Mixing
Spark compression when writing to external Hive table
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I'm inserting into an external hive-parquet table from Spark 2.1 (using df.write.insertInto(...). By setting e.g.
spark.sql("SET spark.sql.parquet.compression.codec=GZIP")
I can switch between SNAPPY,GZIP and uncompressed. I can verify that the file size (and filename ending) is influenced by these settings. I get a file named e.g.
part-00000-5efbfc08-66fe-4fd1-bebb-944b34689e70.gz.parquet
However if I work with partitioned Hive table, this setting does not have any effect, the file size is always the same. In addition, the filename is always
part-00000
Now how can I change (or at least verify) the compression codec of the parquet files in the partitioned case?
My table is :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
WITH SERDEPROPERTIES (
'serialization.format' = '1'
)
STORED AS
INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
apache-spark hive parquet
asked Jan 3 at 14:03
Raphael RothRaphael Roth
12.9k54279
add a comment |
I'm inserting into an external hive-parquet table from Spark 2.1 (using df.write.insertInto(...). By setting e.g.
spark.sql("SET spark.sql.parquet.compression.codec=GZIP")
I can switch between SNAPPY,GZIP and uncompressed. I can verify that the file size (and filename ending) is influenced by these settings. I get a file named e.g.
part-00000-5efbfc08-66fe-4fd1-bebb-944b34689e70.gz.parquet
However if I work with partitioned Hive table, this setting does not have any effect, the file size is always the same. In addition, the filename is always
part-00000
Now how can I change (or at least verify) the compression codec of the parquet files in the partitioned case?
My table is :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
WITH SERDEPROPERTIES (
'serialization.format' = '1'
)
STORED AS
INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
apache-spark hive parquet
asked Jan 3 at 14:03
Raphael RothRaphael Roth
12.9k54279
add a comment |
I'm inserting into an external hive-parquet table from Spark 2.1 (using df.write.insertInto(...). By setting e.g.
spark.sql("SET spark.sql.parquet.compression.codec=GZIP")
I can switch between SNAPPY,GZIP and uncompressed. I can verify that the file size (and filename ending) is influenced by these settings. I get a file named e.g.
part-00000-5efbfc08-66fe-4fd1-bebb-944b34689e70.gz.parquet
However if I work with partitioned Hive table, this setting does not have any effect, the file size is always the same. In addition, the filename is always
part-00000
Now how can I change (or at least verify) the compression codec of the parquet files in the partitioned case?
My table is :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
WITH SERDEPROPERTIES (
'serialization.format' = '1'
)
STORED AS
INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
apache-spark hive parquet
asked Jan 3 at 14:03
Raphael RothRaphael Roth
12.9k54279
I'm inserting into an external hive-parquet table from Spark 2.1 (using df.write.insertInto(...). By setting e.g.
spark.sql("SET spark.sql.parquet.compression.codec=GZIP")
I can switch between SNAPPY,GZIP and uncompressed. I can verify that the file size (and filename ending) is influenced by these settings. I get a file named e.g.
part-00000-5efbfc08-66fe-4fd1-bebb-944b34689e70.gz.parquet
However if I work with partitioned Hive table, this setting does not have any effect, the file size is always the same. In addition, the filename is always
part-00000
Now how can I change (or at least verify) the compression codec of the parquet files in the partitioned case?
My table is :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
WITH SERDEPROPERTIES (
'serialization.format' = '1'
)
STORED AS
INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
apache-spark hive parquet
apache-spark hive parquet
asked Jan 3 at 14:03
Raphael RothRaphael Roth
12.9k54279
asked Jan 3 at 14:03
Raphael RothRaphael Roth
12.9k54279
edited Jan 10 at 8:18
Raphael Roth
asked Jan 3 at 14:03
Raphael RothRaphael Roth
12.9k54279
asked Jan 3 at 14:03
Raphael RothRaphael Roth
12.9k54279
asked Jan 3 at 14:03
Raphael RothRaphael Roth
12.9k54279
12.9k54279
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
As you create external table, I would proceed like this :
First write your parquet dataset with the required compression:
df.write
.partitionBy("year")
.option("compression","<gzip|snappy|none>")
.parquet("<parquet_file_path>")
you can check as before with the file extension.
Then,you can create your external table as follow :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
STORED AS PARQUET
LOCATION '<parquet_file_path>';
If the external table already exists in Hive, you just need to run to refresh your table:
MSCK REPAIR TABLE test;
answered Jan 15 at 15:12
NonontbNonontb
22138
I was asking about inserting into an existing table
– Raphael Roth
Jan 15 at 17:09
UsingSaveMode.Appendwould add some new files with new data and usingMSCK REPAIR TABLEwill refresh the table. But ok I don't use insertInto method
– Nonontb
Jan 15 at 20:42
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
As you create external table, I would proceed like this :
First write your parquet dataset with the required compression:
df.write
.partitionBy("year")
.option("compression","<gzip|snappy|none>")
.parquet("<parquet_file_path>")
you can check as before with the file extension.
Then,you can create your external table as follow :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
STORED AS PARQUET
LOCATION '<parquet_file_path>';
If the external table already exists in Hive, you just need to run to refresh your table:
MSCK REPAIR TABLE test;
answered Jan 15 at 15:12
NonontbNonontb
22138
I was asking about inserting into an existing table
– Raphael Roth
Jan 15 at 17:09
UsingSaveMode.Appendwould add some new files with new data and usingMSCK REPAIR TABLEwill refresh the table. But ok I don't use insertInto method
– Nonontb
Jan 15 at 20:42
add a comment |
As you create external table, I would proceed like this :
First write your parquet dataset with the required compression:
df.write
.partitionBy("year")
.option("compression","<gzip|snappy|none>")
.parquet("<parquet_file_path>")
you can check as before with the file extension.
Then,you can create your external table as follow :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
STORED AS PARQUET
LOCATION '<parquet_file_path>';
If the external table already exists in Hive, you just need to run to refresh your table:
MSCK REPAIR TABLE test;
answered Jan 15 at 15:12
NonontbNonontb
22138
I was asking about inserting into an existing table
– Raphael Roth
Jan 15 at 17:09
UsingSaveMode.Appendwould add some new files with new data and usingMSCK REPAIR TABLEwill refresh the table. But ok I don't use insertInto method
– Nonontb
Jan 15 at 20:42
add a comment |
As you create external table, I would proceed like this :
First write your parquet dataset with the required compression:
df.write
.partitionBy("year")
.option("compression","<gzip|snappy|none>")
.parquet("<parquet_file_path>")
you can check as before with the file extension.
Then,you can create your external table as follow :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
STORED AS PARQUET
LOCATION '<parquet_file_path>';
If the external table already exists in Hive, you just need to run to refresh your table:
MSCK REPAIR TABLE test;
answered Jan 15 at 15:12
NonontbNonontb
22138
As you create external table, I would proceed like this :
First write your parquet dataset with the required compression:
df.write
.partitionBy("year")
.option("compression","<gzip|snappy|none>")
.parquet("<parquet_file_path>")
you can check as before with the file extension.
Then,you can create your external table as follow :
CREATE EXTERNAL TABLE `test`(`const` string, `x` int)
PARTITIONED BY (`year` int)
STORED AS PARQUET
LOCATION '<parquet_file_path>';
If the external table already exists in Hive, you just need to run to refresh your table:
MSCK REPAIR TABLE test;
answered Jan 15 at 15:12
NonontbNonontb
22138
answered Jan 15 at 15:12
NonontbNonontb
22138
answered Jan 15 at 15:12
NonontbNonontb
22138
answered Jan 15 at 15:12
NonontbNonontb
22138
22138
I was asking about inserting into an existing table
– Raphael Roth
Jan 15 at 17:09
UsingSaveMode.Appendwould add some new files with new data and usingMSCK REPAIR TABLEwill refresh the table. But ok I don't use insertInto method
– Nonontb
Jan 15 at 20:42
add a comment |
I was asking about inserting into an existing table
– Raphael Roth
Jan 15 at 17:09
UsingSaveMode.Appendwould add some new files with new data and usingMSCK REPAIR TABLEwill refresh the table. But ok I don't use insertInto method
– Nonontb
Jan 15 at 20:42
I was asking about inserting into an existing table
– Raphael Roth
Jan 15 at 17:09
I was asking about inserting into an existing table
– Raphael Roth
Jan 15 at 17:09
Using
SaveMode.Append would add some new files with new data and using MSCK REPAIR TABLE will refresh the table. But ok I don't use insertInto method– Nonontb
Jan 15 at 20:42
Using
SaveMode.Append would add some new files with new data and using MSCK REPAIR TABLE will refresh the table. But ok I don't use insertInto method– Nonontb
Jan 15 at 20:42
add a comment |
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