MQAnalyzer
This sheet summarizes value, pricing and use cases. From here you can move to the dedicated areas or to the client portal for the next action.
Enterprise management, monitoring and analytics platform for IBM MQ: real-time visibility, AI-based anomaly detection, message store and multi-tenancy management. Reduces MTTD from hours to seconds.
What it solves
- Nessuna visibilita' real-time sulla profondita' delle code IBM MQ
- No proactive alerts: problems are discovered after the incident
- Limited MQ Explorer: no historical analytics nor automation
- Un incidente su coda critica costa 5.000-50.000 EUR
What it does
MQAnalyzer is an enterprise, web-based, multi-tenant platform for managing and monitoring IBM MQ messaging infrastructures from a single 'single pane of glass'. It replaces desktop tools designed for individual users with a browser-accessible console intended for teams operating dozens of Queue Managers across DEV/TEST/PROD environments, featuring isolation per organization and granular access control.
Its differentiator is the Message Store + Resend: messages are persisted with automatic compression, made searchable in full-text, and re-injectable onto the original queue. This function enables audit, debug, and forensic analysis, which competing tools do not offer.
| Module | What it does |
|---|---|
| Monitoring & Dashboard | Code depth, active codes, QM metrics, real-time alerts and topology |
| Message Store + Resend | Compressed message persistence, full-text search, re-injection to queue |
| Analytics & Insight | Time series metrics, trends, anomaly detection, AI-assisted summaries |
| Alerting & Notifications | Threshold rules with multi-channel notifications (Email, Slack, Teams, Webhook, Telegram) |
| Workflow / Automation | Orchestration of steps (search, bulk resend, webhook) with cron or webhook triggers |
| MQ Administration | Management of queues, channels, listeners and processes with audit and change history |
| Primeur Space Integration | Monitoring transfers, nodes, work queue and correlation with MQ messages |
| Multi-tenancy & Billing | Isolated tenants, RBAC, management of plans and subscriptions |
Key Features
- Real-time monitoring of Queue Manager and queues (depth, throughput, message age) with WebSocket feed.
- Message Store with compression (zstd), full-text payload search and re-injection into the original queue.
- Analytics on time series with trends, top queues, usage heatmaps and automatic anomaly detection.
- Proactive alerting with configurable rules (queue depth, QM down, connection error, anomalies) and multi-channel notifications.
- Workflow engine to automate recurring operations (e.g. nightly retry of failed messages) with cron or webhook triggers.
- IBM MQ administration (creation/modification of queues, channels, listeners) with configuration history and audit trail.
- Native integration with Primeur Space: file transfer monitoring, work queue and end-to-end correlation with MQ messages.
- Built-in multi-tenancy with RBAC for four roles (superadmin, tenant-admin, operator, viewer) and audit log for every action.
- Federated authentication via Keycloak (OIDC/SAML, AD/LDAP/Okta) with MFA and API key.
- Integrations with N8N, Zapier, Make.com and Power Automate through inbound/outbound webhooks and REST API OpenAPI.
Architecture
Modern web application, async-first. Frontend Angular (SPA, standalone components) with OIDC PKCE authentication and real-time updates via WebSocket. Backend FastAPI (Python 3.12, async) exposing REST /api/v1 and a plugin-based abstraction layer for broker providers (IBM MQ via pymqi in production; RabbitMQ/Kafka/ActiveMQ on roadmap). Persistence on a single PostgreSQL 16 with TimescaleDB extension: metrics live as hypertable time-series alongside relational data, for unique SQL queries and native JOINs. Redis 7 for cache and pub/sub WebSocket; background jobs with APScheduler in-process. Delegated authentication to Keycloak (AD/LDAP/Okta federation). Broker credentials encrypted at-rest (Fernet), audit log on every mutating action, integrated observability with OpenTelemetry. Deploy on-prem via Docker Compose or in HA on Kubernetes (Helm chart), with signed images (cosign).
Requirements
- OS host backend: Linux (Debian/RHEL) or Windows Server 2022; container-friendly (Docker Engine 24+ / Kubernetes).
- Runtime: Python 3.12+ (backend, managed by uv); Node.js 20 LTS only at frontend build-time.
- Database: PostgreSQL 16.x with TimescaleDB extension >= 2.13.
- Cache / pub-sub: Redis 7.x (or Memurai 4.x on Windows).
- Identity provider: Keycloak 24.x+ (recommended 26.x) with dedicated realm, for OIDC/SAML and AD/LDAP/Okta federation.
- IBM MQ Provider: IBM MQ Client libraries >= 9.2 (pymqi); access to Queue Manager on port 1414 (default).
- Minimum backend resources: 2 vCPU and 2 GB RAM per replica (4 GB recommended); ~50 GB storage DB for ~1M messages in Message Store.
- TLS required in production (terminated by reverse proxy nginx/Traefik/Caddy).
Compliance & regulations
Regolamento UE 2016/679
Retention configurabile per tenant, diritto all'oblio (redact-in-place dei messaggi), export/portabilita' dati, audit log con redazione automatica di password e segreti.
Direttiva UE 2022/2555
Incident reporting via alert engine e integrazione SIEM, risk management ICT documentato, supply chain security (bandit/pip-audit in CI, lockfile), backup & restore con RTO/RPO.
Regolamento UE 2022/2554
Framework di ICT risk management, classificazione e reporting degli incidenti per severita', resilience testing, gestione del rischio di terze parti (Vendor Risk Questionnaire pre-compilato).
Circ. 285/2013 - sistemi informativi bancari
Audit log (immutabilita' via SIEM export/WORM), segregation of duties tramite RBAC, identificazione univoca utenti federata, tracciabilita' delle modifiche ai dati sensibili.
ISO/IEC 27001 - controlli Annex A
Access control e multi-tenant isolation, autenticazione OIDC + JWKS, cifratura Fernet/TLS, monitoring via OpenTelemetry + audit, secure coding (Ruff + bandit).
Payment Card Industry DSS (solo se in scope)
Applicabile unicamente se il cliente persiste PAN nel Message Store; supporto a mascheramento, cifratura at-rest del payload e accesso ristretto via ruoli dedicati.
Reports & dossiers
- Report di monitoraggio / analyticsPDF, HTML
Report per Queue Manager e periodo, con statistiche, grafici e classifica delle code; ottimizzato per stampa, documentazione e audit.
- Export audit & complianceJSON
Estrazione delle azioni collegate a un utente (accesso/portabilita' GDPR) e del trail di audit, con push verso SIEM esterni (Syslog RFC 5424 / REST).
- Export messaggi (Message Store)JSON, XML, CSV
Esportazione dei messaggi archiviati per analisi, debug e tracciabilita' forense.
- Export topologiaJSON
Grafo della rete MQ (Queue Manager, canali, connessioni) esportabile per documentazione o import in altri tool.
Try it
Frequently asked questions
What does MQAnalyzer do that IBM MQ Explorer doesn't?
Preserves the messages and puts them back in the queue. The Message Store persists the messages with automatic compression, makes them fully text searchable, and allows for requeuing to the original queue. It is the core function around which the product is built, not an accessory: when an integration fails, the question is not which message was lost but how to return it.
Supports Kafka or RabbitMQ?
Today MQAnalyzer talks to IBM MQ. The architecture is plugin-based and RabbitMQ, Kafka, and ActiveMQ are on the roadmap: we say it as it is instead of letting it be implied, because when it comes to a platform choice, the difference between 'it's there' and 'it will arrive' matters.
Si collega al nostro Active Directory?
Yes. Authentication goes through Keycloak with OIDC and SAML, and supports federation to Active Directory, LDAP, and Okta. Users remain where they are already managed.
Si installa da noi o è un servizio?
Entrambe le cose: MQAnalyzer è multi-tenant e gira sia on-premise sia in cloud. Nei settori bancario e finanziario, dove il prodotto è pensato per stare, l'installazione presso il cliente è di norma un requisito di capitolato.
What infrastructure does it require?
PostgreSQL 16 with the TimescaleDB extension for time-series metrics, and Redis. The background jobs run within the application process: there is no need to install and maintain a separate processing queue.
Use cases
Banca: MTTD da ore a secondi su code IBM MQ
Monitoring real-time e anomaly detection sugli ambienti MQ critici.
Industria: monitoring multi-sito IBM MQ
Gestione centralizzata multi-tenant degli ambienti MQ.