fluent-plugin-bigquery

Fluentd output plugin to load/insert data into Google BigQuery.

• insert data over streaming inserts
  • for continuous real-time insertions
  • https://developers.google.com/bigquery/streaming-data-into-bigquery#usecases
• (NOT IMPLEMENTED) 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.

Configuration

Streaming inserts

Configure insert specifications with target table schema, with your credentials. This is minimum configurations:

<match dummy>
  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
  
  field_integer time,status,bytes
  field_string  rhost,vhost,path,method,protocol,agent,referer
  field_float   requesttime
  field_boolean bot_access,loginsession
</match>

For high rate inserts over streaming inserts, you should specify flush intervals and buffer chunk options:

<match dummy>
  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
  
  field_integer time,status,bytes
  field_string  rhost,vhost,path,method,protocol,agent,referer
  field_float   requesttime
  field_boolean bot_access,loginsession
</match>

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_records_limit
  • number of records over streaming inserts API call is limited as 100, per second, per table
  • default average rate limit is 100, and spike rate limit is 1000
  • out_bigquery flushes buffer with 100 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
  • 1 is lowest value, without patches on Fluentd v0.10.41 or earlier
  • see patches below

Authentication

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

1. Public-Private key pair
2. Predefined access token (Compute Engine only)

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.

On the other hand, you don't need to explicitly create a service account for fluentd when you run fluentd in Google Compute Engine. In this second 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
  
  auth_method compute_engine
  
  project yourproject_id
  dataset yourdataset_id
  table   tablename
  
  time_format %s
  time_field  time
  
  field_integer time,status,bytes
  field_string  rhost,vhost,path,method,protocol,agent,referer
  field_float   requesttime
  field_boolean bot_access,loginsession
</match>

Table id formatting

table and tables options accept Time#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.

<match dummy>
  type bigquery
  
  ...
  
  project yourproject_id
  dataset yourdataset_id
  table   accesslog_%Y_%m
  
  ...
</match>

Note that the timestamp of logs and the date in the table id do not always match, because there is a time lag between collection and transmission of logs.

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.

<match dummy>
  type bigquery
  
  ...
  
  time_format %s
  time_field  time
  
  field_integer time,response.status,response.bytes
  field_string  request.vhost,request.path,request.method,request.protocol,request.agent,request.referer,remote.host,remote.ip,remote.user
  field_float   request.time
  field_boolean request.bot_access,request.loginsession
</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
  
  ...
  
  time_format %s
  time_field  time
  
  schema_path /path/to/httpd.schema
  field_integer time
</match>

where /path/to/httpd.schema is a path to the JSON-encoded schema file which you used for creating the table on BigQuery.

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
  
  ...
  
  time_format %s
  time_field  time
  
  fetch_schema true
  field_integer time
</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.

Specifying insertId property

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.

<match dummy>
  type bigquery

  ...

  insert_id_field uuid
  field_string uuid
</match>

TODO

• support Load API
  • with automatically configured flush/buffer options
• support optional data fields
• support NULLABLE/REQUIRED/REPEATED field options in field list style of configuration
• OAuth installed application credentials support
• Google API discovery expiration
• Error classes
• check row size limits

Authors

• @tagomoris: First author, original version
• KAIZEN platform Inc.: Maintener, Since 2014.08.19