How to choose the right NoSQL database

“Wondering how to choose the best NoSQL database, we’ve got you covered”

These days, NoSQL databases become a good choice for big data and analytics projects because of working effectively with large sets of distributed data. In this article, we will give you deeper insight about solutions like MongoDB, Elasticsearch, OrientDB, Hadoop and Cassandra.

1. MongoDB

Famous for being the most prevalent NoSQL database management system (DBMS), MongoDB is document-oriented and coded in C++. Invented to support high volumes of data, MongoDB carries on a logic of horizontal scalability with sharding and assists to implement a MapReduce system.

One of noticeable features of MongoDB in its 3rd version is that it allows to conduct  advanced research such as geospatial, faceted search, do research on some text as well as define the language, ignore “stop words” (“and”, “or”, “the”…in English for example). Besides, documents are stored in BSON (Binary + JSON) on computer, resulting in some disk space and a better performance.
Only accessing it through the protocol because of no API REST interface is a main downside of using this method. However, to narrow the gap, some external projects give a measure acting as the interface between an API REST and the protocol on the other side. It is possible for  Full-text search yet not in depth. It is can be inconvenience for users because of the lack of  some functionalities such as “ More like this” which is to help users to search for related documents.

2. Elasticsearch

Elasticsearch is another Another well-known cloud-based NoSQL database programmed in Java using Lucene. It is of plugins and tools that you have to pay for.

Elasticsearch has the ability  to implement complicated search on high volumes of data. Horizontal scalability becomes more effortless  since  you merely need to establish a new service. The invention of Elasticsearch has intention to prompt a ‘no SPOF’ (no Single Point Of Failure) engine -i.e. in a cluster of several Elasticsearch.  The data would be kept and the service would continue to work in case that a node would turn off. Without matching a schema,  users can store flat documents like JSON objects.

Put it in another way, using Elasticsearch as a main database system  is not good because  it’s a search engine but a database. It takes users some time before the data would be ready to work. Unlike MongoDB, Elasticsearch will do two queries to handle several documents.

3. OrientDB

Released in 2010 and a 2.0 version in 2015, OrientDB, open source and free for any use, emphasizes on graph-document.

It has no any leader nor any election between nodes from the cluster so OrientDB . In order to be more tolerant towards node failure without interrupting the service or data loss, the data is copied exactly and shared between the various nodes. By being scalable, OrientDB has set up some clusters at the class level to be more efficient. This enables you to search in the User class to find back all the users or to search in one of the clusters to limit the number of results. Besides, OrientDB helps you find quickly relationships with a native function especially when using a social network to find and suggest to users the friends of friends at different stages.

In spite of promises OrientDB made, we do only find few user feedback from a production with a large amount of data. The community isn’t quite big around this tool, which can be quite frightening if a problem might occur.

4. Hadoop & Hive

Hadoop is a Java framework helping some tools from the same ecosystem connect onto it. Thanks to MapReduce jobs, Hadoop abstracts the fact that the load is handed out and run as if the data was stored on one disk. And, Hive- a Java software, will connect itself onto Hadoop and run queries close to SQL syntax
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In the process of working, Hadoop aims to analyse a enormous volume of data shared through some servers. Take it as an example. To retrieving all the tweets with a particular hashtag to analyse the level of satisfaction towards a brand is one of useful functions of it.
One of downsides of this solution is that SQL queries are compiled in MapReduc job for a small-sized data or this tool is not suitable for many servers. Due to not being a search engine, Hive does not undertakes a ‘full-text’ search or faceted search.

5. Cassandra

Invented by Facebook and released in 2008, Cassandra is column-oriented and open source. It is the preference’ s big companies: eBay, Netflix, Github, Spotify, Instagram…

One of great functions of Cassandra is that it can help a strong scalability and to guarantee a high availability. The power is enhanced in proportion to nodes added as users add a Cassandra node within a cluster. Put it in another way, no need to worry about adding a node as it can be the case with other DBMS.

The schema is supposed to be specified in advance since the system remains column-oriented. In duration of retrieval, it is harsh. Also, Retrieval is not exhaustive, no like, no ‘full-text’ or faceted search.

To sum up

Each tool is given to deal with issues arising on specific projects. Combining NoSQL DBMS and/or also add a SQL solution like MySQL or PostgreSQL would be the best solution.  From all given information mentioned  above,  you will select the best fit for the desired task.

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