tkati-dashboard — dataflow graph viewer

Reads a serialized tkati dataflow directory (see docs/dataflow-serialization.md) and serves a local web page rendering it as a graph — no live runtime process required, no manifest to maintain, just a directory of *.json/*.yaml/*.yml fragment files (freely mixable — both encodings merge into the same graph identically).

Usage

tkati-dashboard path/to/dataflow-dir

Then open http://127.0.0.1:8000/ in a browser. The page fetches /api/graph and renders it top-to-bottom with React Flow, laid out by dagre using each node's real measured box (from its label text, via canvas measurement — no DOM mount needed) rather than a flat grid, so a node with a long broker/topic string pushes its neighbors aside instead of overlapping them. Source/sink nodes (kafka-topic, clickhouse-table) and processing nodes are colored differently, and stream edges are labeled by kind plus, for a Kafka consumer edge, its group_id and live lag. Click a node to fill the always-visible inspector panel on the right with its full connection/config/schema details; drag its left edge to resize it (the width is remembered across reloads).

Try it with the bundled example

examples/simple-pipeline is the smallest interesting dataflow: two Kafka topics and one processing node (raw-events → dedup → deduped-events). Its connection blocks point at localhost:9092, so if you have a broker there, seed it with sample events first:

uv run python packages/tkati-dashboard/examples/simple-pipeline/seed_kafka.py
uv run tkati-dashboard packages/tkati-dashboard/examples/simple-pipeline

Then open http://127.0.0.1:8000/, click the raw-events or deduped-events node, and click "Load latest events" in the side panel to see the real messages the seed script just produced (raw-events includes two intentional duplicate event_ids so you can see what the dedup node in between is for).

For a bigger graph exercising fragment merging across topics.json, tables.json, nodes.json, and edges.json (two raw topics → dedup → a sessionize/enrich node → a ClickHouse table, plus a side branch straight to another table), see examples/analytics-pipeline — its topics aren't seeded with data, so "Latest events" there will error unless you produce to them yourself.

Options:

• --host (default 127.0.0.1)
• --port (default 8000)

The dataflow directory is re-read on every request to /api/graph, so editing the fragments and refreshing the browser picks up the change without restarting the server.

Node panel

The panel on the right is always there, like an inspector rather than a popup — it shows a placeholder until you click a node, then shows that node's full metadata: connection settings, config, and schema (field → type). Clicking empty canvas clears the selection back to the placeholder. Every section has a clickable header to collapse/expand it — a collapsed section stays mounted, just hidden, so collapsing a live-fetching section and reopening it doesn't re-fetch. For a kafka-topic node, the panel also fetches two more, live views:

• GET /api/nodes/{id}/snapshot connects to connection.broker/connection.topic and shows the most recent messages on that topic (newest last), parsed as JSON, using a throwaway consumer group that never commits offsets. It only fetches on demand — click "Load latest events" — and a "↻ Refresh" button afterward pulls a fresh batch on request rather than automatically. Each message renders as its own pretty-printed JSON block (fields reordered to match schema, when there is one) rather than a table — real messages commonly carry 5-20 fields, too many to lay out sensibly as table columns in a side panel — with a small header showing that message's Kafka-level partition, offset, and timestamp, alongside its parsed body.
• GET /api/nodes/{id}/topic-stats shows the topic's partitioning and replication (per-partition leader/replicas/in-sync-replicas, flagging any under-replicated partition) and its topic-level config — retention.ms, retention.bytes, cleanup.policy, segment.bytes, compression.type, max.message.bytes — via tkati_dashboard.topic_stats, marking each value that differs from the broker default. Partition/replica info comes from the same topic metadata lookup as the snapshot/lag features (_kafka_metadata.py); the config comes from a separate read-only AdminClient.describe_configs() call.

These are two of the places tkati-dashboard talks to a live broker rather than just the serialized directory — best-effort conveniences for the panel, not something the graph view itself depends on: a broker that's unreachable, or a topic that doesn't exist, shows an inline error in that section instead of breaking the page.

Consumer lag

For every stream edge whose consumer.group_id is set and whose source is a kafka-topic, the page also fetches GET /api/nodes/{topic_id}/consumer-lag?group_id=... and appends the result to the edge's label (e.g. stream · group: orders-dedup · lag: 4). This is another place tkati-dashboard talks to a live broker: it looks up group_id's committed offset with Consumer.committed() and compares it to the topic's high watermark — it never subscribes or polls as that group, so it can't join it, trigger a rebalance, or otherwise disturb a real pipeline's consumer. A group that has never committed an offset is reported as fully behind (lag = the topic's full size); an unreachable broker shows lag: n/a on that edge instead of failing the page.

Validation

tkati_dashboard.dataflow.load_dataflow enforces the rules from the serialization doc: the directory must contain at least one *.json/*.yaml/*.yml fragment, node ids must be unique (or identically redefined) across fragments, edges must reference existing nodes, and a node's schema, when present, must use field types known to tkati_core.type_mapping — schema itself is always optional, since it isn't always on hand for a real-world node. A validation failure surfaces as an HTTP 422 with the error message, shown inline on the page instead of a blank graph.