Local Storm cluster (cluster mode) with Docker Compose

Brings up a real, distributed Storm cluster on your machine — dev ZooKeeper + Nimbus + two Supervisors + UI plus an observability stack (Pushgateway + Prometheus + Grafana).

Two supervisors with 2 worker slots each (4 slots total) are intentional: a topology submitted with topology.workers >= 2 lands one worker per supervisor container, so its inter-worker tuple traffic actually crosses the network — the only path where tuple serialization happens. The slot count lives in storm.yaml (supervisor.slots.ports); raise it there if you want more workers.

Architecture

All containers share one Docker bridge network (storm) and resolve each other by service name. Host-published ports are shown in (). The metrics plane is detailed under Metrics & reports.

   host:  docker compose exec nimbus  storm jar ...
                          |  submit topology
  ========================|=========== docker network: storm ==============
                          v
   +-----------+      +---------------+      +-----------+
   | ZooKeeper |<---->| Nimbus  :6627 |<---->|  UI :8080 |
   +-----------+      +-------+-------+      +-----------+
                              |  assign workers
              +---------------+----------------+
              v                                v
   +--------------------+   tuples     +--------------------+
   | supervisor1        |<============>| supervisor2        |
   |   worker :6700     | (network hop)|   worker :6700     |
   +--------------------+              +--------------------+

   metrics:  Nimbus --> Pushgateway   and   workers --> graphite-exporter,
             both scraped by  Prometheus :9090  -->  Grafana :3000

Layout

Prerequisites

Platform: Linux or macOS (or Windows via WSL2). The helper scripts are bash and call mvn (not mvn.cmd), and netsim.sh relies on Linux tc/netem. Native Windows is not supported yet.

Build the distribution, the Docker image, and stage the Prometheus reporter onto Nimbus's classpath — one command:

dev-tools/cluster/build-image.sh

It rebuilds storm-client-bin + final-package (so both the daemon lib and the worker lib-worker classpaths reflect your code), then builds the storm-local image. Building only final-package -am is not enough: it leaves lib-worker (the worker classpath) stale, so workers run old code.

As a final step it runs prepare-extlib.sh, which fills extlib-daemon/ (git-ignored build artifacts) with the Prometheus reporter jar + runtime deps that docker-compose mounts onto Nimbus. Pass --no-extlib (or set PREPARE_EXTLIB=0) to skip it, or run it standalone after changing only that module:

cd dev-tools/cluster
./prepare-extlib.sh

The Storm version is taken from the repo root pom.xml (project.version). build-image.sh reads it and writes dev-tools/cluster/.env; the compose file references it as ${STORM_VERSION} (image tag, build arg, and the storm-perf jar path), so everything tracks the pom automatically. To pin a different version, run with STORM_VERSION=x.y.z or edit .env.

Run

cd dev-tools/cluster
docker compose up --build -d      # build the image and start everything
docker compose ps                 # all services Up, zookeeper healthy
docker compose logs -f nimbus     # follow a daemon

Tear down — use -v so the metrics are deleted too:

docker compose down -v

Prometheus and Grafana store their data on disk in the named volumes prometheus-data / grafana-data (Prometheus retention is capped at --storage.tsdb.retention.time=2h to keep them small). A plain docker compose down keeps the containers' networks gone but leaves those volumes on disk; down -v is what deletes them. The datasource and dashboards are re-provisioned from files on the next up, so wiping the volumes loses only metrics history and ad-hoc Grafana UI edits.

Smoke test: submit a topology

The Nimbus container has the storm-perf jar, a sample input file and the config mounted under /topology. Submit the word-count topology (runs ~120s):

docker compose exec -d nimbus \
  storm jar /topology/storm-perf.jar \
  org.apache.storm.perf.FileReadWordCountTopo 120 /topology/topo.yaml

Watch it in the UI, or via REST:

curl -s http://localhost:8080/api/v1/topology/summary | python3 -m json.tool

FileReadWordCountTopo-cluster.yaml sets topology.workers: 2, so the two workers land on supervisor1 and supervisor2 — verify with the topology page (Worker Resources) that the two workers sit on different hosts.

It is a 3-stage pipeline; spreading it across two workers makes at least one edge a network hop (where tuple serialization happens):

   FileReadSpout  --shuffle (network hop)-->  SplitSentenceBolt  --fieldsGrouping-->  CountBolt
   (emits text lines)                         (emits words)                           (counts)

Simulating network latency and jitter

Inter-worker traffic between containers is near-instant (~0.05 ms), which hides the network cost. netsim.sh adds realistic latency/jitter/loss to the worker hosts with Linux tc/netem. The Storm image has no tc, so the script injects the qdisc from a throwaway helper container sharing each supervisor's network namespace — no image rebuild needed.

./netsim.sh add 50 10 0   # 50 ms delay, 10 ms jitter, 0% loss on each supervisor
./netsim.sh ping          # verify: worker<->worker RTT jumps to ~100 ms (2x egress)
./netsim.sh show          # inspect the active qdisc
./netsim.sh clear         # remove shaping

netem shapes all egress from each supervisor (inter-worker tuples and heartbeats to Nimbus/ZK), so keep the delay moderate (≤ ~150 ms) or heartbeats may time out. With both supervisors delayed by D, worker round-trip latency is ~2*D.

Why the script sets a huge queue limit. netem‘s default queue is only 1000 packets. Under a high-throughput perf topology that buffer overflows at the added delay and drops tuples even with loss 0%, which collapses TCP and back-pressures the spout to zero throughput (you’ll see transferred 0). netsim.sh therefore sets limit 1000000 (override as the 4th arg) so the queue can hold rate * delay without dropping. If you ever apply tc netem by hand, remember to add a large limit.

Metrics & reports (Prometheus + Grafana)

Two metric paths feed Prometheus, both push-based (so ephemeral workers need no scrape targets), and Grafana auto-loads a dashboard for each:

   Nimbus --push--> Pushgateway:9091 -----------------scrape-------------+
                                                                         v
   supervisor1 worker --+                                          Prometheus:9090 --> Grafana:3000
                        +-- graphite:9109 --> graphite-exporter:9108 --scrape--+         |
   supervisor2 worker --+                                                                +--> "Storm Cluster"
                                                                                         +--> "Storm Metrics v2"

1. Cluster summary — Storm Cluster dashboard Nimbus → Pushgateway → Prometheus. Nimbus runs Storm's PrometheusPreparableReporter (enabled via -c overrides in docker-compose.yml, jars from extlib-daemon/) and pushes cluster-summary metrics every 10s. Prometheus scrapes the Pushgateway (honor_labels keeps job="nimbus").
2. Metrics v2 (per-worker/topology) — Storm Metrics v2 dashboard workers → graphite-exporter → Prometheus. Every worker runs the GraphiteStormReporter (configured in storm.yaml under topology.metrics.reporters) and emits its full Dropwizard metric set in Graphite plaintext to the graphite-exporter, which graphite-mapping.yml turns into labelled storm_worker{...} / storm_topology{...} series.

The pushed series are cluster-level (not per-topology): summary_cluster_num_supervisors, summary_cluster_num_topologies, summary_cluster_num_total_workers, summary_cluster_num_total_used_workers, nimbus_total_cpu, nimbus_available_cpu_non_negative, nimbus_total_memory, and the summary_topologies_assigned_* histograms. Quick check:

curl -s 'http://localhost:9090/api/v1/query?query=summary_cluster_num_total_workers'

Storm Metrics v2 dashboard

Metrics v2 are emitted per task (org.apache.storm.metrics2.TaskMetrics), so the dashboard is filtered by a chained topology → host → component → task variable set, and every series carries topology_id, host, component, task, port labels.

graphite-mapping.yml models TaskMetrics explicitly into clean metrics. Each is per (component, task); the key label is the metric key — the own output stream for emit/transfer, or the sourceComponent:sourceStream for the input metrics (execute/ack/fail/latency):

Counts/rates are sampling-scaled (topology.stats.sample.rate), so they estimate true values; .m1_rate is tuples/s averaged over 1 minute. The jitter metrics are RFC 3550 EWMA latency-variation estimators and only flow when topology.stats.ewma.enable: true (set in storm.yaml) — pair them with netsim.sh to see network jitter propagate into per-task latency variation.

Everything else falls through to generic series, still fully queryable:

• storm_worker{metric=...} — __skipped-*, __backpressure-last-overflow-count, __send-iconnection-*, doHeartbeat-calls.
• storm_topology{component="__system"} — per-worker JVM (task=-1): memory.heap.*, memory.non-heap.*, memory.pools.*, GC.*.{count,time}, threads.*.

List everything currently flowing:

curl -s http://localhost:9090/api/v1/label/metric/values | python3 -m json.tool

Notes

• The bundled storm dev-zookeeper is single-node and for development only; it does not snapshot. Swap in a real ZooKeeper for anything beyond local testing.
• Heaps are kept small in storm.yaml so the whole cluster fits on a laptop. Bump worker.childopts / *.childopts for heavier topologies.
• To run a different topology, mount its jar into the nimbus service (see the volumes: of that service) and storm jar it the same way.