spark.sql.hive.convertMetastoreParquet is true. I can't repro the issue of
scanning all partitions now.. : P
Anyway, I found another email thread "Re: Spark Sql behaves strangely with
tables with a lot of partitions"
I observe the same issue as Jerrick, spark driver will call listStatus
for the
Is there any chance that " spark.sql.hive.convertMetastoreParquet" is
turned off?
Cheng
On 11/4/15 5:15 PM, Rex Xiong wrote:
Thanks Cheng Lian.
I found in 1.5, if I use spark to create this table with partition
discovery, the partition pruning can be performed, but for my old
table
SPARK-11153 should be irrelevant because you are filtering on a
partition key while SPARK-11153 is about Parquet filter push-down and
doesn't affect partition pruning.
Cheng
On 11/3/15 7:14 PM, Rex Xiong wrote:
We found the query performance is very poor due to this issue
We found the query performance is very poor due to this issue
https://issues.apache.org/jira/plugins/servlet/mobile#issue/SPARK-11153
We usually use filter on partition key, the date, it's in string type in
1.3.1 and works great.
But in 1.5, it needs to do parquet scan for all partitions.
Add back this thread to email list, forgot to reply all.
2015年10月31日 下午7:23,"Michael Armbrust" 写道:
> Not that I know of.
>
> On Sat, Oct 31, 2015 at 12:22 PM, Rex Xiong wrote:
>
>> Good to know that, will have a try.
>> So there is no easy way to
What Storage Format?
> On 30 Oct 2015, at 12:05, Rex Xiong wrote:
>
> Hi folks,
>
> I have a Hive external table with partitions.
> Every day, an App will generate a new partition day=-MM-dd stored by
> parquet and run add-partition Hive command.
> In some cases, we
Hi folks,
I have a Hive external table with partitions.
Every day, an App will generate a new partition day=-MM-dd stored by
parquet and run add-partition Hive command.
In some cases, we will add additional column to new partitions and update
Hive table schema, then a query across new and old
>
> We have tried schema merging feature, but it's too slow, there're hundreds
> of partitions.
>
Which version of Spark?