Document Databases: Be Honest

Matty Stratton's blog post explores the strengths and limitations of MongoDB, particularly when handling time-series data, and provides guidance on whether to stick with it or consider alternatives like Postgres or TimescaleDB. The post acknowledges MongoDB's advantages, such as schema flexibility and ease of scaling for document-shaped data, but highlights its struggles with workloads that resemble time-series data due to inefficiencies in its storage engine and lack of native time-based partitioning. While transitioning to vanilla Postgres might seem like a viable solution, it also faces challenges with high-frequency time-series ingestion, revealing that the real issue lies in the choice between a document store and a purpose-built time-series storage system. The author advises using a decision framework to assess whether tuning, migration, or a change in architecture is necessary, particularly recommending TimescaleDB for sustained high-volume time-series workloads for its compatibility with the Postgres ecosystem and more suitable storage architecture.

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