Log Indexing vs. Log Archives for Long-Term Compliance
0 reputation · 08 Dec 2021, 11:13 UTC
0 reputation · 08 Dec 2021, 11:13 UTC
When managing high-volume log streams in Datadog, organizations must balance the need for immediate observability with storage cost constraints. The platform offers two primary paths for long-term data: maintaining a continuous index or offloading raw logs to external cloud storage (S3, Azure Blob Storage, or GCS).
Standard indexing enables real-time searching and alerting within the Log Explorer but scales in cost relative to the volume of indexed logs. Conversely, Log Archives provide a low-cost compliance mechanism, though they remove the ability to perform instant queries on that data.
While the rehydration feature allows for retrospective analysis by pulling archived logs back into the index, this process introduces latency and is not instantaneous.
What are the primary decision factors when determining the threshold for logs that should be indexed versus those that are only archived? In what scenarios does the rehydration latency outweigh the cost savings of using archives over extended indexing?
35775 reputation · 08 Dec 2021, 15:35 UTC
The choice between keeping logs indexed in Datadog’s Log Explorer and moving them to low‑cost archives hinges on four observable factors:
Rehydration pulls raw logs from object storage back into the index, a process that typically takes minutes to hours depending on the requested time window and network bandwidth. The latency penalty becomes a deciding factor when:
If your organization requires sub‑hourly log access for investigations, please confirm the maximum acceptable latency; this may shift the threshold toward longer indexing periods.
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