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Version: Java (Groovy)

rollup

The deephaven rollup method creates a rollup table from a source table given zero or more aggregations and zero or more grouping columns to create hierarchy from.

Syntax​

source.rollup(aggregations)
source.rollup(aggregations, includeConstituents)
source.rollup(aggregations, groupByColumns...)
source.rollup(aggregations, includeConstituents, groupByColumns...)

Parameters​

ParameterTypeDescription
aggregationsCollection<? extends Aggregation>

One or more aggregations.

includeConstituents optionalboolean

Whether or not to include constituent rows at the leaf level. The default value is False.

groupByColumnsString...

One or more columns to group on and create hierarchy from.

Methods​

Instance​

  • getAggregations - Get the aggregations performed in the rollup operation.
  • getGroupByColumns - Get the groupByColumns used for the rollup operation.
  • getLeafNodeType - Get the NodeType at the leaf level.
  • getNodeDefinition(nodeType) - Get the TableDefinition that should be exposed to node table consumers.
  • includesConstituents - Returns a boolean indicating whether or not the constituent rows at the lowest level are included.
  • makeNodeOperationsRecorder(nodeType) - Get a recorder for per-node operations to apply during snapshots of the requested NodeType.
  • translateAggregatedNodeOperationsForConstituentNodes(aggregatedNodeOperationsToTranslate) - Translate node operations for aggregated nodes to the closest equivalent for a constituent node.
  • withFilters(filters...) - Create a new rollup table that will apply a set of filters to the groupByColumns of the rollup table.
  • withNodeOperations(nodeOperations...) - Create a new rollup table that will apply the recorded operations to nodes when gathering snapshots.

Returns​

A rollup table.

Examples​

The following example creates a rollup table from a source table of insurance expense data. The aggregated average of the bmi and expenses columns are calculated, then the table is rolled up by the region and age column.

import static io.deephaven.csv.CsvTools.readCsv
import io.deephaven.api.agg.spec.AggSpec
import io.deephaven.api.agg.Aggregation

insurance = readCsv("https://media.githubusercontent.com/media/deephaven/examples/main/Insurance/csv/insurance.csv")

aggList = [Aggregation.of(AggSpec.avg(), "bmi", "expenses")]

insuranceRollup = insurance.rollup(aggList, false, "region", "age")

The following example creates a rollup table from real-time source data. The source data updates 10,000 times per second. The result rollup table can be expanded by the N column to show unique values of M for each N. The aggregated average and sum are calculated for the Value and Weight, respectively.

import io.deephaven.api.agg.spec.AggSpec
import io.deephaven.api.agg.Aggregation

aggList = [Aggregation.of(AggSpec.avg(), "Value"), Aggregation.of(AggSpec.sum(), "Weight")]

rows = emptyTable(1_000_000).updateView("Group = i", "N = i % 347", "M = i % 29")
changes = timeTable("PT00:00:00.0001").view("Group = i % 1_000_000", "LastModified = Timestamp", "Value = (i * Math.sin(i)) % 6977", "Weight = (i * Math.sin(i)) % 7151").lastBy("Group")

source = rows.join(changes, "Group")

result = source.rollup(aggList, false, "N", "M")

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