard, ODMG. Anticipating a full integration with SQL, rasdaman collections represent a binary relation with the first attribute being an object identifier and the second being the array. This allows the establishment of foreign key references between arrays and regular relational tuples.

Raster Query Language

The rasdaman query language, rasql, embeds itself into standard SQL and its set-oriented processing. On the new attribute type, multi-dimensional arrays, a set of extra operations is provided which all are based on a minimal set of algebraically defined core operators, an array constructor (which establishes a new array and fills it with values) and an array condenser (which, similarly to SQL aggregates, derives scalar summary information from an array). The query language is declarative (and, hence, optimizable) and safe in evaluation - that is: every query is guaranteed to return after a finite number of processing steps.

The rasql query guide [8] provides details, here some examples may illustrate its use:

selectc[*:*,100:200,*:*,42]fromClimateSimulationsasc
selectimg*(img.green>130)fromLandsatArchiveasimg

Note: this is a very naive phrasing of vegetation search; in practice one would use the NDVI formula, use null values for cloud masking, and several more techniques.

selectimgfromMRIasimg,Masksasmwheresome_cells(img>250andm)
selectpng(c[*:*,*:*,100,42])fromClimateSimulationsasc

Architecture

Storage management

Sample array tiling in rasdaman Sample tiling of an array for storage in rasdaman.png
Sample array tiling in rasdaman

Raster objects are partitioned into tiles. [9] Aside from a regular subdivision, any user or system generated partitioning is possible. As tiles form the unit of disk access, it is important that the tiling pattern is adjusted to the query access patterns; several tiling strategies assist in establishing a well-performing tiling. A geo index is employed to quickly determine the tiles affected by a query. Optionally, tiles are compressed using one of various choices [10] , including lossless and lossy (wavelet) algorithms; independently from that, query results can be compressed for transfer to the client. Both tiling strategy and compression comprise database tuning parameters.

Tiles and tile index are stored on disk in the rasdaman database [11] , together with the data dictionary needed by rasdaman's dynamic type system. For arrays larger than disk space, hierarchical storage management (HSM) support has been developed [12] .

Query processing

Query execution is based on tile streaming [13] . Execution follows a tile streaming paradigm: whenever possible, array tiles addressed by a query are fetched sequentially, and each tile is discarded after processing. This leads to an architecture scalable to data volumes exceeding server main memory by orders of magnitude.

Queries undergo heavy optimization [14] . The server applies algebraic (heuristic) optimisation rules to the query tree where applicable; of the 150 algebraic rewriting rules, 110 are actually optimising while the other 40 serve to transform the query into canonical form. Further, cost-based optimization is applied. Parsing and optimization together take less than a millisecond on a laptop.

Further, queries get parallelized [15] . Rasdaman offers inter-query parallelism (a dispatcher schedules requests into a pool of server processes on a per-transaction basis) and intra-query parallelism (transparent distribution of query subtrees across available cores, GPUs, or cloud nodes).

Client APIs

The primary interface to rasdaman is the query language. Embeddings into C++ and Java APIs allow invocation of queries, as well as client-side convenience functions for array handling. Arrays per se are delivered in the main memory format of the client language and processor architecture, ready for further processing. Data format codecs allow to retrieve arrays in common raster formats, such as CSV, PNG, and NetCDF.

A Web design toolkit, raswct, is provided which makes the creation of Web query frontends easy, including graphical widgets for parametrized query handling, such as sliders for thresholds in queries.

Geo Web Services

A Java servlet, petascope, running as a rasdaman client offers Web service interfaces specifically for geo data access, processing and filtering. The following OGC standards are supported: WMS, WCS, WCPS, and WPS.

For WCS and WCPS, rasdaman is the reference implementation.

Status and license model

Today, rasdaman is a fully-fledged implementation offering select / insert / update / delete array query functionality. It is being used in both research and commercial installations.

In a collaboration of the original code owner, rasdaman GmbH [5] and Jacobs University, a code split was performed in 2008 - 2009 resulting in rasdaman community, [16] an open-source branch, and rasdaman enterprise, the commercial branch. Since then, rasdaman community is being maintained by Jacobs University whereas rasdaman enterprise remains proprietary to rasdaman GmbH. The difference between both variants mainly consists of performance boosters (such as specific optimization techniques) intended to support particularly large databases, user numbers, and complex queries; Details are available on the rasdaman community website. [17]

The rasdaman community license releases the server in GPL and all client parts in LGPL, thereby allowing the use of the system in any kind of license environment.

Impact

Being the first Array DBMS shipped (first prototype available in 1996), rasdaman has shaped this recent database research domain. Concepts of the data and query model (declarativeness, sometimes choice of operators) find themselves in more recent approaches. A deep comparison of Array DBMSs and related technology has been performed by the Research Data Alliance in 2018 [18] .


Standards

In 2008, the Open Geospatial Consortium released the Web Coverage Processing Service standard which defines a raster (often called "datacube") query language based on the concept of a coverage. Operator semantics [19] is influenced by the rasdaman array algebra.

In 2016, INSPIRE (Legal Framework for Spatial Information in Europe [20] ) adopted WCPS as optional component of INSPIRE-WCS [21] .

In 2023, WCPS has been adopted by ISO TC211 as ISO 19123-3:2023 [22] .

In 2024, OGC adopted the same specification as Abstract Topic 6.3 [23] .

In 2019, ISO adopted the rasql array query language as [24] to the SQL standard, with only minor syntactic adjustments to SQL.

Project Use

Two selected projects may illustrate use of rasdaman in geo services.

The open Earth Datacube Playground [25] is a showcase for actionable geo datacubes, offering 1-D through 4-D use cases of raster data access and ad-hoc processing. The showcase is built using rasdaman.

EarthServer [26] is a global federation of independent datacube providers. The combined datacube offerings are made available as a single, homogenized datacube space. Access is completely location transparent: any node can receive a query and answer it, regardless on which federation node the data sit; this includes automatic distributed data fusion. Participation is free for datacube contributors.

Related Research Articles

Denormalization is a strategy used on a previously-normalized database to increase performance. In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data. It is often motivated by performance or scalability in relational database software needing to carry out very large numbers of read operations. Denormalization differs from the unnormalized form in that denormalization benefits can only be fully realized on a data model that is otherwise normalized.

Structured Query Language (SQL) is a domain-specific language used to manage data, especially in a relational database management system (RDBMS). It is particularly useful in handling structured data, i.e., data incorporating relations among entities and variables.

In computing, online analytical processing, or OLAP, is an approach to quickly answer multi-dimensional analytical (MDA) queries. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture.

A coverage is the digital representation of some spatio-temporal phenomenon. ISO 19123 provides the definition:

The SQL SELECT statement returns a result set of rows, from one or more tables.

<span class="mw-page-title-main">TerraLib</span> Geographic information system software library

TerraLib is an open-source geographic information system (GIS) software library. It extends object-relational database management systems (DBMS) to handle spatiotemporal data types.

In computer programming contexts, a data cube is a multi-dimensional ("n-D") array of values. Typically, the term data cube is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data.

A GIS software program is a computer program to support the use of a geographic information system, providing the ability to create, store, manage, query, analyze, and visualize geographic data, that is, data representing phenomena for which location is important. The GIS software industry encompasses a broad range of commercial and open-source products that provide some or all of these capabilities within various information technology architectures.

<span class="mw-page-title-main">Manifold System</span>

Manifold System is a geographic information system (GIS) software package developed by Manifold Software Limited that runs on Microsoft Windows. Manifold System handles both vector and raster data, includes spatial SQL, a built-in Internet Map Server (IMS), and other general GIS features.

Microsoft SQL Server Analysis Services (SSAS) is an online analytical processing (OLAP) and data mining tool in Microsoft SQL Server. SSAS is used as a tool by organizations to analyze and make sense of information possibly spread out across multiple databases, or in disparate tables or files. Microsoft has included a number of services in SQL Server related to business intelligence and data warehousing. These services include Integration Services, Reporting Services and Analysis Services. Analysis Services includes a group of OLAP and data mining capabilities and comes in two flavors multidimensional and tabular, where the difference between the two is how the data is presented. In a tabular model, the information is arranged in two-dimensional tables which can thus be more readable for a human. A multidimensional model can contain information with many degrees of freedom, and must be unfolded to increase readability by a human.

Query optimization is a feature of many relational database management systems and other databases such as NoSQL and graph databases. The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans.

A spatial database is a general-purpose database that has been enhanced to include spatial data that represents objects defined in a geometric space, along with tools for querying and analyzing such data.

<span class="mw-page-title-main">Peter Baumann (computer scientist)</span> German computer scientist

Peter Baumann is a German computer scientist and professor at Constructor University, Bremen, Germany, where he is head of the Large-Scale Scientific Information Systems research group in the Department of Computer Science and Electrical Engineering.

The Open Geospatial Consortium Web Coverage Service Interface Standard (WCS) defines Web-based retrieval of coverages – that is, digital geospatial information representing space/time-varying phenomena.

In database management systems (DBMS), a prepared statement, parameterized statement, or parameterized query is a feature where the database pre-compiles SQL code and stores the results, separating it from data. Benefits of prepared statements are:

<span class="mw-page-title-main">Open Geospatial Consortium</span> Standards organization

The Open Geospatial Consortium (OGC), an international voluntary consensus standards organization for geospatial content and location-based services, sensor web and Internet of Things, GIS data processing and data sharing. It originated in 1994 and involves more than 500 commercial, governmental, nonprofit and research organizations in a consensus process encouraging development and implementation of open standards.

The following is provided as an overview of and topical guide to databases:

<span class="mw-page-title-main">Array DBMS</span> System that provides database services specifically for arrays

An array database management system or array DBMS provides database services specifically for arrays, that is: homogeneous collections of data items, sitting on a regular grid of one, two, or more dimensions. Often arrays are used to represent sensor, simulation, image, or statistics data. Such arrays tend to be Big Data, with single objects frequently ranging into Terabyte and soon Petabyte sizes; for example, today's earth and space observation archives typically grow by Terabytes a day. Array databases aim at offering flexible, scalable storage and retrieval on this information category.

The Web Coverage Processing Service (WCPS) defines a language for filtering and processing of multi-dimensional raster coverages, such as sensor, simulation, image, and statistics data. The Web Coverage Processing Service is maintained by the Open Geospatial Consortium (OGC). This raster query language allows clients to obtain original coverage data, or derived information, in a platform-neutral manner over the Web.

<span class="mw-page-title-main">ClickHouse</span> Open-source database management system

ClickHouse is an open-source column-oriented DBMS for online analytical processing (OLAP) that allows users to generate analytical reports using SQL queries in real-time. ClickHouse Inc. is headquartered in the San Francisco Bay Area with the subsidiary, ClickHouse B.V., based in Amsterdam, Netherlands.

References

  1. ↑ "The rasdaman Open Source Project on Open Hub". Open Hub. Black Duck Software. Retrieved 2020-01-14.
  2. ↑ "Rasdaman License". rasdaman.org. Retrieved 2016-08-01.
  3. ↑ Baumann, P.: On the Management of Multidimensional Discrete Data. VLDB Journal 4(3)1994, Special Issue on Spatial Database Systems, pp. 401 - 444
  4. ↑ "Raster data management in databases". Community Research and Development Information Service (CORDIS).
  5. 1 2 "rasdaman, the Big Data Analytics Server". Rasdaman.com. Retrieved 2022-09-11.
  6. ↑ "Rasdaman - the Agile Array Analytics Engine". www.rasdaman.com. Archived from the original on 24 September 2015. Retrieved 15 January 2022.
  7. ↑ Baumann, P.: A Database Array Algebra for Spatio-Temporal Data and Beyond. Proc. NGITS’99, LNCS 1649, Springer 1999, pp.76-93
  8. ↑ n.n.: Rasdaman Query Language Guide
  9. ↑ Furtado, P., Baumann, P.: Storage of Multidimensional Arrays based on Arbitrary Tiling. Proc. ICDE'99, March 23–26, 1999, Sydney, Australia, pp. 328-336
  10. ↑ A. Dehmel: A Compression Engine for Multidimensional Array Database Systems. PhD Thesis, TU Munich, 2001
  11. ↑ P. Furtado: Storage Management of Multidimensional Arrays in Database Management Systems. PhD Thesis, TU Munich, 2000
  12. ↑ B. Reiner et al: Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems. Proc. DEXA, Aix-en-Provence, France, September 2-6, 2002, Springer LNCS 2453, pp. 689 -700, doi: 10.1007/3-540-46146-9_68
  13. ↑ N. Widmann: Effcient Operation Execution on Multidimensional Array Data. PhD Thesis, TU Munich, 1999
  14. ↑ R. Ritsch: Optimization and Evaluation of Array Queries in Database Management Systems. PhD Thesis, TU Munich, 1999
  15. ↑ K. Hahn: Parallele Anfrageverarbeitung in multidimensionalen Array-Datenbanksystemen. PhD Thesis, TU Munich, 2003
  16. ↑ "rasdaman". rasdaman. 2022-02-28. Retrieved 2022-09-11.
  17. ↑ rasdaman license model
  18. ↑ Baumann, P.: Array Databases: Concepts, Standards, Implementations. Research Data Alliance, Array Database Assessment Working Group, DOI: https://dx.doi.org/10.15497/RDA00024
  19. ↑ Baumann, P.: The OGC Web Coverage Processing Service (WCPS) Standard. Geoinformatica, 14(4)2010, pp. 447-479
  20. ↑ "EU INSPIRE". EU. 2022-02-28. Retrieved 2024-07-18.
  21. ↑ "Technical Guidance for the implementation of INSPIRE Download Services using Web Coverage Services (WCS)". 2016-12-16.
  22. ↑ "ISO 19123-3:2023: Geographic information - Schema for coverage geometry and functions - Part 3: Processing fundamentals". 2023-06-21.
  23. ↑ "2024-07-05" (PDF). 2024-07-05.
  24. ↑ "ISO 9075-15:2023: Information technology – Database languages – SQL – Part 15: Multi-Dimensional Arrays (MDA)". 2023-06-01.
  25. ↑ "Earth Datacube Playground". rasdaman. Retrieved 2024-07-18.
  26. ↑ "EarthServer". Earthserver.eu. Retrieved 2022-09-11.
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