Python calls Elasticsearch to perform addition, deletion, modification and search operations

Basic Operation

1. Connect to the Elasticsearch database

First connect to the Elasticsearch database and then create a custom index

from elasticsearch import Elasticsearch
import random
from elasticsearch import helpers

# Connect to the local Elasticsearch servicees = Elasticsearch(hosts=["http://localhost:9200"])
index_name = "my_index"

# Create an index with an index named "my_index"if (index=index_name):  # If the index exists, delete    (index=index_name)
(index=index_name)  # Create a new index

2. Add random data

Here we create a random data item, including value_num and value_choice items,

# Add random data itemsadd_value_list: list = []
for _ in range(1000):
    num = (1, 100)
    add_value_list.append({
        "_index": index_name,  # Notice!  Parameters determine which index to insert into        "value_num": (1, 100),
        "value_choice": ["c1", 'c2', 'c3'][(0, 2)],
    })
# Insert data in batches(es, add_value_list)

3. Query operation

# Query operation_body_query = {
    "query": {
        "range": {
            "value_num": {
                "gte": 40,  # &gt;= 40
                "lte": 60  # &lt;= 60
            }
        }
    },
    "size": 20,  # Query 20 items}
response = (index=index_name, body=_body_query)
# Print the query resultsfor _hit in response["hits"]["hits"]:
    _value = _hit['_source']
    print("value_num:", _value["value_num"], " value_choice:", _value['value_choice'])

4. Update data items

Here, we will reassign the data from the query data through the document ID and modified data.

# Update operationfor _hit in response["hits"]["hits"]:
    update_body = {"doc": {
        "value_choice": "c4",  # Update the value_choice field to c4    }}
    res = (index=index_name, id=_hit['_id'], body=update_body)

5. Delete data items

# Delete operationfor _hit in response["hits"]["hits"]:
    res = (index=index_name, id=_hit['_id'])

More query methods

1. Query all data

_body_query = {
    "query":{
        "match_all":{}
    }
}

2. Query for a certain value/string: term, match

Match will perform multiple term operations, and the term operation accuracy will be higher

_body_query = {
    "query": {
        "match": {
            "value_choice": "c1"
        }
    }
}

_body_query = {
    "query": {
        "term": {
            "value_choice": "c1"
        }
    }
}

3. If there is one match among multiple options, find it out: terms

_body_query = {
    "query": {
        "terms": {
            "value_choice": ["c1", "c2"],
        }
    }
}

4. Numerical range query: range

Query data >=40 and <=60

_body_query = {
    "query": {
        "range": {
            "value_num": {
                "gte": 40,  # >= 40
                "lte": 60  # <= 60
            }
        }
    }
}

5. Multiple conditions trigger bool at the same time

Boolean query can query multiple conditions at the same time, also known as a combination query. When constructing the dictionary data of the query, query is followed by bool, and then the judgment conditions of bool. There are the following:

filter: filter
Must: Similar to and, all conditions need to be met
Should: Similar to or, as long as one can satisfy
Must_not: Necessary is not satisfied

After writing the judgment conditions, follow the specific details of the query operation in the list of the judgment conditions.

_body_query = {
    "query": {
        "bool": {
            "should": [
                {
                    "match": {"value_choice": "c1"} # value_choice = "c1"
                },
                {
                    "range": {"value_num": {"lte": 50}} # value_num <= 50
                }
            ]
        }
    },
}

6. Specify the number of return values size

Just add the size keyword to the constructed dictionary

_body_query = {
    "query": {
        "range": {
            "value_num": {
                "gte": 40,  # >= 40
                "lte": 60  # <= 60
            }
        }
    },
    "size": 20,
}

7. Return to the specified column _source

_body_query = {
    "query": {
        "range": {
            "value_num": {
                "gte": 40,  # &gt;= 40
                "lte": 60  # &lt;= 60
            }
        }
    },
     "_source": ["value_num"] # Here specify the fields returned}

Complete sample program

from elasticsearch import Elasticsearch
import random
from elasticsearch import helpers

# Connect to the local Elasticsearch servicees = Elasticsearch(hosts=["http://localhost:9200"])
index_name = "my_index"

# Create an index with an index named "my_index"if (index=index_name):  # If the index exists, delete    (index=index_name)
(index=index_name)  # Create a new index
# Generate random dataadd_value_list: list = []
for _ in range(1000):
    num = (1, 100)
    add_value_list.append({
        "_index": index_name,  # Notice!  Parameters determine which index to insert into        "value_num": (1, 100),
        "value_choice": ["c1", 'c2', 'c3'][(0, 2)],
    })
# Insert data in batches(es, add_value_list)

# ================================================_body_query = {
    "query": {
        "range": {
            "value_num": {
                "gte": 40,  # &gt;= 40
                "lte": 60  # &lt;= 60
            }
        }
    },
    "size": 20,
}

response = (index=index_name, body=_body_query)  # Query 10 items# Print the query resultsfor _hit in response["hits"]["hits"]:
    _value = _hit['_source']
    print("value_num:", _value["value_num"], " value_choice:", _value['value_choice'])

# Update operationfor _hit in response["hits"]["hits"]:
    update_body = {"doc": {
        "value_choice": "c4",  # Update the value_choice field to c4    }}
    res = (index=index_name, id=_hit['_id'], body=update_body)

# Delete operationfor _hit in response["hits"]["hits"]:
    res = (index=index_name, id=_hit['_id'])

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