Nicolas Ruflin

How to use Elasticsearch and Time Series Data Streams for observability metrics

With Time Series Data Streams (TSDS), Elasticsearch introduces optimized storage for metrics time series. Check out how we use it for Elastic Observability.

7 min read
How to use Elasticsearch and Time Series Data Streams for observability metrics

Elasticsearch is used for a wide variety of data types — one of these is metrics. With the introduction of Metricbeat many years ago and later our APM Agents, the metric use case has become more popular. Over the years, Elasticsearch has made many improvements on how to handle things like metrics aggregations and sparse documents. At the same time, TSVB visualizations were introduced to make visualizing metrics easier. One concept that was missing that exists for most other metric solutions is the concept of time series with dimensions.

Mid 2021, the Elasticsearch team embarked on making Elasticsearch a much better fit for metrics. The team created Time Series Data Streams (TSDS), which were released in 8.7 as generally available (GA).

This blog post dives into how TSDS works and how we use it in Elastic Observability, as well as how you can use it for your own metrics.

A quick introduction to TSDS

Time Series Data Streams (TSDS) are built on top of data streams in Elasticsearch that are optimized for time series. To create a data stream for metrics, an additional setting on the data stream is needed. As we are using data streams, first an Index Template has to be created:

PUT _index_template/metrics-laptop
  "index_patterns": [
  "data_stream": {},
  "priority": 200,
  "template": {
    "settings": {
      "index.mode": "time_series"
    "mappings": {
      "properties": {
        "": {
          "type": "keyword",
          "time_series_dimension": true
        "packages.sent": {
          "type": "integer",
          "time_series_metric": "counter"
        "memory.usage": {
          "type": "double",
          "time_series_metric": "gauge"

Let's have a closer look at this template. On the top part, we mark the index pattern with metrics-laptop-*. Any pattern can be selected, but it is recommended to use the data stream naming scheme for all your metrics. The next section sets the "index.mode": "time_series" in combination with making sure it is a data_stream: "data_stream": .


Each time series data stream needs at least one dimension. In the example above, is set as a dimension field with "time_series_dimension": true. You can have up to 16 dimensions by default. Not every dimension must show up in each document. The dimensions define the time series. The general rule is to pick fields as dimensions that uniquely identify your time series. Often this is a unique description of the host/container, but for some metrics like disk metrics, the disk id is needed in addition. If you are curious about default recommended dimensions, have a look at this ECS contribution with dimension properties.

Reduced storage and increased query speed

At this point, you already have a functioning time series data stream. Setting the index mode to time series automatically turns on synthetic source. By default, Elasticsearch typically duplicates data three times:

With synthetic source, the _source field is not persisted; instead, it is reconstructed from the doc values. Especially in the metrics use case, there are little benefits to keeping the source.

Not storing it means a significant reduction in storage. Time series data streams sort the data based on the dimensions and the time stamp. This means data that is usually queried together is stored together, which speeds up query times. It also means that the data points for a single time series are stored alongside each other on disk. This enables further compression of the data as the rate at which a counter increases is often relatively constant.

Metric types

But to benefit from all the advantages of TSDS, the field properties of the metrics fields must be extended with the

time_series_metric: {type}
. Several types are supported — as an example, gauge and counter were used above. Giving Elasticsearch knowledge about the metric type allows Elasticsearch to offer more optimized queries for the different types and reduce storage usage further.

When you create your own templates for data streams under the data stream naming scheme, it is important that you set "priority": 200 or higher, as otherwise the built-in default template will apply.

Ingest a document

Ingesting a document into a TSDS isn't in any way different from ingesting documents into Elasticsearch. You can use the following commands in Dev Tools to add a document, and then search for it and also check out the mappings. Note: You have to adjust the @timestamp field to be close to your current date and time.

# Add a document with `` as the dimension
POST metrics-laptop-default/_doc
  # This timestamp neesd to be adjusted to be current
  "@timestamp": "2023-03-30T12:26:23+00:00",
  "": "",
  "packages.sent": 1000,
  "memory.usage": 0.8

# Search for the added doc, _source will show up but is reconstructed
GET metrics-laptop-default/_search

# Check out the mappings
GET metrics-laptop-default

If you do search, it still shows _source but this is reconstructed from the doc values. The additional field added above is @timestamp. This is important as it is a required field for any data stream.

Why is this all important for Observability?

One of the advantages of the Elastic Observability solution is that in a single storage engine, all signals are brought together in a single place. Users can query logs, metrics, and traces together without having to jump from one system to another. Because of this, having a great storage and query engine not only for logs but also metrics is key for us.

Usage of TSDS in integrations

With integrations, we give our users an out of the box experience to integrate with their infrastructure and services. If you are using our integrations, eventually you will automatically get all the benefits of TSDS for your metrics assuming you are on version 8.7 or newer.

Currently we are working through the list of our integration packages, add the dimensions, metric type fields and then turn on TSDS for the metrics data streams. What this means is as soon as the package has all properties enabled, the only thing you have to do is upgrade the integration and everything else will happen automatically in the background.

To visualize your time series in Kibana, use Lens, which has native support built in for TSDS.

Learn more

If you switch over to TSDS, you will automatically benefit from all the future improvements Elasticsearch is making for metrics time series, be it more efficient storage, query performance, or new aggregation capabilities. If you want to learn more about how TSDS works under the hood and all available config options, check out the TSDS documentation. What Elasticsearch supports in 8.7 is only the first iteration of the metrics time series in Elasticsearch.

TSDS can be used since 8.7 and will be in more and more of our integrations automatically when integrations are upgraded. All you will notice is lower storage usage and faster queries. Enjoy!