Performance Optimization Face-Off: AWS DocumentDB vs MongoDB

MongoDB is one of the fastest-growing database technologies. Driven by the power of the document data model and the high performance offered by a modern NoSQL engine, MongoDB has seen rapid adoption across organizations for mission-critical workloads.

Amazon jumped on the MongoDB bandwagon by launching DocumentDB, a new database-as-a-service offering designed to be compatible with existing MongoDB applications, combining the benefits of a fully managed service with high availability and security.

These are only a few of the questions that we answered in our paper:

  • How does DocumentDB stack up in terms of performance versus MongoDB hosted on-prem or on-a-cloud IaaS setup?
  • What are the cost implications of moving to a fully managed database?
  • Does AI-driven performance optimization change the price-performance equation?

Also, in this document you will find: 

  1. Introduction and Result Summary: In this section we highlight the differences between MongoDB and AWS DocumentDB, discussing the problem of sub-optimal configurations. 
  2. Methodology: This section outlines the performance benchmarking and configuration optimization methodologies we used.
  3. Results: We compare the performance of DocumentDB and MongoDB in the default configuration, in terms of throughput (operations per second) and query latency for the two YCSB workloads we tested. We also show the results that we obtained using Akamas.
  4. Conclusions: We summarize key findings in optimizing MongoDB and AWS DocumentDB with Akamas.
  5. Appendix I: In this section we share details about the performance experiments referenced in this study during the period from January to April 2019.

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