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https://issues.apache.org/jira/browse/SPARK-19553?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15870780#comment-15870780
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Nicholas Chammas edited comment on SPARK-19553 at 3/14/17 2:38 PM:
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The utility of 1) would be being able to count items instead of distinct items,
unless I misunderstood what you're saying. I would imagine that just counting
items (as opposed to distinct items) would be cheaper, in addition to being
semantically different.
-I'll open a PR for 3), unless someone else wants to step in and do that.-
was (Author: nchammas):
The utility of 1) would be being able to count items instead of distinct items,
unless I misunderstood what you're saying. I would imagine that just counting
items (as opposed to distinct items) would be cheaper, in addition to being
semantically different.
I'll open a PR for 3), unless someone else wants to step in and do that.
> Add GroupedData.countApprox()
> -----------------------------
>
> Key: SPARK-19553
> URL: https://issues.apache.org/jira/browse/SPARK-19553
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Nicholas Chammas
> Priority: Minor
>
> We already have a
> [{{pyspark.sql.functions.approx_count_distinct()}}|http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.functions.approx_count_distinct]
> that can be applied to grouped data, but it seems odd that you can't just
> get regular approximate count for grouped data.
> I imagine the API would mirror that for
> [{{RDD.countApprox()}}|http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.countApprox],
> but I'm not sure:
> {code}
> (df
> .groupBy('col1')
> .countApprox(timeout=300, confidence=0.95)
> .show())
> {code}
> Or, if we want to mirror the {{approx_count_distinct()}} function, we can do
> that too. I'd want to understand why that function doesn't take a timeout or
> confidence parameter, though. Also, what does {{rsd}} mean? It's not
> documented.
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