Google BigQuery output plugin

This Logstash plugin uploads events to Google BigQuery using the streaming API so data can become available to query nearly immediately.

You can configure it to flush periodically, after N events or after a certain amount of data is ingested.

You must enable BigQuery on your Google Cloud account and create a dataset to hold the tables this plugin generates.

You must also grant the service account this plugin uses access to the dataset.

You can use Logstash conditionals and multiple configuration blocks to upload events with different structures.

This is an example of Logstash config:

output {
   google_bigquery {
     project_id => "folkloric-guru-278"                        (required)
     dataset => "logs"                                         (required)
     csv_schema => "path:STRING,status:INTEGER,score:FLOAT"    (required) 
     json_key_file => "/path/to/key.json"                      (optional) 
     error_directory => "/tmp/bigquery-errors"                 (required)
     date_pattern => "%Y-%m-%dT%H:00"                          (optional)
     flush_interval_secs => 30                                 (optional)
   }
}

The maximum number of messages to upload at a single time. This number must be < 10,000. Batching can increase performance and throughput to a point, but at the cost of per-request latency. Too few rows per request and the overhead of each request can make ingestion inefficient. Too many rows per request and the throughput may drop. BigQuery recommends using about 500 rows per request, but experimentation with representative data (schema and data sizes) will help you determine the ideal batch size.

An approximate number of bytes to upload as part of a batch. This number should be < 10MB or inserts may fail.

Schema for log data. It must follow the format name1:type1(,name2:type2)*. For example, path:STRING,status:INTEGER,score:FLOAT.

The BigQuery dataset the tables for the events will be added to.

Time pattern for BigQuery table, defaults to hourly tables. Must Time.strftime patterns: www.ruby-doc.org/core-2.0/Time.html#method-i-strftime

The location to store events that could not be uploaded due to errors. By default if any message in an insert is invalid all will fail. You can use skip_invalid_rows to allow partial inserts.

Consider using an additional Logstash input to pipe the contents of these to an alert platform so you can manually fix the events.

Or use GCS FUSE to transparently upload to a GCS bucket.

Files names follow the pattern [table name]-[UNIX timestamp].log

Uploads all data this often even if other upload criteria aren’t met.

Indicates if BigQuery should ignore values that are not represented in the table schema. If true, the extra values are discarded. If false, BigQuery will reject the records with extra fields and the job will fail. The default value is false.

mutate {
    remove_field => ["@version","@timestamp","path","host","type", "message"]
}

If Logstash is running within Google Compute Engine, the plugin can use GCE’s Application Default Credentials. Outside of GCE, you will need to specify a Service Account JSON key file.

Schema for log data as a hash. These can include nested records, descriptions, and modes.

Example:

json_schema => {
  fields => [{
    name => "endpoint"
    type => "STRING"
    description => "Request route"
  }, {
    name => "status"
    type => "INTEGER"
    mode => "NULLABLE"
  }, {
    name => "params"
    type => "RECORD"
    mode => "REPEATED"
    fields => [{
      name => "key"
      type => "STRING"
     }, {
      name => "value"
      type => "STRING"
    }]
  }]
}

Google Cloud Project ID (number, not Project Name!).

Insert all valid rows of a request, even if invalid rows exist. The default value is false, which causes the entire request to fail if any invalid rows exist.

BigQuery table ID prefix to be used when creating new tables for log data. Table name will be <table_prefix><table_separator><date>

BigQuery table separator to be added between the table_prefix and the date suffix.

Uploader interval when uploading new files to BigQuery. Adjust time based on your time pattern (for example, for hourly files, this interval can be around one hour).

The codec used for output data. Output codecs are a convenient method for encoding your data before it leaves the output without needing a separate filter in your Logstash pipeline.

Disable or enable metric logging for this specific plugin instance. By default we record all the metrics we can, but you can disable metrics collection for a specific plugin.

Add a unique ID to the plugin configuration. If no ID is specified, Logstash will generate one. It is strongly recommended to set this ID in your configuration. This is particularly useful when you have two or more plugins of the same type. For example, if you have 2 google_bigquery outputs. Adding a named ID in this case will help in monitoring Logstash when using the monitoring APIs.

output {
  google_bigquery {
    id => "my_plugin_id"
  }
}