# fluent-plugin-bigquery
[Fluentd](http://fluentd.org) output plugin to load/insert data into Google BigQuery.
- **Plugin type**: TimeSlicedOutput
* insert data over streaming inserts
* for continuous real-time insertions
* https://developers.google.com/bigquery/streaming-data-into-bigquery#usecases
* load data
* for data loading as batch jobs, for big amount of data
* https://developers.google.com/bigquery/loading-data-into-bigquery
Current version of this plugin supports Google API with Service Account Authentication, but does not support
OAuth flow for installed applications.
## Notice
If you use ruby-2.1 or earlier, you must use activesupport-4.2.x or earlier.
## Configuration
### Options
| name | type | required? | default | description |
| :------------------------------------- | :------------ | :----------- | :------------------------- | :----------------------- |
| method | string | no | insert | `insert` (Streaming Insert) or `load` (load job) |
| buffer_type | string | no | lightening (insert) or file (load) | |
| buffer_chunk_limit | integer | no | 1MB (insert) or 1GB (load) | |
| buffer_queue_limit | integer | no | 1024 (insert) or 32 (load) | |
| buffer_chunk_records_limit | integer | no | 500 | |
| flush_interval | float | no | 0.25 (*insert) or default of time sliced output (load) | |
| try_flush_interval | float | no | 0.05 (*insert) or default of time sliced output (load) | |
| auth_method | enum | yes | private_key | `private_key` or `json_key` or `compute_engine` or `application_default` |
| email | string | yes (private_key) | nil | GCP Service Account Email |
| private_key_path | string | yes (private_key) | nil | GCP Private Key file path |
| private_key_passphrase | string | yes (private_key) | nil | GCP Private Key Passphrase |
| json_key | string | yes (json_key) | nil | GCP JSON Key file path or JSON Key string |
| project | string | yes | nil | |
| table | string | yes (either `tables`) | nil | |
| tables | string | yes (either `table`) | nil | can set multi table names splitted by `,` |
| template_suffix | string | no | nil | can use `%{time_slice}` placeholder replaced by `time_slice_format` |
| auto_create_table | bool | no | false | If true, creates table automatically |
| skip_invalid_rows | bool | no | false | Only `insert` method. |
| max_bad_records | integer | no | 0 | Only `load` method. If the number of bad records exceeds this value, an invalid error is returned in the job result. |
| ignore_unknown_values | bool | 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`) | nil | Schema Definition. It is formatted by JSON. |
| schema_path | string | yes (either `fetch_schema`) | nil | Schema Definition file path. It is formatted by JSON. |
| fetch_schema | bool | yes (either `schema_path`) | false | If true, fetch table schema definition from Bigquery table automatically. |
| fetch_schema_table | string | no | nil | If set, fetch table schema definition from this table, If fetch_schema is false, this param is ignored |
| schema_cache_expire | integer | no | 600 | Value is second. If current time is after expiration interval, re-fetch table schema definition. |
| field_string (deprecated) | string | no | nil | see examples. |
| field_integer (deprecated) | string | no | nil | see examples. |
| field_float (deprecated) | string | no | nil | see examples. |
| field_boolean (deprecated) | string | no | nil | see examples. |
| field_timestamp (deprecated) | string | no | nil | see examples. |
| time_field | string | no | nil | If this param is set, plugin set formatted time string to this field. |
| time_format | string | no | nil | ex. `%s`, `%Y/%m%d %H:%M:%S` |
| replace_record_key | bool | no | false | see examples. |
| replace_record_key_regexp{1-10} | string | no | nil | see examples. |
| convert_hash_to_json (deprecated) | bool | no | false | If true, converts Hash value of record to JSON String. |
| insert_id_field | string | no | nil | Use key as `insert_id` of Streaming Insert API parameter. |
| request_timeout_sec | integer | no | nil | Bigquery API response timeout |
| request_open_timeout_sec | integer | 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) | nil | Type of bigquery time partitioning feature(experimental feature on BigQuery). |
| time_partitioning_expiration | time | no | nil | Expiration milliseconds for bigquery time partitioning. (experimental feature on BigQuery) |
### Standard Options
| name | type | required? | default | description |
| :------------------------------------- | :------------ | :----------- | :------------------------- | :----------------------- |
| localtime | bool | no | nil | Use localtime |
| utc | bool | no | nil | Use utc |
And see http://docs.fluentd.org/articles/output-plugin-overview#time-sliced-output-parameters
## Examples
### Streaming inserts
Configure insert specifications with target table schema, with your credentials. This is minimum configurations:
```apache
@type bigquery
method insert # default
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
time_format %s
time_field time
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"}
]
```
For high rate inserts over streaming inserts, you should specify flush intervals and buffer chunk options:
```apache
@type bigquery
method insert # default
flush_interval 1 # flush as frequent as possible
buffer_chunk_records_limit 300 # default rate limit for users is 100
buffer_queue_limit 10240 # 1MB * 10240 -> 10GB!
num_threads 16
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
time_format %s
time_field time
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"}
]
```
Important options for high rate events are:
* `tables`
* 2 or more tables are available with ',' separator
* `out_bigquery` uses these tables for Table Sharding inserts
* these must have same schema
* `buffer_chunk_limit`
* 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_bigquery` flushes buffer with 500 records for 1 inserts API call
* `buffer_queue_limit`
* BigQuery streaming inserts needs very small buffer chunks
* for high-rate events, `buffer_queue_limit` should be configured with big number
* Max 1GB memory may be used under network problem in default configuration
* `buffer_chunk_limit (default 1MB)` x `buffer_queue_limit (default 1024)`
* `num_threads`
* 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)
* `flush_interval`
* interval between data flushes (default 0.25)
* you can set subsecond values such as `0.15` on Fluentd v0.10.42 or later
See [Quota policy](https://cloud.google.com/bigquery/streaming-data-into-bigquery#quota)
section in the Google BigQuery document.
### Load
```apache
@type bigquery
method load
buffer_type file
buffer_path bigquery.*.buffer
flush_interval 1800
flush_at_shutdown true
try_flush_interval 1
utc
auth_method json_key
json_key json_key_path.json
time_format %s
time_field time
project yourproject_id
dataset yourdataset_id
auto_create_table true
table yourtable%{time_slice}
schema_path bq_schema.json
```
I recommend to use file buffer and long flush interval.
__CAUTION: `flush_interval` default is still `0.25` even if `method` is `load` on current version.__
### Authentication
There are four methods supported to fetch access token for the service account.
1. Public-Private key pair of GCP(Google Cloud Platform)'s service account
2. JSON key of GCP(Google Cloud Platform)'s service account
3. Predefined access token (Compute Engine only)
4. Google application default credentials (http://goo.gl/IUuyuX)
#### Public-Private key pair of GCP's service account
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.
#### JSON key of GCP(Google Cloud Platform)'s service account
You first need to create a service account (client ID),
download its JSON key and deploy the key with fluentd.
```apache
@type bigquery
auth_method json_key
json_key /home/username/.keys/00000000000000000000000000000000-jsonkey.json
project yourproject_id
dataset yourdataset_id
table tablename
...
```
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.
```apache
@type bigquery
auth_method json_key
json_key {"private_key": "-----BEGIN PRIVATE KEY-----\n...", "client_email": "xxx@developer.gserviceaccount.com"}
project yourproject_id
dataset yourdataset_id
table tablename
...
```
#### Predefined access token (Compute Engine only)
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.
```apache
@type bigquery
auth_method compute_engine
project yourproject_id
dataset yourdataset_id
table tablename
time_format %s
time_field time
...
```
#### Application default credentials
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.
1. The environment variable `GOOGLE_APPLICATION_CREDENTIALS` is checked. If this variable is specified it should point to a JSON key file that defines the credentials.
2. The environment variable `GOOGLE_PRIVATE_KEY` and `GOOGLE_CLIENT_EMAIL` are checked. If this variables are specified `GOOGLE_PRIVATE_KEY` should point to `private_key`, `GOOGLE_CLIENT_EMAIL` should point to `client_email` in a JSON key.
3. Well known path is checked. If file is exists, the file used as a JSON key file. This path is `$HOME/.config/gcloud/application_default_credentials.json`.
4. System default path is checked. If file is exists, the file used as a JSON key file. This path is `/etc/google/auth/application_default_credentials.json`.
5. If you are running in Google Compute Engine production, the built-in service account associated with the virtual machine instance will be used.
6. If none of these conditions is true, an error will occur.
### Table id formatting
#### strftime formatting
`table` and `tables` options accept [Time#strftime](http://ruby-doc.org/core-1.9.3/Time.html#method-i-strftime)
format to construct table ids.
Table ids are formatted at runtime
using the local time of the fluentd server.
For example, with the configuration below,
data is inserted into tables `accesslog_2014_08`, `accesslog_2014_09` and so on.
```apache
@type bigquery
...
project yourproject_id
dataset yourdataset_id
table accesslog_%Y_%m
...
```
#### record attribute formatting
The format can be suffixed with attribute name.
__NOTE: This feature is available only if `method` is `insert`. Because it makes performance impact. Use `%{time_slice}` instead of it.__
```apache
...
table accesslog_%Y_%m@timestamp
...
```
If attribute name is given, the time to be used for formatting is value of each row.
The value for the time should be a UNIX time.
#### time_slice_key formatting
Or, the options can use `%{time_slice}` placeholder.
`%{time_slice}` is replaced by formatted time slice key at runtime.
```apache
@type bigquery
...
table accesslog%{time_slice}
...
```
#### record attribute value formatting
Or, `${attr_name}` placeholder is available to use value of attribute as part of table id.
`${attr_name}` is replaced by string value of the attribute specified by `attr_name`.
__NOTE: This feature is available only if `method` is `insert`.__
```apache
...
table accesslog_%Y_%m_${subdomain}
...
```
For example value of `subdomain` attribute is `"bq.fluent"`, table id will be like "accesslog_2016_03_bqfluent".
- any type of attribute is allowed because stringified value will be used as replacement.
- acceptable characters are alphabets, digits and `_`. All other characters will be removed.
### Date partitioned table support
this plugin can insert (load) into date partitioned table.
Use `%{time_slice}`.
```apache
@type bigquery
...
time_slice_format %Y%m%d
table accesslog$%{time_slice}
...
```
But, Dynamic table creating doesn't support date partitioned table yet.
### Dynamic table creating
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`.
```apache
@type bigquery
...
auto_create_table true
table accesslog_%Y_%m
...
```
### Table schema
There are three methods to describe the schema of the target table.
1. List fields in fluent.conf
2. Load a schema file in JSON.
3. 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.
```apache
@type bigquery
...
time_format %s
time_field time
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"}
]
```
This schema accepts structured JSON data like:
```json
{
"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:
```apache
@type bigquery
...
time_format %s
time_field time
schema_path /path/to/httpd.schema
```
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:
```apache
@type bigquery
...
time_format %s
time_field time
fetch_schema true
# fetch_schema_table other_table # if you want to fetch schema from other table
```
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.
### Specifying insertId property
BigQuery uses `insertId` property to detect duplicate insertion requests (see [data consistency](https://cloud.google.com/bigquery/streaming-data-into-bigquery#dataconsistency) in Google BigQuery documents).
You can set `insert_id_field` option to specify the field to use as `insertId` property.
```apache
@type bigquery
...
insert_id_field uuid
schema [{"name": "uuid", "type": "STRING"}]
```
## TODO
* OAuth installed application credentials support
* Google API discovery expiration
* check row size limits
## Authors
* @tagomoris: First author, original version
* KAIZEN platform Inc.: Maintener, Since 2014.08.19
* @joker1007