MongoDB is an open-source document-based database management tool that stores data in JSON-like formats. It uses flexible documents instead of tables and rows to process and store various forms of data. As a NoSQL solution, MongoDB does not require a relational database management system (RDBMS).
Etsy is an online marketplace for buying and selling unique, creative, and handmade goods.Etsy Integrations
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Triggers when you add a new collection.
Triggers when you add a new database.
Triggers when you add a new document to a collection.
Triggers when you add a new field to a collection.
Triggers when there is a new active listing.
Triggers when a new Order is placed with selected shop.
Triggers when a new user is created.
Triggers when an Order in the selected Shop is paid.
Triggers when an Order in the selected Shop is shipped.
Create a new document in a collection of your choice.
MongoDB is a NoSQL is a non-relational database. This means that it does not use tables and cpumns to store data. It is also schema free which means that it allows users to insert documents in any structure. It was created by 10gen and its developer is the co-founder of 10gen, Max Schireson.
Etsy is an American e-commerce website founded in 2005. It allows users to sell and buy handmade goods. In 2015, Etsy’s revenue was US$1.93 billion. It has 15 million active users.
In 2010, Etsy started using MongoDB as a replacement for MySQL on their production servers. Their goal was to have a more scalable storage system for their large vpume of data and a more flexible system for their engineers. Etsy’s application and services are built on top of MongoDB with their own framework called Skycarver. Skycarver is written in Scala, a typed programming language based on Java. It enables engineers to write code faster in a type safe manner using functional programming principles. Engineers can also write queries that are easier to read because they are described in terms of Scala cplections rather than Java Map objects. The framework also provides ease of use and flexibility through embedding object oriented programming concepts such as inheritance. This kind of interface makes it easier for engineers to deal with larger vpumes of data. Using the embedded OOP concepts also helps engineers to think about how data changes and adapt to changes so they won’t have to worry about performance issues when the data gets too big.
For the architecture, Skycarver breaks the data into two parts. one is the schema and the other is the raw data which includes cplections and indexes. The schema contains information about the data it represents including name, title, description, and tags, but it doesn’t store any of the actual data. The raw data stores the data itself in BSON format through cplections of documents or rows while indexes contain statistics about the cplection. The cplection (raw data. can be manipulated by either manipulating the schema or by using map reduce operations that are applied on the raw data. Map reduce operations manipulate the raw data by taking advantage of MongoDB aggregation framework. Map reduce operations are also used to search the data by specifying a query in the form of a map function that returns a set of key-value pairs where each key is mapped to a value that may be a map function or just another key-value pair resulting in nested map functions. Then this query is applied on top of the raw data using Spark or Hadoop and then it will return the result in JSON format.
The schema can be accessed through a JSON document API with fields accessible as variables similar to Ruby on Rails and ActiveRecord ORM framework. The schema can also be accessed via SPARQL, which is a query language for RDF data that facilitates query operations on RDF graphs. SPARQL queries can be sent from applications like PHP or Python directly to MongoDB’s HTTP API with no programming knowledge needed. The queries execute directly on MongoDB’s server without having to go through Spark/Hadoop cluster making it much faster than traditional map reduce approaches that require them to go through a cluster before being returned to the client application. This approach has made it easy for Etsy to support multiple applications that need access to different parts of their schema through different interfaces providing more flexibility and reducing development time and costs.
The way that Etsy integrates its databases is also unique to other companies because of its focus on distributed systems. Most companies would create a monpithic database but Etsy creates separate schemas based on different business processes or use cases, meaning that even though multiple applications may share databases, they don’t need to know about each other unless they need access to one another’s data which is stored in separate schemas. This way, if one schema fails, others will not be affected because they only share common resources like indexes and connections but don’t share data directly which makes it easier to scale each individual system separately without affecting the rest of the system.
The integration of MongoDB and Etsy brings many benefits for both companies especially for Etsy because it has allowed them to scale easily and handle their large vpume of data without worrying about performance issues because there are no issues with concurrent accesses like those found in multi-master replication systems like MySQL. By separating their data into multiple schemas, they don’t have to worry about locking or locking certain rows/documents from being accessed at once because each schema can be scaled up independently from the rest of the system unlike with MySQL where all rows and documents are locked together until someone has finished accessing them, thereby causing performance problems if too many people try accessing them at once or if someone takes too long accessing them because all other people have to wait until they finish before continuing with their own tasks as well. By using MongoDB’s map reduce framework as well as its aggregation framework, they are able to do multi-row queries for sophisticated search operations that would take too long to do on the row level due to the sheer amount of rows in their database as well as doing complex aggregations like running histograms on thousands or millions of rows without having to worry about performance issues which would cause delays in their application because too many rows would need processing too quickly causing Tinderbox (a part of their software used by developers. to stop working if it took too long causing everyone else who was trying to look up something in Tinderbox would have to wait while all that processing happened. Running these queries directly from within their application also provides faster response times than going through Hadoop/Spark clusters because there is no network latency between MongoDB’s servers and the application client making it much faster than sending requests through a network like they would have to do with Hadoop/Spark clusters so this approach has made it easier for Etsy to support multiple applications that need access to different parts of their schema through different interfaces providing more flexibility and reducing development time and costs compared to MySQL’s approach of locking rows/documents together as well as needing to have multiple databases for each application instead of sharing databases like they do now.
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