InfluxDB design insights and tradeoffs
This page documents an earlier version of InfluxDB. InfluxDB v2.6 is the latest stable version. See the equivalent InfluxDB v2.6 documentation: InfluxDB design principles.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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