clickzetta-sql-migration

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Atualizado18 de junho de 2026 às 07:29

Migration guide for SQL workloads moving to ClickZetta Lakehouse from Snowflake, Databricks, or Spark SQL. Covers object concept mapping, syntax differences, function mapping tables, MERGE INTO limitations, strict implicit type conversion, and migration pitfalls. For native ClickZetta SQL syntax reference the Lakehouse documentation. Triggered when the user mentions migration source platforms (Snowflake, Databricks, Spark SQL) together with ClickZetta, asks about specific functions/syntax (IFF, LISTAGG, FLATTEN, TARGET_LAG, APPLY CHANGES INTO, ZORDER, WITH RECURSIVE, WHEN NOT MATCHED BY SOURCE), or asks about compatibility/differences. Keywords: Snowflake migration, Databricks migration, Spark SQL migration, vs Snowflake, vs Spark, vs Databricks, syntax differences, function mapping, implicit type conversion

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clickzetta

clickzetta/clickzetta-skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

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SKILL.md

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name

clickzetta-sql-migration

description

ClickZetta SQL Migration Guide

Use this skill when migrating SQL workloads from Snowflake, Databricks (Delta Lake), or Spark SQL to ClickZetta Lakehouse, or when answering "how does ClickZetta differ from " questions.

For native ClickZetta SQL syntax that does not differ from standard SQL, refer to the ClickZetta Lakehouse documentation.

Reference Documents

Document	When to read
Snowflake migration guide	Migrating from Snowflake — object mapping, type mapping, syntax + function differences
Databricks migration guide	Migrating from Databricks/Delta Lake — APPLY CHANGES, ZORDER, WHEN NOT MATCHED BY SOURCE alternatives
dbt migration guide	Migrating an existing dbt project (Databricks / Snowflake / Redshift → ClickZetta) — adapter switch, project config, materializations, incremental strategies, macros, snapshots
vs Snowflake summary	Cross-platform comparison summary
vs Spark SQL summary	Cross-platform comparison summary
DML differences	INSERT/UPDATE/DELETE/MERGE/COPY syntax that differs from other systems (concise migration view)
Implicit type conversion	The #1 migration error — strict CAST rules for INSERT/UPDATE
Function mapping	Function-by-function mapping tables (Snowflake/Spark/Databricks → ClickZetta) and unsupported functions
DDL reference	Detailed DDL syntax — kept for migration completeness; for native ClickZetta DDL prefer the official documentation
DML reference	Detailed DML syntax — kept for migration completeness; for native ClickZetta DML prefer the official documentation
DQL reference	Detailed DQL syntax — kept for migration completeness; for native ClickZetta DQL prefer the official documentation
Functions reference	Detailed function list — kept for migration completeness; for native ClickZetta functions prefer the official documentation

⚠️ Most Common Migration Pitfalls (Quick Reference)

Scenario	Snowflake / Spark / Databricks	ClickZetta
Implicit string→DATE/TIMESTAMP/BOOLEAN/JSON in INSERT	✅ allowed	❌ Error — must use `CAST` or typed literals (`DATE '...'`, `TIMESTAMP '...'`, `TRUE`/`FALSE`, `PARSE_JSON(...)`)
`IFF(cond, a, b)` (SF)	—	`IF(cond, a, b)`
`ARRAY_SIZE(arr)` (SF)	`size(arr)` (Spark)	`SIZE(arr)` ✅ or `ARRAY_SIZE(arr)` ✅ — both supported
`LISTAGG(col, ',') WITHIN GROUP (...)` (SF)	—	`GROUP_CONCAT(col ORDER BY col SEPARATOR ',')`
`LATERAL FLATTEN(input => arr)` (SF)	—	`LATERAL VIEW EXPLODE(arr)`
`data:key` JSON access (SF)	—	`data['key']`
`OBJECT_CONSTRUCT('k', v)` (SF)	`STRUCT(v AS k)` (Spark)	`named_struct('k', v)`
`VARIANT` type (SF)	—	`JSON` type
`NUMBER(p, s)` (SF)	—	`DECIMAL(p, s)`
`CHARINDEX(sub, s)` (SF)	—	`INSTR(s, sub)` ⚠️ parameter order reversed
`DATEDIFF(day, start, end)` (SF)	`DATEDIFF(end, start)` (Spark)	both supported, ⚠️ Snowflake order has unit as first arg
`WHEN NOT MATCHED BY SOURCE THEN DELETE` (Databricks)	—	❌ Not supported — use MERGE INTO + separate DELETE
`APPLY CHANGES INTO` (DLT)	—	TABLE STREAM + MERGE INTO
`WITH RECURSIVE` (SF/Databricks)	✅ supported	❌ Not supported — iterate via Python/ZettaPark or pre-build helper tables
`BEGIN; COMMIT; ROLLBACK;` (transactions)	✅	❌ Not supported — use MERGE INTO for atomic operations
`TARGET_LAG = '1 minute'` for dynamic tables (SF)	—	`REFRESH INTERVAL 1 MINUTE VCLUSTER xx`
`METADATA$ACTION` for streams (SF)	—	`__change_type` (values: INSERT / UPDATE_BEFORE / UPDATE_AFTER / DELETE)
`OPTIMIZE t ZORDER BY (col)` (Databricks)	—	`OPTIMIZE t` (small file compaction only, no ZORDER)
`STRUCT(1 AS id, 'a' AS name)` (Spark)	—	`named_struct('id', 1, 'name', 'a')`
`TABLESAMPLE (50 PERCENT)`	—	❌ PERCENT not supported — use `ORDER BY RAND() LIMIT n`
`CREATE SEQUENCE` (SF)	—	❌ Not supported — use `IDENTITY(seed)` column (BIGINT only)
`CREATE TEMPORARY TABLE` (SF)	—	❌ Not supported — use CTE
`CHARINDEX` / `EDITDISTANCE` / `SOUNDEX` (SF)	—	`INSTR` (reversed args) / Python UDF / no equivalent

Object Concept Mapping

Snowflake	Databricks	ClickZetta
DATABASE	Catalog (internal)	WORKSPACE
SCHEMA / DATABASE.SCHEMA	Database / Schema	SCHEMA
WAREHOUSE	Cluster / SQL Warehouse	VCLUSTER
STAGE	External Location	VOLUME (+ STORAGE CONNECTION)
STORAGE INTEGRATION	—	STORAGE CONNECTION
SNOWPIPE	Auto Loader	PIPE
STREAM	(Delta CDF / DLT CDC)	TABLE STREAM
DYNAMIC TABLE	DLT (Live Tables)	DYNAMIC TABLE (different syntax)
TASK	Job	Studio Task
SEQUENCE	—	IDENTITY column
SHARE	Delta Sharing	SHARE
—	Unity Catalog (federation)	EXTERNAL CATALOG

Data Type Mapping Quick Reference

Snowflake	Spark / Databricks	ClickZetta
`NUMBER(p, s)` / `NUMERIC`	`DECIMAL(p, s)`	`DECIMAL(p, s)`
`INTEGER` / `NUMBER(10,0)`	`INT` / `BIGINT`	`INT` / `BIGINT`
`VARCHAR(n)` / `TEXT`	`STRING`	`STRING` (recommended) or `VARCHAR(n)`
`TIMESTAMP_LTZ`	`TIMESTAMP`	`TIMESTAMP`
`TIMESTAMP_NTZ`	`TIMESTAMP_NTZ`	`TIMESTAMP_NTZ`
`VARIANT`	—	`JSON`
`ARRAY` (untyped)	`ARRAY<T>`	`ARRAY<T>` (must specify element type)
`OBJECT`	`MAP<K,V>` / `STRUCT<...>`	`MAP<K,V>` or `STRUCT<...>`
`GEOGRAPHY`	—	not supported
—	—	`VECTOR(FLOAT, N)` (ClickZetta-specific)

Migration Workflow Pointers

This skill focuses on SQL syntax compatibility. A complete migration involves more than SQL rewrites:

Object mapping — see table above
Schema/DDL conversion — see migration-snowflake.md and migration-databricks.md
Data movement — typically via object storage (S3/OSS) staging + COPY INTO; not covered in detail here
SQL rewrites — see this skill's reference documents
Application/driver layer — JDBC, Python connector, BI tool reconnection; refer to clickzetta-lakehouse-connect skill
Permission migration — RBAC concept comparison; refer to lakehouse-doc-en official permission, GRANT, masking policy, and network policy docs
Performance tuning re-mapping — Snowflake CLUSTER BY / Databricks ZORDER → ClickZetta partitioning, indexes, and official optimization docs in lakehouse-doc-en

For end-to-end migration planning, combine this skill with the skills listed above.