druid/docs/querying/segmentmetadataquery.md

id	title	sidebar_label
segmentmetadataquery	SegmentMetadata queries	SegmentMetadata

Segment metadata queries return per-segment information about:

• Cardinality of all columns in the segment
• Min/max values of string type columns in the segment
• Estimated byte size for the segment columns if they were stored in a flat format
• Number of rows stored inside the segment
• Interval the segment covers
• Column type of all the columns in the segment
• Estimated total segment byte size in if it was stored in a flat format
• Is the segment rolled up
• Segment id

{
  "queryType":"segmentMetadata",
  "dataSource":"sample_datasource",
  "intervals":["2013-01-01/2014-01-01"]
}

There are several main parts to a segment metadata query:

property	description	required?
queryType	This String should always be "segmentMetadata"; this is the first thing Apache Druid looks at to figure out how to interpret the query	yes
dataSource	A String or Object defining the data source to query, very similar to a table in a relational database. See DataSource for more information.	yes
intervals	A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over.	no
toInclude	A JSON Object representing what columns should be included in the result. Defaults to "all".	no
merge	Merge all individual segment metadata results into a single result	no
context	See Context	no
analysisTypes	A list of Strings specifying what column properties (e.g. cardinality, size) should be calculated and returned in the result. Defaults to ["cardinality", "interval", "minmax"], but can be overridden with using the segment metadata query config. See section analysisTypes for more details.	no
lenientAggregatorMerge	If true, and if the "aggregators" analysisType is enabled, aggregators will be merged leniently. See below for details.	no

The format of the result is:

[ {
  "id" : "some_id",
  "intervals" : [ "2013-05-13T00:00:00.000Z/2013-05-14T00:00:00.000Z" ],
  "columns" : {
    "__time" : { "type" : "LONG", "hasMultipleValues" : false, "size" : 407240380, "cardinality" : null, "errorMessage" : null },
    "dim1" : { "type" : "STRING", "hasMultipleValues" : false, "size" : 100000, "cardinality" : 1944, "errorMessage" : null },
    "dim2" : { "type" : "STRING", "hasMultipleValues" : true, "size" : 100000, "cardinality" : 1504, "errorMessage" : null },
    "metric1" : { "type" : "FLOAT", "hasMultipleValues" : false, "size" : 100000, "cardinality" : null, "errorMessage" : null }
  },
  "aggregators" : {
    "metric1" : { "type" : "longSum", "name" : "metric1", "fieldName" : "metric1" }
  },
  "queryGranularity" : {
    "type": "none"
  },
  "size" : 300000,
  "numRows" : 5000000
} ]

Dimension columns will have type STRING. Metric columns will have type FLOAT or LONG or name of the underlying complex type such as hyperUnique in case of COMPLEX metric. Timestamp column will have type LONG.

If the errorMessage field is non-null, you should not trust the other fields in the response. Their contents are undefined.

Only columns which are dimensions (i.e., have type STRING) will have any cardinality. Rest of the columns (timestamp and metric columns) will show cardinality as null.

intervals

If an interval is not specified, the query will use a default interval that spans a configurable period before the end time of the most recent segment.

The length of this default time period is set in the Broker configuration via: druid.query.segmentMetadata.defaultHistory

toInclude

There are 3 types of toInclude objects.

All

The grammar is as follows:

"toInclude": { "type": "all"}

None

The grammar is as follows:

"toInclude": { "type": "none"}

List

The grammar is as follows:

"toInclude": { "type": "list", "columns": [<string list of column names>]}

analysisTypes

This is a list of properties that determines the amount of information returned about the columns, i.e. analyses to be performed on the columns.

By default, the "cardinality", "interval", and "minmax" types will be used. If a property is not needed, omitting it from this list will result in a more efficient query.

The default analysis types can be set in the Broker configuration via: druid.query.segmentMetadata.defaultAnalysisTypes

Types of column analyses are described below:

cardinality

• cardinality in the result will return the estimated floor of cardinality for each column. Only relevant for dimension columns.

minmax

• Estimated min/max values for each column. Only relevant for dimension columns.

size

• size in the result will contain the estimated total segment byte size as if the data were stored in text format

interval

• intervals in the result will contain the list of intervals associated with the queried segments.

timestampSpec

• timestampSpec in the result will contain timestampSpec of data stored in segments. this can be null if timestampSpec of segments was unknown or unmergeable (if merging is enabled).

queryGranularity

• queryGranularity in the result will contain query granularity of data stored in segments. this can be null if query granularity of segments was unknown or unmergeable (if merging is enabled).

aggregators

• aggregators in the result will contain the list of aggregators usable for querying metric columns. This may be null if the aggregators are unknown or unmergeable (if merging is enabled).

• Merging can be strict or lenient. See lenientAggregatorMerge below for details.

• The form of the result is a map of column name to aggregator.

rollup

• rollup in the result is true/false/null.
• When merging is enabled, if some are rollup, others are not, result is null.

lenientAggregatorMerge

Conflicts between aggregator metadata across segments can occur if some segments have unknown aggregators, or if two segments use incompatible aggregators for the same column (e.g. longSum changed to doubleSum).

Aggregators can be merged strictly (the default) or leniently. With strict merging, if there are any segments with unknown aggregators, or any conflicts of any kind, the merged aggregators list will be null. With lenient merging, segments with unknown aggregators will be ignored, and conflicts between aggregators will only null out the aggregator for that particular column.

In particular, with lenient merging, it is possible for an individual column's aggregator to be null. This will not occur with strict merging.