![]() s3a:\\ => Third gen Executing SQL queries on DataFrame.In order to read ORC files from Amazon S3, use the below prefix to the path along with third-party dependencies and credentials. This supports reading snappy, zlib or no compression, it is not necessary to specify in compression option while reading a ORC file. Use Spark DataFrameReader’s orc() method to read ORC file into DataFrame. Incase to overwrite use overwrite save mode.ĭf.write.mode('append').orc("/tmp/orc/people.orc")ĭf.write.mode('overwrite').orc("/tmp/orc/people.orc") Using append save mode, you can append a DataFrame to an existing ORC file. This creates ORC files with zlib compression. And you can change the compression from default snappy to either none or zlib using an option compression You can notice this on the part file names. Spark by default uses snappy compression while writing ORC file. This method takes a path as an argument where to write a ORC file.Īlternatively, you can also write using format("orc")ĭf.write.format("orc").save("/tmp/orc/data.orc") Spark DataFrameWriter uses orc() method to write or create ORC file from DataFrame. Val df=spark.createDataFrame(data).toDF(columns:_*) Val columns=Seq("firstname","middlename","lastname","dob","gender","salary") Below is a sample DataFrame we use to create an ORC file. Since we don’t have an ORC file to read, first will create an ORC file from the DataFrame. Spark by default supports ORC file formats without importing third party ORC dependencies. By default, it uses SNAPPY when not specified. Spark supports the following compression options for ORC data source. Fast reads: ORC is used for high-speed processing as it by default creates built-in index and has some default aggregates like min/max values for numeric data.Reduces I/O: ORC reads only columns that are mentioned in a query for processing hence it takes reduces I/O.Compression: ORC stores data as columns and in compressed format hence it takes way less disk storage than other formats.ORC file format heavily used as a storage for Apache Hive due to its highly efficient way of storing data which enables high-speed processing and ORC also used or natively supported by many frameworks like Hadoop MapReduce, Apache Spark, Pig, Nifi, and many more. This is similar to other columnar storage formats Hadoop supports such as RCFile, parquet. ORC stands of Optimized Row Columnar which provides a highly efficient way to store the data in a self-describing, type-aware column-oriented format for the Hadoop ecosystem. Spark with Python (PySpark) Tutorial For Beginners What is the ORC file? ![]()
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