TRAM Documentation

Trishul Real-time Aggregation & Mediation

Lightweight, container-native Python daemon for telecom data pipeline orchestration.

Version: 1.3.3

Status: Production-ready

Python: 3.13+

Quick Links

Core Documentation

Getting Started

Architecture - System design, data flow, execution modes, cluster coordination
Deployment Guide - Docker, Kubernetes, TLS, environment variables

Features & Configuration

Connectors - All sources and sinks (SFTP, Kafka, REST, SNMP, ClickHouse, etc.)
Transforms - All transforms and expression syntax
API Reference - REST API endpoints, authentication, rate limiting

Design & Roadmap

Pipeline Controller Design - Historical design notes for the v1.1.x controller transition; current manager/worker architecture is documented in architecture.md
Roadmap - Planned features and version checklist
v1.3.3 Plan - Completed UI revalidation, shared-system consolidation, and release-close record for 1.3.3
Archive
- v1.3.2 Plan - Archived consolidated 1.3.2 design and implementation plan covering stats parity, multi-worker UDP streams, ASN.1 flattening, CDR record shaping, and batch resilience
- v1.3.1 Plan - Archived planning document for the 1.3.1 implementation slice set
- v1.3.0 Plan Architecture Change - Archived design document for the 1.3.0 broadcast-streams rollout

Quick Start

Local Development

pip install -e ".[dev]"
tram version
tram plugins

# Validate a pipeline
tram validate pipelines/minimal.yaml

# Run the daemon
tram daemon &

# Open web UI
open http://localhost:8765/ui/

Docker

docker pull ghcr.io/tosumitdhaka/trishul-ram:latest
docker compose up
curl http://localhost:8765/api/ready

Kubernetes (Helm)

Quick-start examples below use latest. For production, pin image.tag to a specific release such as 1.3.3.

# Standalone mode (SQLite, single pod)
helm install tram oci://ghcr.io/tosumitdhaka/charts/trishul-ram \
  --set image.tag=latest

# Manager + Worker mode (3 workers, dedicated worker image)
helm install tram oci://ghcr.io/tosumitdhaka/charts/trishul-ram \
  --set image.tag=latest \
  --set manager.enabled=true \
  --set worker.replicas=3 \
  --set worker.image.repository=trishul-ram-worker \
  --set worker.image.tag=latest \
  --set apiKey=mysecret

Plugin Registry

Category	Count	Examples
Sources	24	sftp, kafka, rest, snmp_poll, snmp_trap, syslog, webhook, mqtt, amqp, nats, gnmi, sql, clickhouse, influxdb, corba, websocket, prometheus_rw
Sinks	20	sftp, kafka, rest, opensearch, snmp_trap, mqtt, amqp, nats, sql, clickhouse, influxdb, ves, websocket, elasticsearch
Serializers	12	json, ndjson, csv, xml, avro, parquet, protobuf, msgpack, bytes, text, asn1, pm_xml
Transforms	27	rename, cast, filter, aggregate, jmespath, flatten, json_flatten, explode, unnest, select_from_list, coalesce_fields, project, inject_meta, melt, deduplicate, mask, validate, template, enrich, hex_decode

Key Features

Pipeline Management

YAML-based pipeline definitions with ${ENV_VAR} substitution
Hot-reload via REST API or file watcher
Versioning and rollback support
Pipeline templates library

Execution Modes

Batch: Interval, cron, or manual trigger
Stream: Continuous processing (Kafka, NATS, webhooks)
Parallel workers: Multi-threaded processing per pipeline

Observability

Live metrics dashboard (records, errors, latency)
Prometheus metrics export
OpenTelemetry tracing
Per-pipeline run history

Reliability

Dead Letter Queue (DLQ) for failed records
Per-sink retry with exponential backoff
Circuit breakers for failing sinks
Rate limiting per pipeline

Web UI

Bootstrap 5 responsive interface
Pipeline wizard with AI assist
Live metrics and sparklines
Alert rules management
MIB and schema file management

Cluster Mode / Manager + Worker (v1.2.0+)

Manager StatefulSet — scheduler, DB, and UI; dispatches runs to workers via HTTP
Worker StatefulSet — stateless executors; internal agent on :8766, ingress-only webhook receiver on :8767
Dedicated Dockerfile.worker — lighter image without apscheduler/sqlalchemy/UI
Least-loaded dispatch plus multi-worker placement for HTTP push streams
TRAM_MODE=manager / TRAM_MODE=worker — SQLite on manager PVC is sufficient (single writer)
PipelineController — unified pipeline lifecycle authority; core lifecycle states are scheduled, running, stopped, error, with degraded / reconciling surfaced for multi-worker placements in v1.3.0

Security

API key authentication
Browser session authentication (HMAC tokens, shared secret via tram-auth K8s secret)
TLS/HTTPS support
Rate limiting per IP

Architecture Overview

Source → Deserialize → Transform → [per-sink routing] → Serialize → Sink
                                            ↓
                                    (on any error)
                                            ↓
                                        DLQ sink

Plugin-based architecture:

All connectors, transforms, and serializers are plugins
Self-register via decorators at import time
Zero core engine changes to add new plugins

Documentation Structure

docs/
├── index.md                      # This page
├── architecture.md               # System design, data flow, manager+worker
├── api.md                        # REST API reference
├── connectors.md                 # All sources and sinks
├── deployment.md                 # Docker, k8s, environment variables
├── transforms.md                 # Transform reference
├── pipeline-controller-design.md # Historical v1.1.x controller design notes
├── roadmap.md                    # Planned features and version checklist
├── v1.3.3_plan.md                # Current 1.3.3 release record and UI validation plan
├── changelog.md                  # Full release history
├── checklist.md                  # Development checklist
└── archive/                      # Archived version-specific design and planning docs
    ├── v1.3.2-plan.md            # Archived consolidated 1.3.2 backend implementation plan
    ├── v1.3.1-plan.md            # Archived 1.3.1 implementation plan
    └── v1.3.0-plan.md            # Archived 1.3.0 implementation plan

Support & Contributing

Issues: GitHub Issues
Discussions: GitHub Discussions
License: Apache 2.0

Version History

See changelog.md for detailed release notes.

Current Release: v1.3.3 (2026-05-01)

Full UI revalidation completed across Dashboard, Pipelines, Detail, Editor, Runs, Cluster, Templates, Settings, MIBs, Schemas, and Plugins in both manager and standalone mode
Dashboard overview now uses shared records/bytes in/out cards, a bytes-processed load chart with metric toggle and bucket tooltip, contextual Run now handling for manual pipelines, and manual refresh
Cluster now presents runtime state first, merged input/output traffic summaries, per-worker processed records/bytes totals, and clearer active pipeline/live metric truth
Shell, route aliases, and shared renderers were consolidated: #templates now resolves through Pipelines, hash navigation preserves browser history, login boot no longer flashes unauthorized errors, and shared YAML diff/run-monitor helpers back the main flows
Pipeline detail/version flows were tightened with version dedupe, scoped version YAML caching, rollback correctness, shared compare/view/download tooling, and visible failed-run reasons for no-worker batch dispatch
Schemas/MIBs/template/settings surfaces were normalized onto the shared UI system, including search, theme-safe modal/result chrome, and raw-source-aware MIB management

v1.3.2 (2026-04-21)

Standalone live stats: local stream runs now feed StatsStore so /api/pipelines/{name}/placement returns a live single-slot view in standalone mode
Manager operational metrics: 8 new tram_mgr_* Prometheus series for dispatch, redispatch, reconcile actions, placement status, worker health, and callback receipt; /metrics is process-local
UDP multi-worker streams: syslog and snmp_trap sources work in manager mode with kubernetes: enabled: true; per-pipeline NodePort Services; count: N uses manual Endpoints pinned to dispatched workers; L012 enforces the kubernetes requirement
ASN.1 decode improvements: concatenated BER multi-record split, ordered message_classes fallback, and the explicit json_flatten / hex_decode shaping path
CDR shaping primitives: dotted-path support across core transforms plus select_from_list, coalesce_fields, project, conditional drop, and narrow wildcard path support for explicit LTE/SGW/PGW mediation pipelines
Batch resilience: BatchReconciler adopts or clears lost worker-owned batch runs after manager restart or worker loss, reusing the normal final status path
Large-file batch efficiency: record_chunk_size enables bounded serial batch decode windows, and ASN.1 BER payloads now support incremental record parsing instead of one giant in-memory decode
Safer batch file output: serial batch csv / ndjson sinks stage temp files per source unit, publish only on successful source completion, and can optionally run post-batch heap cleanup via post_batch_cleanup: true

v1.3.1 (2026-04-20)

workers.count: N and workers.list placement behavior are now implemented for multi-worker push streams in manager mode
Dedicated per-pipeline Kubernetes Services are available for active webhook and prometheus_rw streams, including pinned-worker workers.list endpoints
File sinks support shared source filename variables, executor-side {field.*} partitioning, and append-mode rolling via max_records, max_time, and max_bytes
SNMP connectors now target pysnmp>=7,<8, and the 1.3.1 pass was revalidated on a live kind cluster for placement, ingress, and scale-down recovery

v1.2.0 (2026-04-10)

Manager + Worker mode (TRAM_MODE=manager/worker) — replaces shared-DB cluster model
Dockerfile.worker — dedicated worker image without scheduler/DB/UI
Helm: manager/worker split introduced; SQLite on RWO PVC (no PostgreSQL required)
WorkerPool — health polling, dispatch, run-complete callback handling

v1.1.4

PipelineController — unified pipeline lifecycle authority; 4-state machine
melt transform; SNMP fixes; DB-backed runtime_status

Last updated: 2026-05-01