Managed

ClickHouse exporter required

If you're using your own distribution, for example the [contrib image](https://github.com/open-telemetry/opentelemetry-collector-contrib), ensure that it includes the [ClickHouse exporter](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/clickhouseexporter).

For this purpose, you're provided with an example OpenTelemetry Collector configuration that uses the ClickHouse exporter with appropriate settings and exposes OTLP receivers. This configuration matches the interfaces and behavior expected by the ClickStack distribution.

An example of this configuration is shown below (environment variables will be pre-populated if copying from the UI):

```
receivers:
  otlp/hyperdx:
    protocols:
      grpc:
        include_metadata: true
        endpoint: "0.0.0.0:4317"
      http:
        cors:
          allowed_origins: ["*"]
          allowed_headers: ["*"]
        include_metadata: true
        endpoint: "0.0.0.0:4318"
processors:
  batch:
  memory_limiter:
    # 80% of maximum memory up to 2G, adjust for low memory environments
    limit_mib: 1500
    # 25% of limit up to 2G, adjust for low memory environments
    spike_limit_mib: 512
    check_interval: 5s
connectors:
  routing/logs:
    default_pipelines: [logs/out-default]
    error_mode: ignore
    table:
      - context: log
        statement: route() where IsMatch(attributes["rr-web.event"], ".*")
        pipelines: [logs/out-rrweb]
exporters:
  debug:
    verbosity: detailed
    sampling_initial: 5
    sampling_thereafter: 200
  clickhouse/rrweb:
    database: default
    endpoint: <clickhouse_cloud_endpoint>
    password: <your_password_here>
    username: default
    ttl: 720h
    logs_table_name: hyperdx_sessions
    timeout: 5s
    retry_on_failure:
      enabled: true
      initial_interval: 5s
      max_interval: 30s
      max_elapsed_time: 300s
  clickhouse:
    database: default
    endpoint: <clickhouse_cloud_endpoint>
    password: <your_password_here>
    username: default
    ttl: 720h
    timeout: 5s
    retry_on_failure:
      enabled: true
      initial_interval: 5s
      max_interval: 30s
      max_elapsed_time: 300s

service:
  pipelines:
    traces:
      receivers: [otlp/hyperdx]
      processors: [memory_limiter, batch]
      exporters: [clickhouse]
    metrics:
      receivers: [otlp/hyperdx]
      processors: [memory_limiter, batch]
      exporters: [clickhouse]
    logs/in:
      receivers: [otlp/hyperdx]
      exporters: [routing/logs]
    logs/out-default:
      receivers: [routing/logs]
      processors: [memory_limiter, batch]
      exporters: [clickhouse]
    logs/out-rrweb:
      receivers: [routing/logs]
      processors: [memory_limiter, batch]
      exporters: [clickhouse/rrweb]
```

For further details on configuring OpenTelemetry collectors, see ["Ingesting with OpenTelemetry."](/docs/use-cases/observability/clickstack/ingesting-data/opentelemetry)

### Start ingestion (optional)[​](#start-ingestion-existing "Direct link to Start ingestion (optional)")

If you have existing applications or infrastructure to instrument with OpenTelemetry, navigate to the relevant guides linked from "Connect an application".

To instrument your applications to collect traces and logs, use the [supported language SDKs](/docs/use-cases/observability/clickstack/sdks) which send data to your OpenTelemetry Collector acting as a gateway for ingestion into Managed ClickStack.

Logs can be [collected using OpenTelemetry Collectors](/docs/use-cases/observability/clickstack/integrations/host-logs) running in agent mode, forwarding data to the same collector. For Kubernetes monitoring, follow the [dedicated guide](/docs/use-cases/observability/clickstack/integrations/kubernetes). For other integrations, see our [quickstart guides](/docs/use-cases/observability/clickstack/integration-guides).

[Vector](https://vector.dev) is a high-performance, vendor-neutral observability data pipeline, 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.

Timestamp required

The only strict requirement for Managed ClickStack, 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.

The following assumes you have an instance of Vector running, pre-configured with ingest pipelines, delivering data.

### Create a database and table[​](#create-database-and-tables "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).

For example, create a database for logs:

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

Then create a table whose schema matches the structure of your log data. The example below assumes a classic Nginx access log format:

```
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);
```

Your table must align with the output schema produced by Vector. Adjust the schema as needed for your data, following the recommended [schema best practices](/docs/best-practices/select-data-types).

We strongly recommend understanding how [Primary keys](/docs/primary-indexes) work in ClickHouse and choosing an ordering key based on your access patterns. See the [ClickStack-specific](/docs/use-cases/observability/clickstack/performance_tuning#choosing-a-primary-key) guidance on choosing a primary key.

Once the table exists, copy the configuration snippet shown. Adjust the input to consume your existing pipelines, as well as the target table and database if required. Credentials should be pre-populated.

For more examples of ingesting data with Vector, see ["Ingesting with Vector"](/docs/use-cases/observability/clickstack/ingesting-data/vector) or the [Vector ClickHouse sink documentation](https://vector.dev/docs/reference/configuration/sinks/clickhouse/) for advanced options.

Navigate to the ClickStack UI

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

Once you have completed setting up ingestion and started to send data, select "Next".

- OpenTelemetry- Vector

If you've ingested OpenTelemetry data using this guide, data sources are created automatically and no further setup is required. You can start exploring ClickStack right away. You'll be directed to the search view with a source automatically selected so you can begin querying immediately.

That's it — you’re all set 🎉.

If you've ingested via Vector data or another source, you will be prompted to configure the 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.