# 队列Cohorts

• 每月硬盘驱动器故障统计信息（按月）Monthly hard drive failure statistics by month
• 每周供应商交付业绩（按周）Weekly supplier delivery performance by week
• 每月班级平均成绩（按月）Monthly average class GPA’s by month

## 数据格式Data format

SQL Analytics 要求输入示例包含以下字段：SQL Analytics expects your input samples to have the following fields:

• 队列日期：唯一标识队列的日期。Cohort Date: the date that uniquely identifies a cohort. 假设你要按注册日期来可视化显示每月的用户活动，则 2018 年 1 月注册的所有用户的队列日期为 2018 年 1 月 1 日。Suppose you’re visualizing monthly user activity by sign-up date, your cohort date for all users that signed-up in January 2018 would be January 1st, 2018. 2 月注册的所有用户的队列日期为 2018 年 2 月 1 日。The cohort date for any user that signed-up in February would be February 1st, 2018.
• 时间段：从队列日期到本示例为止经过的时间段计数。Period: a count of how many periods transpired since the cohort date as of this sample. 如果你要按注册月份对用户进行分组，则时间段将是自这些用户注册以来的月份计数。If you are grouping users by sign-up month, then your period will be the count of months since these users signed up. 在上例中，对 1 月注册的用户在 7 月的活动进行度量将得到时间段值 7，因为在 1 月与 7 月之间经过了 7 个时间段。In the above example, a measurement of activity in July for users that signed up in January would yield a period value of 7 because seven periods have transpired between January and July.
• 满足目标的计数：此队列在给定时间段内的表现的实际度量值。Count Satisfying Target: your actual measurement of this cohort’s performance in the given period. 在上例中，如果 1 月注册的 30 位用户在 7 月均有活动，则满足目标的计数将为 30。In the above example, if thirty users who signed up in January showed activity in July then the Count Satisfying Target would be 30.
• 总队列大小：SQL Analytics 将用于计算队列在给定时间段内目标满意度百分比的分母。Total Cohort Size: the denominator that SQL Analytics will use to calculate the percentage of a cohort’s target satisfaction for a given period. 继续上面的示例，如果有 72 位用户在 1 月注册，则总队列大小为 72。Continuing the example above, if seventy-two users signed up in January then the Total Cohort Size would be 72. 呈现可视化效果时，SQL Analytics 会将该值显示为 `41.67%` (`32 ÷ 72`)。When the visualization is rendered, SQL Analytics would display the value as `41.67%` (`32 ÷ 72`).