Fluentd output plugin to load/insert data into Google BigQuery.
- Plugin type: Output
- insert data over streaming inserts
- plugin type is
bigquery_insert - for continuous real-time insertions
- https://developers.google.com/bigquery/streaming-data-into-bigquery#usecases
- plugin type is
- load data
- plugin type is
bigquery_load - for data loading as batch jobs, for big amount of data
- https://developers.google.com/bigquery/loading-data-into-bigquery
- plugin type is
Current version of this plugin supports Google API with Service Account Authentication, but does not support OAuth flow for installed applications.
| plugin version | fluentd version | ruby version |
|---|---|---|
| v0.4.x | 0.12.x | 2.0 or later |
| v1.x.x | 0.14.x or later | 2.2 or later |
| v2.x.x | 0.14.x or later | 2.3 or later |
| v3.x.x | 1.x or later | 2.7 or later |
If you use official alpine based fluentd docker image (https://github.com/fluent/fluentd-docker-image),
You need to install bigdecimal gem on your own dockerfile.
Because alpine based image has only minimal ruby environment in order to reduce image size.
And in most case, dependency to embedded gem is not written on gemspec.
Because embbeded gem dependency sometimes restricts ruby environment.
| name | type | required? | placeholder? | default | description |
|---|---|---|---|---|---|
| auth_method | enum | yes | no | private_key | private_key or json_key or compute_engine or application_default |
| string | yes (private_key) | no | nil | GCP Service Account Email | |
| private_key_path | string | yes (private_key) | no | nil | GCP Private Key file path |
| private_key_passphrase | string | yes (private_key) | no | nil | GCP Private Key Passphrase |
| json_key | string | yes (json_key) | no | nil | GCP JSON Key file path or JSON Key string |
| location | string | no | no | nil | BigQuery Data Location. The geographic location of the job. Required except for US and EU. |
| project | string | yes | yes | nil | |
| dataset | string | yes | yes | nil | |
| table | string | yes (either tables) |
yes | nil | |
| tables | array(string) | yes (either table) |
yes | nil | can set multi table names splitted by , |
| auto_create_table | bool | no | no | false | If true, creates table automatically |
| ignore_unknown_values | bool | no | no | false | Accept rows that contain values that do not match the schema. The unknown values are ignored. |
| schema | array | yes (either fetch_schema or schema_path) |
no | nil | Schema Definition. It is formatted by JSON. |
| schema_path | string | yes (either fetch_schema) |
yes | nil | Schema Definition file path. It is formatted by JSON. |
| fetch_schema | bool | yes (either schema_path) |
no | false | If true, fetch table schema definition from Bigquery table automatically. |
| fetch_schema_table | string | no | yes | nil | If set, fetch table schema definition from this table, If fetch_schema is false, this param is ignored |
| schema_cache_expire | integer | no | no | 600 | Value is second. If current time is after expiration interval, re-fetch table schema definition. |
| request_timeout_sec | integer | no | no | nil | Bigquery API response timeout |
| request_open_timeout_sec | integer | no | no | 60 | Bigquery API connection, and request timeout. If you send big data to Bigquery, set large value. |
| time_partitioning_type | enum | no (either day) | no | nil | Type of bigquery time partitioning feature. |
| time_partitioning_field | string | no | no | nil | Field used to determine how to create a time-based partition. |
| time_partitioning_expiration | time | no | no | nil | Expiration milliseconds for bigquery time partitioning. |
| clustering_fields | array(string) | no | no | nil | One or more fields on which data should be clustered. The order of the specified columns determines the sort order of the data. |
| name | type | required? | placeholder? | default | description |
|---|---|---|---|---|---|
| template_suffix | string | no | yes | nil | can use %{time_slice} placeholder replaced by time_slice_format |
| skip_invalid_rows | bool | no | no | false | |
| insert_id_field | string | no | no | nil | Use key as insert_id of Streaming Insert API parameter. see. https://docs.fluentd.org/v1.0/articles/api-plugin-helper-record_accessor |
| add_insert_timestamp | string | no | no | nil | Adds a timestamp column just before sending the rows to BigQuery, so that buffering time is not taken into account. Gives a field in BigQuery which represents the insert time of the row. |
| allow_retry_insert_errors | bool | no | no | false | Retry to insert rows when an insertErrors occurs. There is a possibility that rows are inserted in duplicate. |
| require_partition_filter | bool | no | no | false | If true, queries over this table require a partition filter that can be used for partition elimination to be specified. |
| name | type | required? | placeholder? | default | description |
|---|---|---|---|---|---|
| source_format | enum | no | no | json | Specify source format json or csv or avro. If you change this parameter, you must change formatter plugin via <format> config section. |
| max_bad_records | integer | no | no | 0 | If the number of bad records exceeds this value, an invalid error is returned in the job result. |
| name | type | required? | default | description |
|---|---|---|---|---|
| @type | string | no | memory (insert) or file (load) | |
| chunk_limit_size | integer | no | 1MB (insert) or 1GB (load) | |
| total_limit_size | integer | no | 1GB (insert) or 32GB (load) | |
| chunk_records_limit | integer | no | 500 (insert) or nil (load) | |
| flush_mode | enum | no | interval | default, lazy, interval, immediate |
| flush_interval | float | no | 1.0 (insert) or 3600 (load) | |
| flush_thread_interval | float | no | 0.05 (insert) or 5 (load) | |
| flush_thread_burst_interval | float | no | 0.05 (insert) or 5 (load) |
And, other params (defined by base class) are available
see. https://github.com/fluent/fluentd/blob/master/lib/fluent/plugin/output.rb
It is replacement of previous version time_field and time_format.
For example.
<inject>
time_key time_field_name
time_type string
time_format %Y-%m-%d %H:%M:%S
</inject>
| name | type | required? | default | description |
|---|---|---|---|---|
| hostname_key | string | no | nil | |
| hostname | string | no | nil | |
| tag_key | string | no | nil | |
| time_key | string | no | nil | |
| time_type | string | no | nil | |
| time_format | string | no | nil | |
| localtime | bool | no | true | |
| utc | bool | no | false | |
| timezone | string | no | nil |
see. https://github.com/fluent/fluentd/blob/master/lib/fluent/plugin_helper/inject.rb
This section is for load mode only.
If you use insert mode, used formatter is json only.
Bigquery supports csv, json and avro format. Default is json
I recommend to use json for now.
For example.
source_format csv
<format>
@type csv
fields col1, col2, col3
</format>
see. https://github.com/fluent/fluentd/blob/master/lib/fluent/plugin_helper/formatter.rb
Configure insert specifications with target table schema, with your credentials. This is minimum configurations:
<match dummy>
@type bigquery_insert
auth_method private_key # default
email xxxxxxxxxxxx-xxxxxxxxxxxxxxxxxxxxxx@developer.gserviceaccount.com
private_key_path /home/username/.keys/00000000000000000000000000000000-privatekey.p12
# private_key_passphrase notasecret # default
project yourproject_id
dataset yourdataset_id
table tablename
schema [
{"name": "time", "type": "INTEGER"},
{"name": "status", "type": "INTEGER"},
{"name": "bytes", "type": "INTEGER"},
{"name": "vhost", "type": "STRING"},
{"name": "path", "type": "STRING"},
{"name": "method", "type": "STRING"},
{"name": "protocol", "type": "STRING"},
{"name": "agent", "type": "STRING"},
{"name": "referer", "type": "STRING"},
{"name": "remote", "type": "RECORD", "fields": [
{"name": "host", "type": "STRING"},
{"name": "ip", "type": "STRING"},
{"name": "user", "type": "STRING"}
]},
{"name": "requesttime", "type": "FLOAT"},
{"name": "bot_access", "type": "BOOLEAN"},
{"name": "loginsession", "type": "BOOLEAN"}
]
</match>For high rate inserts over streaming inserts, you should specify flush intervals and buffer chunk options:
<match dummy>
@type bigquery_insert
<buffer>
flush_interval 0.1 # flush as frequent as possible
total_limit_size 10g
flush_thread_count 16
</buffer>
auth_method private_key # default
email xxxxxxxxxxxx-xxxxxxxxxxxxxxxxxxxxxx@developer.gserviceaccount.com
private_key_path /home/username/.keys/00000000000000000000000000000000-privatekey.p12
# private_key_passphrase notasecret # default
project yourproject_id
dataset yourdataset_id
tables accesslog1,accesslog2,accesslog3
schema [
{"name": "time", "type": "INTEGER"},
{"name": "status", "type": "INTEGER"},
{"name": "bytes", "type": "INTEGER"},
{"name": "vhost", "type": "STRING"},
{"name": "path", "type": "STRING"},
{"name": "method", "type": "STRING"},
{"name": "protocol", "type": "STRING"},
{"name": "agent", "type": "STRING"},
{"name": "referer", "type": "STRING"},
{"name": "remote", "type": "RECORD", "fields": [
{"name": "host", "type": "STRING"},
{"name": "ip", "type": "STRING"},
{"name": "user", "type": "STRING"}
]},
{"name": "requesttime", "type": "FLOAT"},
{"name": "bot_access", "type": "BOOLEAN"},
{"name": "loginsession", "type": "BOOLEAN"}
]
</match>Important options for high rate events are:
tables- 2 or more tables are available with ',' separator
out_bigqueryuses these tables for Table Sharding inserts- these must have same schema
buffer/chunk_limit_size- max size of an insert or chunk (default 1000000 or 1MB)
- the max size is limited to 1MB on BigQuery
buffer/chunk_records_limit- number of records over streaming inserts API call is limited as 500, per insert or chunk
out_bigqueryflushes buffer with 500 records for 1 inserts API call
buffer/queue_length_limit- BigQuery streaming inserts needs very small buffer chunks
- for high-rate events,
buffer_queue_limitshould be configured with big number - Max 1GB memory may be used under network problem in default configuration
chunk_limit_size (default 1MB)xqueue_length_limit (default 1024)
buffer/flush_thread_count- threads for insert api calls in parallel
- specify this option for 100 or more records per seconds
- 10 or more threads seems good for inserts over internet
- less threads may be good for Google Compute Engine instances (with low latency for BigQuery)
buffer/flush_interval- interval between data flushes (default 0.25)
- you can set subsecond values such as
0.15on Fluentd v0.10.42 or later
See Quota policy section in the Google BigQuery document.
<match bigquery>
@type bigquery_load
<buffer>
path bigquery.*.buffer
flush_at_shutdown true
timekey_use_utc
</buffer>
auth_method json_key
json_key json_key_path.json
project yourproject_id
dataset yourdataset_id
auto_create_table true
table yourtable%{time_slice}
schema_path bq_schema.json
</match>I recommend to use file buffer and long flush interval.
There are four 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
- Predefined access token (Compute Engine only)
- Google application default credentials (http://goo.gl/IUuyuX)
The examples above use the first one. You first need to create a service account (client ID), download its private key and deploy the key with fluentd.
You first need to create a service account (client ID), download its JSON key and deploy the key with fluentd.
<match dummy>
@type bigquery_insert
auth_method json_key
json_key /home/username/.keys/00000000000000000000000000000000-jsonkey.json
project yourproject_id
dataset yourdataset_id
table tablename
...
</match>You can also provide json_key as embedded JSON string like this.
You need to only include private_key and client_email key from JSON key file.
<match dummy>
@type bigquery_insert
auth_method json_key
json_key {"private_key": "-----BEGIN PRIVATE KEY-----\n...", "client_email": "[email protected]"}
project yourproject_id
dataset yourdataset_id
table tablename
...
</match>When you run fluentd on Googlce Compute Engine instance, you don't need to explicitly create a service account for fluentd. In this authentication method, you need to add the API scope "https://www.googleapis.com/auth/bigquery" to the scope list of your Compute Engine instance, then you can configure fluentd like this.
<match dummy>
@type bigquery_insert
auth_method compute_engine
project yourproject_id
dataset yourdataset_id
table tablename
...
</match>The Application Default Credentials provide a simple way to get authorization credentials for use in calling Google APIs, which are described in detail at http://goo.gl/IUuyuX.
In this authentication method, the credentials returned are determined by the environment the code is running in. Conditions are checked in the following order:credentials are get from following order.
- The environment variable
GOOGLE_APPLICATION_CREDENTIALSis checked. If this variable is specified it should point to a JSON key file that defines the credentials. - The environment variable
GOOGLE_PRIVATE_KEYandGOOGLE_CLIENT_EMAILare checked. If this variables are specifiedGOOGLE_PRIVATE_KEYshould point toprivate_key,GOOGLE_CLIENT_EMAILshould point toclient_emailin a JSON key. - Well known path is checked. If file is exists, the file used as a JSON key file. This path is
$HOME/.config/gcloud/application_default_credentials.json. - System default path is checked. If file is exists, the file used as a JSON key file. This path is
/etc/google/auth/application_default_credentials.json. - If you are running in Google Compute Engine production, the built-in service account associated with the virtual machine instance will be used.
- If none of these conditions is true, an error will occur.
this plugin supports fluentd-0.14 style placeholder.
table and tables options accept Time#strftime
format to construct table ids.
Table ids are formatted at runtime
using the chunk key time.
see. https://docs.fluentd.org/configuration/buffer-section
For example, with the configuration below,
data is inserted into tables accesslog_2014_08_02, accesslog_2014_08_03 and so on.
<match dummy>
@type bigquery_insert
...
project yourproject_id
dataset yourdataset_id
table accesslog_%Y_%m_%d
<buffer time>
timekey 1d
</buffer>
...
</match>NOTE: In current fluentd (v1.15.x), The maximum unit supported by strftime formatting is the granularity of days
The format can be suffixed with attribute name.
CAUTION: format is different with previous version
<match dummy>
...
table accesslog_${status_code}
<buffer status_code>
</buffer>
...
</match>If attribute name is given, the time to be used for formatting is value of each row. The value for the time should be a UNIX time.
Instead, Use strftime formatting.
strftime formatting of current version is based on chunk key. That is same with previous time_slice_key formatting .
this plugin can insert (load) into date partitioned table.
Use placeholder.
<match dummy>
@type bigquery_load
...
table accesslog$%Y%m%d
<buffer time>
timekey 1d
</buffer>
...
</match>But, Dynamic table creating doesn't support date partitioned table yet.
And streaming insert is not allowed to insert with $%Y%m%d suffix.
If you use date partitioned table with streaming insert, Please omit $%Y%m%d suffix from table.
When auto_create_table is set to true, try to create the table using BigQuery API when insertion failed with code=404 "Not Found: Table ...".
Next retry of insertion is expected to be success.
NOTE: auto_create_table option cannot be used with fetch_schema. You should create the table on ahead to use fetch_schema.
<match dummy>
@type bigquery_insert
...
auto_create_table true
table accesslog_%Y_%m
...
</match>Also, you can create clustered table by using clustering_fields.
There are three methods to describe the schema of the target table.
- List fields in fluent.conf
- Load a schema file in JSON.
- Fetch a schema using BigQuery API
The examples above use the first method. In this method, you can also specify nested fields by prefixing their belonging record fields.
<match dummy>
@type bigquery_insert
...
schema [
{"name": "time", "type": "INTEGER"},
{"name": "status", "type": "INTEGER"},
{"name": "bytes", "type": "INTEGER"},
{"name": "vhost", "type": "STRING"},
{"name": "path", "type": "STRING"},
{"name": "method", "type": "STRING"},
{"name": "protocol", "type": "STRING"},
{"name": "agent", "type": "STRING"},
{"name": "referer", "type": "STRING"},
{"name": "remote", "type": "RECORD", "fields": [
{"name": "host", "type": "STRING"},
{"name": "ip", "type": "STRING"},
{"name": "user", "type": "STRING"}
]},
{"name": "requesttime", "type": "FLOAT"},
{"name": "bot_access", "type": "BOOLEAN"},
{"name": "loginsession", "type": "BOOLEAN"}
]
</match>This schema accepts structured JSON data like:
{
"request":{
"time":1391748126.7000976,
"vhost":"www.example.com",
"path":"/",
"method":"GET",
"protocol":"HTTP/1.1",
"agent":"HotJava",
"bot_access":false
},
"remote":{ "ip": "192.0.2.1" },
"response":{
"status":200,
"bytes":1024
}
}The second method is to specify a path to a BigQuery schema file instead of listing fields. In this case, your fluent.conf looks like:
<match dummy>
@type bigquery_insert
...
schema_path /path/to/httpd.schema
</match>where /path/to/httpd.schema is a path to the JSON-encoded schema file which you used for creating the table on BigQuery. By using external schema file you are able to write full schema that does support NULLABLE/REQUIRED/REPEATED, this feature is really useful and adds full flexbility.
The third method is to set fetch_schema to true to enable fetch a schema using BigQuery API. In this case, your fluent.conf looks like:
<match dummy>
@type bigquery_insert
...
fetch_schema true
# fetch_schema_table other_table # if you want to fetch schema from other table
</match>If you specify multiple tables in configuration file, plugin get all schema data from BigQuery and merge it.
NOTE: Since JSON does not define how to encode data of TIMESTAMP type, you are still recommended to specify JSON types for TIMESTAMP fields as "time" field does in the example, if you use second or third method.
BigQuery uses insertId property to detect duplicate insertion requests (see data consistency in Google BigQuery documents).
You can set insert_id_field option to specify the field to use as insertId property.
insert_id_field can use fluentd record_accessor format like $['key1'][0]['key2'].
(detail. https://docs.fluentd.org/v1.0/articles/api-plugin-helper-record_accessor)
<match dummy>
@type bigquery_insert
...
insert_id_field uuid
schema [{"name": "uuid", "type": "STRING"}]
</match>- OAuth installed application credentials support
- Google API discovery expiration
- check row size limits
- @tagomoris: First author, original version
- KAIZEN platform Inc.: Maintener, Since 2014.08.19
- @joker1007
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent