embulk-output-bigquery

Embulk output plugin to load/insert data into Google BigQuery using direct insert

Overview

load data into Google BigQuery as batch jobs for big amount of data https://developers.google.com/bigquery/loading-data-into-bigquery

Plugin type: output
Resume supported: no
Cleanup supported: no
Dynamic table creating: yes

NOT IMPLEMENTED

insert data over streaming inserts
- for continuous real-time insertions
- Please use other product, like fluent-plugin-bigquery
- https://developers.google.com/bigquery/streaming-data-into-bigquery#usecases

Current version of this plugin supports Google API with Service Account Authentication, but does not support OAuth flow for installed applications.

INCOMPATIBILITY CHANGES

v0.3.x has incompatibility changes with v0.2.x. Please see CHANGELOG.md for details.

formatter option (formatter plugin support) is dropped. Use source_format option instead. (it already exists in v0.2.x too)
encoders option (encoder plugin support) is dropped. Use compression option instead (it already exists in v0.2.x too).
mode: append mode now expresses a transactional append, and mode: append_direct is one which is not transactional.

Configuration

Original options

name	type	required?	default	description
mode	string	optional	"append"	See Mode
auth_method	string	optional	"private_key"	`private_key` , `json_key` or `compute_engine`
service_account_email	string	required when auth_method is private_key		Your Google service account email
p12_keyfile	string	required when auth_method is private_key		Fullpath of private key in P12(PKCS12) format
json_keyfile	string	required when auth_method is json_key		Fullpath of json key
project	string	required if json_keyfile is not given		project_id
dataset	string	required		dataset
table	string	required		table name
auto_create_dataset	boolean	optional	false	automatically create dataset
auto_create_table	boolean	optional	false	See Dynamic Table Creating
schema_file	string	optional		/path/to/schema.json
template_table	string	optional		template table name. See Dynamic Table Creating
prevent_duplicate_insert	boolean	optional	false	See Prevent Duplication
job_status_max_polling_time	int	optional	3600 sec	Max job status polling time
job_status_polling_interval	int	optional	10 sec	Job status polling interval
is_skip_job_result_check	boolean	optional	false	Skip waiting Load job finishes. Available for append, or delete_in_advance mode
with_rehearsal	boolean	optional	false	Load `rehearsal_counts` records as a rehearsal. Rehearsal loads into REHEARSAL temporary table, and delete finally. You may use this option to investigate data errors as early stage as possible
rehearsal_counts	integer	optional	1000	Specify number of records to load in a rehearsal
abort_on_error	boolean	optional	true if max_bad_records is 0, otherwise false	Raise an error if number of input rows and number of output rows does not match
column_options	hash	optional		See Column Options
default_timezone	string	optional	UTC
default_timestamp_format	string	optional	%Y-%m-%d %H:%M:%S.%6N
payload_column	string	optional	nil	See Formatter Performance Issue
payload_column_index	integer	optional	nil	See Formatter Performance Issue
gcs_bucket	stringr	optional	nil	See GCS Bucket
auto_create_gcs_bucket	boolean	optional	false	See GCS Bucket

Client or request options

name	type	required?	default	description
timeout_sec	integer	optional	300	Seconds to wait for one block to be read
open_timeout_sec	integer	optional	300	Seconds to wait for the connection to open
retries	integer	optional	5	Number of retries
application_name	string	optional	"Embulk BigQuery plugin"	User-Agent
sdk_log_level	string	optional	nil (WARN)	Log level of google api client library

Options for intermediate local files

name	type	required?	default	description
path_prefix	string	optional		Path prefix of local files such as "/tmp/prefix_". Default randomly generates with tempfile
sequence_format	string	optional	.%d.%d	Sequence format for pid, thread id
file_ext	string	optional		The file extension of local files such as ".csv.gz" ".json.gz". Default automatically generates from `source_format` and `compression`
skip_file_generation	boolean	optional		Load already generated local files into BigQuery if available. Specify correct path_prefix and file_ext.
delete_from_local_when_job_end	boolean	optional	true	If set to true, delete generate local files when job is end
compression	string	optional	"NONE"	Compression of local files (`GZIP` or `NONE`)

source_format is also used to determine formatter (csv or jsonl).

Same options of bq command-line tools or BigQuery job's propery

Following options are same as bq command-line tools or BigQuery job's property.

name	type	required?	default	description
source_format	string	required	"CSV"	File type (`NEWLINE_DELIMITED_JSON` or `CSV`)
max_bad_records	int	optional	0
field_delimiter	char	optional	","
encoding	string	optional	"UTF-8"	`UTF-8` or `ISO-8859-1`
ignore_unknown_values	boolean	optional	0
allow_quoted_newlines	boolean	optional	0	Set true, if data contains newline characters. It may cause slow procsssing

Example

out:
  type: bigquery
  mode: append
  auth_method: private_key   # default
  service_account_email: ABCXYZ123ABCXYZ123.gserviceaccount.com
  p12_keyfile: /path/to/p12_keyfile.p12
  project: your-project-000
  dataset: your_dataset_name
  table: your_table_name
  compression: GZIP
  source_format: NEWLINE_DELIMITED_JSON

mode

5 modes are provided.

append

Load to temporary table.
Copy temporary table to destination table. (WRITE_APPEND)

append_direct

Insert data into existing table directly. This is not transactional, i.e., if fails, the target table could have some rows inserted.

replace

Load to temporary table.
Copy temporary table to destination table. (WRITE_TRUNCATE)

is_skip_job_result_check must be false when replace mode

replace_backup

Load to temporary table.
Copy destination table to backup table. (dataset_old, table_old)
Copy temporary table to destination table. (WRITE_TRUNCATE)

is_skip_job_result_check must be false when replace_backup mode.

delete_in_advance

Delete destination table, if it exists.
Load to destination table.

Authentication

There are three methods supported to fetch access token for the service account.

Public-Private key pair of GCP(Google Cloud Platform)'s service account
JSON key of GCP(Google Cloud Platform)'s service account
Pre-defined access token (Google Compute Engine only)

Public-Private key pair of GCP's service account

You first need to create a service account (client ID), download its private key and deploy the key with embulk.

out:
  type: bigquery
  auth_method: private_key   # default
  service_account_email: ABCXYZ123ABCXYZ123.gserviceaccount.com
  p12_keyfile: /path/to/p12_keyfile.p12

JSON key of GCP's service account

You first need to create a service account (client ID), download its json key and deploy the key with embulk.

out:
  type: bigquery
  auth_method: json_key
  json_keyfile: /path/to/json_keyfile.json

You can also embed contents of json_keyfile at config.yml.

out:
  type: bigquery
  auth_method: json_key
  json_keyfile:
    content: |
      {
          "private_key_id": "123456789",
          "private_key": "-----BEGIN PRIVATE KEY-----\nABCDEF",
          "client_email": "..."
       }

Pre-defined access token(GCE only)

On the other hand, you don't need to explicitly create a service account for embulk when you run embulk in Google Compute Engine. In this third authentication method, you need to add the API scope "https://www.googleapis.com/auth/bigquery" to the scope list of your Compute Engine VM instance, then you can configure embulk like this.

out:
  type: bigquery
  auth_method: compute_engine

Table id formatting

table and option accept Time#strftime format to construct table ids. Table ids are formatted at runtime using the local time of the embulk server.

For example, with the configuration below, data is inserted into tables table_2015_04, table_2015_05 and so on.

out:
  type: bigquery
  table: table_%Y_%m

Dynamic table creating

When auto_create_table is set to true, try to create the table using BigQuery API.

If table already exists, insert into it.

There are 3 ways to set schema.

Set schema.json

Please set file path of schema.json.

out:
  type: bigquery
  auto_create_table: true
  table: table_%Y_%m
  schema_file: /path/to/schema.json

Set template_table in dataset

Plugin will try to read schema from existing table and use it as schema template.

out:
  type: bigquery
  auto_create_table: true
  table: table_%Y_%m
  template_table: existing_table_name

Guess from Embulk Schema

Plugin will try to guess BigQuery schema from Embulk schema. It is also configurable with column_options. See Column Options.

Column Options

Column options are used to aid guessing BigQuery schema, or to define conversion of values:

column_options: advanced: an array of options for columns
- name: column name
- type: BigQuery type such as BOOLEAN, INTEGER, FLOAT, STRING, TIMESTAMP, and RECORD. See belows for supported conversion type.
- boolean: BOOLEAN, STRING (default: BOOLEAN)
- long: BOOLEAN, INTEGER, FLOAT, STRING, TIMESTAMP (default: INTEGER)
- double: INTEGER, FLOAT, STRING, TIMESTAMP (default: FLOAT)
- string: BOOLEAN, INTEGER, FLOAT, STRING, TIMESTAMP, RECORD (default: STRING)
- timestamp: INTEGER, FLOAT, STRING, TIMESTAMP (default: TIMESTAMP)
- json: STRING, RECORD (default: STRING)
- mode: BigQuery mode such as NULLABLE, REQUIRED, and REPEATED (string, default: NULLABLE)
- fields: Describes the nested schema fields if the type property is set to RECORD. Please note that this is required for RECORD column.
- timestamp_format: timestamp format to convert into/from timestamp (string, default is default_timestamp_format)
- timezone: timezone to convert into/from timestamp (string, default is default_timezone).
default_timestamp_format: default timestamp format for column_options (string, default is "%Y-%m-%d %H:%M:%S.%6N")
default_timezone: default timezone for column_options (string, default is "UTC")

Example)

out:
  type: bigquery
  auto_create_table: true
  column_options:
    - {name: date, type: STRING, timestamp_format: %Y-%m-%d, timezone: "Asia/Tokyo"}
    - name: json_column
      type: RECORD
      fields:
        - {name: key1, type: STRING}
        - {name: key2, type: STRING}

NOTE: Type conversion is done in this jruby plugin, and could be slow. See Formatter Performance Issue to improve the performance.

Formatter Performance Issue

embulk-output-bigquery supports formatting records into CSV or JSON (and also formatting timestamp column). However, this plugin is written in jruby, and jruby plugins are slower than java plugins generally.

Therefore, it is recommended to format records with filter plugins written in Java such as embulk-filter-to_json as:

filters:
  - type: to_json
    column: {name: payload, type: string}
    default_format: %Y-%m-%d %H:%M:%S.%6N
out:
  type: bigquery
  payload_column_index: 0 # or, payload_column: payload

Furtheremore, if your files are originally jsonl or csv files, you can even skip a parser with embulk-parser-none as:

in:
  type: file
  path_prefix: example/example.jsonl
  parser:
    type: none
    column_name: payload
out:
  type: bigquery
  payload_column_index: 0 # or, payload_column: payload

Prevent Duplication

prevent_duplicate_insert option is used to prevent inserting same data for modes append or append_direct.

When prevent_duplicate_insert is set to true, embulk-output-bigquery generate job ID from md5 hash of file and other options.

job ID = md5(md5(file) + dataset + table + schema + source_format + file_delimiter + max_bad_records + encoding + ignore_unknown_values + allow_quoted_newlines)

job ID must be unique(including failures) so that same data can't be inserted with same settings repeatedly.

out:
  type: bigquery
  prevent_duplicate_insert: true

GCS Bucket

This is useful to reduce number of consumed jobs, which is limited by 10,000 jobs per project per day.

This plugin originally loads local files into BigQuery in parallel, that is, consumes a number of jobs, say 24 jobs on 24 CPU core machine for example (this depends on embulk parameters such as min_output_tasks and max_threads).

BigQuery supports loading multiple files from GCS with one job (but not from local files, sigh), therefore, uploading local files to GCS and then loading from GCS into BigQuery reduces number of consumed jobs.

Using gcs_bucket option, such strategy is enabled. You may also use auto_create_gcs_bucket to create the specified GCS bucket automatically.

out:
  type: bigquery
  gcs_bucket: bucket_name
  auto_create_gcs_bucket: false

ToDo: Use https://cloud.google.com/storage/docs/streaming if google-api-ruby-client supports streaming transfers into GCS.

Development

Run example:

Prepare a json_keyfile at example/your-project-000.json, then

$ embulk bundle install --path vendor/bundle
$ embulk run -X page_size=1 -b . -l trace example/example.yml

Run test:

$ bundle exec rake test

To run tests which actually connects to BigQuery such as test/test_bigquery_client.rb, prepare a json_keyfile at example/your-project-000.json, then

$ bundle exec ruby test/test_bigquery_client.rb
$ bundle exec ruby test/test_example.rb

Release gem:

Fix gemspec, then

$ bundle exec rake release

ChangeLog

CHANGELOG.md