Slow Performance with Approximate Queries
performanceActiveRisingUsing the 'approximate:true' setting in queries results in a consistent 3x slower performance compared to the default setting.
Score Breakdown
Heuristic ranking from public discussion signals — not a validated prediction of commercial opportunity, demand, or willingness to pay.
Composite 72/100 (High, unvalidated). Top driver: Willingness to pay (30% weight, 22.5 pts).
Heuristic only — often urgency map or random scaffolding on ingest, not measured mention frequency. Maps to XPS relevance (with market size).
LLM/mock judgment of intensity from title/summary text — not ops or ticket data. Maps to XPS quality (with willingness to pay).
LLM/mock purchase-intent guess from text — not invoices, surveys, or paid seats. Maps to XPS quality.
Heuristic/scaffold (often random or fixed on insert) — not a verified mention trajectory. Maps to XPS novelty.
Heuristic/scaffold (often random or fixed) — not TAM research. Maps to XPS relevance (with frequency).
Catalog notes (not predictive analysis)
Slow Performance with Approximate Queries (performance). Catalog heuristic opportunity score: 72/100 — a chosen formula over discussion-signal facets, not evidence of demand, conversion, or willingness to pay. Treat as browsing rank, not a commercial prediction.
Using the 'approximate:true' setting in queries results in a consistent 3x slower performance compared to the default setting.
Source Examples
“ESQL approximate results is slower for date histograms ### Elasticsearch Version 9.6.x (main as of 24 August 2026) ### Installed Plugins _No response_ ### Java Version _bundled_ ### OS Version GCP edge ### Problem Description Query with `approximate:true` is consistently 3x slower than without this setting ### Steps to Reproduce Steps to reproduce, execute the below query Result: Executing this with `approximate: true` comes back in 935ms. This result is consistent (~3x) Expected: Faster than 300ms result (performance without `approximate: true` [Link to this environment (elastic employees only)](https://edge-lite-oblt.kb.us-west2.gcp.elastic-cloud.com/app/discover#/?_tab=(tabId:e637d065-3340-4ee5-a97b-064b2dd4cbbb)&_g=(filters:!(),refreshInterval:(pause:!t,value:60000),time:(from:now-15m,to:now))&_a=(breakdownField:log.level,columns:!('count(*)','BUCKET(@timestamp,%2050,%20%3F_tstart,%20%3F_tend)'),dataSource:(type:esql),filters:!(),hideChart:!f,interval:auto,isApproximate:!f,query:(esql:'FROM%20logs*%20%7C%20STATS%20count(*)%20by%20BUCKET(@timestamp,%2050,%20%3F_tstart,%20%3F_tend)'),sort:!())) ``` POST /_query/async?drop_null_columns=true { "query": "FROM logs* | STATS count(*) by BUCKET(@timestamp, 50, ?_tstart, ?_tend)", "time_zone": "America/New_York", "locale": "en", "include_execution_metadata": true, "settings": { "column_metadata": true }, "params": [ { "_tstart": "2026-08-24T13:29:52.760Z" }, { "_tend": "2026-08-24T13:44:52.760Z" } ], "filter": { "bool": { "must": [], "filter": [ { "range": { "@timestamp": { "format": "strict_date_optional_time", "gte": "2026-08-24T13:29:52.760Z", "lte": "2026-08-24T13:44:52.760Z" } } } ], "should": [], "must_not": [] } }, "approximation": true } ``` ### Logs (if relevant) _No response_”
Competitive Landscape
- Existing solutions are either too expensive or too limited
- Most competitors target enterprise, leaving mid-market underserved
- Community scripts and manual processes are the primary alternative
Recommended Next Steps
- ✓Validate pain intensity with 5-10 target customer interviews
- ✓Build minimal viable solution addressing the core workflow
- ✓Test pricing with early adopters from community forums
Related Pain Points
Target Customers
- IT teams at mid-size organizations (100-2000 employees)
- MSPs and consultants managing multiple client environments
- Teams without dedicated specialist staff for this domain
Monetization Ideas
- 1SaaS subscription model ($99-$499/month depending on scale)
- 2Usage-based pricing aligned with value delivered
- 3Freemium tier to drive adoption and prove value