Google 分析和 BigQuery 之间的会话不匹配，同时取消嵌套命中和 hits.product

Question

当我尝试运行以下查询时，我发现 Google 分析和 BQ 数据之间的数据存在 15% 的差异：

SELECT
  SUM(Sessions) AS Sessions
FROM (
  SELECT
    PARSE_DATE("%Y%m%d",
      date) AS DATE,
    COUNT(DISTINCT CONCAT(fullVisitorId,"-",CAST(visitStartTime AS STRING))) AS Sessions,
    (COUNT(DISTINCT
        CASE
          WHEN totals.bounces = 1 THEN CONCAT(fullVisitorId, CAST(visitStartTime AS STRING))
          ELSE NULL
        END ) / COUNT(DISTINCT CONCAT(fullVisitorId, CAST(visitStartTime AS STRING))))*100 AS Bounce_Rate,
    COUNT(DISTINCT hits.transaction.transactionId) AS Transactions,
    SUM(hits.transaction.transactionRevenue)/1000000 AS Revenue,
    SUM(p.productRevenue)/1000000 AS Product_Revenue,
    (COUNT(DISTINCT hits.transaction.transactionId) / COUNT(DISTINCT CONCAT(CAST(fullVisitorId AS STRING), CAST(visitStartTime AS STRING))))*100 AS Ecommerce_Conversion_Rate,
    (SUM(hits.transaction.transactionRevenue)/1000000)/COUNT(DISTINCT hits.transaction.transactionId) AS Avg_Order_Value,
    SUM(hits.item.itemQuantity) / COUNT(hits.transaction.transactionId) AS Avg_Quantity,
    device.deviceCategory AS DeviceCategory,
    channelGrouping AS DefaultChannelGrouping,
    CONCAT(trafficSource.source," / ",trafficSource.medium) AS Source_Medium
  FROM
    `[Project_ID].[Dataset].ga_sessions_2019*`,
    UNNEST(hits) AS hits,
    UNNEST(hits.product) AS p
  GROUP BY
    DATE,
    DeviceCategory,
    DefaultChannelGrouping,
    Source_Medium )
WHERE
  DATE BETWEEN "2019-11-17"
  AND "2019-11-23"

但是当我去掉 UNNEST(hits.product) AS p 时，我得到了较低级别的差异。我想知道如何 UNNEST hits 和 hits.product 数据一起

Answer 1

您正在与产品数组交叉连接。如果缺少产品数组，则交叉连接将导致 NULL - 有效地擦除整个命中，有时甚至是整个会话（如果只有一个没有产品信息的命中）。您必须 LEFT JOIN 使用产品数组以防止删除 hits/sessions.

FROM `[Project_ID].[Dataset].ga_sessions_2019*` AS t
  CROSS JOIN UNNEST(hits) AS h 
  LEFT JOIN  UNNEST(product) AS p

或简而言之

FROM `[Project_ID].[Dataset].ga_sessions_2019*` AS t, t.hits h LEFT JOIN h.product p

Google 分析和 BigQuery 之间的会话不匹配，同时取消嵌套命中和 hits.product

Sessions Mismatch between Google analytics and BigQuery while unnesting hits and hits.product together

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