Databases
AuraScore 79/100

Productivity Workspace Document Store Indexing Checklist

Engineering checklist for optimizing secondary indexes, shard distribution, and query latency in collaborative document databases.

Use this checklist when tuning database performance for document-heavy productivity tools, ensuring rapid query execution, balanced write costs, and bounded storage growth.

Template

Role: Staff Database Reliability Engineer for High-Throughput Productivity Applications.

Context

  • Workspace platform: {{workspace_platform}}
  • Document database engine: {{database_engine}}
  • Primary query patterns: {{primary_query_patterns}}
  • Maximum acceptable p95 latency: {{target_p95_latency_ms}}
  • Horizontal partitioning key: {{partition_strategy}}
  • Dedicated index storage ceiling: {{max_storage_budget_gb}}

Task

Deliver an operational indexing and query optimization checklist for database engineers managing {{workspace_platform}}, ensuring index overhead stays within {{max_storage_budget_gb}} while meeting a {{target_p95_latency_ms}} latency target on {{database_engine}}.

Method

  1. Analyze current execution plans (EXPLAIN / ANALYZE) for {{primary_query_patterns}} to identify full collection scans.
  2. Audit existing secondary and compound indexes on {{database_engine}} to detect unused or duplicate indexes.
  3. Evaluate index cardinality and prefix ordering to optimize compound index prefix reuse.
  4. Measure the write-amplification impact of candidate indexes during peak collaborative editing spikes.
  5. Validate partition distribution along {{partition_strategy}} to prevent query scatter-gather bottlenecks across shards.
  6. Verify partial/sparse index feasibility for optional metadata fields to conserve memory.
  7. Test memory residency of active working sets and cache hit ratios against {{max_storage_budget_gb}} limits.

Constraints

  • MUST specify the metric or command used to confirm each checklist step.
  • MUST NOT exceed {{max_storage_budget_gb}} across combined primary and secondary indexes.
  • All index recommendations must directly support {{primary_query_patterns}}.
  • Keep steps strictly operational and applicable to {{database_engine}}.

Output format

1. Performance Baseline Summary

  • Structured table listing target latencies, shard key, and storage limits.

2. Query Plan & Scan Detection Checklist

  • 3-4 markdown checklist items [ ] focused on index utilization and scan elimination.

3. Index Structure & Compaction Checklist

  • 4-5 markdown checklist items [ ] focused on compound fields, partial indexes, and memory footprint.

4. Partitioning & Shard Alignment Checklist

  • 3-4 markdown checklist items [ ] focused on {{partition_strategy}} routing and cross-shard queries.

Self-review

  • Confirm that the latency target of {{target_p95_latency_ms}} is integrated into the validation criteria.
  • Ensure each section includes markdown checkboxes [ ] with explicit measurement commands.
  • Verify that write overhead and storage limits are addressed alongside read latency.
AuraScore breakdown
79/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering10/12 · Adequate

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

developers
developers-databases
research-productivity-operations
databases
nosql
query-optimization