> ## Documentation Index
> Fetch the complete documentation index at: https://docs.condense.io/llms.txt
> Use this file to discover all available pages before exploring further.

# ClickHouse (Output, Store)

**Connector Type:** Output Connector

**Description**

Writes structured data from Condense into ClickHouse for ultra-fast analytical querying.

**Brief Description**

The ClickHouse Store Output Connector enables Condense to write event streams from Kafka-native topics into ClickHouse tables. ClickHouse is a high-performance columnar database optimized for real-time analytical processing (OLAP). By integrating with ClickHouse, this connector enables low-latency persistence of structured streaming data for large-scale analytics, dashboards, and time-series applications.

This connector is well suited for IoT telemetry, financial event processing, log analytics, and other high-volume analytical workloads.

**Links to Relevant Documents**

* [https://clickhouse.com/docs](https://clickhouse.com/docs)
* [https://clickhouse.com/docs/sql-reference](https://clickhouse.com/docs/sql-reference)
* [https://clickhouse.com/docs/interfaces](https://clickhouse.com/docs/interfaces)

**Connector Overview**

| **Feature**          | **Value**                                    |
| :------------------- | :------------------------------------------- |
| Connector Type       | Output                                       |
| Stream Type          | Database (OLAP)                              |
| Supported Operations | Insert                                       |
| Target System        | ClickHouse cluster or standalone server      |
| Authentication       | Username/Password, SSL                       |
| Input                | Kafka-native topic from Condense             |
| Storage Format       | Structured rows mapped to ClickHouse columns |

**Core Functionality**

1. Writes Kafka messages into ClickHouse tables.
2. Supports efficient batch inserts.
3. Maps JSON payload fields to ClickHouse columns.
4. Supports secure authentication using username/password and SSL.
5. Optimized for high-throughput analytical workloads.

**How It Works in Condense**

1. The connector subscribes to a configured Kafka-native topic.
2. Incoming JSON messages are parsed into structured records.
3. Fields are mapped to the destination ClickHouse table schema.
4. Records are inserted into ClickHouse using the HTTP interface or native protocol.
5. Messages are batched before insertion to improve performance.

**Configuration**

| **Category**   | **Field Name** | **Description**                          | **Required** |
| :------------- | :------------- | :--------------------------------------- | :----------: |
| General        | Title          | Unique connector name                    |      Yes     |
| ClickHouse     | URL            | ClickHouse server hostname or IP address |      Yes     |
| ClickHouse     | Port           | ClickHouse HTTP or TCP port              |      Yes     |
| ClickHouse     | Database Name  | Target database                          |      Yes     |
| ClickHouse     | Table Name     | Destination table                        |      Yes     |
| Authentication | User           | ClickHouse username                      |      Yes     |
| Authentication | Password       | ClickHouse password                      |      Yes     |
| Condense       | Topic (Input)  | Kafka-native topic to consume from       |      Yes     |

**Field-by-Field Explanation**

**1. Title**

Unique identifier for this connector inside Condense.

**Example:**

`ClickHouse_Output_Orders`

**2. URL**

Hostname or IP address of the ClickHouse server.

**Example:**

`clickhouse.company.com`

**3. Port**

Port used by ClickHouse.

Common values:

* 8123 (HTTP)
* 9000 (Native TCP)

**4. Database Name**

Target ClickHouse database.

**Example:**

`analytics`

**5. Table Name**

Destination ClickHouse table where records will be inserted.

**Example:**

`events_stream`

**6. User**

Username used to authenticate with ClickHouse.

**Example:**

`default`

**7. Password**

Password associated with the ClickHouse user.

**8. Topic (Input)**

Kafka-native Condense topic from which records are consumed.

**Example:**

`processed_events`

**Sample Configuration Example**

```json theme={null}
{
  "title": "ClickHouse_Output_Orders",
  "url": "clickhouse.company.com",
  "port": 8123,
  "databaseName": "analytics",
  "tableName": "orders_processed",
  "user": "default",
  "password": "secret123",
  "inputTopic": "processed_order_stream"
}
```

**Troubleshooting and Common Issues**

**1. Connection Refused**

**Cause:** Incorrect URL or port.

**Solution:** Verify the ClickHouse endpoint, firewall rules, and network connectivity.

**2. Authentication Error**

**Cause:** Invalid username or password.

**Solution:** Verify the configured credentials and ensure the user has permission to write to the target database.

**3. Schema Mismatch**

**Cause:** Incoming JSON fields do not match the ClickHouse table schema.

**Solution:** Update the Condense transform or modify the ClickHouse table schema.

**4. Slow Inserts**

**Cause:** Small batch size or network latency.

**Solution:** Increase the batch size and optimize network connectivity.

**Advanced Considerations**

**1. Data Types**

Ensure incoming JSON fields match the destination ClickHouse column types such as `Int32`, `String`, or `DateTime`.

**2. Indexing**

Use appropriate primary keys, partition keys, and ordering keys to improve query performance.

**3. Cluster Deployments**

When using distributed ClickHouse clusters, configure sharding and replication according to your deployment architecture.

**4. Security**

Use SSL/TLS for production deployments and securely manage authentication credentials.

**Best Practices**

1. Use dedicated tables for different event streams.
2. Define explicit schemas to avoid data type conflicts.
3. Monitor connector logs for failed insert operations.
4. Partition tables using timestamps or business attributes to improve analytical performance.
5. Periodically optimize ClickHouse tables using appropriate maintenance operations.