nosql

Neo4j Scalability

由 倾然丶 夕夏残阳落幕 提交于 2020-01-13 18:38:27
问题 I have read this article. It states, that Neo4j can scale horizontally, but only to increase read performance and fault tolerance... so the stored graph is copied to each server in a cluster. But what if I have a dataset that is larger than one server can store? Does Neo4j fail in this situation? Do I have to scale vertically in this situation and buy larger HDD? Thank you 回答1: Yes. You need enough hard drive space to contain the full graph on all nodes of the cluster, no way around that. If

How to call to mongodb inside my map/reduce functions? Is it a good practice?

由 北慕城南 提交于 2020-01-13 17:57:19
问题 I would like to know if: Firstly: Is it possible to use mongodb functions inside my map/reduce functions, for example: function() { foo = db.myCollection.find({ _id: ObjectId('4ee235ce002c62f393000008')}) print(foo); # returns 'db.myCollection -> undefined' } Secondly: Is it a good practice? For example, I need to map a specific property from the documents referenced for a 'root' document. Or maybe, can I set a habtm relationship on this specific property? Thanks! 回答1: although it is possible

Redis系列-第一篇Redis介绍

由 三世轮回 提交于 2020-01-13 11:30:05
1.Redis介绍 1.1什么Redis Redis是用C语言开发的一个开源的高性能键值对（key-value）内存数据库。 它提供五种数据类型来存储值：字符串类型、散列类型、列表类型、集合类型、有序集合类型 它是一种NoSQL数据库。 1.2 什么是NoSQL NoSQL，即Not-Only SQL（不仅仅是SQL），泛指非关系型的数据库。 什么是关系型数据库？数据结构是一种有行有列的数据库 NoSQL数据库是为了解决高并发、高可用、高可扩展、大数据存储问题而产生的数据库解决方案。 NoSQL可以作为关系型数据库的良好补充，但是不能替代关系型数据库。 1.3NoSQL数据库分类 键值(Key-Value)存储数据库 相关产品： Tokyo Cabinet/Tyrant、Redis、Voldemort、Berkeley DB 典型应用： 内容缓存，主要用于处理大量数据的高访问负载。 数据模型： 一系列键值对 优势： 快速查询 劣势： 存储的数据缺少结构化 列存储数据库 相关产品：Cassandra, HBase, Riak 典型应用：分布式的文件系统 数据模型：以列簇式存储，将同一列数据存在一起 优势：查找速度快，可扩展性强，更容易进行分布式扩展 劣势：功能相对局限 文档型数据库 相关产品：CouchDB、MongoDB 典型应用：Web应用（与Key-Value类似

IN operator in Cassandra doesn't work for table having a column with type-collection(Map or List)

由 北战南征 提交于 2020-01-13 11:23:10
问题 I'm working on Cassandra, trying to get to know how it works. Encountered something strange while using IN operator. Example: Table: CREATE TABLE test_time ( name text, age int, time timeuuid, "timestamp" timestamp, PRIMARY KEY ((name, age), time) ) I have inserted few dummy data. Used IN operator as follows: SELECT * from test_time where name="9" and age=81 and time IN (c7c88000-190e-11e4-8000-000000000000, c7c88000-190e-11e4-7000-000000000000); It worked properly. Then, added a column of

How does Cassandra scale horizontally ?

由 别说谁变了你拦得住时间么 提交于 2020-01-13 08:51:07
问题 I've watched a video on Cassandra database, which turns to be very effective and really explains a lot about Cassandra. I've also ready some article and books about Cassandra but the thing I could not understand is how does Cassandra scale horizontally. By horizontally scale I mean add more nodes to gain more space. As I understand each node has the identical data i.e if one node has 1TB of data and is replicated to other nodes this means all n nodes will each contain 1TB of data. Am I

what's the practical difference between google datastore nosql and google bigquery sql?

由 主宰稳场 提交于 2020-01-13 03:04:30
问题 I want to know how to evaluate one tool over another. My major concern is as following: In google datastore, we define 'kind'. Each 'entities' has 'properties'. Then the datastore backends use those properties to index data for future query. The query itself use almost the same idea in SQL, though different syntax, to filter data and find what we want. If you index every property, the index metadata would be even bigger than real data. Google bigquery uses it's dialect of SQL. And it's fully

what's the practical difference between google datastore nosql and google bigquery sql?

由 杀马特。学长 韩版系。学妹 提交于 2020-01-13 03:03:05
问题 I want to know how to evaluate one tool over another. My major concern is as following: In google datastore, we define 'kind'. Each 'entities' has 'properties'. Then the datastore backends use those properties to index data for future query. The query itself use almost the same idea in SQL, though different syntax, to filter data and find what we want. If you index every property, the index metadata would be even bigger than real data. Google bigquery uses it's dialect of SQL. And it's fully

Redis入门

由 时光毁灭记忆、已成空白 提交于 2020-01-12 17:41:15
Redis入门 redis是一款高性能的NOSQL系列的非关系型数据库。 1.1.什么是NOSQL NoSQL(NoSQL = Not Only SQL)，意即“不仅仅是SQL”，是一项全新的数据库理念，泛指非关系型的数据库。 随着互联网web2.0网站的兴起，传统的关系数据库在应付web2.0网站，特别是超大规模和高并发的SNS类型的web2.0纯动态网站已经显得力不从心，暴露了很多难以克服的问题，而非关系型的数据库则由于其本身的特点得到了非常迅速的发展。NoSQL数据库的产生就是为了解决大规模数据集合多重数据种类带来的挑战，尤其是大数据应用难题。 1.1.1. NOSQL和关系型数据库比较 优点： 1）成本：nosql数据库简单易部署，基本都是开源软件，不需要像使用oracle那样花费大量成本购买使用，相比关系型数据库价格便宜。 2）查询速度：nosql数据库将数据存储于缓存之中，关系型数据库将数据存储在硬盘中，自然查询速度远不及nosql数据库。 3）存储数据的格式：nosql的存储格式是key,value形式、文档形式、图片形式等等，所以可以存储基础类型以及对象或者是集合等各种格式，而数据库则只支持基础类型。 4）扩展性：关系型数据库有类似join这样的多表查询机制的限制导致扩展很艰难。 缺点： 1）维护的工具和资料有限，因为nosql是属于新的技术

Is it possible to work with OrientDB using C#?

由 不问归期 提交于 2020-01-12 07:15:28
问题 Are there any implementations, api or examples of OrientDB and C#. The reason I am looking at OrientDB is becuase it's the only one that I found that is a combination of Graph and Document. Any suggestions on how I should try this. My next choice is RavenDB, but I am not sure if it supports joins or linked documents? Any thoughts... 回答1: I just developed a first version of a REST client for OrientDB. http://netorientdb.codeplex.com 回答2: OrientDB has an official binary driver for .NET look

How to structure a DynamoDB database to allow queries for trending posts?

由 我们两清 提交于 2020-01-12 05:45:05
问题 I am planning on using the following formula to calculate "trending" posts: Trending Score = (p - 1) / (t + 2)^1.5 p = votes (points) from users. t = time since submission in hours. I am looking for advice on how to structure my database tables so that I can query for trending posts with DynamoDB (a nosql database service from Amazon). DynamoDB requires a Primary Key for each item in a table. The Primary Key can consist of 2 parts: the Hash Attribute (string or number) and the Range Attribute
订阅 nosql