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. JNI-based implementation to achieve comparable performance to the native C++ version. from pyspark.sql import SparkSession spark ('CSV to ORC Conversion').getOrCreate () Read the CSV file into a DataFrame csvdf ('path/to/csv/file. SnappyOutputStream uses only 32KB+ in default. To enable snappy compression, you can set the compression option to snappy. Features Fast compression/decompression around 200400MB/sec. See details in connector article -> Dataset properties section. snappy-java is a Java port of the snappy, a fast C++ compresser/decompresser developed by Google. Before Snappy became available by Google in 2011, LZO was the default. In most cases, enabling Snappy or LZO by default is a good choice, because they have a low performance overhead and provide space savings. Framing enables decompression of streaming or file data that cannot be entirely maintained in memory. Snappy and LZO use fewer CPU resources than GZIP, but do not provide as high of a compression ratio. Some implementations of Snappy allow for framing. Snappy focuses on high compression and decompression speed rather than the maximum compression of data. Each file-based connector has its own location type and supported properties under location. SNAPPY Compression algorithm that is part of the Lempel-Ziv 77 (LZ7) family. The type property of the dataset must be set to Parquet. This section provides a list of properties supported by the Parquet dataset. Dataset propertiesįor a full list of sections and properties available for defining datasets, see the Datasets article. By default, the service uses min 64 MB and max 1G. 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. The flag Xms specifies the initial memory allocation pool for a Java Virtual Machine (JVM), while Xmx specifies the maximum memory allocation pool. Note when using copy activity to decompress ZipDeflate file(s) and write to file-based sink data store, files will be extracted to the folder: //.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. The compression section has two properties: Type: the compression codec, which can be GZIP, Deflate, BZIP2, or ZipDeflate.
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