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Druid Firehoses

Firehoses describe the data stream source. They are pluggable and thus the configuration schema can and will vary based on the type of the firehose.

Field	Type	Description	Required
type	String	Specifies the type of firehose. Each value will have its own configuration schema, firehoses packaged with Druid are described below.	yes

We describe the configuration of the Kafka firehose example, but there are other types available in Druid (see below).

consumerProps is a map of properties for the Kafka consumer. The JSON object is converted into a Properties object and passed along to the Kafka consumer.
feed is the feed that the Kafka consumer should read from.
parser represents a parser that knows how to convert from String representations into the required InputRow representation that Druid uses. This is a potentially reusable piece that can be found in many of the firehoses that are based on text streams. The spec in the example describes a JSON feed (new-line delimited objects), with a timestamp column called "timestamp" in ISO8601 format and that it should not include the dimension "value" when processing. More information about the options available for the parser are available below.

Available Firehoses

There are several firehoses readily available in Druid, some are meant for examples, others can be used directly in a production environment.

KafkaFirehose

This firehose acts as a Kafka consumer and ingests data from Kafka.

StaticS3Firehose

This firehose ingests events from a predefined list of S3 objects.

TwitterSpritzerFirehose

See Examples. This firehose connects directly to the twitter spritzer data stream.

RandomFirehose

See Examples. This firehose creates a stream of random numbers.

RabbitMqFirehose

This firehose ingests events from a define rabbit-mq queue.

LocalFirehose

This Firehose can be used to read the data from files on local disk. It can be used for POCs to ingest data on disk. A sample local firehose spec is shown below:

{
    "type"    : "local",
    "filter"   : "*.csv",
    "parser" : {
      "timestampSpec": {
        "column": "mytimestamp",
        "format": "yyyy-MM-dd HH:mm:ss"
      },
      "data": {
        "format": "csv",
        "columns": [...],
        "dimensions": [...]
      }
    }
}

property	description	required?
type	This should be "local".	yes
filter	A wildcard filter for files. See here for more information.	yes
data	A data spec similar to what is used for batch ingestion.	yes

IngestSegmentFirehose

This Firehose can be used to read the data from existing druid segments. It can be used ingest existing druid segments using a new schema and change the name, dimensions, metrics, rollup, etc. of the segment. A sample ingest firehose spec is shown below -

{
    "type"    : "ingestSegment",
    "dataSource"   : "wikipedia",
    "interval" : "2013-01-01/2013-01-02"
}

property	description	required?
type	ingestSegment. Type of firehose	yes
dataSource	A String defining the data source to fetch rows from, very similar to a table in a relational database	yes
interval	A String representing ISO-8601 Interval. This defines the time range to fetch the data over.	yes
dimensions	The list of dimensions to select. If left empty, no dimensions are returned. If left null or not defined, all dimensions are returned.	no
metrics	The list of metrics to select. If left empty, no metrics are returned. If left null or not defined, all metrics are selected.	no
filter	See Filters	yes

CombiningFirehose

This firehose can be used to combine and merge data from a list of different firehoses. This can be used to merge data from more than one firehoses.

{
    "type"  :   "combining",
    "delegates" : [ { firehose1 }, { firehose2 }, ..... ]
}

property	description	required?
type	combining	yes
delegates	list of firehoses to combine data from	yes

Parsing Data

There are several ways to parse data.

StringInputRowParser

This parser converts Strings.

MapInputRowParser

This parser converts flat, key/value pair maps.

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