OSS Istio Observability Stack¶
Open-source Istio lacks a built-in observability solution, forcing organizations to integrate and manage a complex stack of disparate tools including -
- Prometheus - for metrics collection,
- Grafana - for dashboards,
- Kiali - for service mesh visualization, and
- Zipkin or Jaeger - for distributed tracing,
creating operational overhead and data silos that require significant expertise to configure, maintain, and correlate across multi-cluster environments.
1. Monitoring with Prometheus + Grafana¶
- Prometheus is pull based metrics scrapper, that offers its metrics source to grafana as a data source.
- Istio community provided grafana dashboard for monitoring
Architecture and Challenges¶
graph TB
subgraph "Architecture"
Grafana[Grafana Dashboards]
GlobalProm[Global Prometheus<br/>Federation]
C1[Cluster 1<br/>Prometheus]
C2[Cluster 2<br/>Prometheus]
C3[Cluster 3<br/>Prometheus]
Grafana --> GlobalProm
GlobalProm -.-> C1
GlobalProm -.-> C2
GlobalProm -.-> C3
end
subgraph "🔴 Federation Problems"
FP1[🔴 Single Point of Failure<br/>Global Prometheus down = no data]
FP2[🔴 No Authentication<br/>Open endpoints]
FP3[🔴 Network Issues<br/>Cross-cloud connectivity]
FP4[🔴 Scrape Timeouts<br/>Large payloads fail]
FP5[🔴 Data Loss<br/>Failed scrapes = missing metrics]
end
subgraph "🔴 Grafana Problems"
GP1[🔴 Manual Dashboard Sync<br/>N clusters = N configurations]
GP2[🔴 Query Performance<br/>Slow federated queries]
GP3[🔴 Data Source Complexity<br/>Multiple Prometheus instances]
GP4[🔴 Inconsistent Views<br/>Different data per cluster]
GP5[🔴 Alert Management<br/>Duplicate/conflicting alerts]
end
subgraph "🔴 Storage & Scale Problems"
SP1[🔴 Expensive Storage<br/>Local disks, limited retention]
SP2[🔴 High Cardinality<br/>Istio metrics = memory issues]
SP3[🔴 Manual Scaling<br/>Each cluster needs setup]
SP4[🔴 Cost Explosion<br/>Linear growth with clusters]
end
subgraph "🔴 Operational Problems"
OP1[🔴 Complex Troubleshooting<br/>Multi-layer debugging]
OP2[🔴 Certificate Management<br/>Manual rotation required]
OP3[🔴 Version Coordination<br/>Istio + Prometheus + Grafana]
OP4[🔴 Expert Team Required<br/>Full-time platform engineers]
end
GlobalProm -.-> FP1
Grafana -.-> GP1
C1 -.-> SP1
style FP1 fill:#ffcdd2
style FP2 fill:#ffcdd2
style FP3 fill:#ffcdd2
style FP4 fill:#ffcdd2
style FP5 fill:#ffcdd2
style GP1 fill:#ffcdd2
style GP2 fill:#ffcdd2
style GP3 fill:#ffcdd2
style GP4 fill:#ffcdd2
style GP5 fill:#ffcdd2
style SP1 fill:#ffcdd2
style SP2 fill:#ffcdd2
style SP3 fill:#ffcdd2
style SP4 fill:#ffcdd2
style OP1 fill:#ffcdd2
style OP2 fill:#ffcdd2
style OP3 fill:#ffcdd2
style OP4 fill:#ffcdd2Enterprise Prometheus/Grafana Challenges:
- Complex Federation & Aggregation: Multi-cluster deployments require complex federation architectures with individual cluster configurations plus additional layers like Thanos/Cortex, creating significant operational overhead and network coordination challenges
- Security & Authentication Gaps: Local Prometheus endpoints lack proper authentication, and metrics are often transported via plain text without HTTPS configuration, exposing sensitive operational data
- Scalability & Data Issues: Service mesh environments generate high cardinality metrics that overwhelm Prometheus's limited local storage, causing federation data loss and retention problems across clusters
- Operational Burden: Manual Grafana dashboard management across teams, multi-cluster connectivity coordination, and the complexity of maintaining federated metric collection create substantial operational overhead for enterprise environments
2. Kiali - Service Mesh Visualization Tool¶
Main Issues Highlighted:
- Fragmented Observability: Each cluster requires its own Kiali instance, creating operational silos where administrators must check multiple dashboards to get a complete picture.
- Cross-Cluster Blind Spots: While services communicate across clusters (shown by dotted lines), Kiali instances can't provide comprehensive visibility into these inter-cluster connections.
- Operational Overhead: Operations teams must maintain and access multiple Kiali instances, leading to context switching and potential oversight of issues.
- No Unified Analytics: Metrics and insights are scattered across instances, making it difficult to correlate events or perform root cause analysis across the entire mesh.
- Configuration Drift: With separate instances, there's risk of inconsistent configurations and policies across clusters, making standardization challenging.
The diagram emphasizes how the distributed nature of Kiali deployments creates gaps in visibility and increases complexity for teams managing large-scale, multi-cluster service mesh environments.
graph TB
subgraph "Cluster A"
KA[Kiali Instance A]
IA[Istio Control Plane A]
SA1[Service A1]
SA2[Service A2]
PA[Prometheus A]
KA --> IA
KA --> PA
IA --> SA1
IA --> SA2
end
subgraph "Cluster B"
KB[Kiali Instance B]
IB[Istio Control Plane B]
SB1[Service B1]
SB2[Service B2]
PB[Prometheus B]
KB --> IB
KB --> PB
IB --> SB1
IB --> SB2
end
subgraph "Cluster C"
KC[Kiali Instance C]
IC[Istio Control Plane C]
SC1[Service C1]
SC2[Service C2]
PC[Prometheus C]
KC --> IC
KC --> PC
IC --> SC1
IC --> SC2
end
%% Cross-cluster service communications
SA1 -.->|Cross-cluster traffic| SB1
SB2 -.->|Cross-cluster traffic| SC1
SA2 -.->|Cross-cluster traffic| SC2
%% Operator/Admin access
OP[Operations Team]
OP -->|Must access separately| KA
OP -->|Must access separately| KB
OP -->|Must access separately| KC
subgraph "❌ Key Disadvantages"
D1[🔍 No Unified Cross-Cluster View]
D2[👥 Multiple UI Instances to Monitor]
D3[🔗 Limited Cross-Cluster Traffic Visibility]
D4[⚙️ Configuration Drift Risk]
D5[📊 Fragmented Metrics & Analytics]
D6[🔧 Higher Management Overhead]
D7[🚫 No Central Policy Visualization]
D8[⏱️ Time-Consuming Troubleshooting]
end
%% Styling
classDef cluster fill:#e1f5fe,stroke:#01579b,stroke-width:2px
classDef kiali fill:#ff9800,stroke:#e65100,stroke-width:2px
classDef istio fill:#466bb0,stroke:#1565c0,stroke-width:2px
classDef service fill:#4caf50,stroke:#2e7d32,stroke-width:2px
classDef disadvantage fill:#ffcdd2,stroke:#d32f2f,stroke-width:2px
classDef operator fill:#9c27b0,stroke:#6a1b9a,stroke-width:2px
class KA,KB,KC kiali
class IA,IB,IC istio
class SA1,SA2,SB1,SB2,SC1,SC2 service
class D1,D2,D3,D4,D5,D6,D7,D8 disadvantage
class OP operator3. Distributed Tracing Challenges with Zipkin¶
- Each cluster has its own Zipkin collector, making centralized trace analysis difficult
- Cross-cluster service calls create distributed traces that are hard to correlate
- Different geographic locations introduce network and timing complications
Summary¶
While Prometheus+Grafana, Kiali, and Zipkin provide valuable observability capabilities individually, managing them together in enterprise multi-cluster environments creates significant operational overhead and data fragmentation challenges. These fragmented observability silos, combined with the multi-cluster traceability issues, sampling inconsistencies, and configuration drift we've discussed, make it clear that enterprises need a unified, enterprise-grade observability platform like Tetrate Service Bridge (TSB), which provides integrated metrics, tracing, and service mesh observability in a single, cohesive platform designed specifically for multi-cluster, multi-cloud environments with enterprise security, governance, and operational requirements.