Oracle Database Monitoring
Tuning Pack を買わずに済む Oracle 監視。
Obsfly は v$session、v$sql、v$active_session_history、v$sysstat を読み取り — Tuning Pack も Diagnostic Pack も $42k の監査リスクも不要です。
Why monitor Oracle
Oracle has the most comprehensive instrumentation of any database — and the most expensive proprietary tools to use it. Obsfly surfaces the views every Oracle DBA already knows, with modern UX and per-DB pricing.
What we scrape
Obsfly reads Oracle through the surfaces operators already know. No driver changes, no extensions installed by us, no agent on the database itself.
v$session / v$session_wait
Live sessions, wait events, blocking sessions.
v$sqlarea / v$sql / v$sqlstats
Per-SQL_ID execution stats: executions, elapsed_time, buffer_gets, disk_reads.
v$active_session_history (ASH)
1-second sampling of active sessions for retroactive analysis.
v$system_event / v$session_event
Wait event histograms across the instance and per session.
v$pgastat / v$sgastat
PGA / SGA memory utilization.
v$dataguard_stats / v$archive_dest_status
Data Guard apply lag, transport state.
Key metrics tracked
Common Oracle pains, and how Obsfly surfaces each
Library cache contention spike
Sign
wait_class = Concurrency dominates v$session_wait; cursor: pin S wait on X events.
Fix
Hard-parsing storms — find the offending app issuing literal SQL instead of bind variables.
Buffer busy waits on a single object
Sign
v$segment_statistics ranks one segment top for buffer_busy_waits.
Fix
Hot block contention. Reverse-key index, partition the table, or reorganize.
Data Guard apply lag growing
Sign
v$dataguard_stats apply_lag climbs; transport_lag stable.
Fix
Standby IO bottleneck or single-threaded apply. Verify Real-Time Apply, MRP slave count.
vs Datadog DBM for Oracle
Obsfly features for Oracle
Feature
Query Summary
Top-N normalized queries with p50 / p95 / p99 latency, QPS, total time, rows touched, and plan-change history.
Feature
Query Activity
Live query stream with wait events, lock chains, slow-query alerts, and sample-once-per-second activity snapshots.
Feature
Anomaly Detection
ML-driven anomaly detection on every metric. Forecast bands, change-point detection, no thresholds to tune.
FAQ
Do I need the Diagnostics or Tuning Pack?+
No. Obsfly uses the publicly licensed v$ views. ASH and AWR are licensed; we use v$active_session_history when available, and fall back to v$session sampling otherwise.
What Oracle versions?+
11g R2, 12c, 19c, 21c, 23c. RAC and Data Guard supported.
Container databases (CDB/PDB) — supported?+
Yes. Multi-tenant Oracle scrapes per-PDB stats and rolls up to the CDB level.
Deep dives on Oracle
Postgres
Postgres slow queries: 12 causes and how to find each one
A field-tested playbook for diagnosing a slow Postgres query in production — from missing indexes to plan flips to bloated tables — with the SQL to find each cause and the fix.
AI
Anomaly detection on database metrics: why thresholds fail and what works
A walk through forecast bands, change-point detection, multi-variate anomaly, and the seasonality math that makes 'p99 over 200ms' the wrong alert by default — with the Postgres example that broke our last threshold.