parquet hadoop

BackgroundWith the advent of the Big Data era, more and more data flows to the Hadoop ecosystem, while the ability to get valuable data from terabytes and even petabytes of data is even more important for a product and company, and a number of open source data analysis engines emerge during the rapid development of the Hadoop ecosystem , such as Hive, Spark SQL, Impala, Presto and so on, but also produced a

Tags: java se javase roc ring condition ADA tle related diffOne: Parquet use best practices for Spark SQL 1, in the past the entire industry of big data analysis of the technology stack pipeline generally divided into two ways: A) Result Service (can be placed in db), Sparksql/impala, HDFs parquet, HDFs, Mr/hive/spark (equivalent ETL), Data Source , may also be used as a data service via JDBC/ODBC); B) Resu

ImportJava.io.BufferedReader;ImportJava.io.File;ImportJava.io.FileReader;Importjava.io.IOException;ImportJava.util.Random;Importorg.apache.hadoop.conf.Configuration;ImportOrg.apache.hadoop.fs.Path;ImportOrg.apache.log4j.Logger;ImportOrg.apache.parquet.example.data.Group;Importorg.apache.parquet.example.data.GroupFactory;Importorg.apache.parquet.example.data.simple.SimpleGroupFactory;ImportOrg.apache.parquet.hadoop.ParquetReader;ImportOrg.apache.parquet.hadoop.ParquetReader.Builder;ImportOrg.apac

Compared with the row-based storage engine of traditional databases, the column-based storage engine has a higher compression ratio and less I/O operations, especially when there are many data columns, however, each operation only queries and computes several columns, and the column-based storage engine is more cost-effective. Currently, in open-source implementations, the most famous columnar storage engines are Parquet and ORC, and they are both Apa

I. Environmental descriptionHadoop cluster: Using the test Hadoop cluster, node:hadoop230hadoop231hadoop232hadoop233These machine configuration, the specific parameters can be referred to as follows:Number of CPUs: 2Number of CPU Threads: 32Memory: 128GBDisk: 48TBWith the same queue on the test fleet, all queries are non-concurrent, using the entire cluster's resources.Hive uses the official hive 1.2.1 version, launched using the Hiveserver2 method, u

Brief introductionApache Parquet is a columnstore format used primarily for the Hadoop ecosystem. Regardless of the data processing framework, model, and programming language. Cloudera's Big Data Online analysis (OLAP) project uses this format as a column store in Impala. Parquet is a columnstore internal to Twitter, currently open source and hosting the code in

The Columnstore engine has a higher compression ratio, fewer IO operations than a traditional database row storage engine, especially when there are many columns of data, but each time the query and calculation is done for only a few columns, the Columnstore engines are more cost-effective.Currently in the open source implementation, the most famous Columnstore engine is parquet and Orc, and they are the top projects of Apache, playing an important ro

In order to optimize the performance of various tools prior to MapReduce and MR , a number of different storage methods have emerged in the Hadoop built-in data storage format. such as optimizing the rcfileof Hive performance, and with Impala to achieve Google Dremel features ( similar to even the feature of the superset ) parquet and so on. Come and study together today. The evolution of data storage in HD

1.spark Read the compressed file of HDFs GZ spark1.5 later versions support direct reading of files in GZ format, no difference from reading other plain text files.Start the spark shell interface and read the GZ file in the same way as a plain text file: Sc.textfile ("/your/path/*.gz"). map{...} The above code will take care of the need to read GZ compressed files. 2.spark Read parquet format file Spark naturally supports files in

Reprinted from Dong's Blog The Columnstore engine has a higher compression ratio and less IO operations than the traditional row storage engine (note: Columnstore is not all-powerful, many scenarios are still more efficient), especially in the number of data columns (column), but each action is only for a few columns of the story, The Columnstore engine is more cost effective. In the Internet Big Data application scenario, in most cases, the data volume is very large and the number of data field

There is a problem with spark SQL 1.2.x:When we try to access multiple parquet files in a query, if the field names and types in these parquet files are exactly the same, except for the order of the fields, for example, a file is name string, id int, and the other file is ID int, name String, the query will error, throwing an exception to the metadata merge.In 1.3, this problem has actually been solved. The

Import org.apache.spark.SparkConf import org.apache.spark.SparkContext Import Org.apache.spark.sql.SQLContext Import org.apache.spark.sql.DataFrame Import Org.apache.spark.sql.SaveMode Object Genericloadsave { def main (args:array[string]): unit = { Val conf = New sparkconf () . Setappname ("Genericloadsave") . Setmaster ("local") val sc = new Sparkcontext (conf) val sqlcontext = new SqlContext (SC) /read a parquet