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// Code generated from OpenAPI specs by Databricks SDK Generator. DO NOT EDIT.

package com.databricks.sdk.service.catalog;

import com.databricks.sdk.support.Generated;
import com.databricks.sdk.support.ToStringer;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.Collection;
import java.util.Objects;

@Generated
public class MonitorMetric {
  /**
   * Jinja template for a SQL expression that specifies how to compute the metric. See [create
   * metric definition].
   *
   * [create metric definition]:
   * https://docs.databricks.com/en/lakehouse-monitoring/custom-metrics.html#create-definition
   */
  @JsonProperty("definition")
  private String definition;

  /**
   * A list of column names in the input table the metric should be computed for. Can use
   * ``":table"`` to indicate that the metric needs information from multiple columns.
   */
  @JsonProperty("input_columns")
  private Collection inputColumns;

  /** Name of the metric in the output tables. */
  @JsonProperty("name")
  private String name;

  /** The output type of the custom metric. */
  @JsonProperty("output_data_type")
  private String outputDataType;

  /**
   * Can only be one of ``"CUSTOM_METRIC_TYPE_AGGREGATE"``, ``"CUSTOM_METRIC_TYPE_DERIVED"``, or
   * ``"CUSTOM_METRIC_TYPE_DRIFT"``. The ``"CUSTOM_METRIC_TYPE_AGGREGATE"`` and
   * ``"CUSTOM_METRIC_TYPE_DERIVED"`` metrics are computed on a single table, whereas the
   * ``"CUSTOM_METRIC_TYPE_DRIFT"`` compare metrics across baseline and input table, or across the
   * two consecutive time windows. - CUSTOM_METRIC_TYPE_AGGREGATE: only depend on the existing
   * columns in your table - CUSTOM_METRIC_TYPE_DERIVED: depend on previously computed aggregate
   * metrics - CUSTOM_METRIC_TYPE_DRIFT: depend on previously computed aggregate or derived metrics
   */
  @JsonProperty("type")
  private MonitorMetricType typeValue;

  public MonitorMetric setDefinition(String definition) {
    this.definition = definition;
    return this;
  }

  public String getDefinition() {
    return definition;
  }

  public MonitorMetric setInputColumns(Collection inputColumns) {
    this.inputColumns = inputColumns;
    return this;
  }

  public Collection getInputColumns() {
    return inputColumns;
  }

  public MonitorMetric setName(String name) {
    this.name = name;
    return this;
  }

  public String getName() {
    return name;
  }

  public MonitorMetric setOutputDataType(String outputDataType) {
    this.outputDataType = outputDataType;
    return this;
  }

  public String getOutputDataType() {
    return outputDataType;
  }

  public MonitorMetric setType(MonitorMetricType typeValue) {
    this.typeValue = typeValue;
    return this;
  }

  public MonitorMetricType getType() {
    return typeValue;
  }

  @Override
  public boolean equals(Object o) {
    if (this == o) return true;
    if (o == null || getClass() != o.getClass()) return false;
    MonitorMetric that = (MonitorMetric) o;
    return Objects.equals(definition, that.definition)
        && Objects.equals(inputColumns, that.inputColumns)
        && Objects.equals(name, that.name)
        && Objects.equals(outputDataType, that.outputDataType)
        && Objects.equals(typeValue, that.typeValue);
  }

  @Override
  public int hashCode() {
    return Objects.hash(definition, inputColumns, name, outputDataType, typeValue);
  }

  @Override
  public String toString() {
    return new ToStringer(MonitorMetric.class)
        .add("definition", definition)
        .add("inputColumns", inputColumns)
        .add("name", name)
        .add("outputDataType", outputDataType)
        .add("typeValue", typeValue)
        .toString();
  }
}