WARNING: Version 6.2 of Kibana has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Querying Elasticsearch
editQuerying Elasticsearch
editBy default, Vega’s data element
can use embedded and external data with a "url"
parameter. Kibana adds support for the direct Elasticsearch queries by overloading
the "url"
value.
Here is an example of an Elasticsearch query that counts the number of documents in all indexes. The query uses @timestamp field to filter the time range, and break it into histogram buckets.
// An object instead of a string for the url value // is treated as a context-aware Elasticsearch query. url: { // Filter the time picker (upper right corner) with this field %timefield%: @timestamp // Apply dashboard context filters when set %context%: true // Which indexes to search index: _all // The body element may contain "aggs" and "query" subfields body: { aggs: { time_buckets: { date_histogram: { // Use date histogram aggregation on @timestamp field field: @timestamp // interval value will depend on the daterange picker // Use an integer to set approximate bucket count interval: { %autointerval%: true } // Make sure we get an entire range, even if it has no data extended_bounds: { min: { %timefilter%: "min" } max: { %timefilter%: "max" } } // Use this for linear (e.g. line, area) graphs // Without it, empty buckets will not show up min_doc_count: 0 } } } // Speed up the response by only including aggregation results size: 0 } }
The full result has this kind of structure:
{ "aggregations": { "time_buckets": { "buckets": [{ "key_as_string": "2015-11-30T22:00:00.000Z", "key": 1448920800000, "doc_count": 28 }, { "key_as_string": "2015-11-30T23:00:00.000Z", "key": 1448924400000, "doc_count": 330 }, ...
Note that "key"
is a unix timestamp, and can be used without conversions by the
Vega date expressions.
For most graphs we only need the list of the bucket values, so we use format: {property: "aggregations.time_buckets.buckets"}
expression to focus on just the data we need.
Query may be specified with individual range and dashboard context as
well. This query is equivalent to "%context%": true, "%timefield%": "@timestamp"
,
except that the timerange is shifted back by 10 minutes:
{ body: { query: { bool: { must: [ // This string will be replaced // with the auto-generated "MUST" clause "%dashboard_context-must_clause%" { range: { // apply timefilter (upper right corner) // to the @timestamp variable @timestamp: { // "%timefilter%" will be replaced with // the current values of the time filter // (from the upper right corner) "%timefilter%": true // Only work with %timefilter% // Shift current timefilter by 10 units back shift: 10 // week, day (default), hour, minute, second unit: minute } } } ] must_not: [ // This string will be replaced with // the auto-generated "MUST-NOT" clause "%dashboard_context-must_not_clause%" ] } } } }
The "%timefilter%"
can also be used to specify a single min or max
value. As shown above, the date_histogram’s extended_bounds
can be set
with two values - min and max. Instead of hardcoding a value, you may
use "min": {"%timefilter%": "min"}
, which will be replaced with the
beginning of the current time range. The shift
and unit
values are
also supported. The "interval"
can also be set dynamically, depending
on the currently picked range: "interval": {"%autointerval%": 10}
will
try to get about 10-15 data points (buckets).