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Data warehouse ⇄ Developer tools

ClickHouse to Jms integration — real-time, two-way sync

Keep ClickHouse and Jms in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Why teams connect ClickHouse and Jms

Close the gap between analytics and operations: ClickHouse holds the record while Jms runs the day-to-day work, and Stacksync keeps the two in step in real time, in both directions.

ClickHouse is the central store where teams keep Distributed tables, Dictionaries, Tables (MergeTree family), Databases for reporting and analysis; Jms runs the operational side of engineering work — tracking issues, moving messages and events, watching systems, and managing users and access. The two overlap wherever the same operational data matters to both: the Queue, Topic, TextMessage, MapMessage produced in Jms are exactly what analysts want to measure in ClickHouse, and the curated rows in ClickHouse are what should drive the next action in Jms. When that overlap is bridged by nightly ETL or hand-written scripts, dashboards lag a day behind reality and the tools that should react to warehouse signals never see them.

Stacksync syncs Distributed tables, Dictionaries, Tables (MergeTree family), Databases in ClickHouse with Queue, Topic, TextMessage, MapMessage in Jms field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync matches records on a stable external key, keeps every copy consistent, and resolves conflicts by rules you set — so analytics and operations work from the same current data instead of two drifting copies.

Common use cases

  • 01 Consolidate logs and business records from multiple sources into MergeTree tables for retention and reporting.
  • 02 Land product event data alongside synced CRM accounts so analysts join usage and revenue in one place.
  • 03 Publish records changed in a CRM or database as TextMessages onto a JMS Topic so subscribing Java services react in real time.
  • 04 Bridge a legacy IBM MQ or ActiveMQ Queue to a SaaS system of record by consuming each message and writing the record through the SaaS API.

Common sync patterns

One shared record, kept consistent

Where both systems track the same entity, a change on either side propagates to the other, ending the manual reconciliation between the operational copy and the warehouse copy.

Keep user and access records aligned

Where Jms manages users, directory, or access data, those records stay current in ClickHouse — and can be provisioned back from it — so ownership and permissions match across both.

Operational data lands in ClickHouse for analytics

Records created in Jms — issues, events, messages, metrics, or user changes — replicate into ClickHouse tables as they happen, so reporting runs on current data instead of last night's export.

What you can sync between ClickHouse and Jms

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

ClickHouse objects Jms objects How this pairing syncs
Views Saved queries used as curated, read-only sync sources. MapMessage Body of typed name/value pairs that maps directly onto record fields, so no custom body parsing is needed on read or write. Views is specific to ClickHouse and MapMessage to Jms — each maps to any object or custom field on the other side.
Materialized views Insert-time transformations that reshape incoming synced rows into aggregates. BytesMessage Raw binary body for files, protobuf, or opaque payloads; passed through as a byte stream when structured field mapping is not required. Materialized views is specific to ClickHouse and BytesMessage to Jms — each maps to any object or custom field on the other side.
Distributed tables Query-routing tables over cluster shards in self-managed deployments. Durable Subscription Named topic subscription that retains messages while the consumer is offline, so a sync that disconnects does not miss events published in the meantime. Distributed tables is specific to ClickHouse and Durable Subscription to Jms — each maps to any object or custom field on the other side.
Dictionaries In-memory lookup structures refreshed from external sources, sometimes fed by syncs. Message headers and properties JMSCorrelationID, JMSReplyTo, JMSTimestamp, JMSType plus JMSX/application-defined properties; used for correlation, routing, and selector-based filtering. Dictionaries is specific to ClickHouse and Message headers and properties to Jms — each maps to any object or custom field on the other side.
Tables (MergeTree family) Columnar, append-optimized tables that serve as the destination for high-volume sync loads. Dead Letter Queue Provider-managed destination (e.g. ActiveMQ.DLQ, IBM MQ dead-letter queue) where messages exceeding redelivery limits land; read to reconcile failed deliveries. Tables (MergeTree family) is specific to ClickHouse and Dead Letter Queue to Jms — each maps to any object or custom field on the other side.
Databases Namespaces that group tables and scope permissions for sync users. Queue Point-to-point destination where each message is delivered to exactly one consumer. Stacksync consumes messages to load into a database, or publishes messages for a downstream Java service to process. Databases is specific to ClickHouse and Queue to Jms — each maps to any object or custom field on the other side.

How changes propagate between ClickHouse and Jms

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

ClickHouse Jms Interval-based propagation

DetectionStacksync polls ClickHouse for changes on an incremental schedule, reading only records changed since the previous pass. No log-based CDC for consumers.

DeliveryEach detected change is written to Jms through its API, with automatic retries and rate-limit backoff.

Jms ClickHouse Sub-second propagation

DetectionJms notifies Stacksync of record changes through webhook events. Asynchronous push — the broker delivers messages to registered consumers (MessageListener.onMessage) in real time, with no polling.

DeliveryEach detected change is applied to ClickHouse as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Jms: JMS defines no protocol-level rate limits; throughput ceilings, consumer prefetch, and producer flow control are configured on the broker (ActiveMQ, IBM MQ, Solace, TIBCO EMS, etc.).
What ships with ClickHouse ⇄ Jms

Connect ClickHouse and Jms for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every ClickHouse–Jms connection.

Real-time

Two-way sync

Changes in ClickHouse or Jms instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever ClickHouse or Jms data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single ClickHouse or Jms record.

Observability

Monitoring

Track your ClickHouse ⇄ Jms sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between ClickHouse and Jms.

How the ClickHouse and Jms connectors work

ClickHouse

Integration surface
Native TCP protocol and HTTP interface; standard SQL dialect, with MySQL and PostgreSQL wire compatibility available
Authentication
Database credentials (username/password); ClickHouse Cloud issues per-service credentials over TLS
Change detection
No log-based CDC for consumers; incremental reads use polling on monotonic columns, and ClickHouse is usually the destination rather than the source
Capabilities
read · write

Jms

Integration surface
JMS / Jakarta Messaging API (classic API and simplified JMSContext) over provider transports such as OpenWire, AMQP, IBM MQ, or STOMP
Authentication
Username/password credentials passed to ConnectionFactory.createConnection(); ConnectionFactory and Destinations resolved via JNDI. Transport security (TLS) and stronger auth (SASL, JAAS, client certificates) are broker-implementation-specific.
Change detection
Asynchronous push — the broker delivers messages to registered consumers (MessageListener.onMessage) in real time, with no polling. Message selectors (an SQL-92 subset over headers/properties) filter delivery. There is no modified-date polling or CDC replay, and queue consumption is destructive.
Capabilities
read · write · webhooks
Rate limits
JMS defines no protocol-level rate limits; throughput ceilings, consumer prefetch, and producer flow control are configured on the broker (ActiveMQ, IBM MQ, Solace, TIBCO EMS, etc.).
How it works

How to connect ClickHouse to Jms — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate ClickHouse and Jms with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    ClickHouse connected
    Jms connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the ClickHouse and Jms objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · ClickHouse ⇄ Jms
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    ClickHouse Jms
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

ClickHouse and Jms integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 419 integrations available for ClickHouse and Jms.

Popular · 8 of 419
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