Run downsampling with ILMedit
This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
This is a simplified example that allows you to see quickly how downsampling works as part of an ILM policy to reduce the storage size of a sampled set of metrics. The example uses typical Kubernetes cluster monitoring data. To test out downsampling with ILM, follow these steps:
Prerequisitesedit
Refer to time series data stream prerequisites.
Before running this example you may want to try the Run downsampling manually example.
Create an index lifecycle policyedit
Create an ILM policy for your time series data. While not required, an ILM policy is recommended to automate the management of your time series data stream indices.
To enable downsampling, add a Downsample action and set
fixed_interval to the downsampling interval at
which you want to aggregate the original time series data.
In this example, an ILM policy is configired for the hot phase. The downsample
takes place after the initial index rollover, which for demonstration
purposes is set to run after five minutes.
PUT _ilm/policy/datastream_policy
{
"policy": {
"phases": {
"hot": {
"actions": {
"rollover" : {
"max_age": "5m"
},
"downsample": {
"fixed_interval": "1h"
}
}
}
}
}
}
Create an index templateedit
This creates an index template for a basic data stream. The available parameters for an index template are described in detail in Set up a time series data stream.
For simplicity, in the time series mapping all time_series_metric parameters
are set to type gauge, but the counter metric type 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 time series
dimensions: host, namespace, node, and pod. The time series dimensions
are not changed by the downsampling process.
PUT _index_template/datastream_template
{
"index_patterns": [
"datastream*"
],
"data_stream": {},
"template": {
"settings": {
"index": {
"mode": "time_series",
"number_of_replicas": 0,
"number_of_shards": 2
},
"index.lifecycle.name": "datastream_policy"
},
"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
}
}
}
}
}
}
}
Ingest time series dataedit
Use a bulk API request to automatically create your TSDS and index a set of ten documents.
Important: Before running this bulk request you need to update the
timestamps to within three to five hours after your current time. That is,
search 2022-06-21T15 and replace with your present date, and adjust the hour
to your current time plus three hours.
PUT /datastream/_bulk?refresh
{"create": {}}
{"@timestamp":"2022-06-21T15:49:00Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":91153,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":463314616},"usage":{"bytes":307007078,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":585236},"rss":{"bytes":102728},"pagefaults":120901,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:45:50Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":124501,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":982546514},"usage":{"bytes":360035574,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":1339884},"rss":{"bytes":381174},"pagefaults":178473,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:44:50Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":38907,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":862723768},"usage":{"bytes":379572388,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":431227},"rss":{"bytes":386580},"pagefaults":233166,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:44:40Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":86706,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":567160996},"usage":{"bytes":103266017,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":1724908},"rss":{"bytes":105431},"pagefaults":233166,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:44:00Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":150069,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":639054643},"usage":{"bytes":265142477,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":1786511},"rss":{"bytes":189235},"pagefaults":138172,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:42:40Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":82260,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":854735585},"usage":{"bytes":309798052,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":924058},"rss":{"bytes":110838},"pagefaults":259073,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:42:10Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":153404,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":279586406},"usage":{"bytes":214904955,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":1047265},"rss":{"bytes":91914},"pagefaults":302252,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:40:20Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":125613,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":822782853},"usage":{"bytes":100475044,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":2109932},"rss":{"bytes":278446},"pagefaults":74843,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:40:10Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":100046,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":567160996},"usage":{"bytes":362826547,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":1986724},"rss":{"bytes":402801},"pagefaults":296495,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
{"create": {}}
{"@timestamp":"2022-06-21T15:38:30Z","kubernetes":{"host":"gke-apps-0","node":"gke-apps-0-0","pod":"gke-apps-0-0-0","container":{"cpu":{"usage":{"nanocores":40018,"core":{"ns":12828317850},"node":{"pct":2.77905e-05},"limit":{"pct":2.77905e-05}}},"memory":{"available":{"bytes":1062428344},"usage":{"bytes":265142477,"node":{"pct":0.01770037710617187},"limit":{"pct":9.923134671484496e-05}},"workingset":{"bytes":2294743},"rss":{"bytes":340623},"pagefaults":224530,"majorpagefaults":0},"start_time":"2021-03-30T07:59:06Z","name":"container-name-44"},"namespace":"namespace26"}}
View the resultsedit
Now that you’ve created and added documents to the data stream, check to confirm the current state of the new index.
GET _data_stream
If the ILM policy has not yet been applied, your results will be like the
following. Note the original index_name: .ds-datastream-<timestamp>-000001.
{
"data_streams": [
{
"name": "datastream",
"timestamp_field": {
"name": "@timestamp"
},
"indices": [
{
"index_name": ".ds-datastream-2022.08.26-000001",
"index_uuid": "5g-3HrfETga-5EFKBM6R-w"
},
{
"index_name": ".ds-datastream-2022.08.26-000002",
"index_uuid": "o0yRTdhWSo2pY8XMvfwy7Q"
}
],
"generation": 2,
"status": "GREEN",
"template": "datastream_template",
"ilm_policy": "datastream_policy",
"hidden": false,
"system": false,
"allow_custom_routing": false,
"replicated": false,
"time_series": {
"temporal_ranges": [
{
"start": "2022-08-26T13:29:07.000Z",
"end": "2022-08-26T19:29:07.000Z"
}
]
}
}
]
}
Next, run a search query:
GET datastream/_search
The query returns your ten newly added documents.
{
"took": 17,
"timed_out": false,
"_shards": {
"total": 4,
"successful": 4,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10,
"relation": "eq"
},
...
By default, index lifecycle management checks every ten minutes for indices that
meet policy criteria. Wait for about ten minutes (maybe brew up a quick coffee
or tea ☕ ) and then re-run the GET _data_stream request.
GET _data_stream
After the ILM policy has taken effect, the original
.ds-datastream-2022.08.26-000001 index is replaced with a new, downsampled
index, in this case downsample-6tkn-.ds-datastream-2022.08.26-000001.
{
"data_streams": [
{
"name": "datastream",
"timestamp_field": {
"name": "@timestamp"
},
"indices": [
{
"index_name": "downsample-6tkn-.ds-datastream-2022.08.26-000001",
"index_uuid": "qRane1fQQDCNgKQhXmTIvg"
},
{
"index_name": ".ds-datastream-2022.08.26-000002",
"index_uuid": "o0yRTdhWSo2pY8XMvfwy7Q"
}
],
...
Run a search query on the datastream.
GET datastream/_search
The new downsampled index contains just one document that includes the min,
max, sum, and value_count statistics based off of the original sampled
metrics.
{
"took": 6,
"timed_out": false,
"_shards": {
"total": 4,
"successful": 4,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "downsample-6tkn-.ds-datastream-2022.08.26-000001",
"_id": "0eL0wC_4-45SnTNFAAABgtpz0wA",
"_score": 1,
"_source": {
"@timestamp": "2022-08-26T14:00:00.000Z",
"_doc_count": 10,
"kubernetes.host": "gke-apps-0",
"kubernetes.namespace": "namespace26",
"kubernetes.node": "gke-apps-0-0",
"kubernetes.pod": "gke-apps-0-0-0",
"kubernetes.container.cpu.usage.nanocores": {
"min": 38907,
"max": 153404,
"sum": 992677,
"value_count": 10
},
"kubernetes.container.memory.available.bytes": {
"min": 279586406,
"max": 1062428344,
"sum": 7101494721,
"value_count": 10
},
"kubernetes.container.memory.pagefaults": {
"min": 74843,
"max": 302252,
"sum": 2061071,
"value_count": 10
},
"kubernetes.container.memory.rss.bytes": {
"min": 91914,
"max": 402801,
"sum": 2389770,
"value_count": 10
},
"kubernetes.container.memory.usage.bytes": {
"min": 100475044,
"max": 379572388,
"sum": 2668170609,
"value_count": 10
},
"kubernetes.container.memory.workingset.bytes": {
"min": 431227,
"max": 2294743,
"sum": 14230488,
"value_count": 10
},
"kubernetes.container.cpu.usage.core.ns": 12828317850,
"kubernetes.container.cpu.usage.limit.pct": 0.000027790500098490156,
"kubernetes.container.cpu.usage.node.pct": 0.000027790500098490156,
"kubernetes.container.memory.majorpagefaults": 0,
"kubernetes.container.memory.usage.limit.pct": 0.00009923134348355234,
"kubernetes.container.memory.usage.node.pct": 0.017700377851724625,
"kubernetes.container.name": "container-name-44",
"kubernetes.container.start_time": "2021-03-30T07:59:06.000Z"
}
}
]
}
}
Use the data stream stats API to get statistics for the data stream, including the storage size.
GET /_data_stream/datastream/_stats?human=true
{
"_shards": {
"total": 4,
"successful": 4,
"failed": 0
},
"data_stream_count": 1,
"backing_indices": 2,
"total_store_size": "16.6kb",
"total_store_size_bytes": 17059,
"data_streams": [
{
"data_stream": "datastream",
"backing_indices": 2,
"store_size": "16.6kb",
"store_size_bytes": 17059,
"maximum_timestamp": 1661522400000
}
]
}
This example demonstrates how downsampling works as part of an ILM policy to reduce the storage size of metrics data as it becomes less current and less frequently queried.
You can also try our Run downsampling manually example to learn how downsampling can work outside of an ILM policy.