Kibana是一个开源的数据可视化和探索工具,通常与Elasticsearch一起使用。通过REST API使用Kibana查询实际上是通过Elasticsearch的查询API来执行搜索操作,因为Kibana本身是基于Elasticsearch构建的。
{
"query": {
"match": {
"field_name": "search_term"
}
}
}
{
"query": {
"bool": {
"must": [
{ "match": { "field1": "value1" } },
{ "match": { "field2": "value2" } }
],
"filter": [
{ "range": { "timestamp": { "gte": "now-1d/d" } } }
]
}
}
}
{
"size": 0,
"aggs": {
"group_by_field": {
"terms": {
"field": "category.keyword",
"size": 10
}
}
}
}
原因:
解决方案:
GET /index_name/_mapping
explain: true
参数查看评分细节原因:
解决方案:
原因:
解决方案:
from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search
client = Elasticsearch(['http://localhost:9200'])
s = Search(using=client, index="your_index") \
.query("match", title="python") \
.filter("range", date={"gte": "2023-01-01"}) \
.aggregations.bucket("per_category", "terms", field="category.keyword")
response = s.execute()
for hit in response:
print(hit.title)
for category in response.aggregations.per_category.buckets:
print(f"{category.key}: {category.doc_count}")
curl -X POST "http://localhost:9200/your_index/_search" -H 'Content-Type: application/json' -d'
{
"query": {
"bool": {
"must": [
{ "match": { "title": "search term" } }
],
"filter": [
{ "term": { "status": "published" } }
]
}
},
"aggs": {
"popular_tags": {
"terms": { "field": "tags.keyword" }
}
}
}
'
通过掌握这些技术,您可以有效地通过REST API在Kibana/Elasticsearch环境中执行各种查询操作。
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