parquet

I have multiple jobs that I want to execute in parallel that append daily data into the same path using partitioning. e.g. dataFrame.write(). partitionBy("eventDate", "category") .mode(Append) .parquet("s3://bucket/save/path"); Job 1 - category = "billing_events" Job 2 - category = "click_events" Both of these jobs will truncate any existing partitions that exist in the s3 bucket prior to execution and then save the resulting parquet files to their respective partitions. i.e. job 1 - > s3://bucket/save/path/eventDate=20160101/channel=billing_events job 2 - > s3://bucket/save/path/eventDate

转载本文需注明出处：微信公众号EAWorld，违者必究。 引言： Spark是在借鉴了MapReduce之上发展而来的，继承了其分布式并行计算的优点并改进了MapReduce明显的缺陷。Spark主要包含了Spark Core、Spark SQL、Spark Streaming、MLLib和GraphX等组件。 本文主要分析了 Spark RDD 以及 RDD 作为开发的不足之处，介绍了 SparkSQL 对已有的常见数据系统的操作方法，以及重点介绍了普元在众多数据开发项目中总结的基于 SparkSQL Flow 开发框架。 目录： 一、Spark RDD 二、基于Spark RDD数据开发的不足 三、SparkSQL 四、SparkSQL Flow 一、Spark RDD RDD（Resilient Distributed Dataset）叫做弹性分布式数据集，是Spark中最基本的数据抽象，它代表一个不可变、可分区、元素可并行计算的集合。 RDD具有数据流模型的特点：自动容错、位置感知性调度和可伸缩性。 //Scala 在内存中使用列表创建 val lines = List(“A”, “B”, “C”, “D” …) val rdd:RDD = sc.parallelize(lines); //以文本文件创建 val rdd:RDD[String] = sc.textFile(

I would like to ingest data into s3 from kinesis firehose formatted as parquet. So far I have just find a solution that implies creating an EMR, but I am looking for something cheaper and faster like store the received json as parquet directly from firehose or use a Lambda function. Thank you very much, Javi. Good news, this feature was released today! Amazon Kinesis Data Firehose can convert the format of your input data from JSON to Apache Parquet or Apache ORC before storing the data in Amazon S3. Parquet and ORC are columnar data formats that save space and enable faster queries To enable,

Having this 12 byte array (int96) to timestamp. [128 76 69 116 64 7 0 0 48 131 37 0] How do I cast it to timestamp? I understand the first 8 byte should be cast to int64 millisecond that represent an epoch datetime. The first 8 bytes are time in nanosecs, not millisecs. They are not measured from the epoch either, but from midnight. The date part is stored separatly in the last 4 bytes as Julian day number . Here is the result of an experiment I did earlier that may help. I stored '2000-01-01 12:34:56' as an int96 and dumped with parquet-tools: $ parquet-tools dump hdfs://path/to/parquet/file

Having some issues with the way that Spark is interpreting columns for parquet. I have an Oracle source with confirmed schema (df.schema() method): root |-- LM_PERSON_ID: decimal(15,0) (nullable = true) |-- LM_BIRTHDATE: timestamp (nullable = true) |-- LM_COMM_METHOD: string (nullable = true) |-- LM_SOURCE_IND: string (nullable = true) |-- DATASET_ID: decimal(38,0) (nullable = true) |-- RECORD_ID: decimal(38,0) (nullable = true) Which is then saved as Parquet - df.write().parquet() method - with corresponding message type (determined by Spark): message spark_schema { optional int64 LM_PERSON