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Database Monitoring

Find the query behind the slow request

Find slow queries and follow each one into the trace and service behind it. Monitor with zero-config eBPF, OpenTelemetry spans, or direct SQL, whichever depth your environment needs.

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Latency, workload, and impact — KloudMate database monitoring KloudMate · Databases Databases · Overview Latency, workload, and impact P99 latency 420ms Read / write 76 / 24 Hot tables 4 Database Throughput Latency Throughput orders-db primary · read heavy postgres 412ms 30 ops/min inventory-db write pressure rising mysql 233ms 18 ops/min catalog-db steady postgres 96ms 12 ops/min sessions cache · low latency redis 3ms 240 ops/min Related trace checkout · /db/postgres · the dominant span in the request waterfall

A slow query means little until you see the request it slowed.

KloudMate keeps query behavior wired to the services and traces it affects, so you can move from a slow query or hot table straight to the request path that exposed it.

What teams can do with Database Monitoring

Choose the right monitoring depth, connect database behavior to the services it affects, and see the moment a query touches sensitive data, all from one agent.

Start with the right monitoring depth

Use eBPF-powered DAM for zero-configuration visibility, OpenTelemetry for trace-linked spans, or direct monitoring for deeper SQL analytics.

Track latency and workload shape

Surface P99 query latency, query operations, read vs. write mix, and table-level hotspots before they become a visible regression.

Correlate queries with traces

Open the service trace that contains the database span so you can see which request path actually carries the bottleneck.

See sensitive-data access in real time

DAM's ML classifier flags queries that touch PII or PHI and tags them with a risk fingerprint, inside your own infrastructure, before any telemetry leaves your network.

Understand database impact on application performance

The useful investigation path is not just finding a slow query. It is confirming which service, trace, or workload made that query business-critical.

01

Choose your monitoring method

Pick DAM, OpenTelemetry, or direct monitoring based on how much setup and depth the environment allows.

02

Watch query latency and workload mix

Use latency, throughput, and read/write ratios to identify the tables, query families, or databases changing first.

03

Open the affected trace

Follow the dominant database span into the service trace to confirm which request path is carrying the slowdown.

04

Escalate with evidence

Hand the issue to the owning team with the query family, related service, and trace evidence already attached.

Where latency is accumulating — KloudMate database monitoring KloudMate · Databases Databases · Query families Where latency is accumulating Observed DBs 4 Mode DAM + OTel P99 watchlist 6 Query family Calls Latency Throughput orders.select_by_customer read heavy postgres 412ms 1.2k calls inventory.update_stock write pressure mysql 233ms 840 calls payments.lookup_session cache misses rising redis 151ms 9.1k calls catalog.search_filters fan-out pattern postgres 108ms 2.3k calls Suggested next view Open Trace Explorer · the request path carrying orders.select_by_customer

Track slow queries, latency, and workload hotspots

Database monitoring is most useful when it shows which tables, query families, or workloads are driving latency instead of just reporting one average number.

  • Use DAM for zero-configuration profiling of supported databases at the kernel level
  • Track tail latency and workload characteristics such as read/write ratio and hot tables
  • Choose deeper direct monitoring when you need execution plans or schema-level analysis
From slow query to incident evidence — KloudMate correlation Application correlation From slow query to incident evidence 01 Latency rises
orders query crosses the tail-latency threshold
 02 Trace confirms
checkout waterfall dominated by a postgres span
 03 Owner found
inventory + payments share the DB bottleneck
 04 Response
alert carries the query family and trace
 Trace span db.statement hotspot same request path tops recent traces Operational Connection pressure 71% approaching steady-state limit

Correlate database slowdowns with services and traces

Database work only becomes operationally meaningful when teams can confirm which service path or release turned a query issue into a production incident.

  • Follow a slow query or hot table into the service and trace behind it
  • Use OpenTelemetry database spans when the request path itself matters most
  • Attach the affected query family to alerts or incidents instead of filing context-free tickets
Queries flagged for PII and PHI — KloudMate DAM ML classifier KloudMate · Databases DAM · ML Classifier Queries flagged for PII and PHI Inspected 12.4k PII 38 PHI 6 Classified inside your infrastructure. Raw values never leave your network Query Detected Sensitivity SELECT email, ssn FROM users email · ssn PII SELECT diagnosis FROM patient_records diagnosis PHI SELECT brand, last4 FROM cards card PII UPDATE orders SET status = ? WHERE id = ? - Clean

Catch sensitive-data access without the data leaving

KloudMate's eBPF monitoring (DAM) watches every query at the kernel level, no credentials, no code changes. Its ML classifier flags queries touching PII or PHI in real time and tags them with a risk fingerprint, all inside your own infrastructure, before any telemetry is exported.

  • eBPF captures every query at the kernel level, no database credentials, no code changes
  • An ML classifier flags PII and PHI in query text as it happens
  • Risk fingerprints are added before telemetry leaves. Raw values never egress your network
  • One agent covers MySQL, PostgreSQL, and Redis
KloudMate AI

Use KloudMate Assistant to explain a database slowdown

Assistant can summarize which database signal changed first, call out the related service traces, and suggest whether the team should open query detail, connection pressure, or application logs next.

  • Explain Highlight the query family or table most likely driving the regression
  • Correlate Connect database spans to the request paths carrying the visible impact
  • Prioritize Point responders toward the next useful query, trace, or log search
Explore platform
What is slowing checkout? — KloudMate Auto-RCA Assistant summary What is slowing checkout? Q
Summarize whether the regression is caused by the database or the application tier.
 Assistant · likely cause
  • Tail latency rose first on orders.select_by_customer, then propagated into checkout traces.
  • The strongest correlation is the postgres span cluster inside checkout and inventory requests.
  • Compare recent query shape changes before treating this as an application-only issue.
 First signal P99 query latency increased orders.select_by_customer Related request path checkout → inventory → postgres same 30 minute window Suggested next check Trace waterfall + query detail look for recent query pattern changes

Related Features

Keep the rest of the workflow close by so teams can move between detection, investigation, and response without losing context.

APM & Distributed Tracing

Trace requests across services, inspect dependencies, and move from latency symptoms to request-level evidence.

Learn more

Log Management

Search, filter, inspect, and stream logs with trace and incident context attached.

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Alerting

Build precise rules, route alerts by label, group related firings into one, and attach a likely cause automatically.

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Incident Management

Coordinate response, ownership, escalation, and telemetry context in one incident workflow.

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From telemetry to root cause,
in one platform.

Connect your OpenTelemetry pipeline, AWS integrations, or eBPF agent. Distributed tracing, log management, alerting, and AI-assisted investigation: unified, with predictable pricing.

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