Rapid growth of sensor-based, IoTs, social media, financial data like stock market activities and many other information streaming platforms created opportunity to design a whole new database which can capture streaming information with highlighting the importance of time into it. This is so because even traditional RDBMS was not able to efficiently handle complex business logic and rules which is based on time series with high transaction volumes of data. Business scenario which are full of aggregations based on time make RDBMS databases uncomfortable. The situation goes only worsen when these business scenarios to be run on history data. This leads to the evolvement of TSDBs as rescuer for many organizations who are struggling with such scenarios.
With these opportunities created many organizations jumped into this market. At one hand, organizations like IBM have developed “Informix” as their own TSDBs, on the other hand, Open source community has also contributed into this area with multiple products like Prometheus, Druid, Atlas, Graphite etc. Many of TSDBs products are enhancement upon their base RDBMS product or added features to capture Timeseries information. These can act like both in many scenarios, however, there are few which is built entirely from scratch to suit the requirements of capturing streaming information. Since, these databases are built from scratch for Time series information manipulations only, they seem to be robust enough. An example for such TSDBs is InfluxDB
Any TSDB provides facility of organizing time series information as per organization’s need. This give flexibility to the user to place all their needed aggregations applied onto the time series information and can retrieve as it is. This makes the retrieval faster than usual. Since, TSDB is write optimized, they can apply these aggregations and store it on different locations which helps user retrieve information as it is and quickest manner.