# IMPORT INTO

The `IMPORT INTO` statement imports CSV or Avro data into an existing table. `IMPORT INTO` appends new rows onto the table.

## Considerations

- `IMPORT INTO` only works for existing tables. For information on how to import data into new tables, see `IMPORT`.
- `IMPORT INTO` cannot be used within a transaction or during a rolling upgrade.
- `IMPORT INTO` invalidates all foreign keys on the target table. To validate the foreign key(s), use the `VALIDATE CONSTRAINT` statement.
- `IMPORT INTO` cannot be used to insert data into a column for an existing row. To do this, use `INSERT`.

Note:

Optimize your applications by following our Import Performance Best Practices.

## Required privileges

Only members of the `admin` role can run `IMPORT INTO`. By default, the `root` user belongs to the `admin` role.

## Synopsis

Note:

While importing into an existing table, the table is taken offline.

## Parameters

Parameter | Description
--- | ---
`table_name` | The name of the table you want to import into.
`column_name` | The table columns you want to import. Note: Currently, target columns are not enforced.
`file_location` | The URL of a CSV or Avro file containing the table data. This can be a comma-separated list of URLs. For an example, see Import into an existing table from multiple CSV files below.
`<option> [= <value>]` | Control your import's behavior with CSV import options or Avro import options.

### Import file URLs

URLs for the files you want to import must use the format shown below. For examples, see Example file URLs.

    [scheme]://[host]/[path]?[parameters]

Location | Scheme | Host | Parameters
--- | --- | --- | ---
Amazon | `s3` | Bucket name | `AUTH` 1 (optional; can be `implicit` or `specified`), `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_SESSION_TOKEN`
Azure | `azure` | N/A (see Example file URLs | `AZURE_ACCOUNT_KEY`, `AZURE_ACCOUNT_NAME`
Google Cloud 2 | `gs` | Bucket name | `AUTH` (optional; can be `default`, `implicit`, or `specified`), `CREDENTIALS`
HTTP 3 | `http` | Remote host | N/A
NFS/Local 4 | `nodelocal` | `nodeID` or `self` 5 (see Example file URLs) | N/A
S3-compatible services 6 | `s3` | Bucket name | `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_SESSION_TOKEN`, `AWS_REGION` 7 (optional), `AWS_ENDPOINT`

Note:

The location parameters often contain special characters that need to be URI-encoded. Use Javascript's encodeURIComponent function or Go language's url.QueryEscape function to URI-encode the parameters. Other languages provide similar functions to URI-encode special characters.

Note:

If your environment requires an HTTP or HTTPS proxy server for outgoing connections, you can set the standard `HTTP_PROXY` and `HTTPS_PROXY` environment variables when starting CockroachDB.

New in v20.1: If you cannot run a full proxy, you can disable external HTTP(S) access (as well as custom HTTP(S) endpoints) when performing bulk operations (e.g., `BACKUP`, `RESTORE`, etc.) by using the `--external-io-disable-http` flag. You can also disable the use of implicit credentials when accessing external cloud storage services for various bulk operations by using the `--external-io-disable-implicit-credentials` flag.

- 1 If the `AUTH` parameter is not provided, AWS connections default to `specified` and the access keys must be provided in the URI parameters. If the `AUTH` parameter is `implicit`, the access keys can be omitted and the credentials will be loaded from the environment.

- 2 If the `AUTH` parameter is not specified, the `cloudstorage.gs.default.key` cluster setting will be used if it is non-empty, otherwise the `implicit` behavior is used. If the `AUTH` parameter is `implicit`, all GCS connections use Google's default authentication strategy. If the `AUTH` parameter is `default`, the `cloudstorage.gs.default.key` cluster setting must be set to the contents of a service account file which will be used during authentication. If the `AUTH` parameter is `specified`, GCS connections are authenticated on a per-statement basis, which allows the JSON key object to be sent in the `CREDENTIALS` parameter. The JSON key object should be base64-encoded (using the standard encoding in RFC 4648).

- 3 You can create your own HTTP server with Caddy or nginx. A custom root CA can be appended to the system's default CAs by setting the `cloudstorage.http.custom_ca` cluster setting, which will be used when verifying certificates from HTTPS URLs.

- 4 The file system backup location on the NFS drive is relative to the path specified by the `--external-io-dir` flag set while starting the node. If the flag is set to `disabled`, then imports from local directories and NFS drives are disabled.

- 5 New in v20.1: Using a `nodeID` is required and the data files will be in the `extern` directory of the specified node. In most cases (including single-node clusters), using `nodelocal://1/<path>` is sufficient. Use `self` if you do not want to specify a `nodeID`, and the individual data files will be in the `extern` directories of arbitrary nodes; however, to work correctly, each node must have the `--external-io-dir` flag point to the same NFS mount or other network-backed, shared storage.

- 6 A custom root CA can be appended to the system's default CAs by setting the `cloudstorage.http.custom_ca` cluster setting, which will be used when verifying certificates from an S3-compatible service.

- 7 The `AWS_REGION` parameter is optional since it is not a required parameter for most S3-compatible services. Specify the parameter only if your S3-compatible service requires it.

#### Example file URLs

Location | Example
--- | ---
Amazon S3 | `s3://acme-co/employees?AWS_ACCESS_KEY_ID=123&AWS_SECRET_ACCESS_KEY=456`
Azure | `azure://employees?AZURE_ACCOUNT_KEY=123&AZURE_ACCOUNT_NAME=acme-co`
Google Cloud | `gs://acme-co`
HTTP | `http://localhost:8080/employees`
NFS/Local | `nodelocal://1/path/employees`, `nodelocal://self/nfsmount/backups/employees` 5

### CSV import options

You can control the `IMPORT` process's behavior using any of the following key-value pairs as a `<option> [= <value>]`.

Option | Value | Example
--- | --- | ---
`delimiter` | The unicode character that delimits columns in your rows. Default: `,`. | To use tab-delimited values: `IMPORT INTO foo (..) CSV DATA ('file.csv') WITH delimiter = e'\t';`
`comment` | The unicode character that identifies rows to skip. | `IMPORT INTO foo (..) CSV DATA ('file.csv') WITH comment = '#';`
`nullif` | The string that should be converted to NULL. | To use empty columns as NULL: `IMPORT INTO foo (..) CSV DATA ('file.csv') WITH nullif = '';`
`skip` | The number of rows to be skipped while importing a file. Default: `'0'`. | To import CSV files with column headers: `IMPORT INTO ... CSV DATA ('file.csv') WITH skip = '1';`
`decompress` | The decompression codec to be used: `gzip`, `bzip`, `auto`, or `none`. Default: `'auto'`, which guesses based on file extension (`.gz`, `.bz`, `.bz2`). `none` disables decompression. | `IMPORT INTO ... WITH decompress = 'bzip';`

For examples showing how to use these options, see the `IMPORT` - Examples section.

For instructions and working examples showing how to migrate data from other databases and formats, see the Migration Overview. For information on how to import data into new tables, see `IMPORT`.

### Avro import options

Option | Description | Example
--- | --- | ---
`strict_validation` | Rejects Avro records that do not have a one-to-one mapping between Avro fields to the target CockroachDB schema. By default, CockroachDB ignores unknown Avro fields and sets missing SQL fields to `NULL`. CockroachDB will also attempt to convert the Avro field to the CockroachDB data type; otherwise, it will report an error. | `IMPORT INTO foo (..) AVRO DATA ('file.avro') WITH strict_validation;`
`records_terminated_by` | The unicode character to indicate new lines in the input binary or JSON file. This is not needed for Avro OCF. Default: `\n` | To use tab-terminated records: `IMPORT INTO foo (..) AVRO DATA ('file.csv') WITH records_terminated_by = e'\t';`
`data_as_binary_records` | Use when importing a binary file containing Avro records. The schema is not included in the file, so you need to specify the schema with either the `schema` or `schema_uri` option. | `IMPORT INTO foo (..) AVRO DATA ('file.bjson') WITH data_as_binary_records, schema_uri='..';`
`data_as_json_records` | Use when importing a JSON file containing Avro records. The schema is not included in the file, so you need to specify the schema with either the `schema` or `schema_uri` option. | `IMPORT INTO foo (..) AVRO DATA ('file.bjson') WITH data_as_json_records, schema='{ "type": "record",..}';`
`schema` | The schema of the Avro records included in the binary or JSON file. This is not needed for Avro OCF. | See `data_as_json_records` example above.
`schema_uri` | The URI of the file containing the schema of the Avro records include in the binary or JSON file. This is not needed for Avro OCF. | See `data_as_binary_records` example above.

## Requirements

### Prerequisites

Before using `IMPORT INTO`, you should have:

- An existing table to import into (use `CREATE TABLE`).
- The CSV or Avro data you want to import, preferably hosted on cloud storage. This location must be equally accessible to all nodes using the same import file location. This is necessary because the `IMPORT INTO` statement is issued once by the client, but is executed concurrently across all nodes of the cluster. For more information, see the Import file location section below.

Warning:

Column values cannot be generated by `DEFAULT` when importing; an import must include a value for every column specified in the `IMPORT INTO` statement. To use `DEFAULT` values, your file must contain values for the column upon import, or you can add the column or alter the column after the table has been imported.

### Available storage

Each node in the cluster is assigned an equal part of the imported data, and so must have enough temp space to store it. In addition, data is persisted as a normal table, and so there must also be enough space to hold the final, replicated data. The node's first-listed/default `store` directory must have enough available storage to hold its portion of the data.

On `cockroach start`, if you set `--max-disk-temp-storage`, it must also be greater than the portion of the data a node will store in temp space.

### Import file location

We strongly recommend using cloud/remote storage (Amazon S3, Google Cloud Platform, etc.) for the data you want to import, but local files are supported as well.

To import a local file, you have the following options:

- Option 1. Run a local file server to make the file accessible from all nodes.

- Option 2. Make the file accessible from a local node's store. You can do this by using `cockroach nodelocal upload` or by manually placing the file in the `extern` directory:

  1. Create an `extern` directory on a node's store. The pathname will differ depending on the `--store` flag passed to `cockroach start` (if any), but will look something like `/path/to/cockroach-data/extern/`.
  2. Copy the file to a node's `extern` directory.
  3. Assuming the file is called `data.sql` and you uploaded it to node 1, you can access it in your `IMPORT` statement using the following import file URL: `'nodelocal://1/data.sql'`.

## Performance

All nodes are used during the import job, which means all nodes' CPU and RAM will be partially consumed by the `IMPORT` task in addition to serving normal traffic.

## Viewing and controlling import jobs

After CockroachDB successfully initiates an import into an existing table, it registers the import as a job, which you can view with `SHOW JOBS`.

After the import has been initiated, you can control it with `PAUSE JOB`, `RESUME JOB`, and `CANCEL JOB`.

Note:

If initiated correctly, the statement returns when the import is finished or if it encounters an error. In some cases, the import can continue after an error has been returned (the error message will tell you that the import has resumed in background).

Warning:

Pausing and then resuming an `IMPORT INTO` job will cause it to restart from the beginning.

## Examples

### Import into an existing table from a CSV file

Amazon S3:

    > IMPORT INTO customers (id, name)
        CSV DATA (
          's3://acme-co/customers.csv?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder]&AWS_SESSION_TOKEN=[placeholder]'
        );

Azure:

    > IMPORT INTO customers (id, name)
        CSV DATA (
          'azure://acme-co/customer-import-data.csv?AZURE_ACCOUNT_KEY=hash&AZURE_ACCOUNT_NAME=acme-co'
        );

Google Cloud:

    > IMPORT INTO customers (id, name)
        CSV DATA (
          'gs://acme-co/customers.csv'
        );

Note:

The column order in your statement must match the column order in the CSV being imported, regardless of the order in the existing table's schema.

### Import into an existing table from multiple CSV files

Amazon S3:

    > IMPORT INTO customers (id, name)
        CSV DATA (
          's3://acme-co/customers.csv?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder]',
          's3://acme-co/customers2.csv?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder',
          's3://acme-co/customers3.csv?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder]',
          's3://acme-co/customers4.csv?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder]',
        );

Azure:

    > IMPORT INTO customers (id, name)
        CSV DATA (
          'azure://acme-co/customer-import-data1.1.csv?AZURE_ACCOUNT_KEY=hash&AZURE_ACCOUNT_NAME=acme-co',
          'azure://acme-co/customer-import-data1.2.csv?AZURE_ACCOUNT_KEY=hash&AZURE_ACCOUNT_NAME=acme-co',
          'azure://acme-co/customer-import-data1.3.csv?AZURE_ACCOUNT_KEY=hash&AZURE_ACCOUNT_NAME=acme-co',
          'azure://acme-co/customer-import-data1.4.csv?AZURE_ACCOUNT_KEY=hash&AZURE_ACCOUNT_NAME=acme-co',
          'azure://acme-co/customer-import-data1.5.csv?AZURE_ACCOUNT_KEY=hash&AZURE_ACCOUNT_NAME=acme-co',
        );

Google Cloud:

    > IMPORT INTO customers (id, name)
        CSV DATA (
          'gs://acme-co/customers.csv',
          'gs://acme-co/customers2.csv',
          'gs://acme-co/customers3.csv',
          'gs://acme-co/customers4.csv',
        );

### Import into an existing table from an Avro file

New in v20.1: Avro OCF data, JSON records, or binary records can be imported. The following are examples of importing Avro OCF data.

To specify the table schema in-line:

Amazon S3:

    > IMPORT INTO customers
        AVRO DATA (
          's3://acme-co/customers.avro?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder]&AWS_SESSION_TOKEN=[placeholder]'
        );

Azure:

    > IMPORT INTO customers
        AVRO DATA (
          'azure://acme-co/customers.avro?AZURE_ACCOUNT_KEY=hash&AZURE_ACCOUNT_NAME=acme-co'
        );

Google Cloud:

    > IMPORT INTO customers
        AVRO DATA (
          'gs://acme-co/customers.avro'
        );

For more detailed information about importing data from Avro and examples, see Migrate from Avro.

## Known limitations

- While importing into an existing table, the table is taken offline.
- After importing into an existing table, constraints will be un-validated and need to be re-validated.
- Imported rows must not conflict with existing rows in the table or any unique secondary indexes.
- `IMPORT INTO` works for only a single existing table, and the table must not be interleaved.
- `IMPORT INTO` cannot be used within a transaction.

- `IMPORT INTO` can sometimes fail with a "context canceled" error, or can restart itself many times without ever finishing. If this is happening, it is likely due to a high amount of disk contention. This can be mitigated by setting the `kv.bulk_io_write.max_rate` cluster setting to a value below your max disk write speed. For example, to set it to 10MB/s, execute:

      > SET CLUSTER SETTING kv.bulk_io_write.max_rate = '10MB';

- `IMPORT INTO` cannot be used on a table with a `DEFAULT` expression for any of its columns.

## See also

- Create a File Server
- `IMPORT`
- Migration Overview