This page has instructions for migrating data from Avro files into CockroachDB using IMPORT.

Tip:

For best practices for optimizing import performance in CockroachDB, see Import Performance Best Practices.

Step 1. Export data to Avro

Please refer to the documentation of your database for instructions on exporting data to Avro.

You will need to export one file per table, with the following requirements:

Data type mapping

Avro data types will be flexibly mapped to the target schema; that is, Avro and CockroachDB SQL types do not need to match exactly. By default, CockroachDB ignores unknown Avro fields and sets any columns to NULL if they were not set in the Avro record.

Use the table below for data type mappings:

Avro Data Type CockroachDB Data Type
BOOL BOOL, STRING
INT INT, STRING
FLOAT FLOAT, STRING
STRING STRING
BYTES BYTES, STRING
ARRAY ARRAY, STRING
UUID STRING
DATE STRING
TIME STRING
INTERVAL STRING
TIMESTAMP STRING
JSON STRING
BIT STRING
DECIMAL STRING
Note:

CockroachDB will attempt to convert the Avro data type to the CockroachDB data type; otherwise, it will report an error.

Step 2. Host the files where the cluster can access them

Each node in the CockroachDB cluster needs to have access to the files being imported. There are several ways for the cluster to access the data; for more information on the types of storage IMPORT can pull from, see the following:

Tip:

We strongly recommend using cloud storage such as Amazon S3 or Google Cloud to host the data files you want to import.

Step 3. Import the Avro

To import Avro data:

Import an object container file

An object container file (OCF) is a self-contained Avro file and includes both the table schema and records. For Avro OCF, there is one available import option:

  • strict_validation, which rejects Avro records that do not have a one-to-one data type mapping to the target schema. By default, CockroachDB ignores unknown Avro fields and sets missing SQL fields to NULL.
Note:

The following example uses sample data from Teradata.

For example, to import the data from userdata1.avro into an employees table, issue the following IMPORT statement:

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> IMPORT TABLE employees (
        registration_dttm STRING,
        id INT,
        first_name STRING,
        last_name STRING,
        email STRING,
        gender STRING,
        ip_address STRING,
        cc INT,
        country STRING,
        birthdate STRING,
        salary FLOAT,
        title STRING,
        comments STRING
  )
  AVRO DATA (
        's3://[bucket-placeholder]/userdata1.avro?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder]'
    );
        job_id       |  status   | fraction_completed | rows | index_entries | bytes
---------------------+-----------+--------------------+------+---------------+---------
  535041064396062721 | succeeded |                  1 | 1000 |             0 | 162825
(1 row)
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> SELECT * FROM employees LIMIT 5;
   registration_dttm   | id | first_name | last_name |          email           | gender |   ip_address   |        cc        |        country         | birthdate  |  salary   |          title           | comments
-----------------------+----+------------+-----------+--------------------------+--------+----------------+------------------+------------------------+------------+-----------+--------------------------+-----------
  2016-02-03T07:55:29Z |  1 | Amanda     | Jordan    | ajordan0@com.com         | Female | 1.197.201.2    | 6759521864920116 | Indonesia              | 3/8/1971   |  49756.53 | Internal Auditor         | 1E+02
  2016-02-03T17:04:03Z |  2 | Albert     | Freeman   | afreeman1@is.gd          | Male   | 218.111.175.34 |             NULL | Canada                 | 1/16/1968  | 150280.17 | Accountant IV            |
  2016-02-03T01:09:31Z |  3 | Evelyn     | Morgan    | emorgan2@altervista.org  | Female | 7.161.136.94   | 6767119071901597 | Russia                 | 2/1/1960   | 144972.51 | Structural Engineer      |
  2016-02-03T12:36:21Z |  4 | Denise     | Riley     | driley3@gmpg.org         | Female | 140.35.109.83  | 3576031598965625 | China                  | 4/8/1997   |  90263.05 | Senior Cost Accountant   |
  2016-02-03T05:05:31Z |  5 | Carlos     | Burns     | cburns4@miitbeian.gov.cn |        | 169.113.235.40 | 5602256255204850 | South Africa           |            | NULL      |                          |
(5 rows)

Repeat the above for each OCF you want to import.

Note:

To import data into an existing table, use IMPORT INTO.

Note:

You will need to run ALTER TABLE ... ADD CONSTRAINT to add any foreign key relationships.

Import binary or JSON records

In addition to importing Avro OCF, you can also import a binary or JSON file containing Avro records:

  • To import a binary file, use the data_as_binary_records option.
  • To import a JSON file, use the data_as_json_records option.

The schema is not included in these types of files, so you need to specify the schema. You need to either:

  • Specify the schema inline with the schema option.
  • Specify the schema by pointing to the file with the schema_uri option.

There are additional import options you can use when importing binary and JSON files:

  • strict_validation, which rejects Avro records that do not have a one-to-one data type mapping to the target schema. By default, CockroachDB ignores unknown Avro fields and sets missing SQL fields to NULL.
  • records_terminated_by, which specifies the unicode character used to indicate new lines in the input binary or JSON file (default: \n).
Note:

The following example uses sample data generated by Avro tools.

For example, to import the data from simple-schema.json into an simple table, issue the following IMPORT statement:

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> IMPORT TABLE simple
  CREATE USING
      's3://[bucket-placeholder]/simple-schema.sql?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder]'
  AVRO DATA (
        's3://[bucket-placeholder]/simple-sorted.json?AWS_ACCESS_KEY_ID=[placeholder]&AWS_SECRET_ACCESS_KEY=[placeholder]'
    )
  WITH
       data_as_json_records,
       schema = '{ "type": "record",
  "name": "simple",
  "fields":
    [
      { "name": "i", "type": "int" },
      { "name": "s", "type": "string" },
      { "name": "b", "type": ["null", "bytes"] }
    ]
}';
        job_id       |  status   | fraction_completed | rows | index_entries | bytes
---------------------+-----------+--------------------+------+---------------+--------
  535294613223669761 | succeeded |                  1 | 1000 |             0 | 50372
(1 row)
> SELECT * FROM  simple LIMIT 5;
       i      |                 s                 |                   b
--------------+-----------------------------------+---------------------------------------------...
  -2135825688 | dcpamywjlvtohbbtbtpypubccu        | \303\204\303\264\303\216\027\303\221\017\303...
  -2135463332 | rmspluxnumigrpbrkfmuktphnmfskt    | \303\232\017i>{b.~\302\277\177A\302\264\303\...
  -2132354298 | mebfxrhurtngsqvlyjechuglymuxfjpvv | \303\2541E\302\277\302\2714\302\257\303\201\...
  -2131856455 | hfrgfefflpopvtemrspaixitncghwqfrr | NULL
  -2116408431 | thuosfwm                          | \016s\026\303\264\303\247\302\201\302\264o\3...
(5 rows)

Repeat the above for each binary or JSON file you want to import.

Note:

To import data into an existing table, use IMPORT INTO.

Note:

You will need to run ALTER TABLE ... ADD CONSTRAINT to add any foreign key relationships.

See also



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