ClickHouse -> BigQuery

This guide is a copy/paste-ready starting point for loading data from ClickHouse into BigQuery with dpone.

When to use this path

Use this path when ClickHouse is the system of record or ingestion boundary and BigQuery is the landing, warehouse, event-log, or downstream replication target.

Copy/paste manifest

name: clickhouse_to_bigquery_example

source:
  type: clickhouse
  connection_id: clickhouse_analytics
  connection_type: vault
  table:
    database: analytics
    name: orders
  options:
    partition_column: event_date
    batch_size: 100000

sink:
  type: bigquery
  connection_id: bigquery_dwh
  connection_type: vault
  table:
    dataset: landing
    name: orders
  strategy:
    mode: incremental_merge
    unique_key: order_id
    merge_policy: delete_insert
    duplicate_policy: fail
  options:
    write_disposition: WRITE_APPEND
    batch_size: 50000

state:
  type: bigquery
  connection_id: bigquery_dwh
  table:
    dataset: etl_state
    name: dpone_state

quality:
  mode: fail
  checks:
    - type: min_rows
      threshold: 1
    - type: source_target_count
      tolerance_pct: 0.1

observability:
  artifacts:
    enabled: true
    path: .dpone/runs/clickhouse_to_bigquery

Run it locally:

dpone plan examples/clickhouse_to_bigquery.yaml --format md
dpone batch run examples/clickhouse_to_bigquery.yaml

Supported load strategies

Strategy	Status	Notes
full_refresh	Supported	Uses staging first, then applies the target-specific finalization plan.
incremental_append	Supported	Uses staging first, then applies the target-specific finalization plan.
incremental_merge	Supported	Default merge_policy: delete_insert; shadow_swap is available for DB targets.
replace	Supported	Uses staging first, then applies the target-specific finalization plan.
partition_replace	Supported	Replaces target partitions represented by staging partition.column; see Load strategies for native/fallback paths.
snapshot_reconciliation	Supported	Uses staging first, then applies the target-specific finalization plan.

See Load strategies for the detailed algorithm for each strategy.

Runtime algorithm

flowchart TD
    A["Resolve manifest and registry entries"] --> B["Create ClickHouse source"]
    B --> C["Plan bounded extract"]
    C --> D["Read through native SELECT streaming or file export"]
    D --> E["Emit ExtractResult with schema and artifact"]
    E --> F["Plan schema evolution"]
    F --> G["Create BigQuery staging or event batch"]
    G --> H["Load through load job into staging table followed by set-based finalization"]
    H --> I["Apply finalization strategy"]
    I --> J["Run quality and reconciliation checks"]
    J --> K["Advance state only after success"]

Strategy behavior

• full_refresh: extract the selected source boundary, load into staging, and replace the target according to the target's safe finalization path.
• incremental_append: extract only the incremental boundary and append rows through staging or event production.
• incremental_merge: load into staging, validate duplicates, then use delete_insert by default; shadow_swap is available where table swaps are supported.
• replace: reload a bounded predicate window through staging and then atomically replace the matching target slice.
• snapshot_reconciliation: compare the latest source snapshot with the target key set and apply configured physical-delete or soft-delete behavior through staging-first plans.
• partition_replace: extract a complete partition slice, load it into staging, and replace only partitions represented by partition.column.

Schema evolution and type mapping

Schema evolution is enabled by default and runs before the staging/final load path:

1. Read source schema from ExtractResult.schema.
2. Introspect the BigQuery target schema.
3. Apply safe additions and widening operations.
4. Fail breaking changes by default.
5. If configured, route incompatible type changes to __dpone__nc__<column>.

Use Schema evolution and Type mapping matrix when adding columns or changing source types.

Runbook

1. Start with dpone doctor --profile local and fix missing extras or native clients.
2. Run dpone plan <manifest> --format md and review source boundary, staging path, schema evolution, state, and quality checks.
3. Run a small bounded window first.
4. Inspect the run artifact under .dpone/runs/clickhouse_to_bigquery.
5. For incremental jobs, verify state before enabling a schedule.
6. For delete-aware jobs, run reconciliation in report-only mode before enabling physical deletes.
7. Promote the manifest through GitOps after the plan and artifact are reviewed.

Cross-links

• Source -> sink matrix
• Load strategies
• Schema evolution
• Type mapping matrix
• Reconciliation and CDC
• Performance guide

Type contracts and physical design

This flow supports the shared dpone type-governance stack:

• Type inference for source metadata, sampled profiling, confidence, and empty string vs NULL behavior.
• Schema contracts for explicit logical column types, enforcement modes, and __dpone__nc__* variant columns.
• Physical design for target-specific DDL such as concrete SQL types, indexes, partitioning, compression, ClickHouse LowCardinality, and BigQuery clustering.

Use dpone schema infer --manifest ... and dpone schema physical-plan --manifest ... before enabling new table DDL in production.