A time series database is a type of software system that has been optimized for storing and reading time series data. This type of data is defined as a series of datapoints that are individually coupled with a timestamp. Early adopters of the timeseries database were industrial applications that were in need efficiently storingsensory data such as temperature and flow. These types of niche databases became commonly labeled as process/data historians or simply historians for short. MOPSsys, a company in Örnsköldsvik, has been developing historians for 40 years with their most recent one being MOPS HISTORIAN, which is operated by over 120 mills across 20 countries. For process historians, read and write speeds are crucial in order to provide fast and responsive trends, curves and profiles for operators and engineers. Therefore, it is not uncommon for these historians to relax some typical database requirements such as ACID (Atomic, Consistent, Isolated, Durable) transactions to increase performance. One of the crucial parts of designing a process historian is to decide on how data is stored for optimalperformance. This leads to an interesting discussion about how the data should be stored on disk for optimal historian performance. This thesis explores andtests different storage formats suited for historians such as the current storage format in MOPS Historian, Log-Structured Merge trees (LSM) and Indexed Sequential Access Methods (ISAM). The benchmarks include basic reads and writes with heavy emphasis put on range queries. These storage formats are also benchmarked against other databases that exist on the market, those being PostgreSQL and TimescaleDB, a PostgreSQL extension. Results from testing these candidates showed that PostgreSQL and TimescaleDB performed the best and that ISAM and LSM had different strengths and weaknesses depending on the scenario.