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Posted to user@spark.apache.org by daily <as...@foxmail.com> on 2018/10/17 00:41:20 UTC
回复： SparkSQL read Hive transactional table
Hi,
Spark version: 2.3.0
Hive version: 2.1.0
Best regards.
------------------ 原始邮件 ------------------
发件人: "Gourav Sengupta"<go...@gmail.com>;
发送时间: 2018年10月16日(星期二) 晚上6:35
收件人: "daily"<as...@foxmail.com>;
抄送: "user"<us...@spark.apache.org>; "dev"<de...@spark.apache.org>;
主题: Re: SparkSQL read Hive transactional table
Hi,
can I please ask which version of Hive and Spark are you using?
Regards,
Gourav Sengupta
On Tue, Oct 16, 2018 at 2:42 AM daily <as...@foxmail.com> wrote:
Hi,
I use HCatalog Streaming Mutation API to write data to hive transactional table, and then, I use SparkSQL to read data from the hive transactional table. I get the right result.
However, SparkSQL uses more time to read hive orc bucket transactional table, beacause SparkSQL read all columns(not The columns involved in SQL) so it uses more time.
My question is why that SparkSQL read all columns of hive orc bucket transactional table, but not the columns involved in SQL? Is it possible to control the SparkSQL read the columns involved in SQL?
For example:
Hive Table:
create table dbtest.t_a1 (t0 VARCHAR(36),t1 string,t2 double,t5 int ,t6 int) partitioned by(sd string,st string) clustered by(t0) into 10 buckets stored as orc TBLPROPERTIES ('transactional'='true');
create table dbtest.t_a2 (t0 VARCHAR(36),t1 string,t2 double,t5 int ,t6 int) partitioned by(sd string,st string) clustered by(t0) into 10 buckets stored as orc TBLPROPERTIES ('transactional'='false');
SparkSQL:
select sum(t1),sum(t2) from dbtest.t_a1 group by t0;
select sum(t1),sum(t2) from dbtest.t_a2 group by t0;
SparkSQL's stage Input size:
dbtest.t_a1=113.9 GB,
dbtest.t_a2=96.5 MB
Best regards.
Re: SparkSQL read Hive transactional table
Posted by Gourav Sengupta <go...@gmail.com>.
Hi,
I think that the speed of ORC has been improved in latest versions. Any
chance you could use the latest version?
Regards,
Gourav Sengupta
On 17 Oct 2018 6:11 am, "daily" <as...@foxmail.com> wrote:
Hi,
Spark version: 2.3.0
Hive version: 2.1.0
Best regards.
------------------ 原始邮件 ------------------
*发件人:* "Gourav Sengupta"<go...@gmail.com>;
*发送时间:* 2018年10月16日(星期二) 晚上6:35
*收件人:* "daily"<as...@foxmail.com>;
*抄送:* "user"<us...@spark.apache.org>; "dev"<de...@spark.apache.org>;
*主题:* Re: SparkSQL read Hive transactional table
Hi,
can I please ask which version of Hive and Spark are you using?
Regards,
Gourav Sengupta
On Tue, Oct 16, 2018 at 2:42 AM daily <as...@foxmail.com> wrote:
> Hi,
>
> I use HCatalog Streaming Mutation API to write data to hive transactional
> table, and then, I use SparkSQL to read data from the hive transactional
> table. I get the right result.
> However, SparkSQL uses more time to read hive orc bucket transactional
> table, beacause SparkSQL read all columns(not The columns involved in SQL)
> so it uses more time.
> My question is why that SparkSQL read all columns of hive orc bucket
> transactional table, but not the columns involved in SQL? Is it possible to
> control the SparkSQL read the columns involved in SQL?
>
>
>
> For example:
> Hive Table:
> create table dbtest.t_a1 (t0 VARCHAR(36),t1 string,t2 double,t5 int ,t6
> int) partitioned by(sd string,st string) clustered by(t0) into 10 buckets
> stored as orc TBLPROPERTIES ('transactional'='true');
>
> create table dbtest.t_a2 (t0 VARCHAR(36),t1 string,t2 double,t5 int ,t6
> int) partitioned by(sd string,st string) clustered by(t0) into 10 buckets
> stored as orc TBLPROPERTIES ('transactional'='false');
>
> SparkSQL:
> select sum(t1),sum(t2) from dbtest.t_a1 group by t0;
> select sum(t1),sum(t2) from dbtest.t_a2 group by t0;
>
> SparkSQL's stage Input size:
>
> dbtest.t_a1=113.9 GB,
>
> dbtest.t_a2=96.5 MB
>
>
>
> Best regards.
>
>
>
>