![]() In case when the property was ABCDE the query didn't failed, but table wasn't been created. create table NEW_TABLE stored as parquet tblproperties ('pression'='ABCDE') as select * from OLD_TABLE Note: I tried to run the same query directly from Hive and in case when the property was equals to SNAPPY table was created successfully with proper compression (i.e. Example: Writing query results to a different format. Example: Specifying data storage and compression formats. CREATE TABLE (ORC) In TBLPROPERTIES, specify orc.compress NONE. Example: Creating an empty copy of an existing table. Step1 : Create a temporary table in Hive Step 2: Create a ORC foramtted table in Hive Step 3: Load data to ORC table from the Temp table Step 4: drop the temporary table. Use Snappy if you can handle higher disk usage for the performance benefits (lower CPU + Splittable). Learn about the compression support in Athena for various storage file formats. Example: Selecting specific columns from one or more tables. This make me think that TBLPROPERTIES are just ignored by Spark SQL. Example: Duplicating a table by selecting all columns. ABCDE the code still works fine with exception that compression is still GZIP: hiveContext.sql("create table NEW_TABLE stored as parquet tblproperties ('pression'='ABCDE') as select * from OLD_TABLE")Īnd Hue "Metastore Tables" -> TABLE -> "Properties" shows: | Parameter | Value | If I change SNAPPY to any other string e.g. Orc package is built in Spark thus there is no need to install the package like Avro format: spark-submit orc-example.py Once the script is executed successfully, the script will create data in the local file system as the screenshot shows: About. The following code creates table in PARQUET format, but with GZIP compression: hiveContext.sql("create table NEW_TABLE stored as parquet tblproperties ('pression'='SNAPPY') as select * from OLD_TABLE")īut in the Hue "Metastore Tables" -> TABLE -> "Properties" it still shows: | Parameter | Value | I have a hive managed partition table (4 partitions) which has 2TB of data and it is stored as ORC tables with no compression. OUTPUTFORMAT '.ql.io.parquet.I need to create a Hive table from Spark SQL which will be in the PARQUET format and SNAPPY compression. Below is a sample DataFrame we use to create an ORC file. Note currently Copy activity doesn't support LZO when read/write ORC files. Supported types are none, zlib, snappy (default), and lzo. When reading from ORC files, Data Factories automatically determine the compression codec based on the file metadata. Since we don’t have an ORC file to read, first will create an ORC file from the DataFrame. The compression codec to use when writing to ORC files. INPUTFORMAT '.ql.io.parquet.MapredParquetInputFormat' SNAPPY ZLIB LZO NONE Create a DataFrame Spark by default supports ORC file formats without importing third party ORC dependencies. The best format for performance is parquet with snappy compression, which is the default in Spark 2.x. CREATE TABLE encrypted ( ssn STRING, email STRING, name STRING ) USING ORC OPTIONS ( .path 'kms://httplocalhost:9600/kms', 'hadoop', orc.encrypt 'pii:ssn,email', orc. I noticed that while storing ORC file I did not provide compress option and I used option(compression, snappy) while saving the file and it appears the. Spark can be extended to support many more formats with external data sources - for more information, see Apache Spark packages. My table is : CREATE EXTERNAL TABLE `test`(`const` string, `x` int) Spark supports many formats, such as csv, json, xml, parquet, orc, and avro. ![]() ![]() Now how can I change (or at least verify) the compression codec of the parquet files in the partitioned case? However if I work with partitioned Hive table, this setting does not have any effect, the file size is always the same. I can verify that the file size (and filename ending) is influenced by these settings. I can switch between SNAPPY,GZIP and uncompressed. I'm inserting into an external hive-parquet table from Spark 2.1 (using df.write.insertInto(.). ![]()
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