In the world of large-scale log analytics and observability, engineers face a constant, frustrating trade-off: keep data hot and instantly accessible at a high cost, or archive it to cold storage, sacrificing queryability. For years, this binary choice has forced DevOps and SRE teams to set aggressive retention policies, effectively throwing away valuable business and security insights simply because it was too expensive to keep them live.

OCI OpenSearch Service’s Searchable Snapshots feature doesn’t just nudge this boundary; it breaks it entirely.

This isn’t just another storage tier. It’s a new architectural approach where cold data remains in low-cost OCI Object Storage, but—critically—remains 100% queryable. The most-needed data segments are pulled on demand into a dedicated cache on new dedicated search nodes.

This post isn’t just a “how-to.” It’s a guide on how to fundamentally rethink your data retention strategy. We’ll explore:

  • The strategic value of Searchable Snapshots.
  • How does this new architecture work to separate query compute from storage?
  • Honest performance trade-offs between remote (snapshot) and local indexes.
  • My recommendations for sizing, cost optimization, and real-world adoption.

Whether you’re running a massive observability pipeline or a growing analytics workload, it’s time to stop deleting data and start leveraging it.

What Are Searchable Snapshots?

Searchable Snapshots enable you to mount read-only snapshots of indices stored in an OCI Object Storage bucket and query them as if they were live indices. Instead of full restores to block storage, only the required Lucene segments are fetched to a local file cache on search nodes.

  • Cost savings with Cold storage: Older indices are snapshotted and offloaded to OCI Object Storage at a fraction of the cost of block storage.
  • Functional and performance transparency: Queries transparently retrieve necessary Lucene segments into a local LRU cache.
  • Seamless integration: Combined with an ISM (Index State Management) policy, indices transition from hot to cold automatically.

This approach reduces the need for large primary data nodes, cutting block storage and compute expenses.

Understanding the New Conceptual Architecture

Implementing Searchable Snapshots on OCI represents a shift in cluster topology, separating compute for hot data (ingest) from compute for cold data (historical queries). The architecture stands on three main pillars:

  1. Dedicated Search Nodes: First, you provision dedicated search nodes alongside your existing master and data nodes. These new nodes are specifically designed to handle the query load for remote data, each with its own block volume serving as a local cache.
  2. Object Storage Repository: Second, this new architecture connects to a low-cost OCI Object Storage repository. You register this repository, typically leveraging a Resource Principal for secure, seamless authentication between the OpenSearch service and Object Storage.
  3. Automated Data Lifecycle: Finally, the real power lies in automating the data lifecycle with an Index State Management (ISM) policy. You define the “hot-to-cold” transition—for example, a policy that automatically snapshots any index older than 90 days and then seamlessly converts it into a “remote” (searchable snapshot) index. The data is now in Object Storage, but it remains fully queryable.

Once this policy is attached to your index templates, the entire lifecycle management process—and the resulting cost savings—becomes hands-off.

Note: This article provides a high-level strategic overview. For a complete, step-by-step tutorial on configuring the ISM policies, registering your repository, and deploying search nodes, please see our detailed guides on the Oracle Help Center (OHC) Learn.

Performance Comparison: Remote vs. Local Indexes

Searchable snapshots introduce a small, logical performance trade-off in exchange for massive storage savings. We ran several benchmarks comparing remote (snapshot-backed) and local (fully restored) indexes to understand the real-world impact.

Here are the key editorial takeaways from our testing:

  • Cold Starts vs. Warm Caches: On its very first query (a “cold” run), a remote index will show latency spikes as it streams segments from Object Storage. However, once the cache is “warm,” subsequent queries see a dramatic performance increase, with latencies approaching those of local-only indexes.
  • Impact of Concurrent Load: As the concurrent search load increased, the cache warmed quickly, and the latency difference between local and remote indexes became minimal. Under heavy, sustained load, searchable snapshots sustained ~85–90% of local throughput.
  • Reduced CPU on Data Nodes: A critical finding was that CPU utilization on data nodes was significantly lower in the remote-index (searchable snapshot) cluster. This is because the new search nodes absorbed the query load, freeing up the data nodes to focus on ingest.

In conclusion, while searchable snapshots incur a modest initial latency penalty during cold cache conditions, they rapidly approach local index performance—delivering up to 90% of local throughput under sustained load—while significantly reducing storage costs and data-node CPU usage. This makes them an ideal solution for OCI OpenSearch workloads with large cold data sets.

Cost & Operational Benefits

  • Storage savings: OCI Object Storage rates are typically 60–80% lower than block storage.
  • Compute efficiency: Data nodes focus on ingesting and serving hot data; search nodes handle queries for archived data.
  • Operational automation: Simplify lifecycle management with ISM policies, reducing manual snapshot and retention tasks.

Beyond the Benchmarks: A Strategic Shift

The performance graphs and setup steps are vital, but they only tell part of the story. The true value of Searchable Snapshots lies in the strategic shift it enables for your entire organization.

As engineers, we’ve been conditioned to think of data retention in terms of cost and liability. We’ve all set 30- or 90-day ISM policies that ultimately delete data, even though we’re discarding potentially crucial information for long-term trend analysis, security audits, or machine learning models.

Searchable Snapshots fundamentally change this cost-benefit analysis. The modest, one-time latency hit for an initial cold query (which, as our tests show, quickly vanishes as the cache warms up) is an insignificant price to pay for having years of historical data at your fingertips. This feature moves log analytics from a short-term tactical tool to a long-term strategic asset. You no longer have to choose between cost and accessibility.

From Theory to Practice: A Strategic Rollout

This feature is powerful, but it’s not a magic wand. A smart rollout is key. Based on my experience and our benchmark findings, I recommend a practical, phased approach rather than an all-at-once migration:

  1. Audit Your Access Patterns: Before changing any ISM policies, identify your true data tiers. Don’t just look at index age; look at access patterns as well. Which indices are only touched for occasional audits or end-of-quarter reports? These are your prime candidates for Searchable Snapshots.
  2. Rethink “Hot” vs. “Warm”: This feature is perfect for the “warm” tier. Your hot data (e.g., the last 7-14 days) should absolutely remain on high-performance local block storage. But the data from 15-90 days, which is queried infrequently but must be available, is the sweet spot. You may find that your “hot” window can shrink dramatically.
  3. Right-Size Your Entire Cluster: As the benchmarks show, this feature offloads significant query CPU load from data nodes to the new search nodes. This isn’t just a storage saving; it’s a compute saving. You can now provision your hot data nodes primarily for ingest performance, letting the search nodes handle the bulk of the historical query load. As a starting guideline, we found that dedicating 20–40% of your data nodes to search nodes is a solid baseline (e.g., a minimum of ceil(data_nodes * 0.2)). When provisioning these new nodes, match their OCPU and memory to your data nodes for consistent performance benchmarks, and allocate the majority (e.g., 90%) of each search node’s block volume to the file cache to maximize its effectiveness.

Conclusion: Stop Deleting, Start Analyzing

OCI OpenSearch’s Searchable Snapshot feature is more than just a cost-saving tool; it’s an operational game-changer. It unlocks the full value of your data—every bit of it—without the operational complexity or financial penalties of traditional storage models. By separating compute from storage, you can finally build a truly scalable, long-term observability and analytics platform on OCI.

Ready to stop deleting data and start analyzing it?

Explore OCI OpenSearch Service today and learn more about building your next-gen analytics solution.