What are Time Series Databases?

In this video I go over an introduction of time series databases. At first I talk about what is time series data and what are some examples of it. Then I take a look at what makes time series data unique and how its different from other relational data.

From there, I look at a few example queries that can be efficiently answered by time series databases. These queries are not performant in other databases, such as relational, document or graph databases.

Finally, I am going to wrap it up by looking at some existing time series databases.

Time series databases are purpose built to store time series data. Time series data is nothing but a sequence of data points over a time interval. Some examples include: Sensors, Weather, System monitoring, Website activity and Stock price.

There are several challenges of time series data:

Very high write throughput. How do you ingest data into DB?
Regular & periodic writes
Large range scans of many records
Data needs to be compressed due to volume and homogeneity
Native support of time based queries & analytics (Summarization)

With time series data, we want to do certain unique queries, such as rollups by date or max/min every month/year, or maybe averages over a period of time. These are expensive queries, but time series databases let us get results of these queries very quickly, as quickly as sub 100 milliseconds.

There are quite a few time series databases out there already, such as, QuestDB, TimestreamDB, InfluxDB, Amazon Timestream. Some NoSQL databases such as Cassandra is also very good for time series data.

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