parquet

Parquet-backed Hive table: array column not queryable in Impala

由 流过昼夜 提交于 2019-12-01 06:27:08
问题 Although Impala is much faster than Hive, we used Hive because it supports complex (nested) data types such as arrays and maps. I notice that Impala, as of CDH5.5, now supports complex data types. Since it's also possible to run Hive UDF's in Impala, we can probably do everything we want in Impala, but much, much faster. That's great news! As I scan through the documentation, I see that Impala expects data to be stored in Parquet format. My data, in its raw form, happens to be a two-column

Achieve concurrency when saving to a partitioned parquet file

由 一个人想着一个人 提交于 2019-12-01 06:19:33
问题 When writing a dataframe to parquet using partitionBy : df.write.partitionBy("col1","col2","col3").parquet(path) It would be my expectation that each partition being written were done independently by a separate task and in parallel to the extent of the number of workers assigned to the current spark job. However there is actually only one worker/task running at a time when writing to the parquet. That one worker is cycling through each of the partitions and writing out the .parquet files

Write Parquet format to HDFS using Java API with out using Avro and MR

由 与世无争的帅哥 提交于 2019-12-01 05:59:56
What is the simple way to write Parquet Format to HDFS (using Java API) by directly creating Parquet Schema of a Pojo, without using avro and MR ? The samples I found were outdated and uses deprecated methods also uses one of Avro, spark or MR. loicmathieu Effectively, there is not a lot of sample available for reading/writing Apache parquet files without the help of an external framework. The core parquet library is parquet-column where you can find some test files reading/writing directly : https://github.com/apache/parquet-mr/blob/master/parquet-column/src/test/java/org/apache/parquet/io

How to change the location of _spark_metadata directory?

由 此生再无相见时 提交于 2019-12-01 05:47:30
问题 I am using Spark Structured Streaming's streaming query to write parquet files to S3 using the following code: ds.writeStream().format("parquet").outputMode(OutputMode.Append()) .option("queryName", "myStreamingQuery") .option("checkpointLocation", "s3a://my-kafka-offset-bucket-name/") .option("path", "s3a://my-data-output-bucket-name/") .partitionBy("createdat") .start(); I get the desired output in the s3 bucket my-data-output-bucket-name but along with the output, I get the _spark_metadata

Spark DataFrames with Parquet and Partitioning

由 让人想犯罪 __ 提交于 2019-12-01 04:31:38
I have not been able to find much information on this topic but lets say we use a dataframe to read in a parquet file that is 10 Blocks spark will naturally create 10 partitions. But when the dataframe reads in the file to process it, won't it be processing a large data to partition ratio because if it was processing the file uncompressed the block size would have been much larger meaning the partitions would be larger as well. So let me clarify, parquet compressed (these numbers are not fully accurate). 1GB Par = 5 Blocks = 5 Partitions which might be decompressed to 5GB making it 25 blocks

Write Parquet format to HDFS using Java API with out using Avro and MR

由 ∥☆過路亽．° 提交于 2019-12-01 03:21:46
问题 What is the simple way to write Parquet Format to HDFS (using Java API) by directly creating Parquet Schema of a Pojo, without using avro and MR ? The samples I found were outdated and uses deprecated methods also uses one of Avro, spark or MR. 回答1: Effectively, there is not a lot of sample available for reading/writing Apache parquet files without the help of an external framework. The core parquet library is parquet-column where you can find some test files reading/writing directly : https:

Disable parquet metadata summary in Spark

由 ↘锁芯ラ 提交于 2019-11-30 20:42:23
I have a spark job (for 1.4.1) receiving a stream of kafka events. I would like to save them continuously as parquet on tachyon. val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2) lines.window(Seconds(1), Seconds(1)).foreachRDD { (rdd, time) => if (rdd.count() > 0) { val mil = time.floor(Duration(86400000)).milliseconds hiveContext.read.json(rdd).toDF().write.mode(SaveMode.Append).parquet(s"tachyon://192.168.1.12:19998/persisted5$mil") hiveContext.sql(s"CREATE TABLE IF NOT EXISTS persisted5$mil USING org.apache.spark.sql.parquet OPTIONS ( path 'tachyon://192.168.1.12

Saving Spark dataFrames as parquet files - no errors, but data is not being saved

由 蹲街弑〆低调 提交于 2019-11-30 20:36:36
I want to save a dataframe as a parquet file in Python, but I am only able to save the schema, not the data itself. I have reduced my problem down to a very simple Python test case, which I copied below from IPYNB. Any advice on what might be going on? In [2]: import math import string import datetime import numpy as np import matplotlib.pyplot from pyspark.sql import * import pylab import random import time In [3]: sqlContext = SQLContext(sc) ​#create a simple 1 column dataframe a single row of data df = sqlContext.createDataFrame(sc.parallelize(xrange(1)).flatMap(lambda x[Row(col1="Test row"

Read multiple parquet files in a folder and write to single csv file using python

由 我是研究僧i 提交于 2019-11-30 20:15:29
问题 I am new to python and I have a scenario where there are multiple parquet files with file names in order. ex: par_file1,par_file2,par_file3 and so on upto 100 files in a folder. I need to read these parquet files starting from file1 in order and write it to a singe csv file. After writing contents of file1, file2 contents should be appended to same csv without header. Note that all files have same column names and only data is split into multiple files. I learnt to convert single parquet to

（译）优化ORC和Parquet文件，提升大SQL读取性能

由 时间秒杀一切 提交于 2019-11-30 17:58:10
本文编译自IBM开发者社区，主要介绍了 HDFS 中小的 ORC 和 Parquet 文件的问题，以及这些小文件如何影响 Big SQL 的读取性能，并探索了为了提高读取性能，使用现有工具将小文件压缩为大文件的可能解决方案。 简介 众所周知，多个 Hadoop 小文件（定义为明显小于 HDFS 块大小的文件，默认情况下为 64MB ）是 Hadoop 分布式文件系统（ HDFS ）中的一个大问题。 HDFS 旨在存储大量数据，理想情况下以大文件的形式存储。在 HDFS 中存储大量小文件，而不是存储较少的大文件，这在管理文件的目录树时给 NameNode 增加了额外的开销。此外， MapReduce 和其他读取 HDFS 文件的作业也会受到负面影响，因为它将涉及与 HDFS 的更多通信以获取文件信息。 小文件读取性能问题对于存储格式更为严重，在存储格式中，元数据被嵌入文件中以描述所存储的复杂内容。 IBM Db2 Big SQL 使用的两种常见文件存储格式是 ORC 和 Parquet ，这些文件格式以列格式存储数据，以优化读取和过滤列的子集。 ORC 和 Parquet 格式将有关列和行组的信息编码到文件本身中，因此，在对文件中的数据进行解压缩、反序列化和读取之前，需要处理元数据。由于这种开销，处理以逻辑方式捆绑在一起的这些格式的多个小型文件（例如，属于 Big SQL
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