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Time Series

A time series is a sequence of data points reported at a consistent interval over time. The time interval between successive data points is called the step size. In Atlas, each time series is paired with metadata called tags that allow us to query and group the data.


A set of key value pairs associated with a time series. Each time series must have at least one tag with a key of name. To make it more concrete, here is an example of a tag set represented as a JSON object:

  "name":       "server.requestCount",
  "status":     "200",
  "endpoint":   "api",
  "":     "fooserver",
  "nf.cluster": "fooserver-main",
  "nf.stack":   "main",
  "nf.region":  "us-east-1",
  "":    "us-east-1c",
  "nf.node":    "i-12345678"

Usage of tags typically falls into two categories:

  1. Namespace. These are tags necessary to qualify a name, so that it can be meaningfully aggregated. Using the sample above, consider computing the sum of all metrics for application fooserver. That number would be meaningless. Properly modelled data should try to make the aggregates meaningful by selecting the name. The sum of all metrics with name = server.requestCount is the overall request count for the service.
  2. Dimensions. These are tags used to filter the data to a meaningful subset. They can be used to see the number of successful requests across the cluster by querying for status = 200 or the number of requests for a single node by querying for nf.node = i-12345678. Most tags should fall into this category.

When creating metrics, it is important to carefully think about how the data should be tagged. See the naming docs for more information.


A metric is a specific quantity being measured, e.g., the number of requests received by a server. In casual language about Atlas metric is often used interchangeably with time series. A time series is one way to track a metric and is the method supported by Atlas. In most cases there will be many time series for a given metric. Going back to the example, request count would usually be tagged with additional dimensions such as status and node. There is one time series for each distinct combination of tags, but conceptually it is the same metric.

Data Point

A data point is a triple consisting of tags, timestamp, and a value. It is important to understand at a high level how data points correlate with the measurement. Consider requests hitting a server, this would typically be measured using a counter. Each time a request is received the counter is incremented. There is not one data point per increment, a data point represents the behavior over a span of time called the step size. The client library will sample the counter once for each interval and report a single value.

Suppose that each circle in the diagram below represents a request:

1:00       1:01       1:02       1:03

There are 5 requests shown, 4 from 1:00 to 1:01, and 1 from 1:02 to 1:03. Assuming all requests incremented the same time series, i.e. all other dimensions such as status code are the same, then this would result in three data points. For counters values are always a rate per second, so for a one minute step size the total number of requests would be divided by 60 seconds. So the values stored would be:

Time Value
1:01 4 / 60 = 0.0667
1:02 0 / 60 = 0.0000
1:03 1 / 60 = 0.0167

Step Size

The amount of time between two successive data points in a time series. For Atlas the datapoints will always be on even boundaries of the step size. If data is not reported on step boundaries, it will get normalized to the boundary.