Import and Parse SQL

Who this is for

For users who already have existing SQL or structured data files and want to evolve from current structures instead of starting from blank input.

What this solves

You can quickly convert external SQL or structured data files into editable configuration, then continue incremental design and reduce repeated input.

Prerequisites

• You have identified the correct database type for the source data.
• SQL text or files are accessible and within the system-supported length/size.

Steps

SQL Import

1. Click Import SQL in the top bar. Result: the import dialog opens and enters the Validate step.
2. In Source Database, choose the correct database type and paste SQL. Result: the system parses with that dialect.
3. Click Next to complete validation. Result: on pass, it enters Preview; on failure, it shows error messages and location details.
4. In Preview, inspect fields, indexes, and grantees. Adjust field order or remove unnecessary fields if needed. Result: import content is confirmed before final apply.
5. Enter Confirm and execute import. Result: parsed results are written into the current workspace; if source SQL includes a schema, the system splits it into Schema Name and the bare Table Name, then you can continue editing and generate new SQL.

Batch SQL Import

1. In the import dialog, paste multiple SQL statements (such as multiple CREATE TABLE). Result: the system recognizes all table structures.
2. In preview, check parsing results and conflicts for each table. Result: if duplicate table names or field names exist, the system marks conflicts and provides merge strategy options.
3. Select merge strategy (overwrite, skip, rename) and execute import. Result: multiple SQL statements are applied sequentially according to strategy, greatly reducing batch migration cost.

CSV / Excel / JSON Schema Import

1. Click Import Data at the top and select file type (CSV, Excel, or JSON Schema). Result: the corresponding file selector opens.
2. Upload the file and confirm encoding and delimiter (CSV/Excel). Result: the system parses file content and extracts field names and types.
3. In preview, check auto-recognized field types and manually adjust as needed. Result: type mapping better matches target database conventions.
4. Confirm import. Result: field structure is written into the current workspace; you can continue supplementing indexes, privileges, and foreign key configurations.

Supported statement and file scope

• SQL: CREATE TABLE, CREATE INDEX, ALTER TABLE, GRANT
• CSV: first row as field names, subsequent rows as data; system infers types from data content
• Excel: supports .xlsx and .xls; automatically recognizes first row as headers
• JSON Schema: supports standard JSON Schema format; fields in properties are parsed as table fields

Done when

• After import, table name, fields, indexes, and grantees appear in the current workspace. If a schema exists, Schema Name is also filled correctly.
• DDL and DCL outputs on the right are no longer empty and match expected structure.
• You can continue incremental adjustments based on imported results.

Common pitfalls and failure handling

• Dialect mismatch: the same SQL may parse differently under different database types. Change Source Database first and retry.
• If older SQL stores the object name as schema.table, the system will try to split it into Schema Name + Table Name. Check the split result after import.
• SQL too long: it fails directly when beyond limits. Split into core table-creation segments before import.
• Parse failure: keep a minimal reproducible segment first, then add statements back piece by piece to locate the problematic part.
• No valid table structure recognized: usually caused by missing key table statements or incomplete syntax. Check the CREATE TABLE body first.
• CSV/Excel type inference deviation: auto-inference is based on sampled data; dates and amounts are easily misidentified. Check column by column after import.
• JSON Schema nested complexity: deeply nested objects or array types may not directly map to flat table fields. Simplify schema before import.

Troubleshooting

• If SQL dialect differences are large, verify database type selection first.
• For complex expressions, minimize statements first, then import incrementally to locate issues.
• When file import fails, check whether file encoding is UTF-8 and whether Excel files are password-protected.