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Storage and operations of spatial data in MongoDB

In this example, we use the official C # Driver to store and query spatial data (vectors) in MongoDB) read vector element space information and attribute tables, and write them to MongoDB. Read the shapefile file and convert the space information to

"Four" MongoDB index management

First, the index introductionIn MongoDB, indexes are used to support efficient queries. If there is no index, MONGODB must scan each document in the entire collection to find a matching document. However, if an appropriate index is established,

MongoDB launches multi-instance and MongoDB create Delete, backup, copy

MongoDB start multi-instance and MongoDB create Delete, backup, copy enable multi-instance creation data and log storage locations with sufficient single server resources# 数据存放位置[[email protected] ~] mkdir -p

MongoDB Geospatial Indexes and queries

I. MongoDB INTRODUCTION ( provides a series of indexing and querying mechanisms to handle geospatial information. This section describes the geo-index characteristics of

MongoDB Learning--Index types and attributes

Index typeMONGDB indexes are divided into the following types: Single-key index, composite index, geospatial Index, full-text index, and hash indexSingle-key index (single Field Indexes)An index created on a key is a single-key index, which is the

MongoDB series five (geo-spatial Index and query).

First, the latitude and longitude of the representation wayThe storage of latitude and longitude in MongoDB has its own set of specifications (mainly for the purpose of establishing a geospatial index on that field). There are two ways, namely

Using MongoDB to develop LBS application practice "turn"

Recently, as a commando, with colleagues in a blitz to build a simple lbs system. The current mainstream approach is to use MongoDB, because it has encapsulated the common LBS basic operations (such as looking for nearby people), the function is

MongoDB Learning Note four: Index

The index is used to speed up the query. Creating a database index is like determining how to organize the index of a book. But your advantage is knowing what queries will be made in the future and what needs to be found quickly. For example: All

Ruby Operation MongoDB (Advanced 11)--Spatial information search geospatial

The corresponding function of text search is introduced in the previous blog post.The MongoDB database provides a series of indexing and querying mechanisms for processing operations of spatial information. This blog post will show you how to create

Storage and manipulation of spatial data in MongoDB

This article uses the official C # Driver to implement storage in MongoDB, querying spatial data (vectors)storage of spatial dataIn this example, the vector feature space information and the attribute table are read from a vector file (shapefile

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