mongodb

问题 I'm looking for an elegant solution for persisting localized data in MongoDB in my Spring application. In the example below there is a basic solution for persisting localized data for the field description . As already suggested in how-to-do-i18n-with-mongodb the schema for localized data is "description": [{ "locale": "es", "value": "someESvalue" }, { "locale": "en", "value": "someENvalue" }] Given that, the entity looks like this: @Document(collection = "foo") public class Foo implements

问题 We have a dataframe of almost 100000 records which i want to upsert in a mongodb collection. My sample code is mentioned below. For keeping it simple in below code, I am generating these data in a for loop and appending lstValues. In actual application, we receive these data from external csv files which we load it into pandas dataframe. We receive almost 98000 records from these external csv files. Also our original mongodb collection already contains almost 1,00,00,00 records and it keeps

问题 We have a dataframe of almost 100000 records which i want to upsert in a mongodb collection. My sample code is mentioned below. For keeping it simple in below code, I am generating these data in a for loop and appending lstValues. In actual application, we receive these data from external csv files which we load it into pandas dataframe. We receive almost 98000 records from these external csv files. Also our original mongodb collection already contains almost 1,00,00,00 records and it keeps

问题 We have a dataframe of almost 100000 records which i want to upsert in a mongodb collection. My sample code is mentioned below. For keeping it simple in below code, I am generating these data in a for loop and appending lstValues. In actual application, we receive these data from external csv files which we load it into pandas dataframe. We receive almost 98000 records from these external csv files. Also our original mongodb collection already contains almost 1,00,00,00 records and it keeps

问题 We have a dataframe of almost 100000 records which i want to upsert in a mongodb collection. My sample code is mentioned below. For keeping it simple in below code, I am generating these data in a for loop and appending lstValues. In actual application, we receive these data from external csv files which we load it into pandas dataframe. We receive almost 98000 records from these external csv files. Also our original mongodb collection already contains almost 1,00,00,00 records and it keeps

问题 When I tried to call up to 200,000 POST requests in NodeJS, it display some errors like heap memory leak. In each POST request, I want to insert the resolved data into localhost mongo DB. It's ok to make 2000 requests at one time but it's really difficult to deal with 200,000 requests. I got stuck in this problem and don't know exactly to resolve it. I really need your help or any suggestions. Thank you in advance for your help. const mongoose = require('mongoose'); const request = require(

问题 I have come a long way in structuring into the following mongodb data collection, but i couldn't finish the aggregation stage, { "test": [ { "_id": "60014aee808bc5033b45c222", "name": "a rogram", "companyName": "company NAme", "website": "https://www.example.comn", "loginUrl": "https://www.example.comn", "description": null, "createdBy": "5fe5cbcdb9ac0f001dccfadf", "createdAt": "2021-01-15T07:57:34.499Z", "updatedAt": "2021-01-15T13:09:09.417Z", "__v": 0, "address": null, "affiliatePlatform":

问题 I have come a long way in structuring into the following mongodb data collection, but i couldn't finish the aggregation stage, { "test": [ { "_id": "60014aee808bc5033b45c222", "name": "a rogram", "companyName": "company NAme", "website": "https://www.example.comn", "loginUrl": "https://www.example.comn", "description": null, "createdBy": "5fe5cbcdb9ac0f001dccfadf", "createdAt": "2021-01-15T07:57:34.499Z", "updatedAt": "2021-01-15T13:09:09.417Z", "__v": 0, "address": null, "affiliatePlatform":

问题 I am comparing performance of the two dbs, plus csv - data is 1 million row by 5 column float, bulk insert into sqlite/mongodb/csv, done in python. import csv import sqlite3 import pymongo N, M = 1000000, 5 data = np.random.rand(N, M) docs = [{str(j): data[i, j] for j in range(len(data[i]))} for i in range(N)] writing to csv takes 6.7 seconds: %%time with open('test.csv', 'w', newline='') as file: writer = csv.writer(file, delimiter=',') for i in range(N): writer.writerow(data[i]) writing to

问题 I am comparing performance of the two dbs, plus csv - data is 1 million row by 5 column float, bulk insert into sqlite/mongodb/csv, done in python. import csv import sqlite3 import pymongo N, M = 1000000, 5 data = np.random.rand(N, M) docs = [{str(j): data[i, j] for j in range(len(data[i]))} for i in range(N)] writing to csv takes 6.7 seconds: %%time with open('test.csv', 'w', newline='') as file: writer = csv.writer(file, delimiter=',') for i in range(N): writer.writerow(data[i]) writing to