White space in column name is not supported for Parquet files. Note currently Copy activity doesn't support LZO when read/write Parquet files. Supported types are " none", " gzip", " snappy" (default), and " lzo". When reading from Parquet files, Data Factories automatically determine the compression codec based on the file metadata. The compression codec to use when writing to Parquet files. STORED AS ORC tblproperties (orc.compressSNAPPY). See details in connector article -> Dataset properties section. Similarly you can create a table using snappy compression as below. Each file-based connector has its own location type and supported properties under location. This supports reading snappy, zlib or no compression, it is not necessary to specify in compression option while reading a ORC file. The type property of the dataset must be set to Parquet. Use Spark DataFrameReader’s orc () method to read ORC file into DataFrame. 10 Mb compressed with SNAPPY algorithm will turn into 2. However, there is an opinion that ORC is more compression efficient. This section provides a list of properties supported by the Parquet dataset. Source: (Apache 2.0 license) I did a little test and it seems that both Parquet and ORC offer similar compression ratios. Dataset propertiesįor a full list of sections and properties available for defining datasets, see the Datasets article. By default. However, I have a feeling that ORC is supported by a smaller number of Hadoop projects than Parquet, i.e. Are you sure that the ORC tables you created were with no compression. If None is set, it uses the value specified in .codec. This will override orc.compress and .codec. This can be one of the known case-insensitive shorten names (none, snappy, zlib, and lzo). 10 Mb compressed with SNAPPY algorithm will turn into 2.4Mb in Parquet. compression codec to use when saving to file. By default, the service uses min 64 MB and max 1G. I did a little test and it seems that both Parquet and ORC offer similar compression ratios. This means that JVM will be started with Xms amount of memory and will be able to use a maximum of Xmx amount of memory. Supported data compression formats Blobs and files can be compressed through any of the following compression algorithms: Indicate compression by appending the extension to the name of the blob or file. The flag Xms specifies the initial memory allocation pool for a Java Virtual Machine (JVM), while Xmx specifies the maximum memory allocation pool. If you copy data to/from Parquet format using Self-hosted Integration Runtime and hit error saying "An error occurred when invoking java, message: :Java heap space", you can add an environment variable _JAVA_OPTIONS in the machine that hosts the Self-hosted IR to adjust the min/max heap size for JVM to empower such copy, then rerun the pipeline.Įxample: set variable _JAVA_OPTIONS with value -Xms256m -Xmx16g.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |