Platform · Capabilities

Slow query to root cause
in under 30 seconds.

You see slow database queries linked directly to the application services and code paths that issued them — automatically, without any database-side agents.

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<30s

Query-to-root-cause time
Product capability

100%

Query plans captured — no DB-side agents
Product capability

65%

DB-related incident reduction
Illustrative example

Figures above reflect measured product behavior and representative scenarios — not a guarantee of results in every environment.

Key capabilities

Database Monitoring.

🖹

Query performance intelligence

N+1 patterns, missing indexes, and lock contention surfaced with the exact ORM method that issued the query.

↓ 65% DB incidents · illustrative

🔗

Connection pool monitoring

Pool saturation detected before exhaustion. Leaks identified by service. Backed by actual workload patterns.

📊

Replication & HA health

Primary/replica lag, failover events — correlated with application read performance.

💸

Cost optimisation

Unused indexes, over-provisioned instances — rightsizing backed by 30-day actuals.

Applicare — Database Monitoring⬤ Live product UI

Slow Queries

14/hr

checkout-svc · N+1

Conn Pool

96%

orders-db · near limit

Repl Lag

Normal

0.8s · healthy

DB Cost

Over-prov.

3 instances

🧠 ArcIn: N+1 in checkout-svc: OrderRepository executing 47 SELECTs/request. Fix: eager load on order_items. Expected ↓340ms p99.

Database query intelligence — every SQL query tracked with response times and error counts

Demonstration data — illustrative environment, not a specific customer's metrics

The Core Differentiator

Not just "your database is slow."

Generic database monitoring stops at the query. Applicare keeps going — through the application service, the code path, and into a recommended fix.

Slow query detected

1.8s SELECT flagged on the orders table — above baseline for this endpoint.

orders-db

Database identified

Query traced to the specific table, index path, and wait event driving the latency.

Query plan captured

Application service linked

The query is mapped back to the exact service that issued it — no manual correlation.

checkout-svc

Code path pinpointed

The class and method responsible are surfaced, down to the ORM call site.

OrderRepository

Root cause & fix

ArcIn explains the likely cause in plain English and recommends a specific, reviewable fix.

ArcIn-assisted

Applicare in Action

Don't just find the slow query.
Find the code causing it.

A walkthrough of the N+1 example above, in the order an engineer would actually see it.

Symptom

47 SELECTs/request

checkout endpoint

Service

checkout-svc

order confirmation path

Code path

OrderRepository

N+1 query pattern

Recommended fix

Eager-load order_items

ArcIn-suggested

Expected impact

↓ 340ms p99

Illustrative example

This walkthrough illustrates how Applicare presents an N+1 pattern end-to-end. The 340ms figure is a representative scenario, not a measured customer outcome.

Full Database Monitoring coverage.

🐘

MySQL & PostgreSQL

Query plans, slow query log, replication health, InnoDB buffer pool.

🪸

Oracle & SQL Server

Wait event analysis, execution plan regression, tablespace and license utilisation.

🌿

MongoDB & NoSQL

Collection scan detection, index utilisation, oplog lag, shard balance.

⚡

Redis & Memcached

Hit rate, eviction, memory fragmentation correlated with application performance.

☁️

RDS & Aurora

Full cloud database monitoring with connection pool, replica lag, and cost attribution.

🔓

Query Attribution

Every slow query linked to the service, class, method, and git commit that issued it.

Why Applicare Database Monitoring?

Symptom vs. root cause.

Generic database monitoring tells you a query is slow. Applicare tells you why, and where to fix it.

Capability	Traditional DB monitoring	Applicare
Slow queries	Shown	Shown
Database metrics	Shown	Shown
Wait events / locks	Shown	Shown
Link to application service	Manual investigation required	Correlated automatically
Responsible code path	Not identified	Class and method surfaced
Database ↔ application correlation	Separate tools, separate views	One causal view
Root-cause reasoning	Engineer interprets metrics alone	ArcIn assists with plain-English analysis
Path to fix	Symptom only — investigation is on you	Symptom → cause → actionable fix

Real-World Use Cases

Where this shows up
in production.

🐢

Slow application response

Problem: Users report lag; the app tier looks healthy.
Detects: The specific query and service driving the delay.
Why it matters: Cuts triage from guesswork to a direct pointer.

🔒

Database contention / locking

Problem: Transactions queue behind locks during peak load.
Detects: Blocking sessions and the queries holding locks.
Why it matters: Contention is resolved before it cascades.

🔗

Connection pool exhaustion

Problem: A pool nears its limit and requests start failing.
Detects: Saturation trend and the service leaking connections.
Why it matters: Caught before exhaustion causes an outage.

☁️

Cloud database over-provisioning

Problem: Instances are sized for peak but run idle most days.
Detects: Actual utilisation vs. provisioned capacity, over time.
Why it matters: Rightsizing decisions backed by real usage.

Slow query to root causein under 30 seconds.