Design Insights and Tradeoffs in InfluxDB

InfluxDB is a time-series database. Optimizing for this use-case entails some tradeoffs, primarily to increase performance at the cost of functionality. Below is a list of some of those design insights that lead to tradeoffs:

1. For the time series use case, we assume that if the same data is sent multiple times, it is the exact same data that a client just sent several times.
   • Pro: Simplified conflict resolution increases write performance
   • Con: Cannot store duplicate data; may overwrite data in rare circumstances
2. Deletes are a rare occurrence. When they do occur it is almost always against large ranges of old data that are cold for writes.
   • Pro: Restricting access to deletes allows for increased query and write performance
   • Con: Delete functionality is significantly restricted
3. Updates to existing data are a rare occurrence and contentious updates never happen. Time series data is predominantly new data that is never updated.
   • Pro: Restricting access to updates allows for increased query and write performance
   • Con: Update functionality is significantly restricted
4. The vast majority of writes are for data with very recent timestamps and the data is added in time ascending order.
   • Pro: Adding data in time ascending order is significantly more performant
   • Con: Writing points with random times or with time not in ascending order is significantly less performant
5. Scale is critical. The database must be able to handle a high volume of reads and writes.
   • Pro: The database can handle a high volume of reads and writes
   • Con: The InfluxDB development team was forced to make tradeoffs to increase performance
6. Being able to write and query the data is more important than having a strongly consistent view.
   • Pro: Writing and querying the database can be done by multiple clients and at high loads
   • Con: Query returns may not include the most recent points if database is under heavy load
7. Many time series are ephemeral. There are often time series that appear only for a few hours and then go away, e.g. a new host that gets started and reports for a while and then gets shut down.
   • Pro: InfluxDB is good at managing discontinuous data
   • Con: Schema-less design means that some database functions are not supported e.g. there are no cross table joins
8. No one point is too important.
   • Pro: InfluxDB has very powerful tools to deal with aggregate data and large data sets
   • Con: Points don’t have IDs in the traditional sense, they are differentiated by timestamp and series