Ingesting with Vector

[Edit this page](https://github.com/ClickHouse/clickhouse-docs/blob/main/docs/use-cases/observability/clickstack/ingesting-data/vector.md)

[Vector](https://vector.dev) is a high-performance, vendor-neutral observability data pipeline. It is commonly used to collect, transform, and route logs and metrics from a wide range of sources, and is especially popular for log ingestion due to its flexibility and low resource footprint.

When using Vector with ClickStack, users are responsible for defining their own schemas. These schemas may follow OpenTelemetry conventions, but they can also be entirely custom, representing user-defined event structures. In practice, Vector ingestion is most commonly used for **logs**, where users want full control over parsing and enrichment before data is written to ClickHouse.

This guide focuses on onboarding data into ClickStack using Vector for both ClickStack Open Source and Managed ClickStack. For simplicity, it doesn't cover Vector sources or pipeline configuration in depth. Instead, it focuses on configuring the **sink** that writes data into ClickHouse and ensuring the resulting schema is compatible with ClickStack.

The only strict requirement for ClickStack, whether using the open-source or managed deployment, is that the data includes a **timestamp column** (or equivalent time field), which can be declared when configuring the data source in the ClickStack UI.

## Sending data with Vector[​](#sending-data-with-vector "Direct link to Sending data with Vector")

  

- Managed ClickStack- OpenSource ClickStack

The following guide assumes you have already created a Managed ClickStack service and recorded your service credentials. If you haven't, follow the [Getting Started](/docs/use-cases/observability/clickstack/getting-started/managed) guide for Managed ClickStack until promoted to configure Vector.

Create a database and table

### Create a database and table[​](#create-a-database-table "Direct link to Create a database and table")

Vector requires a table and schema to be defined prior to data ingestion.

First create a database. This can be done via the [ClickHouse Cloud console](/docs/cloud/get-started/sql-console).

In the example below, we use `logs`:

```
CREATE DATABASE IF NOT EXISTS logs
```

Create a table for your data. This should match the output schema of your data. The example below assumes a classic Nginx structure. Adjust accordingly to your data, adhering to [schema best practices](/docs/best-practices/select-data-types). We **strongly recommend** familiarizing yourself with the [concept of Primary keys](/docs/primary-indexes), selecting your primary key based on the guidelines outlined [here](/docs/use-cases/observability/clickstack/performance_tuning#choosing-a-primary-key).

```
CREATE TABLE logs.nginx_logs
(
    `time_local` DateTime,
    `remote_addr` IPv4,
    `remote_user` LowCardinality(String),
    `request` String,
    `status` UInt16,
    `body_bytes_sent` UInt64,
    `http_referer` String,
    `http_user_agent` String,
    `http_x_forwarded_for` LowCardinality(String),
    `request_time` Float32,
    `upstream_response_time` Float32,
    `http_host` String
)
ENGINE = MergeTree
ORDER BY (toStartOfMinute(time_local), status, remote_addr)
```

Nginx primary key

The primary key above assumes typical access patterns in the ClickStack UI for Nginx logs, but may need to be adjusted depending on your workload in production environments.

Add ClickHouse sink to vector configuration

### Add ClickHouse sink to vector configuration[​](#add-clickhouse-sink-to-config "Direct link to Add ClickHouse sink to vector configuration")

Modify your Vector configuration to include the ClickHouse sink, updating the `inputs` field to receive events from your existing pipelines.

This configuration assumes that your upstream Vector pipeline has already **prepared the data to match the target ClickHouse schema**, meaning that fields are parsed, named correctly, and typed appropriately for insertion. See the [**Nginx example below**](#example-dataset-with-vector) for a complete illustration of parsing and normalizing raw log lines into a schema suitable for ClickStack.

```
sinks:
  clickhouse:
    type: clickhouse
    inputs:
      - your_input
    endpoint: "<CLICKHOUSE_ENDPOINT>"
    database: logs
    format: json_each_row
    table: nginx_logs
    skip_unknown_fields: true
    auth:
      strategy: "basic"
      user: "default"
      password: "<CLICKHOUSE_PASSWORD>"
```

By default, we recommend using the **`json_each_row`** format, which encodes each event as a single JSON object per row. This is the default and recommended format for ClickStack when ingesting JSON data, and should be preferred over alternative formats such as JSON objects encoded as strings.

The ClickHouse sink also supports **Arrow stream encoding** (currently in beta). This can offer higher throughput but comes with important constraints: the database and table must be static, as the schema is fetched once at startup, and dynamic routing isn't supported. For this reason, Arrow encoding is best suited for fixed, well-defined ingestion pipelines.

We recommend reviewing the available sink configuration options in the [Vector documentation](https://vector.dev/docs/reference/configuration/sinks/clickhouse):

Note

The example above uses the default user for Managed ClickStack. For production deployments, we recommend [creating a dedicated ingestion user](/docs/use-cases/observability/clickstack/ingesting-data/otel-collector#creating-an-ingestion-user) with appropriate permissions and limits.

Navigate to the ClickStack UI

### Navigate to the ClickStack UI[​](#navigate-to-clickstack-ui "Direct link to Navigate to the ClickStack UI")

Navigate to your Managed ClickStack service and select "ClickStack" from the left-hand menu. If you’ve already completed the onboarding, this will launch the ClickStack UI in a new tab, and you will be automatically authenticated. If not, you can proceed through the onboarding and select “Launch ClickStack” once you’ve selected Vector as your input source.

Create a datasource

### Create a datasource[​](#create-a-datasource-managed "Direct link to Create a datasource")

Create a logs data source. If no data sources exist, you will be prompted to create one on your first login. Otherwise, navigate to Team Settings and add a new data source.

The configuration above assumes an Nginx-style schema with a `time_local` column used as the timestamp. This should be, where possible, the timestamp column declared in the primary key. This column is mandatory.

We also recommend updating the `Default SELECT` to explicitly define which columns are returned in the logs view. If additional fields are available, such as service name, log level, or a body column, these can also be configured. The timestamp display column can also be overridden if it differs from the column used in the table's primary key and configured above.

In the example above, a `Body` column doesn't exist in the data. Instead, it is defined using a SQL expression that reconstructs an Nginx log line from the available fields.

For other possible options, see the [configuration reference](/docs/use-cases/observability/clickstack/config).

Explore the data

### Explore the data[​](#explore-the-data-managed "Direct link to Explore the data")

Navigate to the logs view to explore the data and begin using ClickStack.

Create a database and table

### Create a database and table[​](#create-a-database-table-oss "Direct link to Create a database and table")

Vector requires a table and schema to be defined prior to data ingestion.

First create a database. This can be done via the [ClickHouse Web user interface](/docs/interfaces/http#web-ui) at <http://localhost:8123/play>. Use the default username and password `api:api`.

In the example below, we use `logs`:

```
CREATE DATABASE IF NOT EXISTS logs
```