carto-import-export-data

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🇺🇸

Original

English

🇨🇳

Chinese

Move data into the warehouse from local files / URLs (

carto import

), pull data out (

carto activity export

for usage data; warehouse-native unloads for everything else), and prepare tilesets for performant map rendering of large geospatial datasets.

将数据从本地文件/URL导入仓库（使用

carto import

），导出数据（使用

carto activity export

导出使用数据；其他数据使用仓库原生导出功能），并为大型地理空间数据集的高性能地图渲染准备瓦片集。

The user has a CSV / GeoJSON / Shapefile / GeoParquet file and wants it queryable in the warehouse.
The user wants to refresh an existing table from a remote URL.
The user wants to render a 10M+-row spatial dataset on a map (needs tileset preparation).
The user is bulk-exporting CARTO activity data to disk for offline analysis.

If the user just wants to query a file already in the warehouse, jump to

carto-query-datawarehouse

. If they want to discover what's already there,

carto-explore-datawarehouse

如果用户只是想查询仓库中已有的文件，请跳转至

carto-query-datawarehouse

。如果他们想了解仓库中已有的内容，请查看

carto-explore-datawarehouse

。

bash

undefined

bash

undefined

carto import --file ./data.csv
--connection carto_dw
--destination project.dataset.table

carto import --url https://example.com/data.geojson
--connection carto_dw
--destination my_project.demo.regions

carto import --file ./big.parquet
--connection carto_dw
--destination my_project.demo.huge
--async

carto import --file ./data.csv
--connection carto_dw
--destination my_project.demo.t
--overwrite

undefined

carto import --file ./data.csv
--connection carto_dw
--destination my_project.demo.t
--overwrite

undefined

Topic	Reference
`carto import` — flags, formats, size limits, async	references/imports.md
Tileset preparation for large maps	references/tilesets.md
Exporting data: warehouse-native unloads vs `activity export`	references/exports.md

主题	参考文档
`carto import` — 参数、格式、大小限制、异步模式	references/imports.md
大型地图的瓦片集准备	references/tilesets.md
数据导出：仓库原生导出 vs `activity export`	references/exports.md

--connection
is the connection name (from
```
connections list
```
), not the warehouse project ID. If you only know the project, run
```
carto connections list --json
```
first to find the matching connection.
--destination
is the fully-qualified target name in the warehouse's syntax:
```
project.dataset.table
```
(BigQuery),
```
DATABASE.SCHEMA.TABLE
```
(Snowflake),
```
schema.table
```
(Postgres/Redshift),
```
catalog.schema.table
```
(Databricks),
```
SCHEMA.TABLE
```
(Oracle).
1GB hard limit per file. For larger files, split or pre-stage to cloud storage and use
```
--url
```
to a presigned URL.
--no-autoguessing
skips column type detection — use it when you've prepared a precise schema and don't want CARTO to second-guess types (especially for columns that look numeric but should stay string, like ZIP codes).
Imports are async at the API level. The CLI defaults to polling-to-completion; pass
```
--async
```
to return immediately. The CLI prints a job ID in async mode that you can use to check progress.
For tilesets, the workflow is import → SQL job to materialize a tileset table → reference the tileset in a map. The tileset itself is created in the warehouse, not by the CLI.

--connection
是连接名称（来自
```
connections list
```
），而非仓库项目ID。如果仅知道项目名称，请先运行
```
carto connections list --json
```
查找匹配的连接。
--destination
是仓库语法中的完整目标名称：
```
project.dataset.table
```
（BigQuery）、
```
DATABASE.SCHEMA.TABLE
```
（Snowflake）、
```
schema.table
```
（Postgres/Redshift）、
```
catalog.schema.table
```
（Databricks）、
```
SCHEMA.TABLE
```
（Oracle）。
单个文件大小上限为1GB。对于更大的文件，请拆分或预先存储到云存储，并使用指向预签名URL的
```
--url
```
参数。
--no-autoguessing
会跳过列类型检测 — 当你已准备好精确的 schema，且不希望CARTO自动推断类型时使用（尤其是对于看似数值但应保持字符串类型的列，如邮政编码）。
API层面的导入是异步的。CLI默认会轮询直到完成；传递
```
--async
```
参数可立即返回。异步模式下CLI会打印作业ID，你可以用它检查进度。
对于瓦片集，工作流程是导入 → 通过SQL作业物化瓦片集表 → 在地图中引用瓦片集。瓦片集本身是在仓库中创建的，而非通过CLI创建。