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Updating Existing Data

Once you ingest some data in a dataSource for an interval and create Druid segments, you might want to make changes to the ingested data. There are several ways this can be done.

Updating Dimension Values

If you have a dimension where values need to be updated frequently, try first using lookups. A classic use case of lookups is when you have an ID dimension stored in a Druid segment, and want to map the ID dimension to a human-readable String value that may need to be updated periodically.

Rebuilding Segments (Reindexing)

If lookups are not sufficient, you can entirely rebuild Druid segments for specific intervals of time. Rebuilding a segment is known as reindexing the data. For example, if you want to add or remove columns from your existing segments, or you want to change the rollup granularity of your segments, you will have to reindex your data.

We recommend keeping a copy of your raw data around in case you ever need to reindex your data.

Dealing with Delayed Events (Delta Ingestion)

If you have a batch ingestion pipeline and have delayed events come in and want to append these events to existing segments and avoid the overhead of rebuilding new segments with reindexing, you can use delta ingestion.

Reindexing and Delta Ingestion with Hadoop Batch Ingestion

This section assumes the reader understands how to do batch ingestion using Hadoop. See batch-ingestion for more information. Hadoop batch-ingestion can be used for reindexing and delta ingestion.

Druid uses an inputSpec in the ioConfig to know where the data to be ingested is located and how to read it. For simple Hadoop batch ingestion, static or granularity spec types allow you to read data stored in deep storage.

There are other types of inputSpec to enable reindexing and delta ingestion.

`dataSource`

This is a type of inputSpec that reads data already stored inside Druid.

Field	Type	Description	Required
type	String.	This should always be 'dataSource'.	yes
ingestionSpec	JSON object.	Specification of Druid segments to be loaded. See below.	yes
maxSplitSize	Number	Enables combining multiple segments into single Hadoop InputSplit according to size of segments. Default is none.	no

Here is what goes inside ingestionSpec:

Field	Type	Description	Required
dataSource	String	Druid dataSource name from which you are loading the data.	yes
intervals	List	A list of strings representing ISO-8601 Intervals.	yes
granularity	String	Defines the granularity of the query while loading data. Default value is "none". See Granularities.	no
filter	JSON	See Filters	no
dimensions	Array of String	Name of dimension columns to load. By default, the list will be constructed from parseSpec. If parseSpec does not have an explicit list of dimensions then all the dimension columns present in stored data will be read.	no
metrics	Array of String	Name of metric columns to load. By default, the list will be constructed from the "name" of all the configured aggregators.	no
ignoreWhenNoSegments	boolean	Whether to ignore this ingestionSpec if no segments were found. Default behavior is to throw error when no segments were found.	no

For example

"ioConfig" : {
  "type" : "hadoop",
  "inputSpec" : {
    "type" : "dataSource",
    "ingestionSpec" : {
      "dataSource": "wikipedia",
      "intervals": ["2014-10-20T00:00:00Z/P2W"]
    }
  },
  ...
}

`multi`

This is a composing inputSpec to combine other inputSpecs. This inputSpec is used for delta ingestion. Please note that delta ingestion is not an idempotent operation. We may add change things in future to make it idempotent.

Field	Type	Description	Required
children	Array of JSON objects	List of JSON objects containing other inputSpecs.	yes

For example:

"ioConfig" : {
  "type" : "hadoop",
  "inputSpec" : {
    "type" : "multi",
    "children": [
      {
        "type" : "dataSource",
        "ingestionSpec" : {
          "dataSource": "wikipedia",
          "intervals": ["2014-10-20T00:00:00Z/P2W"]
        }
      },
      {
        "type" : "static",
        "paths": "/path/to/more/wikipedia/data/"
      }
    ]  
  },
  ...
}

Reindexing without Hadoop Batch Ingestion

This section assumes the reader understands how to do batch ingestion without Hadoop using the IndexTask,
which uses a "firehose" to know where and how to read the input data. IngestSegmentFirehose can be used to read data from segments inside Druid. Note that IndexTask is to be used for prototyping purposes only as it has to do all processing inside a single process and can't scale. Please use Hadoop batch ingestion for production scenarios dealing with more than 1GB of data.

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