Hive，如何按具有 null 值的列进行分区，将所有空值放在一个分区中

Question

I am using Hive, and the IDE is Hue. I am trying different key combinations to choose for my partition key(s).

The definition of my original table is as follows:

CREATE External Table `my_hive_db`.`my_table`(
    `col_id` bigint,
    `result_section__col2` string,
    `result_section_col3` string ,
    `result_section_col4` string,
    `result_section_col5` string,
    `result_section_col6__label` string,
    `result_section_col7__label_id` bigint ,
    `result_section_text` string ,
    `result_section_unit` string,
    `result_section_col` string ,
    `result_section_title` string,
    `result_section_title_id` bigint,
    `col13` string,
    `timestamp` bigint,
    `date_day` string
    )
    PARTITIONED BY ( 
      `date_year` string, 
      `date_month` string)
    ROW FORMAT SERDE 
      'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' 
    STORED AS INPUTFORMAT 
      'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' 
    OUTPUTFORMAT 
      'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
    LOCATION
      's3a://some/where/in/amazon/s3'; 

The above code is working properly. But when I create a new table with date_day as partition key, the table is empty and I need to run MSCK Repair Table. However, I am getting the following error:

Error while compiling statement: FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.ddl.DDLTask

When the partion keys were date_year, date_month, MSCK worked properly.

Table definition of the table I am getting the error for is as follows:

CREATE External Table `my_hive_db`.`my_table`(
    `col_id` bigint,
    `result_section__col2` string,
    `result_section_col3` string ,
    `result_section_col4` string,
    `result_section_col5` string,
    `result_section_col6__label` string,
    `result_section_col7__label_id` bigint ,
    `result_section_text` string ,
    `result_section_unit` string,
    `result_section_col` string ,
    `result_section_title` string,
    `result_section_title_id` bigint,
    `col13` string,
    `timestamp` bigint,
    `date_year` string, 
    `date_month` string
  )
    PARTITIONED BY (
     `date_day` string)
    ROW FORMAT SERDE 
      'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' 
    STORED AS INPUTFORMAT 
      'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' 
    OUTPUTFORMAT 
      'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
    LOCATION
      's3a://some/where/in/amazon/s3'; 

After this the following query is empty:

Select * From `my_hive_db`.`my_table` Limit 10;

I therefore ran the following command:

MSCK REPAIR TABLE `my_hive_db`.`my_table`;

And I get the error: Error while compiling statement: FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.ddl.DDLTask

I checked this link as it is exactly the error I am getting, but by using the solution provided:

set hive.msck.path.validation=ignore;
MSCK REPAIR TABLE table_name;

I get a different error:

Error while processing statement: Cannot modify hive.msck.path.validation at runtime. It is not in list of params that are allowed to be modified at runtime.

I think the reason I am getting these errors is that there are more than 200 million records with date_day having null value.

There are 31 distinct date-day not null values. I would like to partition my table with 32 partitions, each for a distinct value of date_day field, and all the null values get into a different partition. Is there a way to do so (partitioning by a column with null values)?

If this can be achieved by spark, I am also open to use it.

This is part of a bigger problem of changing partition keys by recreating a table as mentioned in this link in answer to my other question .

Thank you for your help.

Answer 1

You seem to not understand how Hive's partitioning work. Hive stores data into files on HDFS (or S3, or some other distributed folders). If you create a non-partitioned parquet table called my_schema.my_table , you will see in your distributed storage files stored in a folder

hive/warehouse/my_schema.db/my_table/part_00001.parquet
hive/warehouse/my_schema.db/my_table/part_00002.parquet
...

If you create a table partitioned by a column p_col , the files will look like

hive/warehouse/my_schema.db/my_table/p_col=value1/part_00001.parquet
hive/warehouse/my_schema.db/my_table/p_col=value1/part_00002.parquet
...
hive/warehouse/my_schema.db/my_table/p_col=value2/part_00001.parquet
hive/warehouse/my_schema.db/my_table/p_col=value2/part_00002.parquet
...

The command MSCK repair table allows you to automatically reload the partitions, when you create an external table.

Let's say you have folders on s3 that look like this:

hive/warehouse/my_schema.db/my_table/p_col=value1/part_00001.parquet
hive/warehouse/my_schema.db/my_table/p_col=value2/part_00001.parquet
hive/warehouse/my_schema.db/my_table/p_col=value3/part_00001.parquet

You create an external table with

CREATE External Table my_schema.my_table(
   ... some columns ...
)
PARTITIONED BY (p_col STRING)

the table will be created but empty, because Hive hasn't detected the partitions yet. You run MSCK REPAIR TABLE my_schema.my_table , and Hive will recognize that your partition p_col matches the partitioning scheme on s3 ( /p_col=value1/ ).

From what I could understand from your other question , you are trying to change the partitioning scheme of the table by doing

CREATE External Table my_schema.my_table(
   ... some columns ...
)
PARTITIONED BY (p_another_col STRING)

and you are getting an error message because p_another_col doesn't match with the column used in s3, which was p_col . And this error is perfectly normal, since what you are doing doesn't make sense.

As stated in the other question's answer , you need to create a copy of the first table, with a different partitioning scheme.

You should instead try something like this:

CREATE External Table my_hive_db.my_table_2(
    `col_id` bigint,
    `result_section__col2` string,
    `result_section_col3` string ,
    `result_section_col4` string,
    `result_section_col5` string,
    `result_section_col6__label` string,
    `result_section_col7__label_id` bigint ,
    `result_section_text` string ,
    `result_section_unit` string,
    `result_section_col` string ,
    `result_section_title` string,
    `result_section_title_id` bigint,
    `col13` string,
    `timestamp` bigint,
    `date_year` string, 
    `date_month` string
)
PARTITIONED BY (`date_day` string)

and then populate your new table with dynamic partitioning

INSERT OVERWRITE TABLE my_hive_db.my_table_2 PARTITION(date_day)
SELECT 
  col_id,
  result_section__col2,
  result_section_col3,
  result_section_col4,
  result_section_col5,
  result_section_col6__label,
  result_section_col7__label_id,
  result_section_text,
  result_section_unit,
  result_section_col,
  result_section_title,
  result_section_title_id,
  col13,
  timestamp,
  date_year,
  date_month,
  date_day
FROM my_hive_db.my_table_1