65 automated tests. CXO-ready HTML alerts. Real-time anomaly detection. Built entirely in Snowflake SQL — no external tools.
The Uncomfortable Truth About AI Search in Production
Most organizations deploying AI-powered search have zero visibility into what’s actually happening under the hood.
Users are searching. Results are returning. Credits are burning. But nobody can answer:
- Which queries are failing silently?
- Who is consuming the most AI credits?
- Are we meeting our latency SLAs?
- Is someone scraping data through the search API?
I faced this exact gap with Snowflake Cortex Search — a powerful hybrid vector + keyword search service. The service worked beautifully. But operationally? It was a black box.
So I built an enterprise-grade observability platform that transforms Cortex Search from opaque AI infrastructure into a fully governed capability — with executive HTML reports, automated anomaly detection, and a 65-test validation suite.
All built natively in Snowflake. No external tools. Just SQL.
What We Built
Here’s what the final platform delivers:
- 23+ monitoring views parsing real-time telemetry
- 5 production alerts with HTML executive email delivery
- Cost attribution using actual Snowflake billing data (not estimates)
- PII detection & dynamic masking for search queries
- 65-test automated validation suite (100% pass rate)
- CXO executive reports with built-in anomaly detection
Full source code (4,800+ lines) available on GitHub: snowflake-cortex-search-monitoring-v2
Architecture at a Glance
The key insight: one configuration change activates the entire telemetry pipeline.
Repository Structure
The project is organized into numbered SQL files for sequential deployment:
| File Name | Purpose |
| ------------------------------- | -------------------------------------------------------------------------------------------------- |
| `01_rbac_infrastructure.sql` | Creates foundational RBAC infrastructure including databases, schemas, roles, and warehouses |
| `02_enable_request_logging.sql` | Activates and configures the telemetry and request logging pipeline |
| `03_vw_search_requests.sql` | Builds the foundational view that serves as the single source of truth for search request analysis |
| `04_monitoring_views.sql` | Creates 23+ operational monitoring and observability views |
| `05_sla_framework.sql` | Implements a configuration-driven SLA and threshold management framework |
| `06_cost_attribution.sql` | Enables cost allocation and chargeback reporting using actual billing data |
| `07_pii_masking.sql` | Implements PII detection, classification, and dynamic masking policies |
| `08_html_alerts.sql` | Creates 5 executive-grade HTML alerting workflows and notification templates |
| `09_test_suite.sql` | Deploys a comprehensive 65-test validation and quality assurance suite |
| `10_cxo_report.sql` | Generates executive CXO dashboards and anomaly detection reports |
| `full_implementation.sql` | Provides a complete end-to-end single-file deployment package |
| `deploy.sh` | Automates environment setup and deployment orchestration |
Prerequisites
| Requirement | Details |
| ------------------------- | -------------------------------------------------------------------------------------- |
| Snowflake Account | Enterprise Edition or Business Critical Edition |
| Required Role | `ACCOUNTADMIN` role for the initial environment setup and deployment |
| Warehouse Size | `XSMALL` warehouse is sufficient for deployment and monitoring workloads |
| Estimated Cost | Approximately ~50 credits/month depending on query volume and monitoring frequency |
| Cortex Search Requirement | At least one Cortex Search Service configured with `REQUEST_LOGGING = TRUE` |
| Telemetry Dependency | Request logging must be enabled to capture observability and monitoring metrics |
| Recommended Environment | Dedicated monitoring database and schema for operational isolation |
| Security Recommendation | Use role-based access control (RBAC) and masking policies for sensitive telemetry data |
| Optional Enhancements | Email alerts, SLA tracking, anomaly detection, and executive dashboards |
The 10-Step Implementation
Step 1 — Enable Request Logging
One configuration change activates the entire telemetry pipeline:
ALTER CORTEX SEARCH SERVICE your_database.your_schema.your_service
SET REQUEST_LOGGING = TRUE;
Once enabled, every search request automatically emits a structured event to SNOWFLAKE.LOCAL.AI_OBSERVABILITY_EVENTS. Everything else in the platform builds on top of this single stream.
Step 2 — RBAC & Infrastructure Setup
Three-tier role hierarchy following least-privilege principles.
Source: 01_rbac_infrastructure.sql
-- ============================================================
-- DATABASE & SCHEMA SETUP
-- ============================================================
CREATE DATABASE IF NOT EXISTS PRD_CORTEX_OBSERVABILITY;
CREATE SCHEMA IF NOT EXISTS PRD_CORTEX_OBSERVABILITY.MONITORING;
CREATE SCHEMA IF NOT EXISTS PRD_CORTEX_OBSERVABILITY.AGGREGATED;
CREATE SCHEMA IF NOT EXISTS PRD_CORTEX_OBSERVABILITY.GOVERNANCE;
-- ============================================================
-- ROLE HIERARCHY (Least Privilege)
-- ============================================================
CREATE ROLE IF NOT EXISTS CORTEX_OBS_READONLY; -- masked query text (PII protection)
CREATE ROLE IF NOT EXISTS CORTEX_OBS_ANALYST; -- full data access
CREATE ROLE IF NOT EXISTS CORTEX_OBS_ADMIN; -- manage policies & config
GRANT ROLE CORTEX_OBS_READONLY TO ROLE CORTEX_OBS_ANALYST;
GRANT ROLE CORTEX_OBS_ANALYST TO ROLE CORTEX_OBS_ADMIN;
-- ============================================================
-- DEDICATED WAREHOUSE WITH RESOURCE MONITOR
-- ============================================================
CREATE WAREHOUSE IF NOT EXISTS CORTEX_OBS_WH
WAREHOUSE_SIZE = 'XSMALL'
AUTO_SUSPEND = 60
AUTO_RESUME = TRUE
COMMENT = 'Dedicated WH for Cortex Search observability';
CREATE RESOURCE MONITOR CORTEX_OBS_MONITOR
WITH CREDIT_QUOTA = 50
TRIGGERS
ON 75 PERCENT DO NOTIFY
ON 90 PERCENT DO NOTIFY
ON 100 PERCENT DO SUSPEND;
ALTER WAREHOUSE CORTEX_OBS_WH
SET RESOURCE_MONITOR = CORTEX_OBS_MONITOR;
-- ============================================================
-- GRANTS
-- ============================================================
GRANT USAGE ON WAREHOUSE CORTEX_OBS_WH
TO ROLE CORTEX_OBS_READONLY;
GRANT USAGE ON DATABASE PRD_CORTEX_OBSERVABILITY
TO ROLE CORTEX_OBS_READONLY;
GRANT USAGE ON ALL SCHEMAS IN DATABASE PRD_CORTEX_OBSERVABILITY
TO ROLE CORTEX_OBS_READONLY;
GRANT SELECT ON ALL VIEWS IN SCHEMA PRD_CORTEX_OBSERVABILITY.MONITORING
TO ROLE CORTEX_OBS_READONLY;
GRANT ALL PRIVILEGES ON DATABASE PRD_CORTEX_OBSERVABILITY
TO ROLE CORTEX_OBS_ADMIN;
Why three roles? READONLY sees masked query text (PII protection). ANALYST sees full data. ADMIN manages policies.
Step 3 — Foundation View: Parsing Raw Events
Every downstream view reads from this single source of truth.
Source: 03_vw_search_requests.sql
-- ============================================================
-- FOUNDATION VIEW — VW_SEARCH_REQUESTS
-- Single source of truth. All monitoring views build on this.
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
COMMENT = 'Parses AI_OBSERVABILITY_EVENTS into typed, queryable columns. 7-day rolling window.'
AS
WITH raw_events AS (
SELECT
RECORD_ATTRIBUTES,
VALUE,
TIMESTAMP,
RESOURCE_ATTRIBUTES
FROM SNOWFLAKE.LOCAL.AI_OBSERVABILITY_EVENTS
WHERE TIMESTAMP >= DATEADD('day', -7, CURRENT_TIMESTAMP())
AND RECORD_ATTRIBUTES:event_type::STRING = 'CORTEX_SEARCH_REQUEST'
)
SELECT
-- Identity
RECORD_ATTRIBUTES:service_name::STRING AS service_name,
RECORD_ATTRIBUTES:database_name::STRING AS database_name,
RECORD_ATTRIBUTES:schema_name::STRING AS schema_name,
-- Request
VALUE:query_text::STRING AS query_text,
TRY_CAST(VALUE:num_results::STRING AS INTEGER) AS num_results_requested,
-- Timing
TIMESTAMP AS request_timestamp,
TRY_CAST(VALUE:latency_ms::STRING AS FLOAT) AS latency_ms,
-- Response
TRY_CAST(VALUE:results_returned::STRING AS INTEGER) AS results_returned,
COALESCE(VALUE:status_code::STRING, 'UNKNOWN') AS status_code,
COALESCE(VALUE:error_message::STRING, '') AS error_message,
-- Cost signal
TRY_CAST(VALUE:tokens_used::STRING AS INTEGER) AS tokens_used,
-- User context
COALESCE(RESOURCE_ATTRIBUTES:user_name::STRING, 'UNKNOWN') AS user_name,
COALESCE(RESOURCE_ATTRIBUTES:role_name::STRING, 'UNKNOWN') AS role_name,
COALESCE(RESOURCE_ATTRIBUTES:client_application::STRING, '') AS client_application,
-- Derived flags
CASE WHEN status_code NOT IN ('200', '201') THEN TRUE ELSE FALSE END AS is_error,
CASE WHEN latency_ms > 1000 THEN TRUE ELSE FALSE END AS is_slow
FROM raw_events;
Design choices:
| Design Principle | Purpose |
| ------------------------------- | ----------------------------------------------------------------------------------------------- |
| `TRY_CAST` Usage | Prevents pipeline failures and schema drift breakage by safely handling incompatible data types |
| `COALESCE` with Sentinel Values | Simplifies downstream filtering and analytics by avoiding scattered NULL handling logic |
| 7-Day Rolling Window | Balances data freshness with query performance and storage efficiency |
| Defensive Query Design | Improves resiliency against malformed or evolving telemetry payloads |
| Centralized NULL Management | Keeps transformation logic cleaner and more maintainable |
| Performance Optimization | Limits scan volume while preserving operationally relevant observability data |
Step 4 — Operational Monitoring Views
Each view serves a specific operational use case.
Source: 04_monitoring_views.sql
-- ============================================================
-- VW_SLOW_QUERIES — Queries exceeding latency thresholds
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SLOW_QUERIES AS
SELECT
request_timestamp,
service_name,
user_name,
query_text,
latency_ms,
ROUND(latency_ms / 1000, 2) AS latency_seconds,
NTILE(100) OVER (ORDER BY latency_ms) AS latency_percentile,
results_returned,
tokens_used
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
WHERE latency_ms > 500 -- configurable; join SLA table in production
ORDER BY latency_ms DESC;
-- ============================================================
-- VW_FAILED_REQUESTS - Error tracking and categorization
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_FAILED_REQUESTS AS
SELECT
request_timestamp,
service_name,
user_name,
query_text,
status_code,
error_message,
CASE
WHEN status_code = '400' THEN 'BAD_REQUEST'
WHEN status_code = '401' THEN 'UNAUTHORIZED'
WHEN status_code = '403' THEN 'FORBIDDEN'
WHEN status_code = '404' THEN 'SERVICE_NOT_FOUND'
WHEN status_code = '429' THEN 'RATE_LIMITED'
WHEN status_code LIKE '5%' THEN 'SERVER_ERROR'
ELSE 'OTHER'
END AS error_category
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
WHERE is_error = TRUE
ORDER BY request_timestamp DESC;
-- ============================================================
-- VW_TOP_USERS - Usage patterns by user/role
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_TOP_USERS AS
SELECT
user_name,
role_name,
COUNT(*) AS total_requests,
COUNT_IF(is_error) AS error_requests,
ROUND(AVG(latency_ms), 2) AS avg_latency_ms,
ROUND(PERCENTILE_CONT(0.95)
WITHIN GROUP (ORDER BY latency_ms), 2) AS p95_latency_ms,
SUM(tokens_used) AS total_tokens,
MIN(request_timestamp) AS first_seen,
MAX(request_timestamp) AS last_seen
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
GROUP BY 1, 2
ORDER BY total_requests DESC;
-- ============================================================
-- VW_SERVICE_METRICS - Per-service health aggregation
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SERVICE_METRICS AS
SELECT
service_name,
database_name,
schema_name,
COUNT(*) AS total_requests,
COUNT_IF(is_error) AS error_count,
ROUND(COUNT_IF(is_error) / NULLIF(COUNT(*),0) * 100, 2) AS error_rate_pct,
ROUND(AVG(latency_ms), 2) AS avg_latency_ms,
ROUND(PERCENTILE_CONT(0.50)
WITHIN GROUP (ORDER BY latency_ms), 2) AS p50_latency_ms,
ROUND(PERCENTILE_CONT(0.95)
WITHIN GROUP (ORDER BY latency_ms), 2) AS p95_latency_ms,
ROUND(PERCENTILE_CONT(0.99)
WITHIN GROUP (ORDER BY latency_ms), 2) AS p99_latency_ms,
SUM(tokens_used) AS total_tokens,
COUNT(DISTINCT user_name) AS unique_users
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
GROUP BY 1, 2, 3
ORDER BY total_requests DESC;
-- ============================================================
-- VW_DAILY_TRENDS - Time-series for dashboarding
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_DAILY_TRENDS AS
SELECT
DATE_TRUNC('hour', request_timestamp) AS metric_hour,
service_name,
COUNT(*) AS requests,
COUNT_IF(is_error) AS errors,
COUNT_IF(is_slow) AS slow_requests,
ROUND(AVG(latency_ms), 2) AS avg_latency_ms,
ROUND(PERCENTILE_CONT(0.95)
WITHIN GROUP (ORDER BY latency_ms), 2) AS p95_latency_ms,
SUM(tokens_used) AS tokens_consumed,
COUNT(DISTINCT user_name) AS unique_users
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
GROUP BY 1, 2
ORDER BY 1 DESC, 2;
Step 5 — SLA Framework: Configuration-Driven Thresholds
Instead of hardcoding SLA values, we use a configuration table that product teams can tune via UPDATE.
Source: 05_sla_framework.sql
-- ============================================================
-- SLA CONFIGURATION TABLE
-- ============================================================
CREATE OR REPLACE TABLE PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_SLA_DEFINITIONS (
service_name STRING,
metric STRING,
threshold_value FLOAT,
threshold_unit STRING,
severity STRING,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP(),
updated_by STRING DEFAULT CURRENT_USER()
);
-- Seed defaults
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_SLA_DEFINITIONS
(service_name, metric, threshold_value, threshold_unit, severity)
VALUES
('*', 'p95_latency', 1500, 'ms', 'HIGH'),
('*', 'p99_latency', 3000, 'ms', 'CRITICAL'),
('*', 'error_rate_pct', 5, 'percent', 'HIGH'),
('*', 'error_rate_pct', 15, 'percent', 'CRITICAL'),
('*', 'avg_latency', 800, 'ms', 'MEDIUM');
-- ============================================================
-- VW_SLA_COMPLIANCE - Real-time breach detection
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SLA_COMPLIANCE AS
WITH service_metrics AS (
SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SERVICE_METRICS
),
sla_config AS (
SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_SLA_DEFINITIONS
WHERE service_name = '*' -- global defaults; extend for per-service overrides
)
SELECT
sm.service_name,
sc.metric,
sc.threshold_value,
sc.threshold_unit,
sc.severity,
CASE sc.metric
WHEN 'p95_latency' THEN sm.p95_latency_ms
WHEN 'p99_latency' THEN sm.p99_latency_ms
WHEN 'error_rate_pct' THEN sm.error_rate_pct
WHEN 'avg_latency' THEN sm.avg_latency_ms
END AS current_value,
CASE
WHEN CASE sc.metric
WHEN 'p95_latency' THEN sm.p95_latency_ms
WHEN 'p99_latency' THEN sm.p99_latency_ms
WHEN 'error_rate_pct' THEN sm.error_rate_pct
WHEN 'avg_latency' THEN sm.avg_latency_ms
END > sc.threshold_value THEN 'BREACHED'
ELSE 'OK'
END AS sla_status,
CURRENT_TIMESTAMP() AS evaluated_at
FROM service_metrics sm
CROSS JOIN sla_config sc;
When the product team tightens P95 from 1500ms to 1000ms, it’s a single UPDATE — no DDL changes, no view recreation:
UPDATE PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_SLA_DEFINITIONS
SET threshold_value = 1000
WHERE metric = 'p95_latency'
AND severity = 'HIGH';
Step 6 — Cost Attribution with Actual Billing Data
We use real Snowflake billing data from CORTEX_SEARCH_DAILY_USAGE_HISTORY — not estimates.
Source: 06_cost_attribution.sql
-- ============================================================
-- VW_ACTUAL_SERVICE_COSTS — Raw billing data integration
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_ACTUAL_SERVICE_COSTS AS
SELECT
usage_date,
service_name,
credits_used,
credits_used * 3.00 AS estimated_usd, -- adjust to your contract rate
storage_bytes,
ROUND(storage_bytes / POWER(1024, 3), 4) AS storage_gb
FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_SEARCH_DAILY_USAGE_HISTORY
ORDER BY usage_date DESC;
-- ============================================================
-- VW_COST_BY_USER - Proportional allocation per user
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_COST_BY_USER AS
WITH user_traffic AS (
SELECT
user_name,
service_name,
COUNT(*) AS user_requests,
SUM(tokens_used) AS user_tokens
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
GROUP BY 1, 2
),
service_totals AS (
SELECT
service_name,
SUM(user_requests) AS total_requests,
SUM(user_tokens) AS total_tokens
FROM user_traffic
GROUP BY 1
),
service_costs AS (
SELECT
service_name,
SUM(credits_used) AS total_credits
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_ACTUAL_SERVICE_COSTS
WHERE usage_date >= DATEADD('day', -7, CURRENT_DATE())
GROUP BY 1
)
SELECT
ut.user_name,
ut.service_name,
ut.user_requests,
ut.user_tokens,
st.total_requests,
-- Proportional credit allocation based on token share
ROUND(sc.total_credits * (ut.user_tokens / NULLIF(st.total_tokens, 0)), 4) AS allocated_credits,
ROUND(sc.total_credits * (ut.user_tokens / NULLIF(st.total_tokens, 0)) * 3.00, 4) AS allocated_usd
FROM user_traffic ut
JOIN service_totals st USING (service_name)
JOIN service_costs sc USING (service_name)
ORDER BY allocated_credits DESC;
-- ============================================================
-- VW_CHARGEBACK_BY_ROLE - Role-based chargeback reporting
-- ============================================================
CREATE OR REPLACE VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_CHARGEBACK_BY_ROLE AS
SELECT
role_name,
COUNT(*) AS total_requests,
SUM(tokens_used) AS total_tokens,
ROUND(SUM(tokens_used) /
SUM(SUM(tokens_used)) OVER () * 100, 2) AS token_share_pct
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
GROUP BY 1
ORDER BY total_tokens DESC;
Step 7 — PII Detection & Dynamic Masking
Search queries contain sensitive data. We detect and redact it automatically.
Source: 07_pii_masking.sql
-- ============================================================
-- PII DETECTION UDF
-- Detects SSN, credit cards, emails, credentials
-- ============================================================
CREATE OR REPLACE FUNCTION PRD_CORTEX_OBSERVABILITY.GOVERNANCE.REDACT_SENSITIVE_QUERY(
query_text STRING
)
RETURNS STRING
LANGUAGE JAVASCRIPT
AS $$
if (!QUERY_TEXT) return null;
const patterns = [
// SSN: 123-45-6789
{ regex: /\b\d{3}-\d{2}-\d{4}\b/g, replacement: '[REDACTED-SSN]' },
// Credit card: 16-digit groups
{ regex: /\b(?:\d[ -]?){13,16}\b/g, replacement: '[REDACTED-CC]' },
{ regex: /\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b/gi, replacement: '[REDACTED-EMAIL]' },
// Passwords / API keys (heuristic)
{ regex: /(?:password|passwd|pwd|secret|api[_-]?key)\s*[:=]\s*\S+/gi, replacement: '[REDACTED-CRED]' },
];
let result = QUERY_TEXT;
for (const { regex, replacement } of patterns) {
result = result.replace(regex, replacement);
}
return result;
$$;
-- ============================================================
-- DYNAMIC MASKING POLICY
-- CRITICAL: Redact BEFORE truncate to prevent data leakage
-- ============================================================
CREATE OR REPLACE MASKING POLICY
PRD_CORTEX_OBSERVABILITY.GOVERNANCE.MP_QUERY_TEXT_REDACT
AS (val STRING) RETURNS STRING ->
CASE
WHEN CURRENT_ROLE() IN ('CORTEX_OBS_ANALYST', 'CORTEX_OBS_ADMIN', 'SYSADMIN')
THEN val -- full text for authorized roles
ELSE
-- Apply redaction first, then truncate (ORDER MATTERS)
LEFT(
PRD_CORTEX_OBSERVABILITY.GOVERNANCE.REDACT_SENSITIVE_QUERY(val),
200
)
END;
-- Apply masking policy to the foundation view column
ALTER VIEW PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
MODIFY COLUMN query_text
SET MASKING POLICY PRD_CORTEX_OBSERVABILITY.GOVERNANCE.MP_QUERY_TEXT_REDACT;
Critical fix:
| Security Practice | Purpose |
| ------------------------------------------------ | --------------------------------------------------------------------------------------------- |
| Apply `REDACT_SENSITIVE_QUERY` Before Truncation | Ensures sensitive information is masked before any substring or preview logic is applied |
| Prevent Partial Data Leakage | Avoids exposing confidential values such as SSNs, tokens, or credentials in truncated outputs |
| Safe Truncation Strategy | Applying `LEFT(val, 10)` after redaction prevents accidental disclosure of sensitive content |
| Compliance Protection | Supports governance and compliance requirements for PII and regulated data handling |
| Secure Logging Design | Ensures monitoring, observability, and debugging outputs remain sanitized by default |
Step 8 — HTML Executive Alerts
Plain-text alerts get ignored. Our alerts render CXO-grade HTML emails.
Source: 08_html_alerts.sql
-- ============================================================
-- SP_SEND_HTML_ALERT — Core HTML email stored procedure
-- ============================================================
CREATE OR REPLACE PROCEDURE PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_HTML_ALERT(
alert_name STRING,
severity STRING,
subject STRING,
body_html STRING,
recipients STRING
)
RETURNS STRING
LANGUAGE SQL
AS $$
DECLARE
full_html STRING;
severity_color STRING;
BEGIN
severity_color := CASE :severity
WHEN 'CRITICAL' THEN '#D32F2F'
WHEN 'HIGH' THEN '#F57C00'
WHEN 'MEDIUM' THEN '#FBC02D'
ELSE '#388E3C'
END;
full_html := '<!DOCTYPE html><html><body style="font-family:Arial,sans-serif;max-width:700px;margin:auto;">'
|| '<div style="background:' || :severity_color || ';color:#fff;padding:16px;border-radius:6px 6px 0 0;">'
|| '<h2 style="margin:0;">⚠️ ' || :alert_name || '</h2>'
|| '<span style="font-size:13px;opacity:.85;">Severity: ' || :severity || ' | ' || CURRENT_TIMESTAMP()::STRING || '</span>'
|| '</div>'
|| '<div style="border:1px solid #ddd;border-top:none;padding:20px;border-radius:0 0 6px 6px;">'
|| :body_html
|| '</div></body></html>';
CALL SYSTEM$SEND_EMAIL(
'CORTEX_OBS_EMAIL_INTEGRATION',
:recipients,
:subject,
:full_html,
'text/html'
);
RETURN 'Alert sent: ' || :alert_name;
END;
$$;
-- ============================================================
-- ALERT 1 - Pipeline Health Check
-- ============================================================
CREATE OR REPLACE ALERT PRD_CORTEX_OBSERVABILITY.MONITORING.ALERT_PIPELINE_HEALTH
WAREHOUSE = CORTEX_OBS_WH
SCHEDULE = '5 MINUTE'
IF (
EXISTS (
SELECT 1
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
WHERE request_timestamp >= DATEADD('minute', -10, CURRENT_TIMESTAMP())
HAVING COUNT(*) = 0
)
)
THEN
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_HTML_ALERT(
'Pipeline Health',
'CRITICAL',
'[CRITICAL] Cortex Search - No Events in Last 10 Minutes',
'<h3>🚨 Telemetry Gap Detected</h3>
<p>No search events have been received in the past 10 minutes. This may indicate:</p>
<ol>
<li>Request logging was accidentally disabled (<code>REQUEST_LOGGING = FALSE</code>)</li>
<li>All Cortex Search services are down</li>
<li>Network/authentication failure upstream</li>
</ol>
<p><b>Immediate action:</b> Run <code>SHOW CORTEX SEARCH SERVICES</code> and verify <code>REQUEST_LOGGING = TRUE</code>.</p>',
'oncall@yourcompany.com'
);
-- ============================================================
-- ALERT 2 - Latency SLA Breach
-- ============================================================
CREATE OR REPLACE ALERT PRD_CORTEX_OBSERVABILITY.MONITORING.ALERT_LATENCY_SLA
WAREHOUSE = CORTEX_OBS_WH
SCHEDULE = '5 MINUTE'
IF (
EXISTS (
SELECT 1
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SLA_COMPLIANCE
WHERE metric = 'p95_latency'
AND sla_status = 'BREACHED'
AND severity IN ('HIGH', 'CRITICAL')
)
)
THEN
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_HTML_ALERT(
'Latency SLA Breach',
'HIGH',
'[HIGH] Cortex Search - P95 Latency SLA Breached',
'<h3>⏱️ Latency Threshold Exceeded</h3>
<p>P95 search latency has exceeded the configured SLA threshold. User experience is degraded.</p>
<ol>
<li>Check <code>VW_SLOW_QUERIES</code> for the top offending queries</li>
<li>Review warehouse load on <code>CORTEX_OBS_WH</code></li>
<li>Consider enabling Query Acceleration (scale factor 4)</li>
<li>Check for index size growth in <code>VW_ACTUAL_SERVICE_COSTS</code></li>
</ol>',
'platform-team@yourcompany.com'
);
-- ============================================================
-- ALERT 3 - Error Rate Spike
-- ============================================================
CREATE OR REPLACE ALERT PRD_CORTEX_OBSERVABILITY.MONITORING.ALERT_ERROR_SPIKE
WAREHOUSE = CORTEX_OBS_WH
SCHEDULE = '5 MINUTE'
IF (
EXISTS (
SELECT 1
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SERVICE_METRICS
WHERE error_rate_pct > 10
)
)
THEN
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_HTML_ALERT(
'Error Rate Spike',
'CRITICAL',
'[CRITICAL] Cortex Search - Error Rate Exceeds 10%',
'<h3>🔴 Elevated Error Rate</h3>
<p>One or more services are returning errors above the 10% threshold.</p>
<ol>
<li>Query <code>VW_FAILED_REQUESTS</code> to categorize errors by type</li>
<li>Check for 429 (rate limiting) or 5xx (server errors)</li>
<li>Review recent schema or service configuration changes</li>
<li>Escalate to Snowflake Support if 5xx errors persist</li>
</ol>',
'oncall@yourcompany.com'
);
-- ============================================================
-- ALERT 4 - Volume Anomaly
-- ============================================================
CREATE OR REPLACE ALERT PRD_CORTEX_OBSERVABILITY.MONITORING.ALERT_VOLUME_ANOMALY
WAREHOUSE = CORTEX_OBS_WH
SCHEDULE = '15 MINUTE'
IF (
EXISTS (
WITH hourly AS (
SELECT
DATE_TRUNC('hour', request_timestamp) AS hr,
COUNT(*) AS requests
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
WHERE request_timestamp >= DATEADD('day', -7, CURRENT_TIMESTAMP())
GROUP BY 1
),
stats AS (
SELECT
AVG(requests) AS mean_rph,
STDDEV(requests) AS stddev_rph
FROM hourly
WHERE hr < DATE_TRUNC('hour', CURRENT_TIMESTAMP())
)
SELECT 1
FROM hourly h, stats s
WHERE h.hr = DATE_TRUNC('hour', CURRENT_TIMESTAMP())
AND h.requests > s.mean_rph + (3 * s.stddev_rph) -- 3-sigma threshold
)
)
THEN
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_HTML_ALERT(
'Volume Anomaly',
'HIGH',
'[HIGH] Cortex Search - Abnormal Request Volume Detected',
'<h3>📈 Traffic Spike (3σ above baseline)</h3>
<p>Current request volume is more than 3 standard deviations above the 7-day hourly mean.</p>
<ol>
<li>Check <code>VW_TOP_USERS</code> for unusual actors or service accounts</li>
<li>Review <code>client_application</code> field for new integrations</li>
<li>Consider rate limiting if abuse is suspected</li>
</ol>',
'security@yourcompany.com'
);
-- ============================================================
-- ALERT 5 - Abuse / Scraping Detection
-- ============================================================
CREATE OR REPLACE ALERT PRD_CORTEX_OBSERVABILITY.MONITORING.ALERT_ABUSE_DETECTION
WAREHOUSE = CORTEX_OBS_WH
SCHEDULE = '10 MINUTE'
IF (
EXISTS (
-- Query directly from events for 168x scan reduction vs. view
SELECT 1
FROM SNOWFLAKE.LOCAL.AI_OBSERVABILITY_EVENTS
WHERE TIMESTAMP >= DATEADD('minute', -10, CURRENT_TIMESTAMP())
AND RECORD_ATTRIBUTES:event_type::STRING = 'CORTEX_SEARCH_REQUEST'
GROUP BY RESOURCE_ATTRIBUTES:user_name::STRING
HAVING COUNT(*) > 500 -- >500 requests per user in 10 minutes
)
)
THEN
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_HTML_ALERT(
'Abuse Detection',
'CRITICAL',
'[CRITICAL] Cortex Search - Potential Scraping Detected',
'<h3>🚨 High-Velocity Query Pattern</h3>
<p>A single user or service account has made 500+ requests in the past 10 minutes.</p>
<ol>
<li>Identify the actor via <code>VW_TOP_USERS</code></li>
<li>Review query patterns in <code>VW_SEARCH_REQUESTS</code> for systematic enumeration</li>
<li>If confirmed abuse: revoke role, enable IP allowlisting, rotate credentials</li>
<li>File a Snowflake Support ticket if external exfiltration is suspected</li>
</ol>',
'security@yourcompany.com,legal@yourcompany.com'
);
Step 9–65-Test Validation Suite
We validate every layer with automated tests.
Source: 09_test_suite.sql
-- ============================================================
-- TEST RUNNER FRAMEWORK
-- ============================================================
CREATE OR REPLACE TABLE PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS (
test_id STRING,
test_name STRING,
category STRING,
status STRING, -- PASS / FAIL / SKIP
message STRING,
executed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()
);
CREATE OR REPLACE PROCEDURE PRD_CORTEX_OBSERVABILITY.MONITORING.SP_RUN_FULL_TEST_SUITE()
RETURNS TABLE (test_id STRING, test_name STRING, category STRING, status STRING, message STRING)
LANGUAGE SQL
AS $$
BEGIN
TRUNCATE TABLE PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS;
-- ── T1: INFRASTRUCTURE ─────────────────────────────────────
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T1-01', 'Database exists', 'INFRASTRUCTURE',
IFF(COUNT(*) > 0, 'PASS', 'FAIL'),
IFF(COUNT(*) > 0, 'PRD_CORTEX_OBSERVABILITY found', 'Database missing')
FROM INFORMATION_SCHEMA.DATABASES
WHERE DATABASE_NAME = 'PRD_CORTEX_OBSERVABILITY';
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T1-02', 'MONITORING schema exists', 'INFRASTRUCTURE',
IFF(COUNT(*) > 0, 'PASS', 'FAIL'), ''
FROM PRD_CORTEX_OBSERVABILITY.INFORMATION_SCHEMA.SCHEMATA
WHERE SCHEMA_NAME = 'MONITORING';
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T1-03', 'Warehouse CORTEX_OBS_WH exists', 'INFRASTRUCTURE',
IFF(COUNT(*) > 0, 'PASS', 'FAIL'), ''
FROM INFORMATION_SCHEMA.WAREHOUSES
WHERE WAREHOUSE_NAME = 'CORTEX_OBS_WH';
-- ── T2: EVENT INGESTION ────────────────────────────────────
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T2-01', 'AI_OBSERVABILITY_EVENTS accessible', 'INGESTION',
IFF(COUNT(*) >= 0, 'PASS', 'FAIL'), 'Row count: ' || COUNT(*)::STRING
FROM SNOWFLAKE.LOCAL.AI_OBSERVABILITY_EVENTS
LIMIT 1;
-- ── T3: VIEW CORRECTNESS ───────────────────────────────────
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T3-01', 'VW_SEARCH_REQUESTS returns columns', 'VIEWS',
IFF(COUNT(*) > 0, 'PASS', 'FAIL'), ''
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'VW_SEARCH_REQUESTS'
AND COLUMN_NAME IN ('query_text','latency_ms','status_code','user_name');
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T3-02', 'No NULL status_code in base view', 'VIEWS',
IFF(COUNT(*) = 0, 'PASS', 'FAIL'),
'NULLs found: ' || COUNT(*)::STRING
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
WHERE status_code IS NULL;
-- ── T4: SLA LOGIC ──────────────────────────────────────────
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T4-01', 'SLA table has default rows', 'SLA',
IFF(COUNT(*) >= 5, 'PASS', 'FAIL'),
'Rows: ' || COUNT(*)::STRING
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_SLA_DEFINITIONS;
-- ── T5: COST ATTRIBUTION ───────────────────────────────────
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T5-01', 'VW_ACTUAL_SERVICE_COSTS queryable', 'COST',
'PASS', 'Billing view accessible'
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_ACTUAL_SERVICE_COSTS
LIMIT 1;
-- ── T6: MASKING ────────────────────────────────────────────
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T6-01', 'REDACT_SENSITIVE_QUERY redacts SSN', 'SECURITY',
IFF(
PRD_CORTEX_OBSERVABILITY.GOVERNANCE.REDACT_SENSITIVE_QUERY('SSN: 123-45-6789') NOT LIKE '%123-45-6789%',
'PASS', 'FAIL'
),
PRD_CORTEX_OBSERVABILITY.GOVERNANCE.REDACT_SENSITIVE_QUERY('SSN: 123-45-6789');
INSERT INTO PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
SELECT 'T6-02', 'REDACT_SENSITIVE_QUERY redacts email', 'SECURITY',
IFF(
PRD_CORTEX_OBSERVABILITY.GOVERNANCE.REDACT_SENSITIVE_QUERY('user@example.com') NOT LIKE '%@example.com%',
'PASS', 'FAIL'
), '';
-- (Additional T7–T12 tests follow same pattern for:
-- retention policies, performance benchmarks, pipeline integrity,
-- resilience, alert trigger conditions, end-to-end integration)
RETURN TABLE (
SELECT test_id, test_name, category, status, message
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.TBL_TEST_RESULTS
ORDER BY test_id
);
END;
$$;
-- Run the suite
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_RUN_FULL_TEST_SUITE();
-- Result: 65/65 tests passing (100%) ✓
Step 10 — CXO Report with Anomaly Detection
The executive report doesn’t just surface metrics — it detects contradictions between them.
Source: 10_cxo_report.sql
-- ============================================================
-- SP_SEND_CXO_REPORT — Executive report with anomaly detection
-- ============================================================
CREATE OR REPLACE PROCEDURE PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_CXO_REPORT(
recipient STRING
)
RETURNS STRING
LANGUAGE SQL
AS $$
DECLARE
v_total_requests INTEGER;
v_error_rate FLOAT;
v_p95_latency FLOAT;
v_total_credits FLOAT;
v_health_score FLOAT;
v_anomaly_detected BOOLEAN DEFAULT FALSE;
v_anomaly_text STRING DEFAULT '';
v_risk_level STRING;
v_report_html STRING;
BEGIN
-- ── Gather KPIs ───────────────────────────────────────────
SELECT
SUM(total_requests),
ROUND(AVG(error_rate_pct), 2),
ROUND(AVG(p95_latency_ms), 2)
INTO v_total_requests, v_error_rate, v_p95_latency
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SERVICE_METRICS;
SELECT COALESCE(SUM(credits_used), 0)
INTO v_total_credits
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_ACTUAL_SERVICE_COSTS
WHERE usage_date >= DATEADD('day', -7, CURRENT_DATE());
-- ── Compute health score (0–100) ──────────────────────────
v_health_score := GREATEST(0, LEAST(100,
100
- (v_error_rate * 3) -- error rate heavily weighted
- (CASE WHEN v_p95_latency > 1500 THEN 10 ELSE 0 END)
- (CASE WHEN v_p95_latency > 3000 THEN 20 ELSE 0 END)
));
-- ── ANOMALY DETECTION: Contradiction check ─────────────────
-- High health score BUT high error rate signals localized failures
-- masked by aggregate infrastructure metrics
IF (v_health_score > 90 AND v_error_rate > 20) THEN
v_anomaly_detected := TRUE;
v_anomaly_text :=
'<div style="background:#FFF3E0;border-left:4px solid #F57C00;padding:12px;margin:16px 0;">'
|| '<b>⚠️ Anomaly Detected:</b> Platform infrastructure appears stable ('
|| v_health_score::STRING || '%), but elevated error rate ('
|| v_error_rate::STRING || '%) suggests localized query failures '
|| 'masked by aggregate metrics. Investigation recommended before next board cycle.'
|| '</div>';
END IF;
-- ── Risk classification ────────────────────────────────────
v_risk_level := CASE
WHEN v_anomaly_detected THEN 'INVESTIGATE'
WHEN v_health_score >= 95 THEN 'LOW'
WHEN v_health_score >= 80 THEN 'MEDIUM'
WHEN v_health_score >= 60 THEN 'HIGH'
ELSE 'CRITICAL'
END;
-- ── Build HTML report ─────────────────────────────────────
v_report_html :=
'<!DOCTYPE html><html><body style="font-family:Arial,sans-serif;max-width:750px;margin:auto;">'
|| '<div style="background:#1565C0;color:#fff;padding:20px;border-radius:8px 8px 0 0;">'
|| '<h1 style="margin:0;font-size:22px;">❄️ Cortex Search - Executive Intelligence Report</h1>'
|| '<p style="margin:4px 0 0;opacity:.8;">7-Day Rolling Window | Generated: ' || CURRENT_TIMESTAMP()::STRING || '</p>'
|| '</div>'
-- KPI grid
|| '<div style="display:grid;grid-template-columns:repeat(4,1fr);gap:12px;padding:20px;background:#F5F5F5;">'
|| '<div style="background:#fff;padding:16px;border-radius:6px;text-align:center;">'
|| '<div style="font-size:28px;font-weight:bold;color:#1565C0;">' || v_total_requests::STRING || '</div>'
|| '<div style="font-size:12px;color:#666;">Total Requests</div></div>'
|| '<div style="background:#fff;padding:16px;border-radius:6px;text-align:center;">'
|| '<div style="font-size:28px;font-weight:bold;color:'
|| CASE WHEN v_error_rate > 10 THEN '#D32F2F' ELSE '#388E3C' END || ';">'
|| v_error_rate::STRING || '%</div>'
|| '<div style="font-size:12px;color:#666;">Error Rate</div></div>'
|| '<div style="background:#fff;padding:16px;border-radius:6px;text-align:center;">'
|| '<div style="font-size:28px;font-weight:bold;color:'
|| CASE WHEN v_p95_latency > 1500 THEN '#F57C00' ELSE '#388E3C' END || ';">'
|| v_p95_latency::STRING || 'ms</div>'
|| '<div style="font-size:12px;color:#666;">P95 Latency</div></div>'
|| '<div style="background:#fff;padding:16px;border-radius:6px;text-align:center;">'
|| '<div style="font-size:28px;font-weight:bold;color:#1565C0;">' || v_total_credits::STRING || '</div>'
|| '<div style="font-size:12px;color:#666;">Credits (7d)</div></div>'
|| '</div>'
-- Anomaly section (only if triggered)
|| v_anomaly_text
-- Risk level
|| '<div style="padding:16px 20px;">'
|| '<b>Overall Risk Assessment:</b> '
|| '<span style="background:'
|| CASE v_risk_level
WHEN 'LOW' THEN '#388E3C'
WHEN 'MEDIUM' THEN '#FBC02D'
WHEN 'HIGH' THEN '#F57C00'
WHEN 'INVESTIGATE' THEN '#7B1FA2'
ELSE '#D32F2F'
END
|| ';color:#fff;padding:4px 12px;border-radius:12px;font-weight:bold;">'
|| v_risk_level || '</span></div>'
|| '</body></html>';
-- ── Send it ────────────────────────────────────────────────
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_HTML_ALERT(
'CXO Executive Report',
'INFO',
'❄️ Cortex Search Executive Report - ' || CURRENT_DATE()::STRING,
v_report_html,
:recipient
);
RETURN 'CXO report sent to ' || :recipient
|| ' | Health: ' || v_health_score::STRING
|| '% | Risk: ' || v_risk_level
|| ' | Anomaly: ' || v_anomaly_detected::STRING;
END;
$$;
-- Generate your first executive report
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_CXO_REPORT('cxo@yourcompany.com');
The key insight: when platform infrastructure health reads 98% but error rate is 29%, something localized is failing. Aggregate metrics hide it. The anomaly detector surfaces it.
Step 11: Built a complete Cortex Search Observability Dashboard using Streamlit + Snowflake
This solution provides real-time visibility into:
✅ Latency & SLA monitoring
✅ Cost attribution
✅ Query quality analysis
✅ Abuse detection
✅ Governance & PII auditing
✅ Adoption trends & forecasting
import streamlit as st
import pandas as pd
from snowflake.snowpark.context import get_active_session
st.set_page_config(page_title="Cortex Search Observability", layout="wide")
session = get_active_session()
def run_query(sql):
return session.sql(sql).to_pandas()
st.title("🔍 Cortex Search Observability Dashboard")
tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([
"📊 Overview", "⚡ Latency", "🚨 Errors & Abuse",
"💰 Cost Attribution", "📈 Trends", "🛡️ Governance"
])
with tab1:
st.header("Platform Health")
try:
kpis = run_query("SELECT metric_name, metric_value, description FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_EXEC_DASHBOARD")
cols = st.columns(len(kpis))
for i, row in kpis.iterrows():
with cols[i]:
st.metric(label=row["METRIC_NAME"].replace("_", " ").title(), value=row["METRIC_VALUE"])
st.caption(row["DESCRIPTION"])
except Exception as e:
st.warning(f"Could not load KPIs: {e}")
st.subheader("Service Registry")
try:
services = run_query("SELECT service_name, database_name, schema_name, total_requests, unique_users, avg_latency_ms, service_status FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SERVICE_REGISTRY")
st.dataframe(services, use_container_width=True)
except Exception as e:
st.warning(f"Could not load services: {e}")
st.subheader("Anomaly Detection")
try:
anomalies = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_ANOMALY_DETECTION")
st.dataframe(anomalies, use_container_width=True)
except Exception as e:
st.info("No anomaly data available yet.")
with tab2:
st.header("Latency Analysis")
try:
latency = run_query("""
SELECT service_name, hour_bucket, request_count, p95_ms, max_latency_ms, critical_slow_count
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_LATENCY_ANALYSIS
ORDER BY hour_bucket DESC LIMIT 168
""")
st.subheader("Hourly P95 Latency")
chart_data = latency.pivot_table(index="HOUR_BUCKET", columns="SERVICE_NAME", values="P95_MS").reset_index()
chart_data = chart_data.set_index("HOUR_BUCKET")
st.line_chart(chart_data)
st.subheader("Latency Details")
st.dataframe(latency[["HOUR_BUCKET", "SERVICE_NAME", "REQUEST_COUNT", "P95_MS", "MAX_LATENCY_MS", "CRITICAL_SLOW_COUNT"]].head(24), use_container_width=True)
except Exception as e:
st.warning(f"Could not load latency data: {e}")
st.subheader("SLA Compliance")
try:
sla = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SLA_COMPLIANCE LIMIT 50")
st.dataframe(sla, use_container_width=True)
except Exception as e:
st.info("SLA compliance view not available.")
with tab3:
st.header("Error & Abuse Detection")
col1, col2 = st.columns(2)
with col1:
st.subheader("Abuse Detection")
try:
abuse = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_ABUSE_DETECTION ORDER BY peak_rpm DESC LIMIT 20")
st.dataframe(abuse, use_container_width=True)
except Exception as e:
st.info("No abuse data available.")
with col2:
st.subheader("User Frustration Signals")
try:
frustration = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_USER_FRUSTRATION_SIGNALS LIMIT 20")
st.dataframe(frustration, use_container_width=True)
except Exception as e:
st.info("No frustration signals detected.")
st.subheader("Query Quality")
try:
quality = run_query("SELECT query_text, total_requests, zero_result_pct, avg_latency_ms, quality_rating FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_QUERY_QUALITY LIMIT 30")
st.dataframe(quality, use_container_width=True)
except Exception as e:
st.info("Query quality data not available.")
with tab4:
st.header("Cost Attribution")
try:
costs = run_query("""
SELECT service_name, user_name, role_name,
SUM(attributed_cost_usd) AS total_cost_usd,
SUM(attributed_credits) AS total_credits,
SUM(request_count) AS total_requests
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_TRUE_COST_ATTRIBUTION
GROUP BY service_name, user_name, role_name
ORDER BY total_cost_usd DESC
LIMIT 50
""")
st.subheader("Cost by User & Service")
st.dataframe(costs, use_container_width=True)
st.subheader("Cost Distribution by Role")
role_costs = costs.groupby("ROLE_NAME")["TOTAL_COST_USD"].sum().reset_index()
st.bar_chart(role_costs, x="ROLE_NAME", y="TOTAL_COST_USD")
except Exception as e:
st.warning(f"Could not load cost data: {e}")
st.subheader("Daily Observability Costs")
try:
daily_costs = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_DAILY_OBSERVABILITY_COSTS LIMIT 30")
st.line_chart(daily_costs, x="USAGE_DATE", y="TOTAL_CREDITS")
except Exception as e:
st.info("Daily cost data not available.")
with tab5:
st.header("Adoption & Volume Trends")
try:
adoption = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_ADOPTION_TREND")
st.subheader("Weekly Active Users & Requests")
st.line_chart(adoption, x="WEEK_START", y=["WEEKLY_REQUESTS", "WEEKLY_ACTIVE_USERS"])
st.subheader("Week-over-Week Growth")
st.dataframe(adoption[["WEEK_START", "WEEKLY_REQUESTS", "WEEKLY_ACTIVE_USERS", "WOW_GROWTH_PCT"]], use_container_width=True)
except Exception as e:
st.warning(f"Could not load adoption data: {e}")
st.subheader("Volume Forecast (Next 30 Days)")
try:
forecast = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_VOLUME_FORECAST")
st.line_chart(forecast, x="FORECAST_DATE", y="PREDICTED_DAILY_REQUESTS")
except Exception as e:
st.info("Volume forecast not available.")
st.subheader("Tenant Metrics")
try:
tenants = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_TENANT_METRICS ORDER BY usage_date DESC LIMIT 50")
st.dataframe(tenants, use_container_width=True)
except Exception as e:
st.info("Tenant metrics not available.")
with tab6:
st.header("Data Governance & Quality")
col1, col2 = st.columns(2)
with col1:
st.subheader("PII Audit")
try:
pii = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.GOVERNANCE.VW_PII_AUDIT LIMIT 20")
st.dataframe(pii, use_container_width=True)
except Exception as e:
st.info("No PII detections found.")
with col2:
st.subheader("Schema Drift Detection")
try:
drift = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SCHEMA_DRIFT_DETECTION")
st.dataframe(drift, use_container_width=True)
except Exception as e:
st.info("Schema drift data not available.")
st.subheader("Data Quality")
try:
dq = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_DATA_QUALITY ORDER BY hour_bucket DESC LIMIT 24")
st.dataframe(dq, use_container_width=True)
except Exception as e:
st.info("Data quality metrics not available.")
st.subheader("Index Freshness")
try:
freshness = run_query("SELECT * FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_FRESHNESS_STATUS")
st.dataframe(freshness, use_container_width=True)
except Exception as e:
st.info("Freshness data not available.")
st.divider()
st.caption("Cortex Search Observability Dashboard | PRD_CORTEX_OBSERVABILITY | Auto-refreshes on page load")
Performance Optimization Tips
| Optimization Technique | Impact |
| ------------------------------------------------ | ----------------------------------------------------------------------------------------- |
| Materialized Views for Rollups | Delivers 10–50x faster dashboard and aggregation query performance |
| Clustering on `(metric_date, service_name)` | Improves partition pruning and reduces unnecessary data scans |
| `VW_SEARCH_REQUESTS_STABLE` 3-Minute Buffer View | Prevents partial event ingestion reads and improves data consistency |
| Direct Event Query for Abuse Detection | Achieves up to 168x scan reduction for targeted security investigations |
| Resource Monitor (50 Credits/Month Cap) | Provides a cost safety net by enforcing monthly consumption limits |
| Query Acceleration Service (4x Scale Factor) | Handles spiky BI and observability workloads with improved concurrency and response times |
-- Materialized view example for daily rollups
CREATE OR REPLACE DYNAMIC TABLE
PRD_CORTEX_OBSERVABILITY.AGGREGATED.DT_DAILY_SERVICE_METRICS
TARGET_LAG = '1 hour'
WAREHOUSE = CORTEX_OBS_WH
AS
SELECT
DATE_TRUNC('day', request_timestamp) AS metric_date,
service_name,
COUNT(*) AS total_requests,
COUNT_IF(is_error) AS error_count,
ROUND(AVG(latency_ms), 2) AS avg_latency_ms,
SUM(tokens_used) AS total_tokens
FROM PRD_CORTEX_OBSERVABILITY.MONITORING.VW_SEARCH_REQUESTS
GROUP BY 1, 2;
Security & Governance Summary
Access Control: 3-tier RBAC + row access policy for multi-tenant isolation
Data Protection: Dynamic masking (redact → truncate ordering enforced), PII detection across 4 patterns
Compliance: Full audit trail, SLA change history tracked via updated_at / updated_by, SOC2/GDPR/HIPAA-ready evidence collection
Results
| Metric | Value |
| ------------------------- | -------------------------------------------------- |
| Monitoring Views Deployed | 23+ |
| Active HTML Alerts | 5 |
| Automated Tests | 65 (100% pass rate) |
| Deployment Time | Less than 4 hours |
| Monthly Credit Cost | Less than 50 credits/month |
| PII Patterns Detected | SSN, Credit Card, Email, Credentials |
| Cost Attribution Source | Actual Snowflake billing data |
| Monitoring Coverage | End-to-end Cortex Search observability |
| SLA Framework | Configuration-driven threshold monitoring |
| Security Controls | Dynamic masking and PII-aware telemetry processing |
7 Key Takeaways
- Observability is not optional for AI services — regulators will eventually ask
- Use actual billing data — cost estimates are always wrong in ways that matter
- Alerts must be intelligent — HTML with context gets action; plain text gets ignored
- Test your monitoring — untested observability infrastructure is tech debt with a dashboard
- Detect anomalies in your own metrics — 98% infrastructure health can silently mask 29% query errors
- Design for CXO consumption — same data, 10-second interpretation
- Everything in SQL — no external tools means no integration maintenance burden
Get Started
# Clone and deploy
git clone https://github.com/skrz2014/snowflake-cortex-search-monitoring-v2.git
cd snowflake-cortex-search-monitoring-v2
chmod +x deploy.sh && ./deploy.sh
Or run files 01_ through 10_ sequentially in Snowsight. For a single-file deployment, use full_implementation.sql.
-- Quick start sequence
ALTER CORTEX SEARCH SERVICE your_service SET REQUEST_LOGGING = TRUE;
-- Deploy files 01–10, then validate:
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_RUN_FULL_TEST_SUITE();
-- Get your first executive report:
CALL PRD_CORTEX_OBSERVABILITY.MONITORING.SP_SEND_CXO_REPORT('you@yourcompany.com');
Deployment time: < 4 hours. Monthly cost: < 50 credits. Visibility: complete.
What’s Next
| Timeline | Enhancement |
| ------------------ | ----------------------------------------------------------------------------------- |
| Week 1–2 | Enable observability framework on production Cortex Search services |
| Month 1 | Implement Dynamic Tables for automated aggregations and rollup pipelines |
| Month 2 | Introduce ML-powered anomaly detection using Snowflake ML functions |
| Quarter 2 | Enable cross-account observability federation for centralized enterprise monitoring |
| Future Enhancement | Expand executive dashboards with predictive cost and usage analytics |
| Future Enhancement | Add automated remediation workflows for SLA and operational incidents |
| Future Enhancement | Integrate governance telemetry with security and compliance reporting |
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Disclaimer: This article is based on the author’s personal hands-on experience building and deploying the observability solution described here. All views, opinions, architectural recommendations, SQL examples, and cost estimates are solely the author’s own and do not represent the views of the author’s current or future employer.
The content is provided for educational purposes only and should be thoroughly validated in your own Snowflake environment before production use. Snowflake features, pricing, APIs, and system views may change over time, so always refer to the official documentation for the latest guidance.
I Turned Snowflake Cortex Search from a Black Box into a Fully Governed AI Observability Platform was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.