MicrocosmWorksื—ื“ืฉื ื•ืช ื•ืชื›ื ื•ืŸ ืงื•ืกืžื•ืก ื“ื™ื’ื™ื˜ืœื™
ืื•ื“ื•ืชืฆื•ืจ ืงืฉืจ
MicrocosmWorksืžื—ื“ืฉื™ื ื•ืžืชื›ื ื ื™ื ืงื•ืกืžื•ืก ื“ื™ื’ื™ื˜ืœื™

ืžืกืคืงื™ื ืคืชืจื•ื ื•ืช IT ื—ืฉื•ื‘ื™ื. ืื ื• ื ืœื”ื‘ื™ื ืžื˜ื›ื ื•ืœื•ื’ื™ื”, ืื‘ื˜ื—ื” ื•ืขื•ื–ืจื™ื ืœืขืกืงื™ื ืœืฆืžื•ื— ื‘ืืžืฆืขื•ืช ืชืฉืชื™ืช IT ืืžื™ื ื” ื•ื—ื“ืฉื ื™ืช.

[email protected]
+91 7011868196
New Delhi, India

ืžืจื›ื– ืฆืžื™ื—ื” AI

ืžืจื›ื– AIื—ื“ืฉื ื•ืช ืกื˜ืืจื˜ืืคืžืื™ืฅ ืืจื’ื•ื ื™

ืคืชืจื•ื ื•ืช

ื›ืœ ื”ืคืชืจื•ื ื•ืชืืคืœื™ืงืฆื™ื•ืช ื‘ืจื™ืื•ืช ื•ื›ื•ืฉืจืคืœื˜ืคื•ืจืžืช ื•ื™ื“ืื• AIืคื™ืชื•ื— ืกื•ื›ื ื™ AI

ืžืฉืื‘ื™ื

ืชื•ื‘ื ื•ืชืžื“ืจื™ื›ื™ ืชืขืฉื™ื™ื”ืชื•ื›ื ื™ื•ืช ืžืงืจื” ืฉื™ืžื•ืฉืชื‘ื ื™ื•ืช ืืจื›ื™ื˜ืงื˜ื•ืจื”ืžื—ืงืจื™ ืžืงืจื”

ื—ื‘ืจื”

ืื•ื“ื•ืชื™ื ื•ืฆื•ืจ ืงืฉืจื”ืขื‘ื•ื“ื” ืฉืœื ื•

ืฉื™ืจื•ืชื™ื

ื™ื™ืขื•ืฅ ื“ื™ื’ื™ื˜ืœื™ืชืฉืชื™ืช ืขื ืŸืคื™ืชื•ื— SaaSืคื™ืชื•ื— AIื˜ื›ื ื•ืœื•ื’ื™ื™ืช ื•ื™ื“ืื•
ืคื™ืชื•ื— ERPื”ืชืืžื” ืื™ืฉื™ืช ืฉืœ Zohoืคื™ืชื•ื— Odooืื™ื ื˜ื’ืจืฆื™ื” ืฉืœ Salesforceืคื™ืชื•ื— CRM ืžื•ืชืื ืื™ืฉื™ืช
ืื™ื ื˜ื’ืจืฆื™ื” ืฉืœ QuickBooksืคืชืจื•ื ื•ืช IoTืคื™ืชื•ื— ื‘ืœื•ืงืฆ'ื™ื™ืŸ
ื™ื™ืขื•ืฅ ืกื™ื™ื‘ืจืชืžื™ื›ื” ื˜ื›ื ื™ืช - L3

ยฉ 2026 MicrocosmWorks. ื›ืœ ื”ื–ื›ื•ื™ื•ืช ืฉืžื•ืจื•ืช.

ืžื“ื™ื ื™ื•ืช ืคืจื˜ื™ื•ืชืชื ืื™ ืฉื™ืจื•ืช
ื—ื–ืจื” ืœืžืจื›ื– ื”ืคื™ืชื•ื—
Performance & Scalability

ืฉื™ืจื•ืชื™ ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืœืฉืื™ืœืชื•ืช

ืฉื™ืจื•ืชื™ ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืœืฉืื™ืœืชื•ืช ืžืกื“ ื ืชื•ื ื™ื. ืื ื• ืžื ืชื—ื™ื ื•ืžืชืงื ื™ื ืฉืื™ืœืชื•ืช ืื™ื˜ื™ื•ืช, ืžืชื›ื ื ื™ื ืื™ื ื“ืงืกื™ื ื™ืขื™ืœื™ื ื•ืžืฉื ื™ื ืžื‘ื ื” ืฉืœ ื“ืคื•ืกื™ ื’ื™ืฉื” ืœื ืชื•ื ื™ื ืขื‘ื•ืจ ืฉื™ืคื•ืจื™ื ื‘ืกื“ืจื™ ื’ื•ื“ืœ.

ื”ืชื—ื™ืœื•
ืฉื™ืจื•ืชื™ ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืœืฉืื™ืœืชื•ืช
3x
Avg Performance Gains
99.9%
Availability
1M+
RPM Capacity
<50ms
P95 Latency
ืงื˜ื’ื•ืจื™ื™ืช ืฉื™ืจื•ืช
ื”ื ื“ืกืช ื‘ื™ืฆื•ืขื™ ืฉืื™ืœืชื•ืช
ืžืชืื™ื ืœ
ืืคืœื™ืงืฆื™ื•ืช ืขื ืฉืื™ืœืชื•ืช ืžืกื“ ื ืชื•ื ื™ื ืื™ื˜ื™ื•ืช ื”ื’ื•ืจืžื•ืช ืœ-latency, ืฉื™ืžื•ืฉ ื’ื‘ื•ื” ื‘ืžืขื‘ื“ ืื• ื‘ืขื™ื•ืช lock contention.
ืœื•ื— ื–ืžื ื™ื
ืฉื‘ื•ืข โ€“ 3 ืฉื‘ื•ืขื•ืช

ืœืžื” ืœื‘ื—ื•ืจ ื‘-MicrocosmWorks ืœืื•ืคื˜ื™ืžื™ืฆื™ื™ืช ืฉืื™ืœืชื•ืช?

ืฉืื™ืœืชื•ืช ืื™ื˜ื™ื•ืช ื”ืŸ ื”ื’ื•ืจื ื”ืขื™ืงืจื™ ืœื‘ืขื™ื•ืช ื‘ื™ืฆื•ืขื™ื ื‘ืืคืœื™ืงืฆื™ื•ืช. ืื ื• ืžืฉืชืžืฉื™ื ื‘ื ื™ืชื•ื— ืฉื™ื˜ืชื™ โ€” EXPLAIN plans, ืกื˜ื˜ื™ืกื˜ื™ืงื•ืช, ื“ืคื•ืกื™ ื’ื™ืฉื” ื•ืคืจื•ืคื™ืœ ืขื•ืžืกื™ื โ€” ื›ื“ื™ ืœื–ื”ื•ืช ื‘ื“ื™ื•ืง ืžื“ื•ืข ืฉืื™ืœืชื•ืช ืื™ื˜ื™ื•ืช ื•ืœื™ื™ืฉื ืคืชืจื•ื ื•ืช ืฉืžืกืคืงื™ื ืฉื™ืคื•ืจื™ ืžื”ื™ืจื•ืช ืคื™ 10-100. ืœืœื ื ื™ื—ื•ืฉื™ื, ืจืง ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืžื‘ื•ืกืกืช ื ืชื•ื ื™ื.

ื™ื›ื•ืœื•ืช ืื•ืคื˜ื™ืžื™ืฆื™ื™ืช ื”ืฉืื™ืœืชื•ืช ืฉืœื ื•

  • ื ื™ืชื•ื— ืฉืื™ืœืชื•ืช ื•-EXPLAIN โ€” ืฆืœื™ืœื” ืขืžื•ืงื” ืœืชื•ื›ื ื™ื•ืช ื‘ื™ืฆื•ืข, ื–ื™ื”ื•ื™ ืกืจื™ืงื•ืช ืกื“ืจืชื™ื•ืช, ื‘ืขื™ื•ืช ื‘ืกื“ืจ ื”-join, ื•ื‘ื—ื™ืจื•ืช ืžืชื›ื ืŸ ืœื ืื•ืคื˜ื™ืžืœื™ื•ืช.
  • ืืกื˜ืจื˜ื’ื™ื™ืช ืื™ื ื“ืงืกื™ื โ€” ืชื›ื ื•ืŸ ืืกื˜ืจื˜ื’ื™ื•ืช ืื™ื ื“ืงืก ืžืงื™ืคื•ืช ื”ื›ื•ืœืœื•ืช composite indexes, partial indexes, covering indexes ื•-GIN/GiST indexes ืขื‘ื•ืจ ืกื•ื’ื™ ื ืชื•ื ื™ื ืžื™ื•ื—ื“ื™ื.
  • ื›ืชื™ื‘ื” ืžื—ื“ืฉ ืฉืœ ืฉืื™ืœืชื•ืช โ€” ืฉื™ื ื•ื™ ืžื‘ื ื” ืฉืื™ืœืชื•ืช ื›ื“ื™ ืœื‘ื˜ืœ ื“ืคื•ืกื™ N+1, ืœื‘ืฆืข ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืœ-CTEs, ืœื”ื—ืœื™ืฃ correlated subqueries ื•ืœืžื ืฃ window functions.
  • ืื•ืคื˜ื™ืžื™ืฆื™ื™ืช ืกื›ื™ืžื” โ€” ื ืจืžื•ืœ ืื• ื“ื”-ื ืจืžื•ืœ ืืกื˜ืจื˜ื’ื™, ื”ื•ืกืคืช materialized views ื•ืฉื™ื ื•ื™ ืžื‘ื ื” ื˜ื‘ืœืื•ืช ืขื‘ื•ืจ ื“ืคื•ืกื™ ื”ื’ื™ืฉื” ื”ืืžื™ืชื™ื™ื ืฉืœื›ื.
  • ืื•ืคื˜ื™ืžื™ืฆื™ื™ืช ื—ื™ื‘ื•ืจื™ื ื•-Pools โ€” ื›ื™ื•ื•ื ื•ืŸ connection pools, ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืœื ื™ื”ื•ืœ ื˜ืจื ื–ืงืฆื™ื•ืช ื•ื”ืคื—ืชืช lock contention ืขื‘ื•ืจ ืขื•ืžืกื™ ืขื‘ื•ื“ื” ืžืงื‘ื™ืœื™ื.
  • ื ื™ื˜ื•ืจ ื•ื”ืชืจืื•ืช โ€” ื”ื’ื“ืจืช slow query logging, ืžืขืงื‘ pg_stat_statements ื•ื”ืชืจืื•ืช ืื•ื˜ื•ืžื˜ื™ื•ืช ืขื‘ื•ืจ ืจื’ืจืกื™ื” ื‘ื‘ื™ืฆื•ืขื™ ืฉืื™ืœืชื•ืช.

ืžื—ืกื ื™ืช ื˜ื›ื ื•ืœื•ื’ื™ืช

ืื ื• ืขื•ื‘ื“ื™ื ื‘ืขื™ืงืจ ืขื PostgreSQL (ื›ื•ืœืœ Aurora, Neon, Supabase), MySQL ื•-MongoDB. ื”ื ื™ืชื•ื— ืฉืœื ื• ืžืฉืชืžืฉ ื‘-pg_stat_statements, auto_explain, pganalyze ื•ื‘ืคืจื•ืคื™ืœ ืฉืื™ืœืชื•ืช ืžื•ืชืื ืื™ืฉื™ืช. ื”ืคืชืจื•ื ื•ืช ื›ื•ืœืœื™ื ืืกื˜ืจื˜ื’ื™ื•ืช ืื™ื ื“ืงืกื™ื, ื›ืชื™ื‘ื” ืžื—ื“ืฉ ืฉืœ ืฉืื™ืœืชื•ืช, materialized views ื•ืฉื™ื ื•ื™ื™ื ื‘ืจืžืช ื”ืืคืœื™ืงืฆื™ื”.

ืœืžื™ ื–ื” ืžื™ื•ืขื“

ืืคืœื™ืงืฆื™ื•ืช ืขื ืฉืื™ืœืชื•ืช ืžืกื“ ื ืชื•ื ื™ื ืื™ื˜ื™ื•ืช ื”ื’ื•ืจืžื•ืช ืœ-latency ืœืžืฉืชืžืฉ, ืฉื™ืžื•ืฉ ื’ื‘ื•ื” ื‘ืžืขื‘ื“ ืฉืœ ืžืกื“ ื”ื ืชื•ื ื™ื ืื• lock contention. ื‘ื™ืŸ ืื ืžื“ื•ื‘ืจ ื‘ื›ืžื” ืฉืื™ืœืชื•ืช ืงืจื™ื˜ื™ื•ืช ืฉืžืื˜ื•ืช ืืช ื”ื“ืฉื‘ื•ืจื“ ืฉืœื›ื ืื• ื“ืคื•ืกื™ ืฉืื™ืœืชื•ืช ืžืขืจื›ืชื™ื™ื ื”ื’ื•ืจืžื™ื ืœืื™ื˜ื™ื•ืช ื ืจื—ื‘ืช, ืื ื—ื ื• ืžืชืงื ื™ื ื–ืืช.

ื”ืชื”ืœื™ืš ืฉืœื ื•

1

Query Audit

Identify top slow queries using pg_stat_statements, analyze access patterns, and measure baseline performance.

2

Root Cause Analysis

Run EXPLAIN ANALYZE on each slow query, identify missing indexes, bad joins, and suboptimal patterns.

3

Optimization

Implement index changes, query rewrites, schema adjustments, and caching for most impactful queries.

4

Validation

Benchmark optimized queries, verify no regressions in other queries, and load test under concurrent access.

5

Ongoing Monitoring

Set up slow query tracking, regression alerts, and periodic review cadence for continued optimization.

ืžืขืจืš ื˜ื›ื ื•ืœื•ื’ื™

Databases

PostgreSQLMySQLMongoDBAuroraNeon

Analysis

pg_stat_statementsEXPLAIN ANALYZEauto_explainpganalyze

Optimization

IndexesMaterialized ViewsQuery RewritingPartitioning

Monitoring

pganalyzeDataDogPrometheusCustom Dashboards

ืชืขืฉื™ื•ืช ืฉืื ื• ืžืฉืจืชื™ื

SaaSE-CommerceFinTechAnalyticsHealthcareEnterprise

ืžื•ื›ื ื™ื ืœืชืงืŸ ืืช ื”ืฉืื™ืœืชื•ืช ื”ืื™ื˜ื™ื•ืช ืฉืœื›ื?

ื‘ื•ืื• ื ื”ืคื•ืš ืืช ืžืกื“ ื”ื ืชื•ื ื™ื ืฉืœื›ื ืžืฆื•ื•ืืจ ื‘ืงื‘ื•ืง ืœื ื›ืก ื‘ื™ืฆื•ืขื™ื ื‘ืืžืฆืขื•ืช ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืžืžื•ืงื“ืช ืฉืœ ืฉืื™ืœืชื•ืช.

ืฆืจื• ืงืฉืจืฆืคื• ื‘ื›ืœ ื”ืฉื™ืจื•ืชื™ื

ืฉืืœื•ืช ื ืคื•ืฆื•ืช

ืื ื• ืžื ืชื—ื™ื ืชื•ื›ื ื™ื•ืช ื‘ื™ืฆื•ืข ื‘ืืžืฆืขื•ืช EXPLAIN ANALYZE, ืžื–ื”ื™ื ืื™ื ื“ืงืกื™ื ื—ืกืจื™ื ืื• ืžื™ื•ืชืจื™ื, ื›ื•ืชื‘ื™ื ืžื—ื“ืฉ ืชืช-ืฉืื™ืœืชื•ืช ื›-joins, ืžื‘ื˜ืœื™ื ื“ืคื•ืกื™ N+1, ื•ืžื‘ืฆืขื™ื ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืœืกื˜ื˜ื™ืกื˜ื™ืงื•ืช ื˜ื‘ืœื” ื›ื“ื™ ืœื”ื‘ื˜ื™ื— ืฉืžืชื›ื ืŸ ื”ืฉืื™ืœืชื•ืช ื™ืงื‘ืœ ื”ื—ืœื˜ื•ืช ืื•ืคื˜ื™ืžืœื™ื•ืช.

ืฉื™ืจื•ืชื™ ืื•ืคื˜ื™ืžื™ื–ืฆื™ื™ืช ืฉืื™ืœืชื•ืช ื‘-MicrocosmWorks ื ืขื™ื ื‘ื™ืŸ 20 ืœ-45 ื“ื•ืœืจ ืœืฉืขื”. ืื ื—ื ื• ื‘ื“ืจืš ื›ืœืœ ืžืชื—ื™ืœื™ื ื‘-profiling ืฉืœ ื”ืฉืื™ืœืชื•ืช ื”ืื™ื˜ื™ื•ืช ื‘ื™ื•ืชืจ ืฉืœืš ื•ืžืชืงื ื™ื ืงื•ื“ื ื›ืœ ืืช ืืœื” ื‘ืขืœื•ืช ื”ื”ืฉืคืขื” ื”ื’ื‘ื•ื”ื” ื‘ื™ื•ืชืจ, ื•ืžืกืคืงื™ื ืฉื™ืคื•ืจื™ื ืžื“ื™ื“ื™ื ืชื•ืš ื™ืžื™ื.

ื›ืŸ, ืื ื• ืžื‘ืฆืขื™ื ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืœืžืกื“ื™ ื ืชื•ื ื™ื ื‘ืงื ื” ืžื™ื“ื” ื’ื“ื•ืœ ืชื•ืš ืฉื™ืžื•ืฉ ื‘-Table Partitioning, ื‘-Partial Indexes, ื‘-Materialized Views, ื‘-Covering Indexes ื•ื‘-Query Restructuring, ื›ื“ื™ ืœืฉืžื•ืจ ืขืœ ื–ืžื ื™ ืชื’ื•ื‘ื” ืฉืœ ืคื—ื•ืช ืžืฉื ื™ื™ื”, ืืคื™ืœื• ื‘ื˜ื‘ืœืื•ืช ืขื ืžื™ืœื™ืืจื“ื™ ืฉื•ืจื•ืช.

ื‘ื”ื—ืœื˜. ืื ื• ืžื‘ืงืจื™ื SQL ืฉื ื•ืฆืจ ืขืœ ื™ื“ื™ ORM ืž-Django, SQLAlchemy, Prisma, Hibernate ื•-ORMs ืื—ืจื™ื, ืžื–ื”ื™ื ื‘ืขื™ื•ืช eager/lazy loading, joins ืžื™ื•ืชืจื™ื ื•ืชื‘ื ื™ื•ืช N+1, ื•ืœืื—ืจ ืžื›ืŸ ืžื™ื™ืขืœื™ื ืืช ื”ืฉื™ืžื•ืฉ ื‘-ORM ืื• ืžื•ืกื™ืคื™ื raw SQL ื”ื™ื›ืŸ ืฉืฆืจื™ืš.

ืื ื• ืžืงื™ืžื™ื ื ื™ื˜ื•ืจ ืื•ื˜ื•ืžื˜ื™ ืฉืœ ื‘ื™ืฆื•ืขื™ ืฉืื™ืœืชื•ืช ื‘ืืžืฆืขื•ืช pg_stat_statements ืื• ื›ืœื™ื ื“ื•ืžื™ื, ื™ื•ืฆืจื™ื regression test suites ืœืฉืื™ืœืชื•ืช ืงืจื™ื˜ื™ื•ืช, ื•ืžื’ื“ื™ืจื™ื ื”ืชืจืื•ืช ืฉืžื•ืคืขืœื•ืช ื›ืืฉืจ ื–ืžื ื™ ื‘ื™ืฆื•ืข ื”ืฉืื™ืœืชื•ืช ื—ื•ืจื’ื™ื ืžืงื•ื•ื™ ื‘ืกื™ืก ืžื•ื’ื“ืจื™ื.