This is a bachelor thesis concerning the response time and CPU effects of indexing in relational databases. Analyzing two popular databases, PostgreSQL and MariaDB with a real-world database structure using randomized entries. The experiment was conducted over Docker and its command-line interface without cached values to ensure fair outcomes. The procedure was done throughout seven different CRUD queries with multiple volumes of database entries to discover their strengths and weaknesses when utilizing indexes. The result chapter shows indicators that indexing has an overall enhancing effect on almost all of the queries. It is found that join, update and delete operations benefits the most from non-clustered indexing. PostgreSQL gains the most from indexes while MariaDB has a minuscule improvement in the response time reduction. A greater usage of CPU resources does not seem to correlate with quicker response times.

abstract: The volume of available spatial data has increased tremendously. Such data includes but is not limited to: weather maps, socioeconomic data, vegetation indices, geotagged social media, and more. These applications need a powerful data management platform to support scalable and interactive analytics on big spatial data. Even though existing single-node spatial database systems (DBMSs) provide support for spatial data, they suffer from performance issues when dealing with big spatial data. Challenges to building large-scale spatial data systems are as follows: (1) System Scalability: The massive-scale of available spatial data hinders making sense of it using traditional spatial database management systems. Moreover, large-scale spatial data, besides its tremendous storage footprint, may be extremely difficult to manage and maintain due to the heterogeneous shapes, skewed data distribution and complex spatial relationship. (2) Fast analytics: When the user runs spatial data analytics applications using graphical analytics tools, she does not tolerate delays introduced by the underlying spatial database system. Instead, the user needs to see useful information quickly. In this dissertation, I focus on designing efficient data systems and data indexing mechanisms to bolster scalable and interactive analytics on large-scale geospatial data. I first propose a cluster computing system GeoSpark which extends the core engine of Apache Spark and Spark SQL to support spatial data types, indexes, and geometrical operations at scale. In order to reduce the indexing overhead, I propose Hippo, a fast, yet scalable, sparse database indexing approach. In contrast to existing tree index structures, Hippo stores disk page ranges (each works as a pointer of one or many pages) instead of tuple pointers in the indexed table to reduce the storage space occupied by the index. Moreover, I present Tabula, a middleware framework that sits between a SQL data system and a spatial visualization dashboard to make the user experience with the dashboard more seamless and interactive. Tabula adopts a materialized sampling cube approach, which pre-materializes samples, not for the entire table as in the SampleFirst approach, but for the results of potentially unforeseen queries (represented by an OLAP cube cell). / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020

The goal of the master ́s thesis is to study index methods, spatial data type objects in PostgreSQL database systems and to create SP-GiST index by quadtree in the PostGIS. The PostGIS is spatial database, which extends of PostgreSQL. PostGIS adds support for geographic and spatial objects. It is a big benefit. PostGIS has its own data types, methods and GiST index too, but there is SP-GiST index missing, therefore master's thesis was created.