dbt-clickzetta 1.7.4

The ClickZetta adapter plugin for dbt

dbt-clickzetta

The dbt adapter for ClickZetta Lakehouse.

See the examples/ directory for complete, runnable examples of all features.

Installation

pip install dbt-clickzetta

Requires Python 3.8+ and dbt-core 1.8+.

Note on versions: The legacy dbt-clickzetta 0.2.x series on PyPI requires dbt-core ~1.5 and is no longer maintained. Use dbt-clickzetta >= 1.6 which supports dbt-core 1.8+ and includes full three-part naming (workspace.schema.table), incremental strategies, snapshots, and all modern dbt features.

Quickstart

1. Configure profiles.yml

my_project:
  target: dev
  outputs:
    dev:
      type: clickzetta
      service: cn-shanghai-alicloud.api.clickzetta.com
      instance: your_instance
      workspace: your_workspace
      username: your_username
      password: your_password
      schema: your_schema
      vcluster: default_ap

2. Test connection

dbt debug

3. Run your project

dbt run
dbt test
dbt docs generate

Supported Features

Feature	Supported
table materialization	✅
view materialization	✅
incremental materialization	✅
ephemeral materialization	✅
snapshot (SCD Type 2)	✅
dynamic_table materialization	✅
materialized_view materialization	✅
dbt test (generic + singular)	✅
dbt seed	✅
dbt docs generate	✅ (row count, size, last modified)
dbt source freshness	✅
persist_docs (relation + columns)	✅
Partitioned tables	✅
Clustered tables	✅
Python models	✅
on_schema_change	✅ (append_new_columns, sync_all_columns)
grants	✅
clone materialization	✅ (zero-copy clone + Time Travel clone)
Indexes (Bloomfilter / Inverted / Vector)	✅ (auto-created via indexes config)
Table Stream as source	✅ (declare in sources.yml, reference via source())
VCluster per-model	✅ (via vcluster config)

Incremental Strategies

Strategy	Description
merge (default)	MERGE INTO with unique_key
append	INSERT INTO without deduplication
insert_overwrite	INSERT OVERWRITE with dynamic partition mode
delete+insert	DELETE matching keys then INSERT, suitable for partition replacement without a primary key

{{ config(
    materialized='incremental',
    incremental_strategy='merge',
    unique_key='id'
) }}

Indexes

Supports Bloomfilter, Inverted, and Vector index types. Indexes are created automatically after the table is built:

{{ config(
    materialized='table',
    indexes=[
        {'type': 'bloomfilter', 'columns': ['order_id']},
        {'type': 'inverted', 'columns': ['status'], 'analyzer': 'unicode'},
        {'type': 'vector', 'columns': ['embedding'], 'distance_function': 'cosine_distance', 'scalar_type': 'f32'}
    ]
) }}

VCluster per-model

Assign a specific VCluster to a model for compute resource isolation:

{{ config(
    materialized='table',
    vcluster='large_ap'   -- this model runs on the large_ap cluster
) }}

Utility Macros

Run via dbt run-operation:

# Compact small files (useful after high-frequency incremental writes)
dbt run-operation optimize_table --args '{relation: my_schema.my_table}'
dbt run-operation optimize_table --args '{relation: my_schema.my_table, where: "dt >= current_date() - interval 7 days"}'

# Switch VCluster for the current session
dbt run-operation use_vcluster --args '{vcluster: large_ap}'

# List recently dropped objects available for recovery
dbt run-operation show_tables_history --args '{schema: my_schema}'

# Recover a dropped object (table, dynamic table, materialized view, or stream)
dbt run-operation undrop --args '{relation: my_schema.my_table}'

# Drop an object (type: table | view | dynamic_table | materialized_view | stream)
dbt run-operation drop_object --args '{relation: my_schema.my_table, type: table}'

# Manually refresh a dynamic table
dbt run-operation refresh_dynamic_table --args '{model_name: my_dynamic_table}'

Table Stream as Source

Declare a Table Stream in sources.yml and reference it via source(). The stream appends three system columns to every row: __change_type, __commit_version, __commit_timestamp.

# sources.yml
sources:
  - name: my_streams
    schema: my_schema
    tables:
      - name: orders_stream

-- Option 1: explicit columns (production-safe)
select __change_type, __commit_timestamp, order_id, amount
from {{ source('my_streams', 'orders_stream') }}

-- Option 2: SELECT * EXCEPT — returns only user columns, no hardcoded list
select * except(__change_type, __commit_timestamp, __commit_version)
from {{ source('my_streams', 'orders_stream') }}

Note: SELECT does not advance the stream offset. Only DML statements (INSERT, MERGE, etc.) advance it.

Dynamic Table

{{ config(
    materialized='dynamic_table',
    refresh_interval='5 minutes',
    refresh_vc='default_ap'
) }}
select id, name, amount
from {{ ref('orders') }}

After creation, the table is automatically refreshed once (equivalent to Snowflake's initialize=ON_CREATE). Subsequent refreshes run on the configured interval.

Snapshot

Snapshots use standard dbt SCD Type 2 via MERGE INTO on regular tables (no delta/iceberg required).

{% snapshot orders_snapshot %}
{{ config(
    target_schema='snapshots',
    unique_key='id',
    strategy='timestamp',
    updated_at='updated_at'
) }}
select * from {{ source('raw', 'orders') }}
{% endsnapshot %}

Connection Parameters

Parameter	Required	Description
type	✅	Must be clickzetta
service	✅	API endpoint, e.g. cn-shanghai-alicloud.api.clickzetta.com
instance	✅	Instance name
workspace	✅	Workspace name
username	✅	Username
password	✅	Password
schema	✅	Default schema
vcluster	✅	VCluster name, e.g. default_ap
connect_retries	❌	Connection retry count (default: 3)

Development

# Clone
git clone https://github.com/clickzetta/dbt-clickzetta.git
cd dbt-clickzetta

# Install in editable mode
pip install -e .

# Run unit tests
pip install pytest
pytest tests/unit/

# Run functional tests (requires a real Lakehouse connection)
cp test.env.example test.env
# Fill in test.env with your connection details
pytest tests/functional/

License

Apache 2.0