Handling Single Point Failures and Disaster Recovery in InfluxDB
To mitigate the inherent single‑point‑failure risk of the open‑source InfluxDB community edition, the article proposes deploying multiple InfluxDB instances with concurrent client writes, tracking failed writes, temporarily storing them, and using custom workers to replay data, while addressing timeout, data consistency, and storage considerations.
The open‑source InfluxDB community edition suffers from single‑point‑failure risks and lacks built‑in disaster‑recovery, prompting the need for a simple, reliable solution.
The proposed approach is to run several InfluxDB instances on different machines and have clients write concurrently to all of them, avoiding a proxy that would itself become a single point of failure.
If a particular InfluxDB instance fails and a write is rejected, the failed data and the target node are recorded; these records can be temporarily stored in a database, message queue, log file, or similar storage.
A custom worker then retrieves the recorded failed entries and rewrites them to the healthy InfluxDB nodes, ensuring that data eventually reaches all instances.
Through this method, data across multiple InfluxDB nodes achieves eventual consistency.
Key operational considerations include: setting a timeout for concurrent writes due to varying machine conditions; distinguishing failure types (client‑side 4xx errors versus server‑side 5xx or node crashes) and handling them appropriately; ensuring the temporary storage can handle the write load and meet durability requirements; using explicit timestamps supplied by the client rather than InfluxDB‑generated timestamps for replayed data; and being aware that temporary inconsistencies may appear among nodes during failure periods.
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