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https://issues.apache.org/jira/browse/SPARK-19881?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun updated SPARK-19881:
----------------------------------
Description:
## What changes were proposed in this pull request?
Since Spark 2.0.0, `SET` commands do not pass the values to HiveClient. In most
case, Spark handles well. However, for the dynamic partition insert, users meet
the following misleading situation.
{code}
scala> spark.range(1001).selectExpr("id as key", "id as
value").registerTempTable("t1001")
scala> sql("create table p (value int) partitioned by (key int)").show
scala> sql("insert into table p partition(key) select key, value from t1001")
org.apache.spark.SparkException:
Dynamic partition strict mode requires at least one static partition column.
To turn this off set hive.exec.dynamic.partition.mode=nonstrict
scala> sql("set hive.exec.dynamic.partition.mode=nonstrict")
scala> sql("insert into table p partition(key) select key, value from t1001")
org.apache.hadoop.hive.ql.metadata.HiveException:
Number of dynamic partitions created is 1001, which is more than 1000.
To solve this try to set hive.exec.max.dynamic.partitions to at least 1001.
scala> sql("set hive.exec.max.dynamic.partitions=1001")
scala> sql("set hive.exec.max.dynamic.partitions").show(false)
+--------------------------------+-----+
|key |value|
+--------------------------------+-----+
|hive.exec.max.dynamic.partitions|1001 |
+--------------------------------+-----+
scala> sql("insert into table p partition(key) select key, value from t1001")
org.apache.hadoop.hive.ql.metadata.HiveException:
Number of dynamic partitions created is 1001, which is more than 1000.
To solve this try to set hive.exec.max.dynamic.partitions to at least 1001.
{code}
The last error is the same with the previous one. `HiveClient` does not know
new value 1001. There is no way to change the default value of
`hive.exec.max.dynamic.partitions` of `HiveCilent` with `SET` command.
The root cause is that `hive` parameters are passed to `HiveClient` on
creating. So, the workaround is to use `--hiveconf` when starting
`spark-shell`. However, it is still unchangeable in `spark-shell`. We had
better handle this case without misleading error messages ending infinite loop.
was:
Currently, `SET` command does not pass the values to Hive. In most case, Spark
handles well. However, for the dynamic partition insert, users meet the
following situation.
{code}
scala> spark.range(1001).selectExpr("id as key", "id as
value").registerTempTable("t1001")
scala> sql("create table p (value int) partitioned by (key int)").show
scala> sql("insert into table p partition(key) select key, value from t1001")
org.apache.spark.SparkException: Dynamic partition strict mode requires at
least one static partition column. To turn this off set
hive.exec.dynamic.partition.mode=nonstrict
scala> sql("set hive.exec.dynamic.partition.mode=nonstrict")
scala> sql("insert into table p partition(key) select key, value from t1001")
org.apache.hadoop.hive.ql.metadata.HiveException: Number of dynamic partitions
created is 1001, which is more than 1000. To solve this try to set
hive.exec.max.dynamic.partitions to at least 1001.
scala> sql("set hive.exec.dynamic.partition.mode=1001")
scala> sql("insert into table p partition(key) select key, value from t1001")
org.apache.hadoop.hive.ql.metadata.HiveException: Number of dynamic partitions
created is 1001, which is more than 1000. To solve this try to set
hive.exec.max.dynamic.partitions to at least 1001.
<== Repeat the same error message.
{code}
The root cause is that `hive` parameters are passed to `HiveClient` on
creating. So, The workaround is using `--hiveconf`.
We had better handle this case without misleading error messages.
> Support Dynamic Partition Inserts params with SET command
> ---------------------------------------------------------
>
> Key: SPARK-19881
> URL: https://issues.apache.org/jira/browse/SPARK-19881
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0, 2.1.0
> Reporter: Dongjoon Hyun
> Priority: Minor
>
> ## What changes were proposed in this pull request?
> Since Spark 2.0.0, `SET` commands do not pass the values to HiveClient. In
> most case, Spark handles well. However, for the dynamic partition insert,
> users meet the following misleading situation.
> {code}
> scala> spark.range(1001).selectExpr("id as key", "id as
> value").registerTempTable("t1001")
> scala> sql("create table p (value int) partitioned by (key int)").show
> scala> sql("insert into table p partition(key) select key, value from t1001")
> org.apache.spark.SparkException:
> Dynamic partition strict mode requires at least one static partition column.
> To turn this off set hive.exec.dynamic.partition.mode=nonstrict
> scala> sql("set hive.exec.dynamic.partition.mode=nonstrict")
> scala> sql("insert into table p partition(key) select key, value from t1001")
> org.apache.hadoop.hive.ql.metadata.HiveException:
> Number of dynamic partitions created is 1001, which is more than 1000.
> To solve this try to set hive.exec.max.dynamic.partitions to at least 1001.
> scala> sql("set hive.exec.max.dynamic.partitions=1001")
> scala> sql("set hive.exec.max.dynamic.partitions").show(false)
> +--------------------------------+-----+
> |key |value|
> +--------------------------------+-----+
> |hive.exec.max.dynamic.partitions|1001 |
> +--------------------------------+-----+
> scala> sql("insert into table p partition(key) select key, value from t1001")
> org.apache.hadoop.hive.ql.metadata.HiveException:
> Number of dynamic partitions created is 1001, which is more than 1000.
> To solve this try to set hive.exec.max.dynamic.partitions to at least 1001.
> {code}
> The last error is the same with the previous one. `HiveClient` does not know
> new value 1001. There is no way to change the default value of
> `hive.exec.max.dynamic.partitions` of `HiveCilent` with `SET` command.
> The root cause is that `hive` parameters are passed to `HiveClient` on
> creating. So, the workaround is to use `--hiveconf` when starting
> `spark-shell`. However, it is still unchangeable in `spark-shell`. We had
> better handle this case without misleading error messages ending infinite
> loop.
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