Architecture to Consider for Open Source Database
When making their choice for a Scalable Open-Source Time Series Database, a Distributed Architecture is normally one of their greatest factors.Here’s why:
- Scalability: Distributed databases were designed to scale horizontally, this mean that you can just add more number of servers on the network to handle more data and traffic. This becomes extremely important for time series databases since they usually deal with huge amounts of data.
- Performance: In a peer-to-peer architecture, data is distributed amongst several servers which makes an individual server less taxed. This may end up in enhanced performance, with data I/O operations mainly being responsible for read and write times crucial to time series databases.
- High Availability: Fault tolerance is ensured with distributed databases as data is replicated across different servers, thus if one fails, peers can take over. This will keep the database reachable, therefore ensuring that your application or system receives reliable services.
- Data Locality: Here in some distributed databases data is stored where it is most frequently accessed thus reducing latency and improving performance.
On the other hand, handling a distributed database can actually be harder than the process of handling a conventional, single-node database. There will be things like consistency of data, network partitioning and others, which you should take into account.
How to Choose a Scalable Open Source Time Series Database
In today’s world, we use a lot of devices and systems that generate a ton of data over time. For example, think about the weather reports you see every day, the steps counted by your fitness tracker, or the information about how many people visit a website. All of this data is called time series data because it’s recorded with a timestamp, showing when each piece of information was collected.