aggregateStatistics

Calculates the requested statistics of the given column(s) in a given table.

The available statistics are: count (number of total objects), mean, stdv (standard deviation), variance, skew, kurtosis, sum, min, max, weighted_average, cardinality (unique count), estimated_cardinality, percentile, and percentile_rank.

Estimated cardinality is calculated by using the hyperloglog approximation technique.

Percentiles and percentile ranks are approximate and are calculated using the t-digest algorithm. They must include the desired percentile/percentile_rank. To compute multiple percentiles each value must be specified separately (i.e.\ ‘percentile(75.0),percentile(99.0),percentile_rank(1234.56),percentile_rank(-5)’).

A second, comma-separated value can be added to the percentile statistic to calculate percentile resolution, e.g., a 50th percentile with 200 resolution would be ‘percentile(50,200)’.

The weighted average statistic requires a weight column to be specified in weight_column_name. The weighted average is then defined as the sum of the products of columnName times the weight_column_name values divided by the sum of the weight_column_name values.

Additional columns can be used in the calculation of statistics via additional_column_names. Values in these columns will be included in the overall aggregate calculation–individual aggregates will not be calculated per additional column. For instance, requesting the count and mean of columnName x and additional_column_names y and z, where x holds the numbers 1-10, y holds 11-20, and z holds 21-30, would return the total number of x, y, and z values (30), and the single average value across all x, y, and z values (15.5).

The response includes a list of key/value pairs of each statistic requested and its corresponding value.

Calculates the requested statistics of the given column(s) in a given table.

The available statistics are: count (number of total objects), mean, stdv (standard deviation), variance, skew, kurtosis, sum, min, max, weighted_average, cardinality (unique count), estimated_cardinality, percentile, and percentile_rank.

Estimated cardinality is calculated by using the hyperloglog approximation technique.

Percentiles and percentile ranks are approximate and are calculated using the t-digest algorithm. They must include the desired percentile/percentile_rank. To compute multiple percentiles each value must be specified separately (i.e.\ ‘percentile(75.0),percentile(99.0),percentile_rank(1234.56),percentile_rank(-5)’).

A second, comma-separated value can be added to the percentile statistic to calculate percentile resolution, e.g., a 50th percentile with 200 resolution would be ‘percentile(50,200)’.

The weighted average statistic requires a weight column to be specified in weight_column_name. The weighted average is then defined as the sum of the products of columnName times the weight_column_name values divided by the sum of the weight_column_name values.

Additional columns can be used in the calculation of statistics via additional_column_names. Values in these columns will be included in the overall aggregate calculation–individual aggregates will not be calculated per additional column. For instance, requesting the count and mean of columnName x and additional_column_names y and z, where x holds the numbers 1-10, y holds 11-20, and z holds 21-30, would return the total number of x, y, and z values (30), and the single average value across all x, y, and z values (15.5).

The response includes a list of key/value pairs of each statistic requested and its corresponding value.

Calculates the requested statistics of the given column(s) in a given table.

The available statistics are: count (number of total objects), mean, stdv (standard deviation), variance, skew, kurtosis, sum, min, max, weighted_average, cardinality (unique count), estimated_cardinality, percentile, and percentile_rank.

Estimated cardinality is calculated by using the hyperloglog approximation technique.

Percentiles and percentile ranks are approximate and are calculated using the t-digest algorithm. They must include the desired percentile/percentile_rank. To compute multiple percentiles each value must be specified separately (i.e.\ ‘percentile(75.0),percentile(99.0),percentile_rank(1234.56),percentile_rank(-5)’).

A second, comma-separated value can be added to the percentile statistic to calculate percentile resolution, e.g., a 50th percentile with 200 resolution would be ‘percentile(50,200)’.

The weighted average statistic requires a weight column to be specified in weight_column_name. The weighted average is then defined as the sum of the products of columnName times the weight_column_name values divided by the sum of the weight_column_name values.

Additional columns can be used in the calculation of statistics via additional_column_names. Values in these columns will be included in the overall aggregate calculation–individual aggregates will not be calculated per additional column. For instance, requesting the count and mean of columnName x and additional_column_names y and z, where x holds the numbers 1-10, y holds 11-20, and z holds 21-30, would return the total number of x, y, and z values (30), and the single average value across all x, y, and z values (15.5).

The response includes a list of key/value pairs of each statistic requested and its corresponding value.

Calculates the requested statistics of the given column(s) in a given table.

The available statistics are: count (number of total objects), mean, stdv (standard deviation), variance, skew, kurtosis, sum, min, max, weighted_average, cardinality (unique count), estimated_cardinality, percentile, and percentile_rank.

Estimated cardinality is calculated by using the hyperloglog approximation technique.

Percentiles and percentile ranks are approximate and are calculated using the t-digest algorithm. They must include the desired percentile/percentile_rank. To compute multiple percentiles each value must be specified separately (i.e.\ ‘percentile(75.0),percentile(99.0),percentile_rank(1234.56),percentile_rank(-5)’).

A second, comma-separated value can be added to the percentile statistic to calculate percentile resolution, e.g., a 50th percentile with 200 resolution would be ‘percentile(50,200)’.

The weighted average statistic requires a weight column to be specified in weight_column_name. The weighted average is then defined as the sum of the products of columnName times the weight_column_name values divided by the sum of the weight_column_name values.

Additional columns can be used in the calculation of statistics via additional_column_names. Values in these columns will be included in the overall aggregate calculation–individual aggregates will not be calculated per additional column. For instance, requesting the count and mean of columnName x and additional_column_names y and z, where x holds the numbers 1-10, y holds 11-20, and z holds 21-30, would return the total number of x, y, and z values (30), and the single average value across all x, y, and z values (15.5).

The response includes a list of key/value pairs of each statistic requested and its corresponding value.