Google analytics ga:ga_sessions_YYYMMDD中的itemQuantity(大查询)
我试图使用standardSQL复制GA数量度量(GA:itemQuantity),并查询GA导出到BigQuery日期分区表(GA_sessions_YYYYMMDD) 我尝试了以下操作,但“数量”始终为空:Google analytics ga:ga_sessions_YYYMMDD中的itemQuantity(大查询),google-analytics,google-bigquery,metrics,bigquery-standard-sql,Google Analytics,Google Bigquery,Metrics,Bigquery Standard Sql,我试图使用standardSQL复制GA数量度量(GA:itemQuantity),并查询GA导出到BigQuery日期分区表(GA_sessions_YYYYMMDD) 我尝试了以下操作,但“数量”始终为空: #standardSQL SELECT sum(hit.item.itemQuantity) as quantity FROM `precise-armor-133520.1500218.ga_sessions_20170801` t CROSS JOIN UNNEST(t.hits)
#standardSQL
SELECT
sum(hit.item.itemQuantity) as quantity
FROM `precise-armor-133520.1500218.ga_sessions_20170801` t
CROSS JOIN
UNNEST(t.hits) AS hit
order by 1 ASC;
其他指标工作正常,并与GA UI 100%匹配,因此我假设这不是数据导出问题。例如:
SELECT
sum( totals.totalTransactionRevenue ) as revenue, sum( totals.transactions ) as transactions
FROM `precise-armor-133520.1500218.ga_sessions_201708*` t
CROSS JOIN
UNNEST(t.hits) AS hit
group by `date`
order by `date` asc
这些总数分别与GA UI中的收入和交易(指标)相匹配
GA度量数量(GA:itemQuantity)的标准SQL查询是什么?这行吗
#standardSQL
SELECT
sku,
SUM(qtd) qtd
FROM(
SELECT
ARRAY(SELECT AS STRUCT productSKU sku, productQuantity qtd FROM UNNEST(hits), UNNEST(product) WHERE ecommerceAction.action_type = '6') data
FROM `precise-armor-133520.1500218.ga_sessions_20170801`
),
UNNEST(data)
GROUP BY sku
ORDER BY qtd DESC
LIMIT 1000
不确定您是如何unest
产品字段的,也许这可以解决您的问题。为了在每个日期匹配GA web UI中的“数量”,请使用以下标准SQL:
SELECT
SUM(product.productQuantity)
,`date`
FROM
`precise-armor-133520.1500218.ga_sessions_*`
,UNNEST(hits) AS hits
,UNNEST(hits.product) AS product
WHERE hits.eCommerceAction.action_type = "6"
and _TABLE_SUFFIX between '20170801' and FORMAT_DATE("%Y%m%d", CURRENT_DATE)
group by 2
order by 2 asc
我想知道
hits.product.productQuantity
是否有效?我也尝试过,并在下面的查询中以至少一个返回值结束,但是它与GA web UI中的度量“数量”不匹配。选择sum(p.productQuantity),d from(选择h.product,date
作为d fromprecise-armor-133520.1500218.ga_sessions_201710*
,UNNEST(hits)作为h),UNNEST(product)作为p按d分组按d排序按d排序你对大查询中的字段项数量有什么线索吗?在来这里寻找这个救命的问题之前,我已经浪费了好几个小时来寻找答案。