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https://issues.apache.org/jira/browse/SPARK-16275?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15400844#comment-15400844
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Xiao Li commented on SPARK-16275:
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Yeah, many queries are using it. Below is the list:
auto_join_nulls
auto_join0
auto_join1
auto_join2
auto_join3
auto_join4
auto_join5
auto_join6
auto_join7
auto_join8
auto_join9
auto_join10
auto_join11
auto_join12
auto_join13
auto_join14
auto_join14_hadoop20
auto_join15
auto_join17
auto_join18
auto_join19
auto_join20
auto_join22
auto_join25
auto_join30
auto_join31
correlationoptimizer1
correlationoptimizer2
correlationoptimizer3
correlationoptimizer4
multiMapJoin1
orc_dictionary_threshold
udf_hash
> Implement all the Hive fallback functions
> -----------------------------------------
>
> Key: SPARK-16275
> URL: https://issues.apache.org/jira/browse/SPARK-16275
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Reporter: Reynold Xin
>
> As of Spark 2.0, Spark falls back to Hive for only the following built-in
> functions:
> {code}
> "elt", "hash", "java_method", "histogram_numeric",
> "map_keys", "map_values",
> "parse_url", "percentile", "percentile_approx", "reflect", "sentences",
> "stack", "str_to_map",
> "xpath", "xpath_boolean", "xpath_double", "xpath_float", "xpath_int",
> "xpath_long",
> "xpath_number", "xpath_short", "xpath_string",
> // table generating function
> "inline", "posexplode"
> {code}
> The goal of the ticket is to implement all of these in Spark so we don't need
> to fall back into Hive's UDFs.
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