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During a fifty-minute incident, Sergey Shinder’s team asked its logs roughly seven questions because each search took six to eight minutes. A redesign of how they organized logs and opened incident views cut a request-ID lookup to about four seconds in their system. The case study’s lesson is not that one storage tier guarantees faster searches: the improvement combined narrower queries, service-specific views, and changes to data placement.
Why seven questions were all the team could ask
Shinder describes a June incident involving four people on a call. Logs from 40 services were stored in one index per day, with 30 days kept on hot nodes and no routing or service separation. A request-ID search from a saved view using its default 30-day range scanned roughly 1.4 terabytes. Searches took six to eight minutes, and the team asked about seven questions during the 50-minute incident. These are the author’s reported conditions and measurements, not independently audited results. Read Shinder’s case study.
That delay affected more than convenience. Each slow answer left a hypothesis untested while the incident was still active. As Shinder puts it, “Your tooling has a latency and it is spent inside the outage, at the moment when a person is holding a hypothesis they cannot check.”
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The redesign combined changes to storage organization with changes to the team’s incident workflow:
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- Route logs by service: Instead of putting all services together in daily indices, logs were organized into per-service indices.
- Separate data by age and access pattern: The team kept three days on fast local disk, 25 days on cheaper nodes, and older data in searchable snapshots.
- Narrow the default view: A service view opened on a one-hour time window and already applied the relevant service filter.
- Make recurring questions repeatable: Five incident queries became buttons, so responders could run bounded searches rather than rebuild broad queries under pressure.
The five recurring questions covered error counts by endpoint, failures for one customer ID, the slowest endpoints, the last ten deploys, and counts by error signature. Together, the changes reduced the amount of unrelated data a responder started by searching and made common investigations quicker to launch.
What the before-and-after numbers show—and what they do not
Shinder reports that a request-ID lookup took about four seconds after the changes. He also reports a median query time under ten seconds across the team’s last three incidents. Those figures describe one team’s experience with its particular system; they are not controlled benchmarks or a guarantee of performance elsewhere. The case study does not establish the Elasticsearch version or enough deployment detail to reproduce the results.
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It also does not isolate the effect of any single change. The redesign changed both data placement and search behavior. It would be misleading to attribute the improvement solely to cheaper storage, service-specific indices, or saved queries.
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Why colder storage is a trade-off, not a speed shortcut
Elastic describes hot and warm tiers as options for more recent or frequently searched time-series data, while cold and frozen tiers can reduce storage costs for data accessed less often. Searchable snapshots allow older data to remain searchable, but frozen-tier searches may be slower because Elasticsearch can fetch data from a snapshot repository. See Elastic’s data tiers documentation and searchable snapshots documentation.
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That trade-off matters when designing incident searches: recent, actively investigated logs may warrant faster access, while older records may be retained more economically if they are searched less often. A colder tier does not inherently make a query faster. In Shinder’s account, the shorter default time range, service filter, and bounded incident queries were part of the same redesign as the storage changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make incident searches useful before an outage
Set defaults around the first question responders need to answer
Open the relevant service view with a narrow, useful time range and the service filter already applied. Responders can expand the range or remove the filter when the evidence calls for it, rather than starting every investigation with a search across all services and a month of data.
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Turn recurring hypotheses into bounded queries
Start with questions the team actually asks during incidents: which endpoints are returning errors, whether one customer is affected, which endpoints are slow, what changed in recent deploys, or which error signatures are rising. A saved query or button should make those questions easy to run while keeping its time bounds and filters visible.
Measure the delay at the right scope
Elastic distinguishes end-to-end request duration in query logs from shard-level execution time in slow logs. Query logs can help show how long a request takes from the application’s perspective; slow logs focus on shard execution. Elastic also notes that creating and storing query logs consumes resources. Its guidance is available in the query and slow logging documentation.
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For incident readiness, a team can track query duration and also record how long it takes responders to get their first useful answer. That second measure is Shinder’s incident-review practice, not a standard metric prescribed by Elastic. Together, the measures can reveal whether slow searches—or a confusing workflow—are limiting investigation.
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