I had to create aggregation that counts number of documents containing in date ranges.
My query looks like:
{
"query":{
"range":{
"doc.createdTime":{
"gte":1483228800000,
"lte":1485907199999
}
}
},
"size":0,
"aggs":{
"by_day":{
"histogram":{
"field":"doc.createdTime",
"interval":"604800000ms",
"format":"yyyy-MM-dd'T'HH:mm:ssZZ",
"min_doc_count":0,
"extended_bounds":{
"min":1483228800000,
"max":1485907199999
}
}
}
}
}
Interval: 604800000 equals to 7 days.
As a result, I recive these:
"aggregations": {
"by_day": {
"buckets": [
{
"key_as_string": "2016-12-29T00:00:00+00:00",
"key": 1482969600000,
"doc_count": 0
},
{
"key_as_string": "2017-01-05T00:00:00+00:00",
"key": 1483574400000,
"doc_count": 603
},
{
"key_as_string": "2017-01-12T00:00:00+00:00",
"key": 1484179200000,
"doc_count": 3414
},
{
"key_as_string": "2017-01-19T00:00:00+00:00",
"key": 1484784000000,
"doc_count": 71551
},
{
"key_as_string": "2017-01-26T00:00:00+00:00",
"key": 1485388800000,
"doc_count": 105652
}
]
}
}
As You can mantion that my buckets starts from 29/12/2016, but as a range query do not cover this date. I expect my buckets should start from 01/01/2017 as I pointed in the range query. This problem occurs only in query with interval with number of days greater then 1. In case of any other intervals it works fine. I've tried with day, months and hours and it works fine.
I've tried also to use filtered aggs and only then use date_histogram. Result is the same.
I'm using Elasticsearch 2.2.0 version.
And the question is how I can force date_histogram to start from date I need?
Try to add offset param with value calculated from given formula:
value = start_date_in_ms % week_in_ms = 1483228800000 % 604800000 =
259200000
{
"query": {
"range": {
"doc.createdTime": {
"gte": 1483228800000,
"lte": 1485907199999
}
}
},
"size": 0,
"aggs": {
"by_day": {
"date_histogram": {
"field": "doc.createdTime",
"interval": "604800000ms",
"offset": "259200000ms",
"format": "yyyy-MM-dd'T'HH:mm:ssZZ",
"min_doc_count": 0,
"extended_bounds": {
"min": 1483228800000,
"max": 1485907199999
}
}
}
}
}
Related
I have an aggregation query where I am trying to calculate the max standard deviation of the number of destination ips per IP Address for a certain time range. As everyone knows the common problem with the moving function std_dev aggregation function, the first 2 days' std dev values will always be null and 0 respectively due to no data being taken into account previously.
Here is my aggregation query:
{
"size": 0,
"query": {
"bool": {
"must": [
{
"exists": {
"field": "aggregations.range.buckets.by ip.buckets.by date.buckets.max_dest_ips.value"
}
}
]
}
},
"aggs": {
"range": {
"date_range": {
"field": "Source Time",
"ranges": [
{
"from": "2018-04-25",
"to": "2018-05-02"
}
]
},
"aggs": {
"by ip": {
"terms": {
"field": "IP Address.keyword",
"size": 500
},
"aggs": {
"datehisto": {
"date_histogram": {
"field": "Source Time",
"interval": "day"
},
"aggs": {
"max_dest_ips": {
"sum": {
"field": "aggregations.range.buckets.by ip.buckets.by date.buckets.max_dest_ips.value"
}
},
"max_dest_ips_std_dev": {
"moving_fn": {
"buckets_path": "max_dest_ips",
"window": 3,
"script": "MovingFunctions.stdDev(values, MovingFunctions.unweightedAvg(values))"
}
}
}
}
}
}
}
}
},
"post_filter": {
"range": {
"Source Time": {
"gte": "2018-05-01"
}
}
}
}
Here is a snippet of the response:
{
"key": "192.168.0.1",
"doc_count": 6,
"datehisto": {
"buckets": [
{
"key_as_string": "2018-04-25T00:00:00.000Z",
"key": 1524614400000,
"doc_count": 1,
"max_dest_ips": {
"value": 309
},
"max_dest_ips_std_dev": {
"value": null
}
},
{
"key_as_string": "2018-04-26T00:00:00.000Z",
"key": 1524700800000,
"doc_count": 1,
"max_dest_ips": {
"value": 529
},
"max_dest_ips_std_dev": {
"value": 0
}
},
{
"key_as_string": "2018-04-27T00:00:00.000Z",
"key": 1524787200000,
"doc_count": 1,
"max_dest_ips": {
"value": 408
},
"max_dest_ips_std_dev": {
"value": 110
}
},
{
"key_as_string": "2018-04-28T00:00:00.000Z",
"key": 1524873600000,
"doc_count": 1,
"max_dest_ips": {
"value": 187
},
"max_dest_ips_std_dev": {
"value": 89.96419040682551
}
}
]
}
}
What I want is for the first 2 days' bucket data (25th and 26th) to be filtered and removed from the above bucket results. I have tried the post filter above and the normal query filter below:
"filter": {
"range": {
"Source Time": {
"gte": "2018-04-27"
}
}
}
The Post Filter does nothing and doesn't work. The above filter range query makes the buckets start from the 27th but also makes the standard deviation calculations start on 27th as well (resulting in 27th being null and 28th being 0) when I want it to start from the 25th instead.
Any other alternative solutions? Help is greatly appreciated!
I'm working with documents in Elasticsearch that represent Alerts. These Alerts are activated for a time and then deactivated. They are similar to this schema.
{
"id": 189393,
"sensorId": "1111111",
"activationTime": 1462569310000,
"deactivationTime": 1462785524876,
}
I would like to know the number of active alerts per day. To achieve this I want to perform a Date Histogram Aggregation that returns the days between activation and deactivation and the number of active alerts per day.
What I've tried so far is this query.
{
"query" : {
...
},
"aggs": {
"active_alerts": {
"date_histogram": {
"field": "timestamp",
"interval": "day"
}
}
}
}
However, It returns just the day it was activated.
"aggregations": {
"active_alerts": {
"buckets": [
{
"key_as_string": "2016-05-06T00:00:00.000Z",
"key": 1462492800000,
"doc_count": 1
}
]
}
}
Which I'd like to return are the days between activation and deactivation time and the number of active alerts per day, as shown below.
"aggregations": {
"active_alerts": {
"buckets": [
{
"key_as_string": "2016-05-06T00:00:00.000Z",
"key": 1462492800000,
"doc_count": 1
},
{
"key_as_string": "2016-05-07T00:00:00.000Z",
"key": 1462579200000,
"doc_count": 1
},
{
"key_as_string": "2016-05-08T00:00:00.000Z",
"key": 1462665600000,
"doc_count": 1
}
]
}
}
Thanks.
Finally I've found a solution via script, creating one that emits an array of dates from activation date until deactivation date.
"aggs": {
"active_alerts": {
"date_histogram": {
"interval": "day",
"script": "Date d1 = new Date(doc['activationTime'].value); Date d2 = new Date(doc['deactivationTime'].value); List<Date> dates = new ArrayList<Date>(); (d1..d2).each { date-> dates.add(date.toTimestamp().getTime())}; return dates;"
}
}
}
Thanks.
I think you can only do it with scripted dateHistogram where you add the "missing" days from that interval you have programmatically:
"aggs": {
"active_alerts": {
"date_histogram": {
"interval": "day",
"script": "counter=0;combinedDates=[];currentDate=doc.activationTime.date;while(currentDate.isBefore(doc.deactivationTime.date.getMillis())){combinedDates[counter++]=currentDate.getMillis();currentDate.addDays(1)};combinedDates[counter]=doc.deactivationTime.date.getMillis();return combinedDates"
}
}
}
I have an elasticsearch index with the following document:
{
dates: ["2014-01-31","2014-02-01"]
}
I want to count all the instances of all the days in my index separated by year and month. I hoped to do this using a date histogram aggregation (which is successful for counting non-array properties):
{
"from": 0,
"size": 0,
"aggregations": {
"year": {
"date_histogram": {
"field": "dates",
"interval": "1y",
"format": "yyyy"
},
"aggregations": {
"month": {
"date_histogram": {
"field": "dates",
"interval": "1M",
"format": "M"
},
"aggregations": {
"day": {
"date_histogram": {
"field": "dates",
"interval": "1d",
"format": "d"
}
}
}
}
}
}
}
}
However, I get the following aggregation results:
"aggregations": {
"year": {
"buckets": [
{
"key_as_string": "2014",
"key": 1388534400000,
"doc_count": 1,
"month": {
"buckets": [
{
"key_as_string": "1",
"key": 1388534400000,
"doc_count": 1,
"day": {
"buckets": [
{
"key_as_string": "31",
"key": 1391126400000,
"doc_count": 1
},
{
"key_as_string": "1",
"key": 1391212800000,
"doc_count": 1
}
]
}
},
{
"key_as_string": "2",
"key": 1391212800000,
"doc_count": 1,
"day": {
"buckets": [
{
"key_as_string": "31",
"key": 1391126400000,
"doc_count": 1
},
{
"key_as_string": "1",
"key": 1391212800000,
"doc_count": 1
}
]
}
}
]
}
}
]
}
}
The "day" aggregation ignores the bucket of its parent "month" aggregation, so it processes both elements of the array in each bucket, counting each date twice. The results indicate that two dates appear in each month (and four total), which is obviously incorrect.
I've tried reducing my aggregation to a single date histogram (and bucketing the results in java based on the key) but the doc_count returns as one instead of the number of elements in the array (two in my example). Adding a value_count brings me back to my original issue in which documents that overlap multiple buckets have their dates double-counted.
Is there a way to add a filter to the date histogram aggregations or otherwise modify them in order to count the elements in my date arrays correctly? Alternatively, does Elasticsearch have an option to unwind arrays like in MongoDB? I want to avoid using scripting due to security concerns.
Thanks,
Thomas
Im firing a query, only with date_histogram aggregation like below
{
"aggs": {
"mentionsAnalytics": {
"date_histogram": {
"field": "created_at",
"interval": "day"
}
}
}
Now the response Im getting is not sorted according to date. Following is my response
"aggregations": {
"mentionsAnalytics": {
"buckets": [
{
"key_as_string": "2014-10-17T00:00:00.000Z",
"key": 1413504000000,
"doc_count": 2
}
,
{
"key_as_string": "2015-09-07T00:00:00.000Z",
"key": 1441584000000,
"doc_count": 2
}
,
{
"key_as_string": "2015-09-29T00:00:00.000Z",
"key": 1443484800000,
"doc_count": 2
}
,
{
"key_as_string": "2015-11-09T00:00:00.000Z",
"key": 1447027200000,
"doc_count": 4
}
]
}
}
As you can see, the dates are occuring in random order. Can I make it sorted,by modifying any parameters inside the date_histogram aggregation. I dont prefer to give a sort query, as a seperate query. Is that possible?
{
"aggs": {
"mentionsAnalytics": {
"date_histogram": {
"field": "created_at",
"interval": "day",
"order": {
"_key": "desc"
}
}
}
}
The field to be sorted on from the aggregation response should be given inside order and the type of sorting
We are performing 3 level aggregation for a certain date range we require fetching the distinct "Website" name grouped by distinct "HitCount" vale grouped by "DateTime" intervals. Here, histogram aggregation allows us to fetch the interval based documents, however the "key_as_string" of date is always considered from 12 AM instead of the date range time provided in the query. Depending on the interval period value, the day (24 hrs starting from 12 AM of the from time) is divided and aggregation output is given.
For e.g. we have given the from time as "2015-11-10T11:00:00" and To time as "2015-11-13T11:00:00" with interval of 8 hrs
Following is the query used:
{
"size": 0,
"query": {
"filtered": {
"filter": {
"bool": {
"must": [
{
"range": {
"DateTime": {
"from": "2015-11-10T11:00:00",
"to": "2015-11-13T11:00:00"
}
}
}
]
}
}
}
},
"aggs": {
"Website": {
"terms": {
"field": "Website",
"size": 0,
"order": {
"_count": "desc"
}
},
"aggs": {
"HitCount": {
"terms": {
"field": "HitCount",
"size": 0,
"order": {
"_count": "desc"
}
},
"aggs": {
"DateTime": {
"date_histogram": {
"field": "DateTime",
"interval": "8h",
"min_doc_count": 0,
"extended_bounds": {
"min": 1447153200000,
"max": 1447412400000
}
}
}
}
}
}
}
}
}
The query Output wrt 3rd level DateTime aggregation is:
"DateTime": {
"buckets": [
{
"key_as_string": "2015-11-10T08:00:00.000Z",
"key": 1447142400000,
"doc_count": 62698
}
,
{
"key_as_string": "2015-11-10T16:00:00.000Z",
"key": 1447171200000,
"doc_count": 248118
}
,
{
"key_as_string": "2015-11-11T00:00:00.000Z",
"key": 1447200000000,
"doc_count": 224898
}
,
{
"key_as_string": "2015-11-11T08:00:00.000Z",
"key": 1447228800000,
"doc_count": 221663
}
,
{
"key_as_string": "2015-11-11T16:00:00.000Z",
"key": 1447257600000,
"doc_count": 220935
}
,
{
"key_as_string": "2015-11-12T00:00:00.000Z",
"key": 1447286400000,
"doc_count": 219340
}
,
{
"key_as_string": "2015-11-12T08:00:00.000Z",
"key": 1447315200000,
"doc_count": 218452
}
,
{
"key_as_string": "2015-11-12T16:00:00.000Z",
"key": 1447344000000,
"doc_count": 190
}
,
{
"key_as_string": "2015-11-13T00:00:00.000Z",
"key": 1447372800000,
"doc_count": 0
}
,
{
"key_as_string": "2015-11-13T08:00:00.000Z",
"key": 1447401600000,
"doc_count": 0
}
]
}
Expected Output:
Here, we would expect the intervals to be divided and queried as:
2015-11-10T11:00:00 to 2015-11-10T19:00:00
2015-11-10T19:00:00 to 2015-11-11T03:00:00
2015-11-11T03:00:00 to 2015-11-11T11:00:00
2015-11-11T11:00:00 to 2015-11-11T19:00:00
2015-11-11T19:00:00 to 2015-11-12T03:00:00
2015-11-12T03:00:00 to 2015-11-12T11:00:00
2015-11-12T11:00:00 to 2015-11-12T19:00:00
2015-11-12T19:00:00 to 2015-11-13T03:00:00
2015-11-13T03:00:00 to 2015-11-13T11:00:00
ie. the "key_as_string" output value should be 2015-11-10T11:00:00, 2015-11-10T19:00:00, .... and so on
The above is required as we have given a From & to time of 11 AM so that it can be a updated value of every 8 hrs whenever we fire the query rather than getting a fixed range of time for the whole day.
Note: ES 1.7 is used
The documentation explains that you can use the offset parameter.
So
{
"size": 0,
"query": {
"filtered": {
"filter": {
"bool": {
"must": [
{
"range": {
"DateTime": {
"from": "2015-11-10T11:00:00",
"to": "2015-11-13T11:00:00"
}
}
}
]
}
}
}
},
"aggs": {
"Website": {
"terms": {
"field": "Website",
"size": 0,
"order": {
"_count": "desc"
}
},
"aggs": {
"HitCount": {
"terms": {
"field": "HitCount",
"size": 0,
"order": {
"_count": "desc"
}
},
"aggs": {
"DateTime": {
"date_histogram": {
"field": "DateTime",
"interval": "8h",
"min_doc_count": 0,
"offset": "+11h"
}
}
}
}
}
}
}
}