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* Add analyzer documentation Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add index and search analyzer pages Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Doc review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * More doc review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented editorial comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Update index-analyzers.md Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Co-authored-by: Nathan Bower <nbower@amazon.com>
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layout | title | parent | grand_parent | nav_order | redirect_from | |
---|---|---|---|---|---|---|
default | Date histogram | Bucket aggregations | Aggregations | 20 |
|
Date histogram aggregations
The date_histogram
aggregation uses date math to generate histograms for time-series data.
For example, you can find how many hits your website gets per month:
GET opensearch_dashboards_sample_data_logs/_search
{
"size": 0,
"aggs": {
"logs_per_month": {
"date_histogram": {
"field": "@timestamp",
"interval": "month"
}
}
}
}
{% include copy-curl.html %}
Example response
...
"aggregations" : {
"logs_per_month" : {
"buckets" : [
{
"key_as_string" : "2020-10-01T00:00:00.000Z",
"key" : 1601510400000,
"doc_count" : 1635
},
{
"key_as_string" : "2020-11-01T00:00:00.000Z",
"key" : 1604188800000,
"doc_count" : 6844
},
{
"key_as_string" : "2020-12-01T00:00:00.000Z",
"key" : 1606780800000,
"doc_count" : 5595
}
]
}
}
}
The response has three months worth of logs. If you graph these values, you can see the peak and valleys of the request traffic to your website month over month.