AWS

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Amazon DocumentDB – The Choice for Operating JSON Workloads with Ease

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Introduction

Document database falls as a NoSQL database used to store and query JSON documents. Reasons for having an accelerated adoption of JSON is its flexibility in terms of having a semi-structured and hierarchical nature.

Be it content management or catalog management, documents evolve with time and hence document databases come handy when it comes to representing and managing such documents using JSON document structure which includes

  • Key-Value pairs such as {“subject”: “math”}
  • An Array such as {“subject”:[“math”, “Physics”]}
  • Objects such as {“coordinates”:{“latitude”:44.5,”longitude”:33.8}}

 

Amazon DocumentDB

Credit: Amazon Web Services

What do we have in AWS to cater to the needs of document data model-based workloads?

Considering the needs for efficiently storing JSON documents while providing flexibility in terms of querying, indexing capabilities, AWS offers a fully managed database solution named as Amazon DocumentDB which enables organizations to cater:

  • Document access pattern for the applications
  • With an ease of required compute and storage resources
  • To better serve, especially the heavy read workloads using read replicas
  • Providing high throughput and low latency
  • While ensuring high availability with Multi-AZ deployment options
  • In a fully managed fashion with MongoDB compatibility

  • Cloud Migration
  • Devops
  • AIML & IoT
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How it looks like?

We begin with creating an Amazon DocumentDB cluster which consists of primary instance, replica instance and a differently engineered distributed cluster volume.

Cluster Endpoint enables connection to the primary instance while reader endpoint enables connection to the read replicas. It also has an instance endpoint per node that can be used to establish connection to a specific instance.

Amazon DocumentDB

Credit: Amazon Web Services

 

Capabilities that make Amazon DocumentDB an appropriate choice

  • CRUD (Create, Read, Update, Delete) operations can be performed easily.
    • A collection which is like a table in a relational database can be created which will hold the documents without any schema enforcement thereby giving flexibility to the structure. insertOne( {} ) and insertMany ( [{},…,{}] ) operations can be used.
    • find() operation can be used to query the documents
    • update() operation can be used to update an existing document.
    • remove() operation can be used to remove a document from the collection.
  • Client-side field level encryption, encryption for data at rest as well in transit ensures data protection.
  • Seamless integration with AWS IAM (Identity and Access Management) (Identity and Access Management) adds a layer of security to cater to the needs of authenticating and authorizing identities to work with Amazon DocumentDB while ensuring principle of least privilege and defense in depth.
  • Features such as automatic snapshots, manual snapshots are also provided.
  • An Amazon DocumentDB cluster lives inside of Amazon VPC (Virtual Private Cloud) thereby giving network isolation.

Customer Success Stories – Let us get motivated

An esteemed customer of AWS used Amazon DocumentDB to store search events for driving personalized search recommendations for their customers.

We were able to handle huge IoT (Internet of Things) telemetry volume using Amazon DocumentDB.

Were able to migrate their MongoDB databases to Amazon DocumentDB quickly within a span of 3 months.

Zero Outages, low latency as low as 80 ms, reduction in management and operational overhead

Conclusion

With Amazon DocumentDB, it is super easy to satisfy the document access pattern while being MongoDB compatible in a fully managed, highly secure, highly available and highly performant fashion.

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WRITTEN BY Muhammad Imran

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