Gabriel has been dedicated to databases as a DBA and consultant for the last 12 years. He has lead and participated in multiple projects across many technologies, including Oracle, MySQL, SQL Server and MongoDB. Gabriel defines himself as an automation super fan, he contributed to the development of two custom DBaaS platforms.
Gabriel holds a college degree in electronics, a degree in industrial engineering and he is currently working on his master's thesis (Information Systems engineering). He is also an GCP, Oracle and Microsoft certified professional. Currently he is an Internal Principal Consultant at Pythian specializing in MySQL and MongoDB.
As consultants, we are often asked to perform a one-off assessment of existing environments. These requests involve basically three phases: data collection, analysis, and results presentation. In order to reduce the time spent on low-value tasks, we introduced automation to turn collected data into a deliverable and to assist consultants with the analysis and recommendations.
In this session we are going to share the process details, technologies involved, and the benefits introduced to our organization
Audience Takeaways/Take Back to Work:
- Discover a way to construct/manipulate dynamic documents
- Learn about the benefits of introducing automation into documentation processes
- Hear a few lessons learned during the development of the solution
Either because of a new feature, a bug, or just for archival purposes, it is often necessary to update or remove large amounts of documents in production.
The challenge with this type of operation is not only to design an efficient process query-wise, but to be able to execute it in production without debilitating the servers or causing secondaries to lag.
There are strategies that can be used to create highly controlled write processes that could run for days under the radar, getting the job done without greatly impacting your application's performance.
In this session, I'm going to share with you key points to consider when creating massive write operations in MongoDB, examples of real-life processes executed, and a few lessons learned.