Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. you can think of removing possibility of running facets or sorting on the analyzed fields if you don’t need it, so that no one is able to run such queries on the production environment.Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. For example, when we know that some fields are analyzed they may contain lots of unique values, which can be very memory intensive leading to high memory usage and long garbage collections. The uninverible property should be used when we want to be sure that on our instance or in our cluster certain fields shouldn’t be used for faceting. And with field set to uninvertible=false Solr will not allow building the field cache entries for that field. This is because we don’t have doc values for the title field, because we can’t have it – it is analyzed field. Instead of Solr returning the faceting results we would get an empty array. If we would now run our example query to Solr its behavior will change. Let’s modify our example one last time and let’s set the uninvertible property for the title field to false: Btw, the modified data structure should look as follows: After we modify the schema.xml file and run the query we will still get results as we would expect. Keep in mind that for backward compatibility reason this is the default value for the fields in our data structure. The next thing we should try is modifying our simple example by introducing the uninvertible property for the title field and setting it to true.
Just for the reference, the query looks as follows: Adding uninvertible In our case that shouldn’t be a problem, the query will be executed without any issues.
After indexing some example data let’s try running faceting on on of the field, for example the title one. Nothing special, we have three fields, one of which is the identifier. Let’s start with a very simple data structure that we will use for testing: Let’s look into what happens with various settings of this new property. It allows us to control what Solr will do when it will require data in an uninverted format, so for example when using faceting or sorting. With the recent release of Solr 7.6.0 we got a new option for the fields and field types – the property called uninvertible.