Skip to main content


Columns visualization is built on top of its query engine. For most of the time, your query is composed by

  • list of metrics: a metric is a value or aggregated value from your data set.
  • list of dimensions: a dimension is a key by which you can break metric value into.
  • list of filters: a filter is a condition where you placed to filter your data out.

Metric (value)

A metric is a number, through adding options in metrics, you can find

  • distinct count of one column
  • count of rows
  • aggregation of a number column
    • sum: total value
    • avg: average value
    • min/max: min/max value of a column
    • percentiles: various percentile value for given column

Dimension (key)

It's usually a string/text column, but you can group your selected metrics above into any dimensions.

An important feature here is pivot, pivot is the way you organize values into one dimension while breaking down another. Pivot function/icon only available to a dimension when:

  • there is only one metric in selection.
  • there are multiple dimensions

When pivoting by one dimension, other dimensions will be combined as combined dimensions.


Columns supports query filter and in-graph filter, please make sure not get confused.

In-graph filter is the filter component that goes with your graph, and viewers and toggle on/off to slice the view. Query filter is the conditions you set that will filter your data before computing keys/metrics results for you.

Query filter supports all the operations mostly aligned with standard SQL, you can use % as wildcard for regular expression matching. Here is the list

  • equals (=): a field value has to equal to the specified value.
  • not equals(!=): a field value has to not equal to the specified value.
  • in: same as equals, but multiple options of specified values.
  • not in: same as not equals, but multiple options of specified values.
  • is empty: simply for string values, if it's empty value or no value.
  • is not empty: opposite of is empty
  • is like (matches): field value contains the specified value, support wildcard %
  • is unlike (not matches): field value does not contain the specified value, support wildcard %.
  • is ilike (matches igoring case): it's case insensitive matches.
  • is iunlike (not matches ignoring case): it's case insenstiive matches not.