more on this

1 files changed, 324 insertions, 61 deletions

diff --git a/gemfeed/DRAFT-x-rag-observability.gmi.tpl b/gemfeed/DRAFT-x-rag-observability.gmi.tpl
index 8c1394a7..7c8b3516 100644
--- a/gemfeed/DRAFT-x-rag-observability.gmi.tpl
+++ b/gemfeed/DRAFT-x-rag-observability.gmi.tpl
@@ -10,7 +10,11 @@ This blog post describes my journey adding observability to X-RAG, a distributed
 
 ## What is X-RAG?
 
-X-RAG is a production-grade distributed RAG platform running on Kubernetes. It consists of several independently scalable microservices:
+X-RAG is a production-grade distributed RAG (Retrieval-Augmented Generation) platform running on Kubernetes. The idea behind RAG is simple: instead of asking an LLM to answer questions from its training data alone, you first retrieve relevant documents from your own knowledge base, then feed those documents to the LLM as context. The LLM synthesises an answer grounded in your actual content—reducing hallucinations and enabling answers about private or recent information the model was never trained on.
+
+X-RAG handles the full pipeline: ingest documents, chunk them into searchable pieces, generate vector embeddings, store them in a vector database, and at query time, retrieve relevant chunks and pass them to an LLM for answer generation. The system supports both local LLMs (Florian runs his on a beefy desktop) and cloud APIs like OpenAI. I configured an OpenAI API key since my laptop's CPU isn't fast enough for decent local inference.
+
+X-RAG consists of several independently scalable microservices:
 
 * Search UI: FastAPI web interface for queries
 * Ingestion API: Document upload endpoint
@@ -37,12 +41,182 @@ The data layer includes Weaviate (vector database with hybrid search), Kafka (me
 └─────────────────────────────────────────────────────────────────────────┘
 ```
 
+## Running Kubernetes locally with Kind
+
+X-RAG runs on Kubernetes, but you don't need a cloud account to develop it. The project uses Kind (Kubernetes in Docker)—a tool originally created by the Kubernetes SIG for testing Kubernetes itself.
+
+=> https://kind.sigs.k8s.io/ Kind - Kubernetes in Docker
+
+Kind spins up a full Kubernetes cluster using Docker containers as nodes. The control plane (API server, etcd, scheduler, controller-manager) runs in one container, and worker nodes run in separate containers. Inside these "node containers," pods run just like they would on real servers—using containerd as the container runtime. It's containers all the way down.
+
+```
+┌─────────────────────────────────────────────────────────────────────────┐
+│                           Docker Host                                    │
+├─────────────────────────────────────────────────────────────────────────┤
+│  ┌───────────────────┐  ┌───────────────────┐  ┌───────────────────┐    │
+│  │ xrag-k8-control   │  │ xrag-k8-worker    │  │ xrag-k8-worker2   │    │
+│  │ -plane (container)│  │ (container)       │  │ (container)       │    │
+│  │                   │  │                   │  │                   │    │
+│  │ K8s API server    │  │ Pods:             │  │ Pods:             │    │
+│  │ etcd, scheduler   │  │ • search-ui       │  │ • weaviate        │    │
+│  │                   │  │ • search-service  │  │ • kafka           │    │
+│  │                   │  │ • embedding-svc   │  │ • prometheus      │    │
+│  │                   │  │ • indexer         │  │ • grafana         │    │
+│  └───────────────────┘  └───────────────────┘  └───────────────────┘    │
+└─────────────────────────────────────────────────────────────────────────┘
+```
+
+Why Kind? It gives you a real Kubernetes environment—the same manifests deploy to production clouds unchanged. No minikube quirks, no Docker Compose translation layer. Just Kubernetes.
+
+Florian developed X-RAG on macOS, but it worked seamlessly on my Linux laptop. The only difference was Docker's resource allocation: on macOS you configure limits in Docker Desktop, on Linux it uses host resources directly.
+
+My hardware: a ThinkPad X1 Carbon Gen 9 with an 11th Gen Intel Core i7-1185G7 (4 cores, 8 threads at 3.00GHz) and 32GB RAM. During the hackathon, memory usage peaked around 15GB—comfortable headroom. CPU was the bottleneck; with ~38 pods running across all namespaces (rag-system, monitoring, kube-system, etc.), plus Discord for the remote video call and Tidal streaming hi-res music, things got tight. When rebuilding Docker images or restarting the cluster, Discord video and audio would stutter—my fellow hackers probably wondered why I kept freezing mid-sentence. A beefier CPU would have meant less waiting and smoother calls, but it was manageable.
+
 ## The problem: flying blind
 
-When I joined the hackathon, Florian's X-RAG was functional but opaque. With five services communicating via gRPC, Kafka, and HTTP, debugging was painful. When a search request took 5 seconds instead of the expected 500 milliseconds, there was no visibility into where the time was being spent. Was it the embedding generation? The vector search? The LLM synthesis? Nobody knew.
+When I joined the hackathon, Florian's X-RAG was functional but opaque. With five services communicating via gRPC, Kafka, and HTTP, debugging was cumbersome. When a search request took 5 seconds instead of the expected 500 milliseconds, there was no visibility into where the time was being spent. Was it the embedding generation? The vector search? The LLM synthesis? Nobody knew.
 
 Distributed systems are inherently opaque. Each service logs its own view of the world, but correlating events across service boundaries is archaeology. Grepping through logs on five different pods, trying to mentally reconstruct what happened—not fun. This was the perfect hackathon project: a real problem with tangible results.
 
+## The observability stack
+
+Before diving into implementation, here's what I deployed. The complete stack runs in the monitoring namespace:
+
+```
+$ kubectl get pods -n monitoring
+NAME                                  READY   STATUS
+alloy-84ddf4cd8c-7phjp                1/1     Running
+grafana-6fcc89b4d6-pnh8l              1/1     Running
+kube-state-metrics-5d954c569f-2r45n   1/1     Running
+loki-8c9bbf744-sc2p5                  1/1     Running
+node-exporter-kb8zz                   1/1     Running
+node-exporter-zcrdz                   1/1     Running
+node-exporter-zmskc                   1/1     Running
+prometheus-7f755f675-dqcht            1/1     Running
+tempo-55df7dbcdd-t8fg9                1/1     Running
+```
+
+Each component has a specific role:
+
+* `Grafana Alloy`: The unified collector. Receives OTLP from applications, scrapes Prometheus endpoints, tails log files. Think of it as the central nervous system.
+* `Prometheus`: Time-series database for metrics. Stores counters, gauges, and histograms with 15-day retention.
+* `Tempo`: Trace storage. Receives spans via OTLP, correlates them by trace ID, enables TraceQL queries.
+* `Loki`: Log aggregation. Indexes labels (namespace, pod, container), stores log chunks, enables LogQL queries.
+* `Grafana`: The unified UI. Queries all three backends, correlates signals, displays dashboards.
+* `kube-state-metrics`: Exposes Kubernetes object metrics (pod status, deployments, resource requests).
+* `node-exporter`: Exposes host-level metrics (CPU, memory, disk, network) from each Kubernetes node.
+
+Everything is accessible via port-forwards:
+
+* Grafana: http://localhost:3000 (unified UI for all three signals)
+* Prometheus: http://localhost:9090 (metrics queries)
+* Tempo: http://localhost:3200 (trace queries)
+* Loki: http://localhost:3100 (log queries)
+
+## Grafana Alloy: the unified collector
+
+Before diving into individual signals, I want to highlight Grafana Alloy—the component that ties everything together. Alloy is Grafana's vendor-neutral OpenTelemetry Collector distribution, and it became the backbone of the observability stack.
+
+=> https://grafana.com/docs/alloy/latest/ Grafana Alloy documentation
+
+Why use a centralised collector instead of having each service push directly to backends?
+
+* `Decoupling`: Applications don't need to know about Prometheus, Tempo, or Loki. They speak OTLP, and Alloy handles the translation.
+* `Unified timestamps`: All telemetry flows through one system, making correlation in Grafana more reliable.
+* `Processing pipeline`: Batch data before sending, filter noisy metrics, enrich with labels—all in one place.
+* `Backend flexibility`: Switch from Tempo to Jaeger without changing application code.
+
+Alloy uses a configuration language called River, which feels similar to Terraform's HCL—declarative blocks with attributes. If you've written Terraform, River will look familiar. Here's what we configured for X-RAG:
+
+`Receiving telemetry (OTLP)`:
+```
+otelcol.receiver.otlp "default" {
+  grpc { endpoint = "0.0.0.0:4317" }
+  http { endpoint = "0.0.0.0:4318" }
+  output {
+    metrics = [otelcol.processor.batch.metrics.input]
+    traces  = [otelcol.processor.batch.traces.input]
+  }
+}
+```
+
+Applications push metrics and traces to Alloy on ports 4317 (gRPC) or 4318 (HTTP). Alloy routes them to batch processors.
+
+`Batching for efficiency`:
+```
+otelcol.processor.batch "metrics" {
+  timeout = "5s"
+  send_batch_size = 1000
+  output { metrics = [otelcol.exporter.prometheus.default.input] }
+}
+
+otelcol.processor.batch "traces" {
+  timeout = "5s"
+  send_batch_size = 500
+  output { traces = [otelcol.exporter.otlp.tempo.input] }
+}
+```
+
+Instead of sending each metric individually, Alloy accumulates up to 1000 metrics (or waits 5 seconds) before flushing. This reduces network overhead and protects backends from being overwhelmed.
+
+`Exporting to storage backends`:
+```
+otelcol.exporter.prometheus "default" {
+  forward_to = [prometheus.remote_write.prom.receiver]
+}
+
+otelcol.exporter.otlp "tempo" {
+  client {
+    endpoint = "tempo.monitoring.svc.cluster.local:4317"
+    tls { insecure = true }
+  }
+}
+```
+
+Metrics get converted to Prometheus format and pushed via remote_write. Traces go to Tempo via OTLP.
+
+`Scraping Kubernetes metrics`:
+
+Alloy also pulls metrics from Kubernetes itself—kubelet resource metrics, cAdvisor container metrics, and kube-state-metrics for cluster state:
+
+```
+prometheus.scrape "kubelet_resource" {
+  targets         = discovery.relabel.kubelet.output
+  metrics_path    = "/metrics/resource"
+  scrape_interval = "30s"
+  forward_to      = [prometheus.relabel.kubelet_resource_filter.receiver]
+}
+```
+
+`Collecting logs`:
+
+For logs, Alloy discovers pods via the Kubernetes API, tails their log files from /var/log/pods/, and ships to Loki:
+
+```
+loki.source.kubernetes "pod_logs" {
+  targets    = discovery.relabel.pod_logs.output
+  forward_to = [loki.process.pod_logs.receiver]
+}
+
+loki.write "default" {
+  endpoint {
+    url = "http://loki.monitoring.svc.cluster.local:3100/loki/api/v1/push"
+  }
+}
+```
+
+The full Alloy configuration runs to over 1400 lines with comments explaining each section. It handles:
+
+* OTLP receiver for application metrics and traces
+* Batch processors for efficiency
+* Prometheus exporter with remote_write
+* Tempo exporter for traces
+* Kubelet, cAdvisor, and kube-state-metrics scraping
+* Infrastructure metrics (Redis, Kafka, MinIO exporters)
+* Pod log collection and shipping to Loki
+
+All three signals—metrics, traces, logs—flow through this single component, making Alloy the central nervous system of the observability stack.
+
 ## Step 1: centralised logging with Loki
 
 The first step was getting all logs in one place. I deployed Grafana Loki in the monitoring namespace, with Grafana Alloy running as a DaemonSet on each node to collect logs.
@@ -86,8 +260,6 @@ Now I could query logs with LogQL:
 {namespace="rag-system", container="search-ui"} |= "ERROR"
 ```
 
-=> ./x-rag-observability/loki-explore.png Exploring logs in Grafana with Loki
-
 But there was a problem: logs lacked correlation. I could see that an error occurred in the indexer, but I couldn't trace it back to the specific ingestion request that triggered it.
 
 ## Step 2: metrics with Prometheus
@@ -136,13 +308,75 @@ The breakthrough came with Grafana Alloy as an OpenTelemetry collector. Services
                         └─────────────────────┘
 ```
 
-With Grafana dashboards, I could now see latency percentiles, throughput, and error rates.
+With Grafana dashboards, I could now see latency percentiles, throughput, and error rates. But metrics told me *that* something was wrong—they didn't tell me *where* in the request path the problem occurred.
+
+## Step 3: the breakthrough—distributed tracing
 
-=> ./x-rag-observability/prometheus-metrics.png Prometheus metrics in Grafana
+### Understanding traces, spans, and the trace tree
 
-But metrics told me *that* something was wrong—they didn't tell me *where* in the request path the problem occurred.
+Before diving into the implementation, let me explain the core concepts I learned. A `trace` represents a single request's journey through the entire distributed system. Think of it as a receipt that follows your request from the moment it enters the system until the final response.
 
-## Step 3: the breakthrough—distributed tracing
+Each trace is identified by a `trace ID`—a 128-bit identifier (32 hex characters) that stays constant across all services. When I make a search request, every service handling that request uses the same trace ID: `9df981cac91857b228eca42b501c98c6`.
+
+=> https://www.youtube.com/watch?v=KPGjqus5qFo Quick video explaining the difference between trace IDs and span IDs in OpenTelemetry
+
+Within a trace, individual operations are recorded as `spans`. A span has:
+
+* A `span ID`: 64-bit identifier (16 hex characters) unique to this operation
+* A `parent span ID`: links this span to its caller
+* A `name`: what operation this represents (e.g., "POST /api/search")
+* `Start time` and `duration`
+* `Attributes`: key-value metadata (e.g., `http.status_code=200`)
+
+The first span in a trace is the `root span`—it has no parent. When the root span calls another service, that service creates a `child span` with the root's span ID as its parent. This parent-child relationship forms a `tree structure`:
+
+```
+                        ┌─────────────────────────┐
+                        │      Root Span          │
+                        │  POST /api/search       │
+                        │  span_id: a1b2c3d4...   │
+                        │  parent: (none)         │
+                        └───────────┬─────────────┘
+                                    │
+              ┌─────────────────────┴─────────────────────┐
+              │                                           │
+              ▼                                           ▼
+┌─────────────────────────┐             ┌─────────────────────────┐
+│      Child Span         │             │      Child Span         │
+│  gRPC Search            │             │  render_template        │
+│  span_id: e5f6g7h8...   │             │  span_id: i9j0k1l2...   │
+│  parent: a1b2c3d4...    │             │  parent: a1b2c3d4...    │
+└───────────┬─────────────┘             └─────────────────────────┘
+            │
+            ├──────────────────┬──────────────────┐
+            ▼                  ▼                  ▼
+     ┌────────────┐     ┌────────────┐     ┌────────────┐
+     │ Grandchild │     │ Grandchild │     │ Grandchild │
+     │ embedding  │     │ vector     │     │ llm.rag    │
+     │ .generate  │     │ _search    │     │ _completion│
+     └────────────┘     └────────────┘     └────────────┘
+```
+
+This tree structure answers the critical question: "What called what?" When I see a slow span, I can trace up to see what triggered it and down to see what it's waiting on.
+
+### How trace context propagates
+
+The magic that links spans across services is `trace context propagation`. When Service A calls Service B, it must pass along the trace ID and its own span ID (which becomes the parent). OpenTelemetry uses the W3C `traceparent` header:
+
+```
+traceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01
+             │   │                                │                 │
+             │   │                                │                 └── flags
+             │   │                                └── parent span ID (16 hex)
+             │   └── trace ID (32 hex)
+             └── version
+```
+
+For HTTP, this travels as a request header. For gRPC, it's passed as metadata. For Kafka, it's embedded in message headers. The receiving service extracts this context, creates a new span with the propagated trace ID and the caller's span ID as parent, then continues the chain.
+
+This is why all my spans link together—OpenTelemetry's auto-instrumentation handles propagation automatically for HTTP, gRPC, and Kafka clients.
+
+### Implementation
 
 The real enlightenment came with OpenTelemetry tracing. I integrated auto-instrumentation for FastAPI, gRPC, and HTTP clients, plus manual spans for RAG-specific operations:
 
@@ -190,17 +424,31 @@ Trace ID: 0af7651916cd43dd8448eb211c80319c
 
 Traces are collected by Alloy and stored in Grafana Tempo. In Tempo's UI, I can finally see exactly where time is spent. That 5-second query? Turns out the vector search was waiting on a cold Weaviate connection. Now I knew what to fix.
 
-=> ./x-rag-observability/tempo-trace.png Visualising a trace in Grafana Tempo
+## Infrastructure metrics: Kafka, Redis, MinIO
 
-=> ./x-rag-observability/tempo-trace-detail.png Trace detail showing span attributes
+Application metrics weren't enough. I also needed visibility into the data layer. Each infrastructure component has a specific role in X-RAG and got its own exporter:
 
-## Infrastructure metrics: Kafka, Redis, MinIO
+`Redis` is the caching layer. It stores search results and embeddings to avoid redundant API calls to OpenAI. We collect 25 metrics via oliver006/redis_exporter running as a sidecar, including cache hit/miss rates, memory usage, connected clients, and command latencies. The key metric? `redis_keyspace_hits_total / (redis_keyspace_hits_total + redis_keyspace_misses_total)` tells you if caching is actually helping.
 
-Application metrics weren't enough. I also needed visibility into the data layer. Each infrastructure component got its own exporter:
+`Kafka` is the message queue connecting the ingestion API to the indexer. Documents are published to a topic, and the indexer consumes them asynchronously. We collect 12 metrics via danielqsj/kafka-exporter, with consumer lag being the most critical—it shows how far behind the indexer is. High lag means documents aren't being indexed fast enough.
 
-* Redis: oliver006/redis_exporter as a sidecar, exposing cache hit rates, memory usage, and client connections
-* Kafka: danielqsj/kafka-exporter as a standalone deployment, tracking consumer lag, partition offsets, and broker health
-* MinIO: Native /minio/v2/metrics/cluster endpoint for S3 request rates, error counts, and disk usage
+`MinIO` is the S3-compatible object storage where raw documents are stored before processing. We collect 16 metrics from its native /minio/v2/metrics/cluster endpoint, covering request rates, error counts, storage usage, and cluster health.
+
+You can verify these counts by querying Prometheus directly:
+
+```
+$ curl -s 'http://localhost:9090/api/v1/label/__name__/values' \
+    | jq -r '.data[]' | grep -c '^redis_'
+25
+$ curl -s 'http://localhost:9090/api/v1/label/__name__/values' \
+    | jq -r '.data[]' | grep -c '^kafka_'
+12
+$ curl -s 'http://localhost:9090/api/v1/label/__name__/values' \
+    | jq -r '.data[]' | grep -c '^minio_'
+16
+```
+
+=> https://github.com/florianbuetow/x-rag/blob/main/infra/k8s/monitoring/alloy-config.yaml Full Alloy configuration with detailed metric filtering
 
 Alloy scrapes all of these and remote-writes to Prometheus:
 
@@ -223,8 +471,6 @@ Or check Redis cache effectiveness:
 redis_keyspace_hits_total / (redis_keyspace_hits_total + redis_keyspace_misses_total)
 ```
 
-=> ./x-rag-observability/infrastructure-dashboard.png Infrastructure metrics dashboard
-
 ## Async ingestion trace walkthrough
 
 One of the most powerful aspects of distributed tracing is following requests across async boundaries like message queues. The document ingestion pipeline flows through Kafka, creating spans that are linked even though they execute in different processes at different times.
@@ -282,7 +528,7 @@ $ curl -s "http://localhost:3200/api/traces/b3fc896a1cf32b425b8e8c46c86c76f7" \
 ]
 ```
 
-The trace spans **three services**: ingestion-api, indexer, and embedding-service. The trace context propagates through Kafka, linking the original HTTP request to the async consumer processing.
+The trace spans `three services`: ingestion-api, indexer, and embedding-service. The trace context propagates through Kafka, linking the original HTTP request to the async consumer processing.
 
 ### Step 4: Analyse the async trace
 
@@ -304,17 +550,17 @@ indexer       | db.insert                | 1038ms  ← Store in Weaviate
 
 The total async processing takes ~1.8 seconds, but the user sees a 16ms response. Without tracing, debugging "why isn't my document showing up in search results?" would require correlating logs from three services manually.
 
-**Key insight**: The trace context propagates through Kafka message headers, allowing the indexer's spans to link back to the original ingestion request. This is configured via OpenTelemetry's Kafka instrumentation.
+`Key insight`: The trace context propagates through Kafka message headers, allowing the indexer's spans to link back to the original ingestion request. This is configured via OpenTelemetry's Kafka instrumentation.
 
 ### Viewing traces in Grafana
 
 To view a trace in Grafana's UI:
 
 1. Open Grafana at http://localhost:3000/explore
-2. Select **Tempo** as the data source (top-left dropdown)
-3. Choose **TraceQL** as the query type
+2. Select `Tempo` as the data source (top-left dropdown)
+3. Choose `TraceQL` as the query type
 4. Paste the trace ID: `b3fc896a1cf32b425b8e8c46c86c76f7`
-5. Click **Run query**
+5. Click `Run query`
 
 The trace viewer shows a Gantt chart with all spans, their timing, and parent-child relationships. Click any span to see its attributes.
 
@@ -373,16 +619,16 @@ $ curl -s "http://localhost:3200/api/traces/9df981cac91857b228eca42b501c98c6" \
 
 The raw trace shows spans from multiple services:
 
-* **search-ui**: `POST /api/search` (root span, 2138ms total)
-* **search-ui**: `/xrag.search.SearchService/Search` (gRPC client call)
-* **search-service**: `/xrag.search.SearchService/Search` (gRPC server)
-* **search-service**: `/xrag.embedding.EmbeddingService/Embed` (gRPC client)
-* **embedding-service**: `/xrag.embedding.EmbeddingService/Embed` (gRPC server)
-* **embedding-service**: `openai.embeddings` (OpenAI API call, 647ms)
-* **embedding-service**: `POST https://api.openai.com/v1/embeddings` (HTTP client)
-* **search-service**: `vector_search.query` (Weaviate hybrid search, 13ms)
-* **search-service**: `openai.chat` (LLM answer generation, 1468ms)
-* **search-service**: `POST https://api.openai.com/v1/chat/completions` (HTTP client)
+* `search-ui`: `POST /api/search` (root span, 2138ms total)
+* `search-ui`: `/xrag.search.SearchService/Search` (gRPC client call)
+* `search-service`: `/xrag.search.SearchService/Search` (gRPC server)
+* `search-service`: `/xrag.embedding.EmbeddingService/Embed` (gRPC client)
+* `embedding-service`: `/xrag.embedding.EmbeddingService/Embed` (gRPC server)
+* `embedding-service`: `openai.embeddings` (OpenAI API call, 647ms)
+* `embedding-service`: `POST https://api.openai.com/v1/embeddings` (HTTP client)
+* `search-service`: `vector_search.query` (Weaviate hybrid search, 13ms)
+* `search-service`: `openai.chat` (LLM answer generation, 1468ms)
+* `search-service`: `POST https://api.openai.com/v1/chat/completions` (HTTP client)
 
 ### Step 3: Analyse the trace
 
@@ -398,7 +644,7 @@ Total request:                     2138ms
 │       └── OpenAI chat API:       1463ms
 ```
 
-The bottleneck is clear: **68% of time is spent in LLM answer generation**. The vector search (13ms) and embedding generation (649ms) are relatively fast. Without tracing, I would have guessed the embedding service was slow—traces proved otherwise.
+The bottleneck is clear: `68% of time is spent in LLM answer generation`. The vector search (13ms) and embedding generation (649ms) are relatively fast. Without tracing, I would have guessed the embedding service was slow—traces proved otherwise.
 
 ### Step 4: Search traces with TraceQL
 
@@ -447,33 +693,6 @@ The real power comes from correlating traces, metrics, and logs. When an alert f
 
 Prometheus exemplars link specific metric samples to trace IDs, so I can click directly from a latency spike to the responsible trace.
 
-=> ./x-rag-observability/signal-correlation.png Correlating metrics, traces, and logs in Grafana
-
-## The observability stack
-
-The complete stack runs in the monitoring namespace:
-
-```
-$ kubectl get pods -n monitoring
-NAME                                  READY   STATUS
-alloy-84ddf4cd8c-7phjp                1/1     Running
-grafana-6fcc89b4d6-pnh8l              1/1     Running
-kube-state-metrics-5d954c569f-2r45n   1/1     Running
-loki-8c9bbf744-sc2p5                  1/1     Running
-node-exporter-kb8zz                   1/1     Running
-node-exporter-zcrdz                   1/1     Running
-node-exporter-zmskc                   1/1     Running
-prometheus-7f755f675-dqcht            1/1     Running
-tempo-55df7dbcdd-t8fg9                1/1     Running
-```
-
-Everything is accessible via port-forwards or NodePort:
-
-* Grafana: http://localhost:3000 (unified UI for all three signals)
-* Prometheus: http://localhost:9090 (metrics queries)
-* Tempo: http://localhost:3200 (trace queries)
-* Loki: http://localhost:3100 (log queries)
-
 ## Results: two days well spent
 
 What did two days of hackathon work achieve? The system went from flying blind to fully instrumented:
@@ -484,7 +703,9 @@ What did two days of hackathon work achieve? The system went from flying blind t
 * Grafana dashboards with PromQL queries
 * Trace context propagation across all gRPC calls
 
-The biggest insight from testing? The embedding service wasn't the bottleneck I assumed. Traces revealed that LLM synthesis (120ms average) dominated latency, not embedding generation (45ms). Without tracing, optimisation efforts would have targeted the wrong component.
+The biggest insight from testing? The embedding service wasn't the bottleneck I assumed. Traces revealed that LLM synthesis dominated latency, not embedding generation. Without tracing, optimisation efforts would have targeted the wrong component.
+
+Beyond the technical wins, I had a lot of fun. The hackathon brought together people working on completely different projects, and I got to know some really nice folks during the breaks. There's something energising about being in a room full of people all heads-down on their own challenges—even if you're not collaborating directly, the shared focus is motivating.
 
 ## What's next
 
@@ -494,6 +715,46 @@ The system is now "enlightened," but there's always more:
 * Alerting rules: Prometheus alerts for SLO violations
 * Sampling strategies: For high-traffic production, sample traces to reduce storage costs
 
+## Using Amp for AI-assisted development
+
+I used Amp (formerly Ampcode) throughout this project. While I knew what I wanted to achieve, I let the LLM generate the actual configurations, Kubernetes manifests, and Python instrumentation code.
+
+=> https://ampcode.com/ Amp - AI coding agent by Sourcegraph
+
+My workflow was step-by-step rather than handing over a grand plan:
+
+1. "Deploy Grafana Alloy to the monitoring namespace"
+2. "Verify Alloy is running and receiving data"
+3. "Document what we did to docs/OBSERVABILITY.md"
+4. "Commit with message 'feat: add Grafana Alloy for telemetry collection'"
+5. Hand off context, start fresh: "Now instrument the search-ui with OpenTelemetry to push traces to Alloy..."
+
+Chaining many small, focused tasks worked better than one massive plan. Each task had clear success criteria, and I could verify results before moving on. The LLM generated the River configuration, the OpenTelemetry Python code, the Kubernetes manifests—I reviewed, tweaked, and committed.
+
+I only ran out of the 200k token context window once, during a debugging session that involved restarting the Kubernetes cluster multiple times. The fix required correlating error messages across several services, and the conversation history grew too long. Starting a fresh context and summarising the problem solved it.
+
+Amp automatically selects the best model for the task at hand. Based on the response speed and Sourcegraph's recent announcements, I believe it was using Claude Opus 4.5 for most of my infrastructure work. The quality was excellent—it understood Kubernetes, OpenTelemetry, and Grafana tooling without much hand-holding.
+
+Let me be clear: without the LLM, I'd never have managed to write all these configuration files by hand in two days. The Alloy config alone is 1400+ lines. But I also reviewed every change manually, verified it made sense, and understood what was being deployed. This wasn't vibe-coding—the whole point of the hackathon was to learn. I already knew Grafana and Prometheus from previous work, but OpenTelemetry, Alloy, Tempo, and Loki were all pretty new to me. By reviewing each generated config and understanding why it was structured that way, I actually learned the tools rather than just deploying magic incantations.
+
+Cost-wise, I spent around 20 USD on Amp credits over the two-day hackathon. For the amount of code generated, configs reviewed, and debugging assistance—that's remarkably affordable.
+
+## Other changes along the way
+
+Looking at the git history, I made 25 commits during the hackathon. Beyond the main observability features, there were several smaller but useful additions:
+
+`OBSERVABILITY_ENABLED flag`: Added an environment variable to completely disable the monitoring stack. Set `OBSERVABILITY_ENABLED=false` in `.env` and the cluster starts without Prometheus, Grafana, Tempo, Loki, or Alloy. Useful when you just want to work on application code without the overhead.
+
+`Metrics migration to OpenTelemetry SDK`: The original codebase used prometheus_client for metrics. I migrated everything to OpenTelemetry's metrics SDK so all telemetry (metrics, traces, logs) flows through the same OTLP pipeline to Alloy. One protocol, one collector.
+
+`Removing duplicate spans`: Auto-instrumentation is great until it creates spans that overlap with your manual instrumentation. I had to audit the traces and remove manual spans where FastAPI or gRPC instrumentors already covered the operation.
+
+`Load generator`: Added a `make load-gen` target that fires concurrent requests at the search API. Useful for generating enough trace data to see patterns in Tempo, and for stress-testing the observability pipeline itself.
+
+`Verification scripts`: Created scripts to test that OTLP is actually reaching Alloy and that traces appear in Tempo. Debugging "why aren't my traces showing up?" is frustrating without a systematic way to verify each hop in the pipeline.
+
+`Moving monitoring to dedicated namespace`: Refactored from having observability components scattered across namespaces to a clean `monitoring` namespace. Makes `kubectl get pods -n monitoring` show exactly what's running for observability.
+
 ## Lessons learned
 
 * Start with metrics, but don't stop there—they tell you *what*, not *why*
@@ -515,6 +776,8 @@ The observability-specific files I added during the hackathon:
 * `src/*/metrics.py` — Per-service metric definitions
 * `docs/OBSERVABILITY.md` — Comprehensive observability guide
 
+The best part? Everything I learned during this hackathon—OpenTelemetry instrumentation, Grafana Alloy configuration, trace context propagation, PromQL queries—I can immediately apply at work. Observability patterns are universal, and hands-on experience with a real distributed system beats reading documentation any day.
+
 E-Mail your comments to paul@nospam.buetow.org
 
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