To improve the speed of data processing through more effective use of L1/ L2/元 CPU caches, Spark algorithms and data structures exploit memory hierarchy with cache-aware computation. This takes advantage of modern CPU designs, by keeping all pipelines full to achieve efficiency. Vectorization allows the CPU to operate on vectors, which are arrays of column values from multiple records.Columnar layout for memory data avoids unnecessary I/O and accelerates analytical processing performance on modern CPUs and GPUs.To reduce JVM object memory size, creation, and garbage collection processing, Spark explicitly manages memory and converts most operations to operate directly against binary data.Tungsten builds upon ideas from modern compilers and massively parallel processing (MPP) technologies, such as Apache Drill, Presto, and Apache Arrow. Tungsten is the code name for the Spark project that makes changes to Apache Spark’s execution engine, focusing on improvements to the efficiency of memory and CPU usage. This blog post will first give a quick overview of what changes were made and then some tips to take advantage of these changes. With Apache Spark 2.0 and later versions, big improvements were implemented to enable Spark to execute faster, making lot of earlier tips and best practices obsolete. Spark natively supports ORC data source to read and write an ORC files using orc() method on DataFrameReader and DataFrameWrite.Editor’s Note: MapR products referenced are now part of the HPE Ezmeral Data Fabric. In summary, ORC is a high efficient, compressed columnar format that is capable to store petabytes of data without compromising fast reads. Val df=spark.createDataFrame(data).toDF(columns:_*) Val columns=Seq("firstname","middlename","lastname","dob","gender","salary") Val spark: SparkSession = SparkSession.builder() For smaller datasets, it is still suggestible to use ZLIB. If you have large data set to write, use SNAPPY.
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