parquet

Looking for a concise example on how to read and write Map<String, Object> from/to parquet file in Java or Scala? Here is expected structure, using com.fasterxml.jackson.databind.ObjectMapper as serializer in Java (i.e. looking for equivalent using parquet): public static Map<String, Object> read(InputStream inputStream) throws IOException { ObjectMapper objectMapper = new ObjectMapper(); return objectMapper.readValue(inputStream, new TypeReference<Map<String, Object>>() { }); } public static void write(OutputStream outputStream, Map<String, Object> map) throws IOException { ObjectMapper

How to stop such messages from coming on my spark-shell console. 5 May, 2015 5:14:30 PM INFO: parquet.hadoop.InternalParquetRecordReader: at row 0. reading next block 5 May, 2015 5:14:30 PM INFO: parquet.hadoop.InternalParquetRecordReader: RecordReader initialized will read a total of 89213 records. 5 May, 2015 5:14:30 PM INFO: parquet.hadoop.InternalParquetRecordReader: block read in memory in 2 ms. row count = 120141 5 May, 2015 5:14:30 PM INFO: parquet.hadoop.InternalParquetRecordReader: at row 0. reading next block 5 May, 2015 5:14:30 PM INFO: parquet.hadoop.InternalParquetRecordReader:

the parquet docs from cloudera shows examples of integration with pig/hive/impala. but in many cases I want to read the parquet file itself for debugging purposes. is there a straightforward java reader api to read a parquet file ? Thanks Yang You can use AvroParquetReader from parquet-avro library to read a parquet file as a set of AVRO GenericRecord objects. rishiehari Old method: (deprecated) AvroParquetReader<GenericRecord> reader = new AvroParquetReader<GenericRecord>(file); GenericRecord nextRecord = reader.read(); New method: ParquetReader<GenericRecord> reader = AvroParquetReader.

I have to load up a CSV file from HDFS using Spark into DataFrame . I was wondering if there is a "performance" improvement (query speed) from a DataFrame backed by a CSV file vs one backed by a parquet file? Typically, I load a CSV file like the following into a data frame. val df1 = sqlContext.read .format("com.databricks.spark.csv") .option("header", "true") .option("inferSchema", "true") .load("hdfs://box/path/to/file.csv") On the other hand, loading a parquet file (assuming I've parsed the CSV file, created a schema, and saved it to HDFS) looks like the following. val df2 = sqlContext

1、进入spark-shell窗口 2、 val sqlContext = new org.apache.spark.sql.SQLContext(sc) 3、 val parquetFile = sqlContext.parquetFile("hdfs://cdp/user/az-user/sparkStreamingKafka2HdfsData/part-00000-ff60a7d3-bf91-4717-bd0b-6731a66b9904-c000.snappy.parquet") hdfs://cdp是defaultFS，也可以不写，如下: val parquetFile2 = sqlContext.parquetFile("/user/az-user/sparkStreamingKafka2HdfsData/part-00000-ff60a7d3-bf91-4717-bd0b-6731a66b9904-c000.snappy.parquet") 4、 parquetFile.take(30).foreach(println) 参考: https://www.jianshu.com/p/57b20d9d7b4a?utm_campaign=maleskine&utm_content=note&utm_medium=seo_notes&utm_source

I couldn't find any plain English explanations regarding Apache Parquet files. Such as: What are they? Do I need Hadoop or HDFS to view/create/store them? How can I create parquet files? How can I view parquet files? Any help regarding these questions is appreciated. What is Apache Parquet? Apache Parquet is a binary file format that stores data in a columnar fashion. Data inside a Parquet file is similar to an RDBMS style table where you have columns and rows. But instead of accessing the data one row at a time, you typically access it one column at a time. Apache Parquet is one of the modern

In the spark docs it's clear how to create parquet files from RDD of your own case classes; (from the docs) val people: RDD[Person] = ??? // An RDD of case class objects, from the previous example. // The RDD is implicitly converted to a SchemaRDD by createSchemaRDD, allowing it to be stored using Parquet. people.saveAsParquetFile("people.parquet") But not clear how to convert back, really we want a method readParquetFile where we can do: val people: RDD[Person] = sc.readParquestFile[Person](path) where those values of the case class are defined are those which are read by the method. The best