Apache Spark to ClickHouse Integration
AI-first ETL from Apache Spark into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apache Spark into ClickHouse
Datrise syncs Apache Spark's jobs, stages, tasks, executors, SQL queries, and application metrics into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.
Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.
Ideal for high-volume event analytics that need sub-second aggregation.
Endpoints
Apache Spark: Distributed engine for large-scale data processing.
ClickHouse: Columnar OLAP engine for fast aggregations.
How Apache Spark entities map to ClickHouse
| Apache Spark entity | ClickHouse object | Notes |
|---|---|---|
| jobs | apache_spark_jobs | id PK · custom fields → JSON or Map columns |
| stages | apache_spark_stages | id PK · linked to apache_spark_jobs |
| tasks | apache_spark_tasks | id PK · linked to apache_spark_jobs |
| executors | apache_spark_executors | id PK · linked to apache_spark_jobs |
FAQ
How does Datrise handle Apache Spark's custom fields in ClickHouse?
Flexible values are stored as JSON or Map columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ClickHouse types.
How does the Apache Spark to ClickHouse sync stay up to date?
It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.
Related pipelines
More destinations for Apache Spark
- Apache Spark → DuckDB
- Apache Spark → Amazon Athena
- Apache Spark → Amazon S3 Data Lake
- Apache Spark → Azure Data Lake Storage
- Apache Spark → Azure Synapse
- Apache Spark → Spreadsheets
- Apache Spark → Airtable
- Apache Spark → CSV Files
- Apache Spark → MongoDB
- Apache Spark → Supabase
- Apache Spark → Neon
- Apache Spark → PlanetScale
More sources for ClickHouse
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