如何使用python生成大量數(shù)據(jù)寫(xiě)入es數(shù)據(jù)庫(kù)并查詢操作

更新時(shí)間：2022年09月15日 16:19:11 作者：IT之一小佬

這篇文章主要介紹了如何使用python生成大量數(shù)據(jù)寫(xiě)入es數(shù)據(jù)庫(kù)并查詢操作，文章圍繞主題展開(kāi)詳細(xì)的內(nèi)容介紹，具有一定的參考價(jià)值，需要的小伙伴可以參考一下

前言：

模擬學(xué)生成績(jī)信息寫(xiě)入es數(shù)據(jù)庫(kù)，包括姓名、性別、科目、成績(jī)。

示例代碼1：【一次性寫(xiě)入10000*1000條數(shù)據(jù)】【本人親測(cè)耗時(shí)5100秒】

from elasticsearch import Elasticsearch
from elasticsearch import helpers
import random
import time
es = Elasticsearch(hosts='http://127.0.0.1:9200')
# print(es)
 
names = ['劉一', '陳二', '張三', '李四', '王五', '趙六', '孫七', '周八', '吳九', '鄭十']
sexs = ['男', '女']
subjects = ['語(yǔ)文', '數(shù)學(xué)', '英語(yǔ)', '生物', '地理']
grades = [85, 77, 96, 74, 85, 69, 84, 59, 67, 69, 86, 96, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]
datas = []
 
start = time.time()
# 開(kāi)始批量寫(xiě)入es數(shù)據(jù)庫(kù)
# 批量寫(xiě)入數(shù)據(jù)
for j in range(1000):
    print(j)
    action = [
        {
            "_index": "grade",
            "_type": "doc",
            "_id": i,
            "_source": {
                "id": i,
                "name": random.choice(names),
                "sex": random.choice(sexs),
                "subject": random.choice(subjects),
                "grade": random.choice(grades)
            }
        } for i in range(10000 * j, 10000 * j + 10000)
    ]
    helpers.bulk(es, action)
end = time.time()
print('花費(fèi)時(shí)間：', end - start)

elasticsearch-head中顯示：

示例代碼2：【一次性寫(xiě)入10000*5000條數(shù)據(jù)】【本人親測(cè)耗時(shí)23000秒】

from elasticsearch import Elasticsearch
from elasticsearch import helpers
import random
import time
 
es = Elasticsearch(hosts='http://127.0.0.1:9200')
# print(es)
names = ['劉一', '陳二', '張三', '李四', '王五', '趙六', '孫七', '周八', '吳九', '鄭十']
sexs = ['男', '女']
subjects = ['語(yǔ)文', '數(shù)學(xué)', '英語(yǔ)', '生物', '地理']
grades = [85, 77, 96, 74, 85, 69, 84, 59, 67, 69, 86, 96, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]
datas = []
start = time.time()
# 開(kāi)始批量寫(xiě)入es數(shù)據(jù)庫(kù)
# 批量寫(xiě)入數(shù)據(jù)
for j in range(5000):
    print(j)
    action = [
        {
            "_index": "grade3",
            "_type": "doc",
            "_id": i,
            "_source": {
                "id": i,
                "name": random.choice(names),
                "sex": random.choice(sexs),
                "subject": random.choice(subjects),
                "grade": random.choice(grades)
            }
        } for i in range(10000 * j, 10000 * j + 10000)
    ]
    helpers.bulk(es, action)
end = time.time()
print('花費(fèi)時(shí)間：', end - start)

示例代碼3：【一次性寫(xiě)入10000*9205條數(shù)據(jù)】【耗時(shí)過(guò)長(zhǎng)】

from elasticsearch import Elasticsearch
from elasticsearch import helpers
import random
import time
 
es = Elasticsearch(hosts='http://127.0.0.1:9200')
names = ['劉一', '陳二', '張三', '李四', '王五', '趙六', '孫七', '周八', '吳九', '鄭十']
sexs = ['男', '女']
subjects = ['語(yǔ)文', '數(shù)學(xué)', '英語(yǔ)', '生物', '地理']
grades = [85, 77, 96, 74, 85, 69, 84, 59, 67, 69, 86, 96, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]
datas = []
 
start = time.time()
# 開(kāi)始批量寫(xiě)入es數(shù)據(jù)庫(kù)
# 批量寫(xiě)入數(shù)據(jù)
for j in range(9205):
    print(j)
    action = [
        {
            "_index": "grade2",
            "_type": "doc",
            "_id": i,
            "_source": {
                "id": i,
                "name": random.choice(names),
                "sex": random.choice(sexs),
                "subject": random.choice(subjects),
                "grade": random.choice(grades)
            }
        } for i in range(10000*j, 10000*j+10000)
    ]
    helpers.bulk(es, action)
end = time.time()
print('花費(fèi)時(shí)間：', end - start)

查詢數(shù)據(jù)并計(jì)算各種方式的成績(jī)總分。

示例代碼4：【一次性獲取所有的數(shù)據(jù)，在程序中分別計(jì)算所耗的時(shí)間】

from elasticsearch import Elasticsearch
import time
def search_data(es, size=10):
    query = {
        "query": {
            "match_all": {}
        }
    }
    res = es.search(index='grade', body=query, size=size)
    # print(res)
    return res
if __name__ == '__main__':
    start = time.time()
    es = Elasticsearch(hosts='http://192.168.1.1:9200')
    # print(es)
    size = 10000
    res = search_data(es, size)
    # print(type(res))
    # total = res['hits']['total']['value']
    # print(total)
    all_source = []
    for i in range(size):
        source = res['hits']['hits'][i]['_source']
        all_source.append(source)
        # print(source)
 
    # 統(tǒng)計(jì)查詢出來(lái)的所有學(xué)生的所有課程的所有成績(jī)的總成績(jī)
    start1 = time.time()
    all_grade = 0
    for data in all_source:
        all_grade += int(data['grade'])
    print('所有學(xué)生總成績(jī)之和：', all_grade)
    end1 = time.time()
    print("耗時(shí)：", end1 - start1)
 
    # 統(tǒng)計(jì)查詢出來(lái)的每個(gè)學(xué)生的所有課程的所有成績(jī)的總成績(jī)
    start2 = time.time()
    names1 = []
    all_name_grade = {}
    for data in all_source:
        if data['name'] in names1:
            all_name_grade[data['name']] += data['grade']
        else:
            names1.append(data['name'])
            all_name_grade[data['name']] = data['grade']
    print(all_name_grade)
    end2 = time.time()
    print("耗時(shí)：", end2 - start2)
 
    # 統(tǒng)計(jì)查詢出來(lái)的每個(gè)學(xué)生的每門課程的所有成績(jī)的總成績(jī)
    start3 = time.time()
    names2 = []
    subjects = []
    all_name_all_subject_grade = {}
    for data in all_source:
        if data['name'] in names2:
            if all_name_all_subject_grade[data['name']].get(data['subject']):
                all_name_all_subject_grade[data['name']][data['subject']] += data['grade']
            else:
                all_name_all_subject_grade[data['name']][data['subject']] = data['grade']
        else:
            names2.append(data['name'])
            all_name_all_subject_grade[data['name']] = {}
            all_name_all_subject_grade[data['name']][data['subject']] = data['grade']
    print(all_name_all_subject_grade)
    end3 = time.time()
    print("耗時(shí)：", end3 - start3)
    end = time.time()
    print('總耗時(shí)：', end - start)

運(yùn)行結(jié)果：

在示例代碼4中當(dāng)把size由10000改為 2000000時(shí)，運(yùn)行效果如下所示：

在項(xiàng)目中一般不用上述代碼4中所統(tǒng)計(jì)成績(jī)的方法，面對(duì)大量的數(shù)據(jù)是比較耗時(shí)的，要使用es中的聚合查詢。計(jì)算數(shù)據(jù)中所有成績(jī)之和。

示例代碼5：【使用普通計(jì)算方法和聚類方法做對(duì)比驗(yàn)證】

from elasticsearch import Elasticsearch
import time
def search_data(es, size=10):
    query = {
        "query": {
            "match_all": {}
        }
    }
    res = es.search(index='grade', body=query, size=size)
    # print(res)
    return res
 
def search_data2(es, size=10):
    query = {
        "aggs": {
            "all_grade": {
                "terms": {
                    "field": "grade",
                    "size": 1000
                }
            }
        }
    }
    res = es.search(index='grade', body=query, size=size)
    # print(res)
    return res
 
 if __name__ == '__main__':
    start = time.time()
    es = Elasticsearch(hosts='http://127.0.0.1:9200')
    size = 2000000
    res = search_data(es, size)
    all_source = []
    for i in range(size):
        source = res['hits']['hits'][i]['_source']
        all_source.append(source)
        # print(source)
 
    # 統(tǒng)計(jì)查詢出來(lái)的所有學(xué)生的所有課程的所有成績(jī)的總成績(jī)
    start1 = time.time()
    all_grade = 0
    for data in all_source:
        all_grade += int(data['grade'])
    print('200萬(wàn)數(shù)據(jù)所有學(xué)生總成績(jī)之和：', all_grade)
    end1 = time.time()
    print("耗時(shí)：", end1 - start1)
 
    end = time.time()
    print('200萬(wàn)數(shù)據(jù)總耗時(shí)：', end - start)
 
    # 聚合操作
    start_aggs = time.time()
    es = Elasticsearch(hosts='http://127.0.0.1:9200')
    # size = 2000000
    size = 0
    res = search_data2(es, size)
    # print(res)
    aggs = res['aggregations']['all_grade']['buckets']
    print(aggs)
 
    sum = 0
    for agg in aggs:
        sum += (agg['key'] * agg['doc_count'])
 
    print('1000萬(wàn)數(shù)據(jù)總成績(jī)之和:', sum)
    end_aggs = time.time()
    print('1000萬(wàn)數(shù)據(jù)總耗時(shí)：', end_aggs - start_aggs)

運(yùn)行結(jié)果：

計(jì)算數(shù)據(jù)中每個(gè)同學(xué)的各科總成績(jī)之和。

示例代碼6： 【子聚合】【先分組，再計(jì)算】

from elasticsearch import Elasticsearch
import time
def search_data(es, size=10):
    query = {
        "query": {
            "match_all": {}
        }
    }
    res = es.search(index='grade', body=query, size=size)
    # print(res)
    return res
 def search_data2(es):
    query = {
        "size": 0,
        "aggs": {
            "all_names": {
                "terms": {
                    "field": "name.keyword",
                    "size": 10
                },
                "aggs": {
                    "total_grade": {
                        "sum": {
                            "field": "grade"
                        }
                    }
                }
            }
        }
    }
    res = es.search(index='grade', body=query)
    # print(res)
    return res
 if __name__ == '__main__':
    start = time.time()
    es = Elasticsearch(hosts='http://127.0.0.1:9200')
    size = 2000000
    res = search_data(es, size)
    all_source = []
    for i in range(size):
        source = res['hits']['hits'][i]['_source']
        all_source.append(source)
        # print(source)
 
    # 統(tǒng)計(jì)查詢出來(lái)的每個(gè)學(xué)生的所有課程的所有成績(jī)的總成績(jī)
    start2 = time.time()
    names1 = []
    all_name_grade = {}
    for data in all_source:
        if data['name'] in names1:
            all_name_grade[data['name']] += data['grade']
        else:
            names1.append(data['name'])
            all_name_grade[data['name']] = data['grade']
    print(all_name_grade)
    end2 = time.time()
    print("200萬(wàn)數(shù)據(jù)耗時(shí)：", end2 - start2)
 
    end = time.time()
    print('200萬(wàn)數(shù)據(jù)總耗時(shí)：', end - start)
 
    # 聚合操作
    start_aggs = time.time()
    es = Elasticsearch(hosts='http://127.0.0.1:9200')
    res = search_data2(es)
    # print(res)
 
    aggs = res['aggregations']['all_names']['buckets']
    # print(aggs)
    dic = {}
    for agg in aggs:
        dic[agg['key']] = agg['total_grade']['value']
 
    print('1000萬(wàn)數(shù)據(jù):', dic)
    end_aggs = time.time()
    print('1000萬(wàn)數(shù)據(jù)總耗時(shí)：', end_aggs - start_aggs)

運(yùn)行結(jié)果：

計(jì)算數(shù)據(jù)中每個(gè)同學(xué)的每科成績(jī)之和。

示例代碼7：

from elasticsearch import Elasticsearch
import time
def search_data(es, size=10):
    query = {
        "query": {
            "match_all": {}
        }
    }
    res = es.search(index='grade', body=query, size=size)
    # print(res)
    return res
 def search_data2(es):
    query = {
        "size": 0,
        "aggs": {
            "all_names": {
                "terms": {
                    "field": "name.keyword",
                    "size": 10
                },
                "aggs": {
                    "all_subjects": {
                        "terms": {
                            "field": "subject.keyword",
                            "size": 5
                        },
                        "aggs": {
                            "total_grade": {
                                "sum": {
                                    "field": "grade"
                                }
                            }
                        }
                    }
                }
            }
        }
    }
    res = es.search(index='grade', body=query)
    # print(res)
    return res
 if __name__ == '__main__':
    start = time.time()
    es = Elasticsearch(hosts='http://127.0.0.1:9200')
    size = 2000000
    res = search_data(es, size)
    all_source = []
    for i in range(size):
        source = res['hits']['hits'][i]['_source']
        all_source.append(source)
        # print(source)
 
    # 統(tǒng)計(jì)查詢出來(lái)的每個(gè)學(xué)生的每門課程的所有成績(jī)的總成績(jī)
    start3 = time.time()
    names2 = []
    subjects = []
    all_name_all_subject_grade = {}
    for data in all_source:
        if data['name'] in names2:
            if all_name_all_subject_grade[data['name']].get(data['subject']):
                all_name_all_subject_grade[data['name']][data['subject']] += data['grade']
            else:
                all_name_all_subject_grade[data['name']][data['subject']] = data['grade']
        else:
            names2.append(data['name'])
            all_name_all_subject_grade[data['name']] = {}
            all_name_all_subject_grade[data['name']][data['subject']] = data['grade']
    print('200萬(wàn)數(shù)據(jù):', all_name_all_subject_grade)
    end3 = time.time()
    print("耗時(shí)：", end3 - start3)
    end = time.time()
    print('200萬(wàn)數(shù)據(jù)總耗時(shí)：', end - start)
 
    # 聚合操作
    start_aggs = time.time()
    es = Elasticsearch(hosts='http://127.0.0.1:9200')
    res = search_data2(es)
    # print(res)
    aggs = res['aggregations']['all_names']['buckets']
    # print(aggs)
 
    dic = {}
    for agg in aggs:
        dic[agg['key']] = {}
        for sub in agg['all_subjects']['buckets']:
            dic[agg['key']][sub['key']] = sub['total_grade']['value']
    print('1000萬(wàn)數(shù)據(jù):', dic)
    end_aggs = time.time()
    print('1000萬(wàn)數(shù)據(jù)總耗時(shí)：', end_aggs - start_aggs)

運(yùn)行結(jié)果：

在上面查詢計(jì)算示例代碼中，當(dāng)使用含有1000萬(wàn)數(shù)據(jù)的索引grade時(shí)，普通方法查詢計(jì)算是比較耗時(shí)的，使用聚合查詢能夠大大節(jié)約大量時(shí)間。當(dāng)面對(duì)9205萬(wàn)數(shù)據(jù)的索引grade2時(shí)，這時(shí)使用普通計(jì)算方法所消耗的時(shí)間太大了，在線上開(kāi)發(fā)環(huán)境中是不可用的，所以必須使用聚合方法來(lái)計(jì)算。

示例代碼8：

from elasticsearch import Elasticsearch
import time
def search_data(es):
    query = {
        "size": 0,
        "aggs": {
            "all_names": {
                "terms": {
                    "field": "name.keyword",
                    "size": 10
                },
                "aggs": {
                    "all_subjects": {
                        "terms": {
                            "field": "subject.keyword",
                            "size": 5
                        },
                        "aggs": {
                            "total_grade": {
                                "sum": {
                                    "field": "grade"
                                }
                            }
                        }
                    }
                }
            }
        }
    }
    res = es.search(index='grade2', body=query)
    # print(res)
    return res
 if __name__ == '__main__':
    # 聚合操作
    start_aggs = time.time()
    es = Elasticsearch(hosts='http://127.0.0.1:9200')
    res = search_data(es)
    # print(res)
 
    aggs = res['aggregations']['all_names']['buckets']
    # print(aggs)
 
    dic = {}
    for agg in aggs:
        dic[agg['key']] = {}
        for sub in agg['all_subjects']['buckets']:
            dic[agg['key']][sub['key']] = sub['total_grade']['value']
    print('9205萬(wàn)數(shù)據(jù):', dic)
    end_aggs = time.time()
    print('9205萬(wàn)數(shù)據(jù)總耗時(shí)：', end_aggs - start_aggs)

運(yùn)行結(jié)果：

注意：寫(xiě)查詢語(yǔ)句時(shí)建議使用kibana去寫(xiě)，然后復(fù)制查詢語(yǔ)句到代碼中，kibana會(huì)提示查詢語(yǔ)句。

到此這篇關(guān)于如何使用python生成大量數(shù)據(jù)寫(xiě)入es數(shù)據(jù)庫(kù)并查詢操作的文章就介紹到這了,更多相關(guān)python es 內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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