# Do Time series data streams (TSDS) store non-numeric/non-dimension logs efficiently?

**URL:** <https://discuss.elastic.co/t/do-time-series-data-streams-tsds-store-non-numeric-non-dimension-logs-efficiently/344352>\
**Category:** Elasticsearch\
**Created:** [October 3, 2023, 10:40pm UTC](https://discuss.elastic.co/t/do-time-series-data-streams-tsds-store-non-numeric-non-dimension-logs-efficiently/344352 "2023-10-03T22:40:07Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![micheal\_riff](https://avatars.discourse-cdn.com/v4/letter/m/ea5d25/32.png) [@micheal\_riff](https://discuss.elastic.co/u/micheal_riff)\
**Post date:** [October 3, 2023, 10:40pm UTC](https://discuss.elastic.co/t/do-time-series-data-streams-tsds-store-non-numeric-non-dimension-logs-efficiently/344352/1 "2023-10-03T22:40:07Z")

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Hi,

I have a few questions about time series data streams (TSDS). I've tried looking through the documentation but am still confused on a few small things 🙂

I was wondering if fields that are not stored as dimensions ("time\_series\_dimension": true) or as a metric (ex: "time\_series\_metric": "gauge") were still stored more efficiently than in a normal datastream?

I.e. if I have the mappings

```auto
{
      "properties": {
        "Dimension1": {
          "type": "keyword",
          "time_series_dimension": true
        },
        "Dimension2": {
          "type": "keyword",
          "time_series_dimension": true
        },
        "metric1": {
          "type": "integer",
          "time_series_metric": "gauge"
        },
        "other": {
          "type": "keyword"
        }
        "@timestamp": {
          "type": "date",
          "format": "strict_date_optional_time"
        }
      }
}

```

Would the "other" keyword be stored more efficiently than just using a normal data stream? If they are not stored as efficiently, then should I make every non-numeric field into a dimension? In this example "other" keyword would be a keyword that will only have 5 possible values - and I would like to keep count of logs with those values over time.

Additionally I had one other question. I have some logs I'm turning into metrics in which some are going to have the exact same dimensions and @timestamp. I know that TSDS stores \_id as a hash of dimensions and @timestamp - but this poses a problem that it is counting some of my logs (around every 1000th) as duplicates as they have the same @timestamp (to the millisecond) and dimensions. Is there any solution that allows me to prevent the TSDS from automatically preventing duplicate documents besides hard coding a solution into a dimension through something like logstash?

Thanks!

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<div class="post-metadata">

**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\
**Post date:** [October 31, 2023, 10:40pm UTC](https://discuss.elastic.co/t/do-time-series-data-streams-tsds-store-non-numeric-non-dimension-logs-efficiently/344352/2 "2023-10-31T22:40:36Z")

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