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 Nessie catalog.
Nessie is an open-source transactional catalog for data lakes that provides:
- Git-inspired data version control with branches and commits
- Cross-table transactions and visibility guarantees
- REST API compliance with the Iceberg REST catalog specification
- Open data lake approach supporting Hive, Spark, Dremio, Trino, and more
- Production-ready deployment on Docker or Kubernetes
Local Development Setup
For local development and testing, you can use a containerized Nessie setup. This approach is ideal for learning, prototyping, and development environments.
Prerequisites
- Docker and Docker Compose: Ensure Docker is installed and running
- Sample Setup: You can use the official Nessie docker-compose setup
Setting up Local Nessie Catalog
You can use the official Nessie docker-compose setup which provides a complete environment with Nessie, in-memory version store, and MinIO for object storage.
Step 1: Create a new folder in which to run the example, then create a file docker-compose.yml with the following configuration:
version: '3.8'
services:
nessie:
image: ghcr.io/projectnessie/nessie:latest
ports:
- "19120:19120"
environment:
- nessie.version.store.type=IN_MEMORY
- nessie.catalog.default-warehouse=warehouse
- nessie.catalog.warehouses.warehouse.location=s3://my-bucket/
- nessie.catalog.service.s3.default-options.endpoint=http://minio:9000/
- nessie.catalog.service.s3.default-options.access-key=urn:nessie-secret:quarkus:nessie.catalog.secrets.access-key
- nessie.catalog.service.s3.default-options.path-style-access=true
- nessie.catalog.service.s3.default-options.auth-type=STATIC
- nessie.catalog.secrets.access-key.name=admin
- nessie.catalog.secrets.access-key.secret=password
- nessie.catalog.service.s3.default-options.region=us-east-1
- nessie.server.authentication.enabled=false
depends_on:
minio:
condition: service_healthy
networks:
- iceberg_net
minio:
image: quay.io/minio/minio
ports:
- "9002:9000"
- "9003:9001"
environment:
- MINIO_ROOT_USER=admin
- MINIO_ROOT_PASSWORD=password
- MINIO_REGION=us-east-1
healthcheck:
test: ["CMD", "mc", "ready", "local"]
interval: 5s
timeout: 10s
retries: 5
start_period: 30s
entrypoint: >
/bin/sh -c "
minio server /data --console-address ':9001' &
sleep 10;
mc alias set myminio http://localhost:9000 admin password;
mc mb myminio/my-bucket --ignore-existing;
tail -f /dev/null"
networks:
- iceberg_net
clickhouse:
image: clickhouse/clickhouse-server:head
container_name: nessie-clickhouse
user: '0:0' # Ensures root permissions
ports:
- "8123:8123"
- "9000:9000"
volumes:
- clickhouse_data:/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=
depends_on:
nessie:
condition: service_started
minio:
condition: service_healthy
volumes:
clickhouse_data:
networks:
iceberg_net:
driver: bridgeStep 2: Run the following command to start the services:
docker compose up -dStep 3: Wait for all services to be ready. You can check the logs:
docker-compose logs -fConnecting to Local Nessie Catalog
Connect to your ClickHouse container:
docker exec -it nessie-clickhouse clickhouse-clientThen create the database connection to the Nessie catalog:
SET allow_experimental_database_iceberg = 1;
CREATE DATABASE demo
ENGINE = DataLakeCatalog('http://nessie:19120/iceberg', 'admin', 'password')
SETTINGS catalog_type = 'rest', storage_endpoint = 'http://minio:9002/my-bucket', warehouse = 'warehouse'Querying Nessie catalog tables using ClickHouse
Now that the connection is in place, you can start querying via the Nessie catalog. For example:
USE demo;
SHOW TABLES;If your setup includes sample data (such as the taxi dataset), you should see tables like:
┌─name──────────┐
│ default.taxis │
└───────────────┘To query a table (if available):
SELECT count(*) FROM `default.taxis`;┌─count()─┐
│ 2171187 │
└─────────┘To inspect the table DDL:
SHOW CREATE TABLE `default.taxis`;┌─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://localhost:9002/my-bucket/default/taxis/', 'admin', '[HIDDEN]') │
└───────────────────────────────────────────────────────────────────────────────────────────────┘Loading data from your Data Lake into ClickHouse
If you need to load data from the Nessie catalog into ClickHouse, start by creating a local ClickHouse table:
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 Nessie catalog table via an INSERT INTO SELECT:
INSERT INTO taxis
SELECT * FROM demo.`default.taxis`;