parquet

Perhaps this is well documented, but I am getting very confused how to do this (there are many Apache tools). When I create an SQL table, I create the table using the following commands: CREATE TABLE table_name( column1 datatype, column2 datatype, column3 datatype, ..... columnN datatype, PRIMARY KEY( one or more columns ) ); How does one convert this exist table into Parquet? This file is written to disk? If the original data is several GB, how long does one have to wait? Could I format the original raw data into Parquet format instead? Apache Spark can be used to do this: 1.load your table

I am trying to test how to write data in HDFS 2.7 using Spark 2.1. My data is a simple sequence of dummy values and the output should be partitioned by the attributes: id and key . // Simple case class to cast the data case class SimpleTest(id:String, value1:Int, value2:Float, key:Int) // Actual data to be stored val testData = Seq( SimpleTest("test", 12, 13.5.toFloat, 1), SimpleTest("test", 12, 13.5.toFloat, 2), SimpleTest("test", 12, 13.5.toFloat, 3), SimpleTest("simple", 12, 13.5.toFloat, 1), SimpleTest("simple", 12, 13.5.toFloat, 2), SimpleTest("simple", 12, 13.5.toFloat, 3) ) // Spark's

I want to use parquet in one of my projects as columnar storage. But i dont want to depends on hadoop/hdfs libs. Is it possible to use parquet outside of hdfs? Or What is the min dependency? Investigating the same question I found that apparently it's not possible for the moment. I found this git issue , which proposes decoupling parquet from the hadoop api. Apparently it has not been done yet. In the Apache Jira I found an issue , which asks for a way to read a parquet file outside hadoop. It is unresolved by the time of writing. EDIT: Issues are not tracked on github anymore (first link

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am looking to use Spark Structured streaming to read data from Kafka and process it and write to Hive table. val spark = SparkSession .builder .appName("Kafka Test") .config("spark.sql.streaming.metricsEnabled", true) .config("spark.streaming.backpressure.enabled", "true") .enableHiveSupport() .getOrCreate() val events = spark .readStream .format("kafka") .option("kafka.bootstrap.servers", "xxxxxxx") .option("startingOffsets", "latest") .option("subscribe", "yyyyyy") .load val data = events.select(.....some columns...) data.writeStream

I'm creating an uber jar spark application that I'm spark submitting to an EMR 4.3 cluster, I'm provisioning 4 r3.xlarge instances, one to be the master and the other three as the cores. I have hadoop 2.7.1, ganglia 3.7.2 spark 1.6, and hive 1.0.0 pre-installed from the console. I'm running the following command: spark-submit \ --deploy-mode cluster \ --executor-memory 4g \ --executor-cores 2 \ --num-executors 4 --driver-memory 4g --driver-cores 2 --conf "spark.driver.maxResultSize=2g" --conf "spark.hadoop.spark.sql.parquet.output.committer.class=org.apache.spark.sql.parquet

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I use Apache Spark 2.1.1 (used 2.1.0 and it was the same, switched today). I have a dataset: root |-- muons: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- reco::Candidate: struct (nullable = true) | | |-- qx3_: integer (nullable = true) | | |-- pt_: float (nullable = true) | | |-- eta_: float (nullable = true) | | |-- phi_: float (nullable = true) | | |-- mass_: float (nullable = true) | | |-- vertex_: struct (nullable = true) | | | |-- fCoordinates: struct (nullable = true) | | | | |-- fX: float (nullable =

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I'm trying to write and read Parquet files from SparkSQL. For reasons of schema evolution, I would like to use Avro schemas with my writes and reads. My understanding is that this is possible outside of Spark (or manually within Spark) using e.g. AvroParquetWriter and Avro's Generic API. However, I would like to use SparkSQL's write() and read() methods (which work with DataFrameWriter and DataFrameReader), and which integrate well with SparkSQL (I will be writing and reading Dataset's). I can't for the life of me figure out how to

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: OK! So I decided to use Parquet as storage format for hive tables and before I actually implement it in my cluster, I decided to run some tests. Surprisingly, Parquet was slower in my tests as against the general notion that it is faster then plain text files. Please be noted that I am using Hive-0.13 on MapR Follows the flow of my operations Table A Format - Text Format Table size - 2.5 Gb Table B Format - Parquet Table size - 1.9 Gb [Create table B stored as parquet as select * from A] Table C Format - Parquet with snappy