[[downsampling-manual]]
=== Run downsampling manually
++++
Run downsampling manually
++++
This is a simplified example that allows you to see quickly how
<> works to reduce the storage size of a time series
index. The example uses typical Kubernetes cluster monitoring data. To test out
downsampling, follow these steps:
. Check the <>.
. <>.
. <>.
. <>.
. <>.
[discrete]
[[downsampling-manual-prereqs]]
==== Prerequisites
Refer to <>.
For the example you need a sample data file. Download the file from link:
https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/bltf2fe7a300c3c59f7/631b4bc5cc56115de2f58e8c/sample-k8s-metrics.json[here]
and save it in the local directory where you're running {es}.
[discrete]
[[downsampling-manual-create-index]]
==== Create a time series index
This creates an index for a basic data stream. The available parameters for an
index are described in detail in <>.
The time series boundaries are set so that sampling data for the index begins at
`2022-06-10T00:00:00Z` and ends at `2022-06-30T23:59:59Z`.
For simplicity, in the time series mapping all `time_series_metric` parameters
are set to type `gauge`, but <> such as
`counter` and `histogram` may also be used. The `time_series_metric` values
determine the kind of statistical representations that are used during
downsampling.
The index template includes a set of static
<>: `host`, `namespace`,
`node`, and `pod`. The time series dimensions are not changed by the
downsampling process.
[source,console]
----
PUT /sample-01
{
"settings": {
"index": {
"mode": "time_series",
"time_series": {
"start_time": "2022-06-10T00:00:00Z",
"end_time": "2022-06-30T23:59:59Z"
},
"routing_path": [
"kubernetes.namespace",
"kubernetes.host",
"kubernetes.node",
"kubernetes.pod"
],
"number_of_replicas": 0,
"number_of_shards": 2
}
},
"mappings": {
"properties": {
"@timestamp": {
"type": "date"
},
"kubernetes": {
"properties": {
"container": {
"properties": {
"cpu": {
"properties": {
"usage": {
"properties": {
"core": {
"properties": {
"ns": {
"type": "long"
}
}
},
"limit": {
"properties": {
"pct": {
"type": "float"
}
}
},
"nanocores": {
"type": "long",
"time_series_metric": "gauge"
},
"node": {
"properties": {
"pct": {
"type": "float"
}
}
}
}
}
}
},
"memory": {
"properties": {
"available": {
"properties": {
"bytes": {
"type": "long",
"time_series_metric": "gauge"
}
}
},
"majorpagefaults": {
"type": "long"
},
"pagefaults": {
"type": "long",
"time_series_metric": "gauge"
},
"rss": {
"properties": {
"bytes": {
"type": "long",
"time_series_metric": "gauge"
}
}
},
"usage": {
"properties": {
"bytes": {
"type": "long",
"time_series_metric": "gauge"
},
"limit": {
"properties": {
"pct": {
"type": "float"
}
}
},
"node": {
"properties": {
"pct": {
"type": "float"
}
}
}
}
},
"workingset": {
"properties": {
"bytes": {
"type": "long",
"time_series_metric": "gauge"
}
}
}
}
},
"name": {
"type": "keyword"
},
"start_time": {
"type": "date"
}
}
},
"host": {
"type": "keyword",
"time_series_dimension": true
},
"namespace": {
"type": "keyword",
"time_series_dimension": true
},
"node": {
"type": "keyword",
"time_series_dimension": true
},
"pod": {
"type": "keyword",
"time_series_dimension": true
}
}
}
}
}
}
----
[discrete]
[[downsampling-manual-ingest-data]]
==== Ingest time series data
In a terminal window with {es} running, run the following curl command to load
the documents from the downloaded sample data file:
[source,sh]
----
curl -s -H "Content-Type: application/json" \
-XPOST http:///sample-01/_bulk?pretty \
--data-binary @sample-k8s-metrics.json
----
// NOTCONSOLE
Approximately 18,000 documents are added. Check the search results for the newly
ingested data:
[source,console]
----
GET /sample-01*/_search
----
// TEST[continued]
The query has at least 10,000 hits and returns the first 10. In each document
you can see the time series dimensions (`host`, `node`, `pod` and `container`)
as well as the various CPU and memory time series metrics.
[source,console-result]
----
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": 1,
"hits": [
{
"_index": "sample-01",
"_id": "WyHN6N6AwdaJByQWAAABgYOOweA",
"_score": 1,
"_source": {
"@timestamp": "2022-06-20T23:59:40Z",
"kubernetes": {
"host": "gke-apps-0",
"node": "gke-apps-0-1",
"pod": "gke-apps-0-1-0",
"container": {
"cpu": {
"usage": {
"nanocores": 80037,
"core": {
"ns": 12828317850
},
"node": {
"pct": 0.0000277905
},
"limit": {
"pct": 0.0000277905
}
}
},
"memory": {
"available": {
"bytes": 790830121
},
"usage": {
"bytes": 139548672,
"node": {
"pct": 0.01770037710617187
},
"limit": {
"pct": 0.00009923134671484496
}
},
"workingset": {
"bytes": 2248540
},
"rss": {
"bytes": 289260
},
"pagefaults": 74843,
"majorpagefaults": 0
},
"start_time": "2021-03-30T07:59:06Z",
"name": "container-name-44"
},
"namespace": "namespace26"
}
}
}
...
----
// TEST[skip:todo]
// TEST[continued]
Next, you can run a terms aggregation on the set of time series dimensions (`_tsid`) to
view a date histogram on a fixed interval of one day.
[source,console]
----
GET /sample-01*/_search
{
"size": 0,
"aggs": {
"tsid": {
"terms": {
"field": "_tsid"
},
"aggs": {
"over_time": {
"date_histogram": {
"field": "@timestamp",
"fixed_interval": "1d"
},
"aggs": {
"min": {
"min": {
"field": "kubernetes.container.memory.usage.bytes"
}
},
"max": {
"max": {
"field": "kubernetes.container.memory.usage.bytes"
}
},
"avg": {
"avg": {
"field": "kubernetes.container.memory.usage.bytes"
}
}
}
}
}
}
}
}
----
// TEST[continued]
[discrete]
[[downsampling-manual-run]]
==== Run downsampling for the index
Before running downsampling, the index needs to be set to read only mode:
[source,console]
----
PUT /sample-01/_block/write
----
// TEST[continued]
And now, you can use the <> to
downsample the index, setting the time series interval to one hour:
[source,console]
----
POST /sample-01/_downsample/sample-01-downsample
{
"fixed_interval": "1h"
}
----
// TEST[continued]
Finally, delete the original index:
[source,console]
----
DELETE /sample-01
----
// TEST[continued]
[discrete]
[[downsampling-manual-view-results]]
==== View the results
Re-run your search query:
[source,console]
----
GET /sample-01*/_search
----
// TEST[continued]
In the query results, notice that the number of hits has been reduced to only 288
documents. As well, for each time series metric statistical representations have
been calculated: `min`, `max`, `sum`, and `value_count`.
[source,console-result]
----
"hits": {
"total": {
"value": 288,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "sample-01-downsample",
"_id": "WyHN6N6AwdaJByQWAAABgYNYIYA",
"_score": 1,
"_source": {
"@timestamp": "2022-06-20T23:00:00.000Z",
"_doc_count": 81,
"kubernetes.host": "gke-apps-0",
"kubernetes.namespace": "namespace26",
"kubernetes.node": "gke-apps-0-1",
"kubernetes.pod": "gke-apps-0-1-0",
"kubernetes.container.cpu.usage.nanocores": {
"min": 23344,
"max": 163408,
"sum": 7488985,
"value_count": 81
},
"kubernetes.container.memory.available.bytes": {
"min": 167751844,
"max": 1182251090,
"sum": 58169948901,
"value_count": 81
},
"kubernetes.container.memory.rss.bytes": {
"min": 54067,
"max": 391987,
"sum": 17550215,
"value_count": 81
},
"kubernetes.container.memory.pagefaults": {
"min": 69086,
"max": 428910,
"sum": 20239365,
"value_count": 81
},
"kubernetes.container.memory.workingset.bytes": {
"min": 323420,
"max": 2279342,
"sum": 104233700,
"value_count": 81
},
"kubernetes.container.memory.usage.bytes": {
"min": 61401416,
"max": 413064069,
"sum": 18557182404,
"value_count": 81
}
}
},
...
----
// TEST[skip:todo]
You can now re-run the earlier aggregation. Even though the aggregation runs on
the downsampled data stream that only contains 288 documents, it returns the
same results as the earlier aggregation on the original data stream.
[source,console]
----
GET /sample-01*/_search
{
"size": 0,
"aggs": {
"tsid": {
"terms": {
"field": "_tsid"
},
"aggs": {
"over_time": {
"date_histogram": {
"field": "@timestamp",
"fixed_interval": "1d"
},
"aggs": {
"min": {
"min": {
"field": "kubernetes.container.memory.usage.bytes"
}
},
"max": {
"max": {
"field": "kubernetes.container.memory.usage.bytes"
}
},
"avg": {
"avg": {
"field": "kubernetes.container.memory.usage.bytes"
}
}
}
}
}
}
}
}
----
// TEST[continued]
This example demonstrates how downsampling can dramatically reduce the number of
records stored for time series data, within whatever time boundaries you choose.
It's also possible to perform downsampling on already downsampled data, to
further reduce storage and associated costs, as the time series data ages and
the data resolution becomes less critical.
Downsampling is very easily integrated within an ILM policy. To learn more, try
the <> example.