Update intro.rst

docs/source/intro.rst

@@ -24,7 +24,7 @@ The software architecture behind OCTOPUS v.2 is illustrated in Fig. Sys1. Both s
 
 **Fig. Sys1** Schematic of the OCTOPUS v.2 Google Cloud Platform (GCP) set-up. See the text for more details.
 
-Most components of OCTOPUS v.2 run natively on the GCP apart from GeoServer and Tomcat, which are deployed within a Google Compute Engine using a single bespoke Docker container (https://www.docker.com, last access: 04 May 2023). Tabular data as well as the point and polygon geometries associated with each observation (see below) are stored in a PostgreSQL/PostGIS (https://postgis.net, last access: 04 May 2023) relational database running in Cloud SQL. The latter is a SaaS (Software as a Service) meaning that installation, set-up, and running activities of the database are automatically managed by the GCP, decreasing maintenance overhead and providing a monthly uptime/availability of 99.95 %. Raster data and all CAIRN [#]_ input and output files are stored separately within a Cloud Storage bucket in .zip archives. Unlike the first version of OCTOPUS (`Codilean et al., 2018 <https://doi.org/10.5194/essd-10-2123-2018>`_), the .zip archives in OCTOPUS v.2 no longer include the tabular and vector data that are now hosted in the PostgreSQL/PostGIS relational database. Thus, we avoid duplication and make future maintenance of the data more straightforward. The relational database is linked to a GeoServer instance (Fig. Sys1). GeoServer (http://geoserver.org, last access: 04 May 2023) implements a range of OGC data-sharing standards, including the widely used Web Feature Service (WFS) and the Web Map Service (WMS) standards that allow, in addition to connections from a web browser, direct connections to the database from a variety of desktop geographic information system (GIS) applications, including ArcGIS and QGIS (via WFS; see below) and Google Earth (via WMS). GeoServer exports data to various formats, including GML, JSON, Google Earth KML and KMZ, and Esri shapefile. It is also possible to access data directly from the PostgreSQL/PostGIS database, however, this is restricted and requires authentication via a Google SQL Proxy. GeoServer (along with Tomcat) is hosted in a Google Compute Engine, an IaaS (Infrastructure as a Service) that allows for a virtualised environment to be run on Google hardware. Geoserver and Tomcat currently exist as a single bespoke Docker container due to limitations of the deployed Geoserver and Tomcat versions that cannot run with separate runtimes. More recent Geoserver and Tomcat versions, however, exist as standard Docker containers that can be run independently aligned with a microservice architecture. Utilising these dockerised versions would permit the applications to be run on managed server-less platforms such as Google Cloud Run, allowing modular horizontal scaling. Further, Tomcat's Common Gateway Interface (CGI) that provides functionality to the OCTOPUS frontend, such as downloading files and retrieving study bounding boxes, could also be separated into independent resources that run on Google Cloud Functions and allow for near-infinite horizontal scalability to meet any fluctuations in traffic volume. Next, the OCTOPUS web frontend is deployed in a Cloud Storage bucket and uses the OpenLayers (https://openlayers.org, last access: 04 May 2023) JavaScript library to display the geospatial data served by the GeoServer instance in a web browser (Fig. Sys1). Finally Cloud Load Balancing is used to distribute traffic and to separate connections to the web interface from those directed to GeoServer directly via WFS/WMS from third-party applications. The OCTOPUS v.2 system architecture consists of bot a development and a production environment as illustrated in Fig. Sys1.
+Most components of OCTOPUS v.2 run natively on the GCP apart from GeoServer and Tomcat, which are deployed within a Google Compute Engine using a single bespoke Docker container (https://www.docker.com, last access: 04 May 2023). Tabular data as well as the point and polygon geometries associated with each observation (see below) are stored in a PostgreSQL/PostGIS (https://postgis.net, last access: 04 May 2023) relational database running in Cloud SQL. The latter is a SaaS (Software as a Service) meaning that installation, set-up, and running activities of the database are automatically managed by the GCP, decreasing maintenance overhead and providing a monthly uptime/availability of 99.95 %. Raster data and all CAIRN [#]_ input and output files are stored separately within a Cloud Storage bucket in .zip archives. Unlike the first version of OCTOPUS (`Codilean et al., 2018 <https://doi.org/10.5194/essd-10-2123-2018>`_), the .zip archives in OCTOPUS v.2 no longer include the tabular and vector data that are now hosted in the PostgreSQL/PostGIS relational database. Thus, we avoid duplication and make future maintenance of the data more straightforward. The relational database is linked to a GeoServer instance (Fig. Sys1). GeoServer (http://geoserver.org, last access: 04 May 2023) implements a range of OGC data-sharing standards, including the widely used Web Feature Service (WFS) and the Web Map Service (WMS) standards that allow, in addition to connections from a web browser, direct connections to the database from a variety of desktop geographic information system (GIS) applications, including ArcGIS and QGIS (via WFS; see below) and Google Earth (via WMS). GeoServer exports data to various formats, including GML, JSON, Google Earth KML and KMZ, and Esri shapefile. It is also possible to access data directly from the PostgreSQL/PostGIS database, however, this is restricted and requires authentication via a Google SQL Proxy. GeoServer (along with Tomcat) is hosted in a Google Compute Engine, an IaaS (Infrastructure as a Service) that allows for a virtualised environment to be run on Google hardware. Geoserver and Tomcat currently exist as a single bespoke Docker container due to limitations of the deployed Geoserver and Tomcat versions that cannot run with separate runtimes. More recent Geoserver and Tomcat versions, however, exist as standard Docker containers that can be run independently aligned with a microservice architecture. Utilising these dockerised versions would permit the applications to be run on managed server-less platforms such as Google Cloud Run, allowing modular horizontal scaling. Further, Tomcat's Common Gateway Interface (CGI) that provides functionality to the OCTOPUS frontend, such as downloading files and retrieving study bounding boxes, could also be separated into independent resources that run on Google Cloud Functions and allow for near-infinite horizontal scalability to meet any fluctuations in traffic volume. Next, the OCTOPUS web frontend is deployed in a Cloud Storage bucket and uses the OpenLayers (https://openlayers.org, last access: 04 May 2023) JavaScript library to display the geospatial data served by the GeoServer instance in a web browser (Fig. Sys1). Finally Cloud Load Balancing is used to distribute traffic and to separate connections to the web interface from those directed to GeoServer directly via WFS/WMS from third-party applications. The OCTOPUS v.2 system architecture consists of both a development and a production environment as illustrated in Fig. Sys1.
 
 
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