Add time series histogram to the TSDB docs.

manage-data/data-store/data-streams/downsampling-concepts.md

@@ -54,9 +54,11 @@ Downsampling is applied to the individual backing indices of the TSDS. The downs
 3. For each [time series metric](time-series-data-stream-tsds.md#time-series-metric) field, computes aggregations for all documents in the bucket.
 
     * `gauge` field type:
-        * `min`, `max`, `sum`, and `value_count` are stored as type `aggregate_metric_double`
+        * `min`, `max`, `sum`, and `value_count` are stored as type `aggregate_metric_double`.
     * `counter` field type:
-        * `last_value` is stored.
+        * the last value is stored and the type is preserved.
+    * `histogram` field type: {applies_to}`stack: preview 9.3`
+        * individual histograms are merged into a single histogram which is stored and the type is preserved. In the case of the `histogram` field type, the [T-Digest](elasticsearch://reference/aggregations/search-aggregations-metrics-percentile-aggregation.md) algorithm is used.
 
 4. For all other fields, copies the most recent value to the target index.
 5. Replaces the original index with the downsampled index, then deletes the original index.

manage-data/data-store/data-streams/time-series-data-stream-tsds.md

@@ -69,18 +69,16 @@ You can also simplify dimension definitions by using [pass-through](elasticsearc
 
 Metrics are numeric measurements that change over time. Documents in a TSDS typically contain one or more metric fields. 
 
-To mark a field as a metric, use the `time_series_metric` mapping parameter. This parameter ensures data is stored in an optimal way for time series analysis. The following field types support the `time_series_metric` parameter:
-
-* All [numeric field types](elasticsearch://reference/elasticsearch/mapping-reference/number.md)
-* [`aggregate_metric_double`](elasticsearch://reference/elasticsearch/mapping-reference/aggregate-metric-double.md), for internal use during downsampling (rarely user-populated)
-
-The valid values for `time_series_metric` are `counter` and `gauge`:
+To mark a field as a metric, use the `time_series_metric` mapping parameter. This parameter ensures data is stored in an optimal way for time series analysis. The valid values for `time_series_metric` are `counter`, `gauge` and `histogram`:
 
 `counter`
-:   A cumulative metric that only monotonically increases or resets to `0` (zero). For example, a count of errors or completed tasks that resets when a serving process restarts. 
+:   A cumulative metric that only monotonically increases or resets to `0` (zero). For example, a count of errors or completed tasks that resets when a serving process restarts. A counter is supported by all [numeric field types](elasticsearch://reference/elasticsearch/mapping-reference/number.md)
 
 `gauge`
-:   A metric that represents a single numeric that can arbitrarily increase or decrease. For example, a temperature or available disk space. 
+:   A metric that represents a single numeric that can arbitrarily increase or decrease. For example, a temperature or available disk space. A gauge is supported by all [numeric field types](elasticsearch://reference/elasticsearch/mapping-reference/number.md) and [`aggregate_metric_double`](elasticsearch://reference/elasticsearch/mapping-reference/aggregate-metric-double.md) (for internal use during downsampling, rarely user-populated).
+
+`histogram` {applies_to}`stack: preview 9.3`
+:   A metric that tracks the distribution of numerical values (like request times or sizes) by grouping them into "buckets" allowing you to monitor the shape of the data alongside the total count. A histogram is supported by [`histogram`](elasticsearch://reference/elasticsearch/mapping-reference/histogram.md) and [`exponential_histogram`](elasticsearch://reference/elasticsearch/mapping-reference/exponential-histogram.md). 
 
 #### `_tsid` metadata field [tsid]