COPY INTO <table>

The COPY INTO command in Databend allows you to load data from files located in multiple locations. This is the recommended method for loading a large amount of data into Databend.

One of its key features is that it provides idempotency by keeping track of files that have already been processed for a default period of 7 days, you can customize this behavior using the load_file_metadata_expire_hours global setting.

The files must exist in one of the following locations:

User / Internal / External stages: See Understanding Stages to learn about stages in Databend.
Buckets or containers created in a storage service.
Remote servers from where you can access the files by their URL (starting with "https://...").
IPFS.

Syntax

/* Standard data load */
COPY INTO [<database>.]<table_name>
     FROM { internalStage | externalStage | externalLocation }
[ FILES = ( '<file_name>' [ , '<file_name>' ] [ , ... ] ) ]
[ PATTERN = '<regex_pattern>' ]
[ FILE_FORMAT = ( TYPE = { CSV | TSV | NDJSON | PARQUET} [ formatTypeOptions ] ) ]
[ copyOptions ]

/* Data load with transformation(Only support Parquet format) */
COPY INTO [<database>.]<table_name> [ ( <col_name> [ , <col_name> ... ] ) ]
     FROM ( SELECT [<file_col> ... ]
            FROM { internalStage | externalStage } )
[ FILES = ( '<file_name>' [ , '<file_name>' ] [ , ... ] ) ]
[ PATTERN = '<regex_pattern>' ]
[ FILE_FORMAT = ( TYPE = {PARQUET} [ formatTypeOptions ] ) ]
[ copyOptions ]

internalStage

internalStage ::= @<internal_stage_name>[/<path>]

externalStage

externalStage ::= @<external_stage_name>[/<path>]

externalLocation

This allows you to access data stored outside of Databend, such as in cloud storage services like AWS S3 or Azure Blob Storage. By specifying an external location, you can query data stored there directly from Databend without the need to load it into Databend.

externalLocation ::=
  's3://<bucket>[<path>]'
  CONNECTION = (
        <connection_parameters>
  )

For the connection parameters available for accessing Amazon S3-like storage services, see Connection Parameters.

externalLocation ::=
  'azblob://<container>[<path>]'
  CONNECTION = (
        <connection_parameters>
  )

For the connection parameters available for accessing Azure Blob Storage, see Connection Parameters.

externalLocation ::=
  'gcs://<bucket>[<path>]'
  CONNECTION = (
        <connection_parameters>
  )

For the connection parameters available for accessing Google Cloud Storage, see Connection Parameters.

externalLocation ::=
  'oss://<bucket>[<path>]'
  CONNECTION = (
        <connection_parameters>
  )

For the connection parameters available for accessing Alibaba Cloud OSS, see Connection Parameters.

externalLocation ::=
  'cos://<bucket>[<path>]'
  CONNECTION = (
        <connection_parameters>
  )

For the connection parameters available for accessing Tencent Cloud Object Storage, see Connection Parameters.

externalLocation ::=
  'hdfs://<endpoint_url>[<path>]'
  CONNECTION = (
        <connection_parameters>
  )

For the connection parameters available for accessing HDFS, see Connection Parameters.

externalLocation ::=
  'webhdfs://<endpoint_url>[<path>]'
  CONNECTION = (
        <connection_parameters>
  )

For the connection parameters available for accessing WebHDFS, see Connection Parameters.

externalLocation ::=
  'https://<url>'

You can use glob patterns to specify moran than one file. For example, use

ontime_200{6,7,8}.csv to represents ontime_2006.csv,ontime_2007.csv,ontime_2008.csv.
ontime_200[6-8].csv to represents ontime_2006.csv,ontime_2007.csv,ontime_2008.csv.

externalLocation ::=
  'ipfs://<your-ipfs-hash>'
  CONNECTION = (ENDPOINT_URL = 'https://<your-ipfs-gateway>')

FILES = ( 'file1' [ , 'file2' ... ] )

Specify a list of one or more files names (separated by commas) to be loaded.

PATTERN = 'regex_pattern'

A PCRE2-based regular expression pattern string, enclosed in single quotes, specifying the file names to match. Click here to see an example. For PCRE2 syntax, see http://www.pcre.org/current/doc/html/pcre2syntax.html.

FILE_FORMAT

See Input & Output File Formats.

copyOptions

copyOptions ::=
  [ SIZE_LIMIT = <num> ]
  [ PURGE = <bool> ]
  [ FORCE = <bool> ]
  [ DISABLE_VARIANT_CHECK = <bool> ]
  [ ON_ERROR = { continue | abort | abort_N } ]
  [ MAX_FILES = <num> ]

Parameter	Description	Required
SIZE_LIMIT	Specifies the maximum rows of data to be loaded for a given COPY statement. Defaults to `0` meaning no limits.	Optional
PURGE	If `True`, the command will purge the files in the stage after they are loaded successfully into the table. Default: `False`.	Optional
FORCE	Defaults to `False` meaning the command will skip duplicate files in the stage when copying data. If `True`, duplicate files will not be skipped.	Optional
DISABLE_VARIANT_CHECK	If `True`, this will allow the variant field to insert invalid JSON strings. Default: `False`.	Optional
ON_ERROR	Decides how to handle a file that contains errors: 'continue' to skip and proceed, 'abort' to terminate on error, 'abort_N' to terminate when errors ≥ N. Default is 'abort'. Note: 'abort_N' not available for Parquet files.	Optional
MAX_FILES	Sets the maximum number of files to load that have not been loaded already. The value can be set up to 500; any value greater than 500 will be treated as 500.	Optional

tip

When importing large volumes of data, such as logs, it is recommended to set both PURGE and FORCE to True. This ensures efficient data import without the need for interaction with the Meta server (updating the copied-files set). However, it is important to be aware that this may lead to duplicate data imports.

Examples

1. Loading Data from an Internal Stage

COPY INTO mytable
FROM @my_internal_s1
PATTERN = '.*[.]parquet'
FILE_FORMAT = (TYPE = PARQUET);

2. Loading Data from an External Stage

COPY INTO mytable
FROM @my_external_s1
PATTERN = 'books.*parquet'
FILE_FORMAT = (TYPE = PARQUET);

COPY INTO mytable
FROM @my_external_s1
PATTERN = '.*[.]parquet'
FILE_FORMAT = (TYPE = PARQUET);

3. Loading Data from External Locations

AWS S3-compatible Storage
Azure Blob Storage
Remote Files
IPFS Files

This example reads 10 rows from a CSV file and inserts them into a table:

-- Authenticated by AWS access keys and secrets.
COPY INTO mytable
FROM 's3://mybucket/data.csv'
CONNECTION = (
    ENDPOINT_URL = 'https://<endpoint-URL>'
    ACCESS_KEY_ID = '<your-access-key-ID>'
    SECRET_ACCESS_KEY = '<your-secret-access-key>'
)
FILE_FORMAT = (type = CSV field_delimiter = ',' record_delimiter = '\n' skip_header = 1)
SIZE_LIMIT = 10;

This example loads data from a CSV file without specifying the endpoint URL:

COPY INTO mytable
FROM 's3://mybucket/data.csv'
FILE_FORMAT = (type = CSV field_delimiter = ',' record_delimiter = '\n' skip_header = 1)
SIZE_LIMIT = 10;

This example reads data from a CSV file and inserts it into a table:

COPY INTO mytable
FROM 'azblob://mybucket/data.csv'
CONNECTION = (
    ENDPOINT_URL = 'https://<account_name>.blob.core.windows.net'
    ACCOUNT_NAME = '<account_name>'
    ACCOUNT_KEY = '<account_key>'
)
FILE_FORMAT = (type = CSV);

As shown in this example, data is loaded from three remote CSV files, but a file will be skipped if it contains errors:

COPY INTO mytable
FROM 'https://repo.databend.rs/dataset/stateful/ontime_200{6,7,8}_200.csv'
FILE_FORMAT = (type = CSV)
ON_ERROR = continue;

This example reads data from a CSV file on IPFS and inserts it into a table:

COPY INTO mytable
FROM 'ipfs://<your-ipfs-hash>'
CONNECTION = (endpoint_url = 'https://<your-ipfs-gateway>')
FILE_FORMAT = (type = CSV field_delimiter = ',' record_delimiter = '\n' skip_header = 1);

4. Loading Data with Pattern Matching

This example uses pattern matching to only load from CSV files containing sales in their names:

COPY INTO mytable
FROM 's3://mybucket/'
PATTERN = '.*sales.*[.]csv'
FILE_FORMAT = (type = CSV field_delimiter = ',' record_delimiter = '\n' skip_header = 1);

Where .* is interpreted as zero or more occurrences of any character. The square brackets escape the period character (.) that precedes a file extension.

If you want to load from all the CSV files, use PATTERN = '.*[.]csv':

COPY INTO mytable
FROM 's3://mybucket/'
PATTERN = '.*[.]csv'
FILE_FORMAT = (type = CSV field_delimiter = ',' record_delimiter = '\n' skip_header = 1);

5. Loading Data with AWS IAM Role

-- Authenticated by AWS IAM role and external ID.
COPY INTO mytable
FROM 's3://mybucket/'
CONNECTION = (
    ENDPOINT_URL = 'https://<endpoint-URL>',
    ROLE_ARN = 'arn:aws:iam::123456789012:role/my_iam_role',
    EXTERNAL_ID = '123456'
)
PATTERN = '.*[.]csv'
FILE_FORMAT = (type = CSV field_delimiter = ',' record_delimiter = '\n' skip_header = 1);

6. Loading Data with Compression

This example reads 10 rows from a CSV file compressed as GZIP and inserts them into a table:

COPY INTO mytable
FROM 's3://mybucket/data.csv.gz'
CONNECTION = (
    ENDPOINT_URL = 'https://<endpoint-URL>',
    ACCESS_KEY_ID = '<your-access-key-ID>',
    SECRET_ACCESS_KEY = '<your-secret-access-key>'
)
FILE_FORMAT = (type = CSV field_delimiter = ',' record_delimiter = '\n' skip_header = 1 compression = AUTO)
SIZE_LIMIT = 10;

7. Loading Parquet Files

COPY INTO mytable
FROM 's3://mybucket/'
CONNECTION = (
    ACCESS_KEY_ID = '<your-access-key-ID>',
    SECRET_ACCESS_KEY = '<your-secret-access-key>'
)
PATTERN = '.*[.]parquet'
FILE_FORMAT = (TYPE = PARQUET);

8. Controlling Parallel Processing

In Databend, the max_threads setting specifies the maximum number of threads that can be utilized to execute a request. By default, this value is typically set to match the number of CPU cores available on the machine.

When loading data into Databend with COPY INTO, you can control the parallel processing capabilities by injecting hints into the COPY INTO command and setting the max_threads parameter. For example:

COPY /*+ set_var(max_threads=6) */ INTO mytable FROM @mystage/ pattern='.*[.]parq' FILE_FORMAT=(TYPE=parquet);

For more information about injecting hints, see SET_VAR.

COPY INTO <table>

Syntax

internalStage

externalStage

externalLocation

FILES = ( 'file1' [ , 'file2' ... ] )

PATTERN = 'regex_pattern'

FILE_FORMAT

copyOptions

Examples

1. Loading Data from an Internal Stage

2. Loading Data from an External Stage

3. Loading Data from External Locations

4. Loading Data with Pattern Matching

5. Loading Data with AWS IAM Role

6. Loading Data with Compression

7. Loading Parquet Files

8. Controlling Parallel Processing

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Syntax​

internalStage​

externalStage​

externalLocation​

FILES = ( 'file1' [ , 'file2' ... ] )​

PATTERN = 'regex_pattern'​

FILE_FORMAT​

copyOptions​

Examples​

1. Loading Data from an Internal Stage​

2. Loading Data from an External Stage​

3. Loading Data from External Locations​

4. Loading Data with Pattern Matching​

5. Loading Data with AWS IAM Role​

6. Loading Data with Compression​

7. Loading Parquet Files​

8. Controlling Parallel Processing​

Join our growing community

GitHub

Slack

Twitter

YouTube

Explore Databend Cloud for FREE

Syntax

internalStage

externalStage

externalLocation

FILES = ( 'file1' [ , 'file2' ... ] )

PATTERN = 'regex_pattern'

FILE_FORMAT

copyOptions

Examples

1. Loading Data from an Internal Stage

2. Loading Data from an External Stage

3. Loading Data from External Locations

4. Loading Data with Pattern Matching

5. Loading Data with AWS IAM Role

6. Loading Data with Compression

7. Loading Parquet Files

8. Controlling Parallel Processing