nosql

问题 I have a collection of tasks on my Firestore database like this: "tasks": { v8NC4GovVOPe21fhwtp0 : { id: "v8NC4GovVOPe21fhwtp0" status: 0 dateFrom: 30 april 2019 at 00:00:00 UTC-3 dateTo: 05 may 2019 at 00:00:00 UTC-3 .... }, v7NC4GaxVOPe48ftstp1 : { id:"v7NC4GaxVOPe48ftstp1" status: 0 dateFrom: 29 april 2019 at 00:00:00 UTC-3 dateTo: 02 may 2019 at 00:00:00 UTC-3 .... } } The document has a dateFrom field, which indicates the first date the user can start working on that task and an dateTo

问题 I am modeling a database with Firestore, for a small company. I have a collection of departments. Each department is a document. In the company, there will never be more than 50 employees. I would like to keep each employee as a map. so a department document will be like this; { emp1: { name: 'tom', age:23, email: 'tom@x.com'}, emp2: { name: 'mike', age:35, email: 'mike@x.com'} } I would like to keep the age field private, it should be accessed only by superusers. From what I learnt, it is

问题 I am a beginner in NoSQL DB and Serverless. My app has a table called Trips . The parameters of the tables are {id, route, cost, selling, type, date, LR, asset } and a bunch of other irrelevant document numbers, where id is generated by uuid. Now I want to query the database for giving me Return all trips between a date range using the date parameter. Return all the trips for an asset in given period of time using date and asset parameter. Return all the trips for a route in a given period of

问题 I am working on a migration function that needs to retrieve soccer game results in certain time intervals and update existing bet documents for those games in MongoDB.This code will be called multiple times throughout the day and I'm looking for an optimized solution regarding the computation performance The data structure looks like this. { "games": { "2324754": { "id": "2324754", "comp_id": "1005", "formatted_date": "14.03.2018", "localteam_name": "Barcelona", "localteam_score": "3",

问题 I am working on a migration function that needs to retrieve soccer game results in certain time intervals and update existing bet documents for those games in MongoDB.This code will be called multiple times throughout the day and I'm looking for an optimized solution regarding the computation performance The data structure looks like this. { "games": { "2324754": { "id": "2324754", "comp_id": "1005", "formatted_date": "14.03.2018", "localteam_name": "Barcelona", "localteam_score": "3",

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed last month . Is there a built-in way in SQLite (or similar) to keep the best of both worlds SQL / NoSQL, for small projects, i.e.: stored in a (flat) file like SQLite (no client/server scheme, no server to install; more precisely : nothing else to install except pip install <package> ) possibility

问题 My main question is what is the benefit of integrating Cassandra and Elasticsearch versus using only Elasticsearch? In fact, there are answers to similar questions on StackOverflow (e.g., here and here). But there are some points: A lot of answers are old. Much may have changed in these years. One point that is mentioned is that "Sometimes ElasticSearch loses writes". However, it can be imagined those alleged loses may had been because of some bugs that have been solved in these years. It is

问题 Apache Kafka is an real-time messaging service. It stores streams of data safely in distributed and fault-tolerant. We can filter streaming data when comming producer. I don't understant that why we need NoSQL databases like as MongoDB to store same data in Apache Kafka. The true question is that why we store same data in a NoSQL database and Apache Kafka? I think if we need a NoSQL database, we can collect streams of data from clients in MongoDB at first without the use of Apache Kafka. But,

问题 Apache Kafka is an real-time messaging service. It stores streams of data safely in distributed and fault-tolerant. We can filter streaming data when comming producer. I don't understant that why we need NoSQL databases like as MongoDB to store same data in Apache Kafka. The true question is that why we store same data in a NoSQL database and Apache Kafka? I think if we need a NoSQL database, we can collect streams of data from clients in MongoDB at first without the use of Apache Kafka. But,

问题 I am really finding it tough to figure out the insights about how does a social networking site (Facebook being a reference) manage their comments and notifications for its users. How would they actually store the comments data? also how would a notification be stored and sent to all the users that. An example scenario would be that a friend comments on my status and everyone that has liked my status including me gets a notification for that. Also each user has their own read/unread