Spark to read data stored in myriad sources-Apache Hadoop, Apache Cassandra, Apache HBase, MongoDB, Apache Hive, RDBMSs, and more-and process it all in memory. Spark SQL, Spark Structured Streaming, Spark MLlib, and GraphX Spark achieves simplicity by providing a fundamental abstraction of a simple logical data structure called a Resilient Distributed Dataset (RDD) upon which all other higher-level structured data abstractions, such as DataFrames and Datasets, are constructed. Second, Spark builds its query computations as a directed acyclic graph (DAG) its DAG scheduler and query optimizer construct an efficient computational graph that can usually be decomposed into tasks that are executed in parallel across workers on the cluster In particular, data engineers will learn how to use Spark’s Structured APIs to perform complex data exploration and analysis on both batch and streaming data use Spark SQL for interactive queries use Spark’s built-in and external data sources to read, refine, and write data in different file formats as part of their extract, transform, and load (ETL) tasks and build reliable data lakes with Spark and the open source Delta Lake table format. Specify JDBC Connector for Spark classpath Save JDBC Connector inside Spark root directory Querying with the Spark SQL Shell, Beeline, and Tableau Speeding up and distributing PySpark UDFs with Pandas UDFs Evaluation order and null checking in Spark SQL Spark SQL and DataFrames: Interacting with External Data Sources Reading an ORC file into a Spark SQL table Reading an Avro file into a Spark SQL table Reading a CSV file into a Spark SQL table Reading JSON files into a Spark SQL table Reading Parquet files into a Spark SQL table Data Sources for DataFrames and SQL Tables Temporary views versus global temporary views Spark SQL and DataFrames: Introduction to Built-in Data Sources Typed Objects, Untyped Objects, and Generic Rows Using DataFrameReader and DataFrameWriter Spark’s Structured and Complex Data Types Transformations, Actions, and Lazy Evaluation So the only thing you can set is the compression codec, using dataframe.write().format("orc").option("compression","snappy"). Note that the default compression codec has changed with Spark 2 before that it was zlib This can be one of the known case-insensitive shorten compression (default snappy): compression codec to use when.You can set the following ORC-specific option(s) for writing ORC But again, these properties must be set before creating (or re-creating) the hiveContext.įor ORC and the other formats, you have to resort to format-specific DataFrameWriter options quoting the latest JavaDoc. There are some Spark-specific properties for Parquet, and they are well documented. Spark uses its own SerDe libraries for ORC (and Parquet, JSON, CSV, etc) so it does not have to honor the standard Hadoop/Hive properties. ("orc.compress","") // will now be Snappy Val sparkAlt = .SparkSession.builder().config("orc.compress","snappy").getOrCreate() ("orc.compress","") // depends on Hadoop conf Val hiveContextAlt = new .SQLContext(scAlt) Val scAlt = new ((new ).set("orc.compress","snappy")) Sc.getConf.get("orc.compress","") // depends on Hadoop conf or in your code, by re-creating the SparkContext.either in the hive-site.xml available to Spark at launch time.They are Hive configuration properties, that must be defined before creating the hiveContext object. Orc.compress and the rest are not Spark DataFrameWriter options. You are making two different errors here.
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