ElasticSearch快速入门

ElasticSearch

概念

Elasticsearch（简称 ES）是一个开源的分布式搜索和分析引擎，被广泛运用于全文检索、日志分析、安全监控和人工智能应用中。它旨在帮助用户从海量数据中快速、近乎实时地获取有价值的洞察。

要理解 Elasticsearch，可以抓住以下几个核心概念：

概念 通俗类比 简要说明
索引 (Index) 数据库中的“库”或“表” 拥有相似结构文档的集合，是检索和管理数据的逻辑单元。
文档 (Document) 表中的一行数据 搜索和存储的最小数据单元，格式为灵活的 JSON 对象。
节点 (Node) 集群中的一台服务器 集群中的一个服务器实例，用于存储数据并参与集群的索引与搜索功能。
集群 (Cluster) 多台服务器组成的整体 由一个或多个节点组成，对外提供统一服务，数据在整个集群中分布式存储。
分片 (Shard) 将数据切分成多块 索引被拆分为多个分片，分布在不同节点上，实现水平扩展和并行处理。
副本 (Replica) 数据的备份 分片的完整拷贝，用于提高数据可用性和搜索的并发吞吐量。

ELK 是 Elasticsearch、Logstash、Kibana 三个开源组件的首字母缩写，组合起来形成一套日志收集、存储、分析、可视化的完整解决方案。它的核心价值是：让散落在各台服务器上的日志被集中管理，并且能快速搜索和分析。

安装(linux)

# 以 arch为例

paru -S elasticsearch
paru -S kibana

配置

Kibana 是一个开源的数据可视化和探索工具，专门用于对存储在 Elasticsearch 中的数据进行搜索、分析和图表展示。

简单理解它的角色：

  • Elasticsearch = 存放大量数据的搜索引擎（后端）

  • Kibana = 用户操作数据的图形化界面（前端）

  • 配置elasticsearch:

# ======================== Elasticsearch Configuration =========================
#
# NOTE: Elasticsearch comes with reasonable defaults for most settings.
#       Before you set out to tweak and tune the configuration, make sure you
#       understand what are you trying to accomplish and the consequences.
#
# The primary way of configuring a node is via this file. This template lists
# the most important settings you may want to configure for a production cluster.
#
# Please consult the documentation for further information on configuration options:
# https://www.elastic.co/guide/en/elasticsearch/reference/index.html
#
# ---------------------------------- Cluster -----------------------------------
#
# Use a descriptive name for your cluster:
#
cluster.name: elasticsearch
#
# ------------------------------------ Node ------------------------------------
#
# Use a descriptive name for the node:
#
#node.name: node-1
#
# Add custom attributes to the node:
#
#node.attr.rack: r1
#
# ----------------------------------- Paths ------------------------------------
#
# Path to directory where to store the data (separate multiple locations by comma):
#
path.data: /var/lib/elasticsearch
#
# Path to log files:
#
path.logs: /var/log/elasticsearch
#
# ----------------------------------- Memory -----------------------------------
#
# Lock the memory on startup:
#
#bootstrap.memory_lock: true
#
# Make sure that the heap size is set to about half the memory available
# on the system and that the owner of the process is allowed to use this
# limit.
#
# Elasticsearch performs poorly when the system is swapping the memory.
#
# ---------------------------------- Network -----------------------------------
#
# By default Elasticsearch is only accessible on localhost. Set a different
# address here to expose this node on the network:
#
network.host: 127.0.0.1
#
# By default Elasticsearch listens for HTTP traffic on the first free port it
# finds starting at 9200. Set a specific HTTP port here:
#
http.port: 9200
#
# For more information, consult the network module documentation.
#
# --------------------------------- Discovery ----------------------------------
#
# Pass an initial list of hosts to perform discovery when this node is started:
# The default list of hosts is ["127.0.0.1", "[::1]"]
#
#discovery.seed_hosts: ["host1", "host2"]
#
# Bootstrap the cluster using an initial set of master-eligible nodes:
#
#cluster.initial_master_nodes: ["node-1", "node-2"]
#
# For more information, consult the discovery and cluster formation module documentation.
#
# ---------------------------------- Various -----------------------------------
#
# Allow wildcard deletion of indices:
#
#action.destructive_requires_name: false

#----------------------- BEGIN SECURITY AUTO CONFIGURATION -----------------------
#
# The following settings, TLS certificates, and keys have been automatically      
# generated to configure Elasticsearch security features on 04-06-2026 02:39:18
#
# --------------------------------------------------------------------------------

# Enable security features
xpack.security.enabled: false

xpack.security.enrollment.enabled: false

# Enable encryption for HTTP API client connections, such as Kibana, Logstash, and Agents
xpack.security.http.ssl:
  enabled: false
  # keystore.path: certs/http.p12

# Enable encryption and mutual authentication between cluster nodes
xpack.security.transport.ssl:
  enabled: false
  # verification_mode: certificate
  # keystore.path: certs/transport.p12
  # truststore.path: certs/transport.p12
# Create a new cluster with the current node only
# Additional nodes can still join the cluster later
cluster.initial_master_nodes: ["archlinux"]

# Allow HTTP API connections from anywhere
# Connections are encrypted and require user authentication
http.host: 0.0.0.0

# Allow other nodes to join the cluster from anywhere
# Connections are encrypted and mutually authenticated
#transport.host: 0.0.0.0

#----------------------- END SECURITY AUTO CONFIGURATION -------------------------
  • kibana
# For more configuration options see the configuration guide for Kibana in
# https://www.elastic.co/guide/index.html

# =================== System: Kibana Server ===================
# Kibana is served by a back end server. This setting specifies the port to use.
server.port: 5601

# Specifies the address to which the Kibana server will bind. IP addresses and host names are both valid values.
# The default is 'localhost', which usually means remote machines will not be able to connect.
# To allow connections from remote users, set this parameter to a non-loopback address.
server.host: "0.0.0.0"

# Enables you to specify a path to mount Kibana at if you are running behind a proxy.
# Use the `server.rewriteBasePath` setting to tell Kibana if it should remove the basePath
# from requests it receives, and to prevent a deprecation warning at startup.
# This setting cannot end in a slash.
#server.basePath: ""

# Specifies whether Kibana should rewrite requests that are prefixed with
# `server.basePath` or require that they are rewritten by your reverse proxy.
# Defaults to `false`.
#server.rewriteBasePath: false

# Specifies the public URL at which Kibana is available for end users. If
# `server.basePath` is configured this URL should end with the same basePath.
#server.publicBaseUrl: ""

# The maximum payload size in bytes for incoming server requests.
#server.maxPayload: 1048576

# The Kibana server's name. This is used for display purposes.
#server.name: "your-hostname"

# Block requests to specific routes (exact path match, evaluated before auth).
# server.excludeRoutes: ["/api/status"]

# =================== System: Kibana Server (Optional) ===================
# Enables SSL and paths to the PEM-format SSL certificate and SSL key files, respectively.
# These settings enable SSL for outgoing requests from the Kibana server to the browser.
#server.ssl.enabled: false
#server.ssl.certificate: /path/to/your/server.crt
#server.ssl.key: /path/to/your/server.key

# =================== System: Elasticsearch ===================
# The URLs of the Elasticsearch instances to use for all your queries.
elasticsearch.hosts: ["http://localhost:9200"]

# If your Elasticsearch is protected with basic authentication, these settings provide
# the username and password that the Kibana server uses to perform maintenance on the Kibana
# index at startup. Your Kibana users still need to authenticate with Elasticsearch, which
# is proxied through the Kibana server.
#elasticsearch.username: "kibana_system"
#elasticsearch.password: "pass"

# Kibana can also authenticate to Elasticsearch via "service account tokens".
# Service account tokens are Bearer style tokens that replace the traditional username/password based configuration.
# Use this token instead of a username/password.
# elasticsearch.serviceAccountToken: "my_token"

# Time in milliseconds to wait for responses from the back end or Elasticsearch. This value
# must be a positive integer.
#elasticsearch.requestTimeout: 30000

# The maximum number of sockets that can be used for communications with elasticsearch.
# Defaults to `800`.
#elasticsearch.maxSockets: 1024

# Specifies whether Kibana should use compression for communications with elasticsearch
# Defaults to `false`.
#elasticsearch.compression: false

# List of Kibana client-side headers to send to Elasticsearch. To send *no* client-side
# headers, set this value to [] (an empty list).
#elasticsearch.requestHeadersWhitelist: [ authorization ]

# Header names and values that are sent to Elasticsearch. Any custom headers cannot be overwritten
# by client-side headers, regardless of the elasticsearch.requestHeadersWhitelist configuration.
#elasticsearch.customHeaders: {}

# Time in milliseconds for Elasticsearch to wait for responses from shards. Set to 0 to disable.
#elasticsearch.shardTimeout: 30000

# =================== System: Elasticsearch (Optional) ===================
# These files are used to verify the identity of Kibana to Elasticsearch and are required when
# xpack.security.http.ssl.client_authentication in Elasticsearch is set to required.
#elasticsearch.ssl.certificate: /path/to/your/client.crt
#elasticsearch.ssl.key: /path/to/your/client.key

# Enables you to specify a path to the PEM file for the certificate
# authority for your Elasticsearch instance.
#elasticsearch.ssl.certificateAuthorities: [ "/path/to/your/CA.pem" ]

# To disregard the validity of SSL certificates, change this setting's value to 'none'.
elasticsearch.ssl.verificationMode: none

# =================== System: Logging ===================
# Set the value of this setting to off to suppress all logging output, or to debug to log everything. Defaults to 'info'
#logging.root.level: debug

# Enables you to specify a file where Kibana stores log output.
#logging.appenders.default:
#  type: file
#  fileName: /var/logs/kibana.log
#  layout:
#    type: json

# Example with size based log rotation
#logging.appenders.default:
#  type: rolling-file
#  fileName: /var/logs/kibana.log
#  policy:
#    type: size-limit
#    size: 256mb
#  strategy:
#    type: numeric
#    max: 10
#  layout:
#    type: json

# Logs queries sent to Elasticsearch.
#logging.loggers:
#  - name: elasticsearch.query
#    level: debug

# Logs http responses.
#logging.loggers:
#  - name: http.server.response
#    level: debug

# Logs system usage information.
#logging.loggers:
#  - name: metrics.ops
#    level: debug

# Enables debug logging on the browser (dev console)
#logging.browser.root:
#  level: debug

# =================== System: Other ===================
# The path where Kibana stores persistent data not saved in Elasticsearch. Defaults to data
#path.data: data

# Specifies the path where Kibana creates the process ID file.
#pid.file: /run/kibana/kibana.pid

# Set the interval in milliseconds to sample system and process performance
# metrics. Minimum is 100ms. Defaults to 5000ms.
#ops.interval: 5000

# Specifies locale to be used for all localizable strings, dates and number formats.
# Supported languages are the following: English (default) "en", Chinese "zh-CN", Japanese "ja-JP", French "fr-FR", German "de-DE".
#i18n.locale: "en"

# =================== Frequently used (Optional)===================

# =================== Saved Objects: Migrations ===================
# Saved object migrations run at startup. If you run into migration-related issues, you might need to adjust these settings.

# The number of documents migrated at a time.
# If Kibana can't start up or upgrade due to an Elasticsearch `circuit_breaking_exception`,
# use a smaller batchSize value to reduce the memory pressure. Defaults to 1000 objects per batch.
#migrations.batchSize: 1000

# The maximum payload size for indexing batches of upgraded saved objects.
# To avoid migrations failing due to a 413 Request Entity Too Large response from Elasticsearch.
# This value should be lower than or equal to your Elasticsearch cluster’s `http.max_content_length`
# configuration option. Default: 100mb
#migrations.maxBatchSizeBytes: 100mb

# The number of times to retry temporary migration failures. Increase the setting
# if migrations fail frequently with a message such as `Unable to complete the [...] step after
# 15 attempts, terminating`. Defaults to 15
#migrations.retryAttempts: 15

# =================== Search Autocomplete ===================
# Time in milliseconds to wait for autocomplete suggestions from Elasticsearch.
# This value must be a whole number greater than zero. Defaults to 1000ms
# unifiedSearch.autocomplete.valueSuggestions.timeout: 1000

# Maximum number of documents loaded by each shard to generate autocomplete suggestions.
# This value must be a whole number greater than zero. Defaults to 100_000
#unifiedSearch.autocomplete.valueSuggestions.terminateAfter: 100000

快速入门

​ kibana的默认端口号是5601,直接浏览器输入127.0.0.1:5601访问,然后接入本地的elasticsearch服务,端口号是9200.
​ 之后搜索框搜索dev tools,然后就可以图形化界面进行操作.

索引创建

这段代码创建一个名为 hotel 的索引，并定义其字段映射。

  • PUT /hotel向ES发送一个HTTP的PUT请求,创建一个叫hotel的索引.

  • mappings相当于关系型数据库的表结构定义.

  • properties是一个固定关键字,用来生命文档类型或索引内部包含那些字段,以及每个字段的类型和配置.

  • name/city/price是字段.其中的type代表其类型.

PUT /hotel
{
    "mappings":{
        "properties": {
            "name":{
                "type":"text"
            },
            "city":{
                "type":"keyword"
            },
            "price":{
                "type":"double"            }
        }
    }
}

添加数据

  • hotel指定要插入的索引
  • _doc是文档类型,ES 7.x之后固定写_doc
  • 001手动指定ID为001.指定的话,ES会自动生成随机ID
PUT /hotel/_doc/001
{
    "name":"大胖熊",
    "city":"杭州",
    "price":999.999
}

{
  "_index": "hotel",
  "_id": "001",
  "_version": 1,
  "result": "created",
  "_shards": {
    "total": 2,
    "successful": 1,
    "failed": 0
  },
  "_seq_no": 0,
  "_primary_term": 1
}

搜索文档

  1. 根据_id搜索文档
GET /hotel/_doc/001
  1. 根据普通字段进行搜索文档
GET /hotel/_search
{
    "query":{
        "term":{
            "city":{
                value:"杭州"
            }
        }
    }
}

{
  "took": 3,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 0.2876821,
    "hits": [
      {
        "_index": "hotel",
        "_id": "001",
        "_score": 0.2876821,
        "_source": {
          "name": "大胖熊",
          "city": "杭州",
          "price": 999.999
        }
      }
    ]
  }
}

批量插入

  • _bulk指定要批量插入
  • {"index":{}}:告诉ES这是索引插入操作

也可以指定文档ID比如{"index":{"_id":"002"}}

// 其他操作类型
{"create": {}}     // 插入（如果ID已存在则失败）
{"update": {}}     // 更新
{"delete": {}}     // 删除（只需要操作行，不需要数据行）

PUT /hotel/_bulk
{"index":{}}
{"name":"哈哈","city":"三亚","price":2412.34}
{"index":{}}
{"name":"哈asd哈","city":"2三亚","price":234412.3423}

条件删除

  • _delete_by_query:表示根据下面的条件进行删除文档
POST /hotel/_delete_by_query
{
    "query":{
        "term":{
            "city":{
                "value":"三亚"
            }
        }
    }
}

{
  "took": 31,
  "timed_out": false,
  "total": 1,
  "deleted": 1,
  "batches": 1,
  "version_conflicts": 0,
  "noops": 0,
  "retries": {
    "bulk": 0,
    "search": 0
  },
  "throttled_millis": 0,
  "requests_per_second": -1,
  "throttled_until_millis": 0,
  "failures": []
}

删除索引

DELETE hotel

{
  "acknowledged": true
}

Python API

安装包

uv add elasticsearch

基本API使用

连接

from elasticsearch import Elasticsearch
es = Elasticsearch("http://localhost:9200")

# 如果存在这个索引,先删除同时忽略掉404错误
es.indices.delete(index="py_index01",ingore=404)

创建索引

es.indices.create(index='py_index01',ignore=400)

插入数据

body = {
    "name":"tommy",
    "age" :20,
    "city":"shenzhen",
    "hobbies":"singing,dancing,reading"
}
es.index(index="py_index01",id=1,body=body)

查询

  • 全部查询
es.search(index="py_index01",query={"match_all":{}})
  • 普通查询
# q和q2写法都可以,不过如果关键词参数是body,只能是q完整的写法
q = {
    "term":{
        "name":{
            "value":"asd"
        }
    }
}
# 这里的关键词参数使用query之后,内部可以简略写法.
q2 = {
    "term":{
        "name":"asd"
    }
}
es.search(index="py_index01",query=q2)
  • 多词精确查询
query ={
    "terms":{
        "name":["asd","tomasdmy"]
    }
}
es.search(index="py_index01",query=query)
  • 范围查询
# 范围查询
"""
gt ,gte ,lt ,lte
"""
query = {
    "range":{
        "age":{
            "gt":10
        }
    }
}
es.search(index='py_index01',query=query)
  • exists/missing查询
# exists/missing

query = {
    "exists":{
        "field":"name"
    }
}

es.search(index="py_index01",query=query)
  • bool 过滤
# bool 过滤
"""
must:and
must_not : not
should:or
"""

query = {
    "bool":{
        "must":[
            {"term":{"name":"asd"},}
            {"term":{"age":22}}
        ]
    }
}
es.search(index="py_index01",query=query)
  • 多条件查询
"""
t1:name!=asd
t2:age 
t3:age=22
"""
query = {
    "bool":{
        "must":[
            {"term":{"name":"asd"}},
            {"term":{"Age":22}},
            {"exists":{"field":"age"}}
        ]
    }
}
es.search(index="py_index01",query=query)
  • multi_search
# multi_search,多个字段中分词查询
query = {
    "multi_match":{
        "query":"reading",
        "fields":["name","hobbies"]
    }
}
es.search(index='py_index01',query=query)
  • 通配符查询
# wildcard
query = {
    "wildcard":{
        "name":"a*"
    }
}

es.search(index="py_index01",query=query)
  • regexp
# regexp
query = {
    "regexp":{
        "name":".*mm.*"
    }
}

es.search(index='py_index01',query = query)
  • prefix
# prefix
query = {
    "prefix":{
        "name":"a"
    }
}
es.search(index='py_index01',query = query)
  • phrase_search
# phrass match
query = {
    "match_phrase":{
        "hobbies":"dancing"
    }
}
es.search(index='py_index01',query = query)

删除数据

  • id指定删除
# id 删除数据
es.delete(index='py_index01',id=1)
  • 条件删除
# delete_bu_query
query = {
    "match":{
        "name":"asd"
    }
}
es.delete_by_query(index="py_index01",query = query)

索引删除

es.indices.delete(index='py_index01')

向量操作

  • 批量插入操作

_index:要操作的索引

_source:要插入的数据

from elasticsearch import helpers

# actions = [
#     {"index": {"_index": "vector_index"}},
#     {"name": "苹果", "embedding": [0.2, 0.1, 0.4]},
#     {"index": {"_index": "vector_index"}},
#     {"name": "小船", "embedding": [0.7, 0.2, 0.6]},
#     {"index": {"_index": "vector_index"}},
#     {"name": "香蕉", "embedding": [0.3, 0.1, 0.3]},
# ]

actions = [
    {
        "_index":"vector_index",
        "_source":{
            "name":"苹果",
            "embedding":[0.2,0.1,0.4]
        }
    },
    {
        "_index":"vector_index",
        "_source":{
            "name":"小船",
            "embedding":[0.7,0.2,0.6]
        }
    },
    {
        "_index":"vector_index",
        "_source":{
            "name":"香蕉",
            "embedding":[0.3,0.1,0.3]
        }
    },
]
helpers.bulk(es,actions)
  • 查询所有记录
es.search(index='vector_index',body={"query":{"match_all":{}}})
  • 向量相似性计算

script_score:模板写法,通过脚本计算相似性

match_all:匹配所有文档,也可以使用前面的bool结合布尔查询,或term等进行特定类型搜索

script:下面是使用的脚本内容,其中source是脚本内容,params是参数.也可以对source使用""" """多行代码

# 向量相似性计算
temp = '橘子'
temp_emb = [0.2,0.2,0.4]

query = {
    "query" : {
        "script_score" : {
            "query":{
                "match_all":{}
            },
            "script":{
                "source":"cosineSimilarity(params.queryVector,'embedding') + 1.0",
                "params":{
                    "queryVector":temp_emb
                }
            }
        }
    }
}          

query2 = {
    "size": 2,  # 返回前10个最相似的结果
    "query": {
        "script_score": {
            "query": {"match_all": {}},
            "script": {
                "source": """
                    double sim = cosineSimilarity(params.queryVector, 'embedding');
                    return Math.max(0, sim);  // 或 sim + 1.0
                """,
                "params": {"queryVector": temp_emb}
            }
        }
    }
}  
response = es.search(index="vector_index",body=query2)

response['hits']['hits']
posted @ 2026-06-05 15:11  大胖熊哈  阅读(8)  评论(0)    收藏  举报