Introduction
Time series data is a sequence of data points recorded over a time interval. This type of data is used for measuring events that change over time.
With time series data, each data point consists of a timestamp, one or more attributes, and the event that changes over time.
This data can be used to derive insights into the performance and health of an application, detect anomalies, and identify optimization opportunities.
For example, DevOps engineers might want to view data that measures changes in infrastructure performance metrics.
Key concepts of Timestream
Time series - A sequence of one or more data points (or records) recorded over a time interval. Examples are the price of a stock over time, the CPU or memory utilization of an EC2 instance over time, and the temperature/pressure reading of an IoT sensor over time.
Record - A single data point in a time series.
Dimension - An attribute that describes the meta-data of a time series. A dimension consists of a dimension name and a dimension value.
Consider the following examples:
When considering an AWS Region as a dimension, the dimension name is "region" and the dimension value is "us-east-1"
For an IoT sensor, the dimension name is "device ID" and the dimension value is "12345"
- Measure - The actual value being measured by the record. Examples are the stock price, the CPU or memory utilization, and the temperature or humidity reading. Measures consist of measure names and measure values.
Consider the following examples:
For CPU utilization, the measure name is "CPU utilization" and the measure value is the actual CPU utilization.
Measures can be modeled in Timestream as multi-measure or single-measure records. For more information, see Multi-measure records vs. single-measure records.
Timestamp - Indicates when a measure was collected for a given record. Timestream supports timestamps with nanosecond granularity.
Table - A container for a set of related time series.
Database - A top level container for tables.
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