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Data warehouse ⇄ Database

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

Keep ClickHouse and Jdbc 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 Jdbc

Connect Jdbc and ClickHouse with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Jdbc's rows in ClickHouse, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Jdbc where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in Jdbc sync into ClickHouse in real time, and result tables in ClickHouse sync back into Jdbc, with schema and type mapping between the two systems handled for you.

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 Consolidate several databases from different vendors into one relational store by syncing each through its own JDBC driver.
  • 04 Write records from a CRM, ERP, or another app back into database tables via SQL INSERT and UPDATE so the database stays current.

Common sync patterns

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

Offload heavy reads

Point analytical queries at the synced copy in ClickHouse and keep Jdbc focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Jdbc land in ClickHouse as they change, replacing hand-built CDC and batch extract jobs.

What you can sync between ClickHouse and Jdbc

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 Jdbc objects How this pairing syncs
Views Saved queries used as curated, read-only sync sources. Views Stored SELECT queries exposed like tables; read-only projections synced outbound when raw base tables should not be exposed downstream. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Dictionaries In-memory lookup structures refreshed from external sources, sometimes fed by syncs. Schemas & catalogs Namespaces that group tables and views; the connector targets a schema/catalog and lists its objects from the JDBC metadata to build the sync. Dictionaries is specific to ClickHouse and Schemas & catalogs to Jdbc — 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. Stored procedures & functions Server-side routines callable via JDBC CallableStatement; invoked for custom read or write logic when a table-level mapping is not enough. Tables (MergeTree family) is specific to ClickHouse and Stored procedures & functions to Jdbc — each maps to any object or custom field on the other side.
Databases Namespaces that group tables and scope permissions for sync users. Sequences Server-generated identity values; relevant when writing rows into tables whose keys are assigned by the database rather than the source system. Databases is specific to ClickHouse and Sequences to Jdbc — each maps to any object or custom field on the other side.
Materialized views Insert-time transformations that reshape incoming synced rows into aggregates. Tables The base relational tables in the target database; synced two-way as rows over SQL, with each table's primary key driving upserts and row-level updates. Materialized views is specific to ClickHouse and Tables to Jdbc — each maps to any object or custom field on the other side.
Distributed tables Query-routing tables over cluster shards in self-managed deployments. Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. Distributed tables is specific to ClickHouse and Columns to Jdbc — each maps to any object or custom field on the other side.

How changes propagate between ClickHouse and Jdbc

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 Jdbc 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 applied to Jdbc as a row-level write, with types converted between the two schemas.

Jdbc ClickHouse Interval-based propagation

DetectionStacksync polls Jdbc for changes on an incremental schedule, reading only records changed since the previous pass. No native change feed.

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

Rate-limit considerations

  • Jdbc: No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
What ships with ClickHouse ⇄ Jdbc

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever ClickHouse or Jdbc 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 Jdbc record.

Observability

Monitoring

Track your ClickHouse ⇄ Jdbc 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 Jdbc.

How the ClickHouse and Jdbc 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

Jdbc

Integration surface
JDBC API (java.sql / javax.sql) executing SQL through a JDBC driver, typically a pure-Java Type 4 driver; reaches any relational database with a driver - PostgreSQL, MySQL, SQL Server, Oracle, IBM DB2, and others - via a JDBC URL such as jdbc:postgresql://host:5432/db.
Authentication
A database user's username and password supplied in the JDBC connection (DriverManager or a DataSource), typically over a TLS/SSL-encrypted connection. Some drivers add Kerberos, integrated Windows auth, or cloud IAM-token auth, but the available methods depend on the target database and its driver.
Change detection
No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks.
Capabilities
read · write
Rate limits
No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
How it works

How to connect ClickHouse to Jdbc — 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 Jdbc 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
    Jdbc connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the ClickHouse and Jdbc 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 ⇄ Jdbc
    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 Jdbc
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

ClickHouse and Jdbc 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 465 integrations available for ClickHouse and Jdbc.

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