> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-sync-clickhouse-operator-docs-7e82242.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# REST catalog
> In this guide, we will walk you through the steps to query your data using ClickHouse and the REST Catalog.
export const BetaBadge = () => {
return ;
};
Integration with the REST Catalog works with Iceberg tables only.
This integration supports both AWS S3 and other cloud storage providers.
ClickHouse supports integration with multiple catalogs (Unity, Glue, REST, Polaris, etc.). This guide will walk you through the steps to query your data using ClickHouse and the [REST Catalog](https://github.com/apache/iceberg/blob/main/open-api/rest-catalog-open-api.yaml/) specification.
The REST Catalog is a standardized API specification for Iceberg catalogs, supported by various platforms including:
* **Local development environments** (using docker-compose setups)
* **Managed services** like Tabular.io
* **Self-hosted** REST catalog implementations
As this feature is beta, you will need to enable it using:
`SET allow_database_iceberg = 1;`
Local Development Setup
For local development and testing, you can use a containerized REST catalog setup. This approach is ideal for learning, prototyping, and development environments.
Prerequisites
1. **Docker and Docker Compose**: Ensure Docker is installed and running
2. **Sample Setup**: You can use various docker-compose setups (see Alternative Docker Images below)
Setting up Local REST Catalog
You can use various containerized REST catalog implementations such as **[Databricks docker-spark-iceberg](https://github.com/databricks/docker-spark-iceberg/blob/main/docker-compose.yml?ref=blog.min.io)** which provides a complete Spark + Iceberg + REST catalog environment with docker-compose, making it ideal for testing Iceberg integrations.
**Step 1:** Create a new folder in which to run the example, then create a file `docker-compose.yml` with the configuration from [Databricks docker-spark-iceberg](https://github.com/databricks/docker-spark-iceberg/blob/main/docker-compose.yml?ref=blog.min.io).
**Step 2:** Next, create a file `docker-compose.override.yml` and place the following ClickHouse container configuration into it:
```yaml theme={null}
version: '3.8'
services:
clickhouse:
image: clickhouse/clickhouse-server:25.5.6
container_name: clickhouse
user: '0:0' # Ensures root permissions
ports:
- "8123:8123"
- "9002:9000"
volumes:
- ./clickhouse:/var/lib/clickhouse
- ./clickhouse/data_import:/var/lib/clickhouse/data_import # Mount dataset folder
networks:
- iceberg_net
environment:
- CLICKHOUSE_DB=default
- CLICKHOUSE_USER=default
- CLICKHOUSE_DO_NOT_CHOWN=1
- CLICKHOUSE_PASSWORD=
```
**Step 3:** Run the following command to start the services:
```bash theme={null}
docker compose up
```
**Step 4:** Wait for all services to be ready. You can check the logs:
```bash theme={null}
docker-compose logs -f
```
The REST catalog setup requires that sample data be loaded into the Iceberg tables first. Make sure the Spark environment has created and populated the tables before attempting to query them through ClickHouse. The availability of tables depends on the specific docker-compose setup and sample data loading scripts.
Connecting to Local REST Catalog
Connect to your ClickHouse container:
```bash theme={null}
docker exec -it clickhouse clickhouse-client
```
Then create the database connection to the REST catalog:
```sql theme={null}
SET allow_database_iceberg = 1;
CREATE DATABASE demo
ENGINE = DataLakeCatalog('http://rest:8181/v1', 'admin', 'password')
SETTINGS
catalog_type = 'rest',
storage_endpoint = 'http://minio:9000/lakehouse',
warehouse = 'demo'
```
Querying REST catalog tables using ClickHouse
Now that the connection is in place, you can start querying via the REST catalog. For example:
```sql theme={null}
USE demo;
SHOW TABLES;
```
If your setup includes sample data (such as the taxi dataset), you should see tables like:
```sql title="Response" theme={null}
┌─name──────────┐
│ default.taxis │
└───────────────┘
```
If you don't see any tables, this usually means:
1. The Spark environment hasn't created the sample tables yet
2. The REST catalog service isn't fully initialized
3. The sample data loading process hasn't completed
You can check the Spark logs to see the table creation progress:
```bash theme={null}
docker-compose logs spark
```
To query a table (if available):
```sql theme={null}
SELECT count(*) FROM `default.taxis`;
```
```sql title="Response" theme={null}
┌─count()─┐
│ 2171187 │
└─────────┘
```
**Backticks required**
Backticks are required because ClickHouse doesn't support more than one namespace.
To inspect the table DDL:
```sql theme={null}
SHOW CREATE TABLE `default.taxis`;
```
```sql title="Response" theme={null}
┌─statement─────────────────────────────────────────────────────────────────────────────────────┐
│ CREATE TABLE demo.`default.taxis` │
│ ( │
│ `VendorID` Nullable(Int64), │
│ `tpep_pickup_datetime` Nullable(DateTime64(6)), │
│ `tpep_dropoff_datetime` Nullable(DateTime64(6)), │
│ `passenger_count` Nullable(Float64), │
│ `trip_distance` Nullable(Float64), │
│ `RatecodeID` Nullable(Float64), │
│ `store_and_fwd_flag` Nullable(String), │
│ `PULocationID` Nullable(Int64), │
│ `DOLocationID` Nullable(Int64), │
│ `payment_type` Nullable(Int64), │
│ `fare_amount` Nullable(Float64), │
│ `extra` Nullable(Float64), │
│ `mta_tax` Nullable(Float64), │
│ `tip_amount` Nullable(Float64), │
│ `tolls_amount` Nullable(Float64), │
│ `improvement_surcharge` Nullable(Float64), │
│ `total_amount` Nullable(Float64), │
│ `congestion_surcharge` Nullable(Float64), │
│ `airport_fee` Nullable(Float64) │
│ ) │
│ ENGINE = Iceberg('http://minio:9000/lakehouse/warehouse/default/taxis/', 'admin', '[HIDDEN]') │
└───────────────────────────────────────────────────────────────────────────────────────────────┘
```
Loading data from your Data Lake into ClickHouse
If you need to load data from the REST catalog into ClickHouse, start by creating a local ClickHouse table:
```sql theme={null}
CREATE TABLE taxis
(
`VendorID` Int64,
`tpep_pickup_datetime` DateTime64(6),
`tpep_dropoff_datetime` DateTime64(6),
`passenger_count` Float64,
`trip_distance` Float64,
`RatecodeID` Float64,
`store_and_fwd_flag` String,
`PULocationID` Int64,
`DOLocationID` Int64,
`payment_type` Int64,
`fare_amount` Float64,
`extra` Float64,
`mta_tax` Float64,
`tip_amount` Float64,
`tolls_amount` Float64,
`improvement_surcharge` Float64,
`total_amount` Float64,
`congestion_surcharge` Float64,
`airport_fee` Float64
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(tpep_pickup_datetime)
ORDER BY (VendorID, tpep_pickup_datetime, PULocationID, DOLocationID);
```
Then load the data from your REST catalog table via an `INSERT INTO SELECT`:
```sql theme={null}
INSERT INTO taxis
SELECT * FROM demo.`default.taxis`;
```