snappy

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: Hive 2.1 I have following table definition : CREATE EXTERNAL TABLE table_snappy ( a STRING, b INT) PARTITIONED BY (c STRING) ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' STORED AS INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' LOCATION '/' TBLPROPERTIES ('parquet.compress'='SNAPPY'); Now, I would like to insert data into it : INSERT INTO table_snappy PARTITION (c='something') VALUES ('xyz', 1); However,

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'm currently working on a system that involves exporting a webpage template to a pdf file. I'm using Laravel 5, and saw SnappyPdf . It worked fine until i cloned the system on a different pc with a new vagrant box and now I'm getting this error: The exit status code '1' says something went wrong: stderr: "Loading pages (1/6) [> ] 0% [======> ] 10% QSslSocket: cannot resolve CRYPTO_num_locks QSslSocket: cannot resolve CRYPTO_set_id_callback QSslSocket: cannot resolve CRYPTO_set_locking_callback QSslSocket: cannot resolve sk_free QSslSocket:

I'm having trouble finding a library that allows Parquet files to be written using Python. Bonus points if I can use Snappy or a similar compression mechanism in conjunction with it. Thus far the only method I have found is using Spark with the pyspark.sql.DataFrame Parquet support. I have some scripts that need to write Parquet files that are not Spark jobs. Is there any approach to writing Parquet files in Python that doesn't involve pyspark.sql ? Update (March 2017): There are currently 2 libraries capable of writing Parquet files: fastparquet pyarrow Both of them are still under heavy

Hive 主流文件存储格式对比 1、存储文件的压缩比测试 1.1 测试数据 https : //github.com/liufengji/Compression_Format_Data log . txt 大小为 18.1 M 1.2 TextFile 创建表，存储数据格式为 TextFile create table log_text ( track_time string , url string , session_id string , referer string , ip string , end_user_id string , city_id string ) row format delimited fields terminated by '\t' stored as textfile ; 向表中加载数据 load data local inpath '/home/hadoop/log.txt' into table log_text ; 查看表的数据量大小 dfs - du - h / user / hive / warehouse / log_text ; +------------------------------------------------+--+ | DFS Output | +--------------------------------

package demo02.action; import java.io.File; import java.io.FileOutputStream; import java.io.IOException; import java.io.OutputStreamWriter; import java.nio.file.Files; import java.nio.file.Paths; import java.util.Date; import org.apache.commons.codec.CharEncoding; import org.xerial.snappy.Snappy; /** * 使用snappy压缩算法压缩文件 * @author gujie * */ public class SnappyUtil { public static void main(String[] args) throws IOException { long time1 = new Date().getTime(); //输入文件 File fileread = new File("D:\\Users\\gujie\\Desktop\\js\\46818_19279_4547_50.json"); //压缩后文件 File fileWrite = new File("D:\\Users\

According to this Cloudera post , Snappy IS splittable. For MapReduce, if you need your compressed data to be splittable, BZip2, LZO, and Snappy formats are splittable, but GZip is not. Splittability is not relevant to HBase data. But from the hadoop definitive guide, Snappy is NOT splittable. There are also some confilitcting information on the web. Some say it's splittable, some say it's not. Both are correct but in different levels. According with Cloudera blog http://blog.cloudera.com/blog/2011/09/snappy-and-hadoop/ One thing to note is that Snappy is intended to be used with a container

上次写了篇 《 SpringBoot迭代发布JAR瘦身配置 》， 但有一个问题，所有的第三方JAR位于lib目录中，不利于传输到服务器，因此应该考虑将此目录压缩打包，再传输到服务器，服务器解压即可使用。 经过一番google，找到类似的plugin（ maven-assembly-plugin ），看官网的介绍： The Assembly Plugin for Maven is primarily intended to allow users to aggregate the project output along with its dependencies, modules, site documentation, and other files into a single distributable archive. 大致意思就是：Assembly Plugin 主要是为了允许用户将项目输出及其依赖项、模块、文档和其他文件聚合到一个可发布的文件中。通俗一点就是可以定制化自己想要打包的文件，支持打包的格式有：zip、tar、tar.gz (or tgz)、tar.bz2 (or tbz2)、tar.snappy、tar.xz (or txz)、jar、dir、war。 接下来我就根据自己的需求定制我想要的包，我想把 target/lib 目录压缩打包， 在pom的配置如下：

因为产品需要，这两天研究了一下Hadoop Snappy。先不说什么各个压缩算法之间的性能对比，单是这个安装过程，就很痛苦。网上有很多博友写Hadoop Snappy安装过程，大部分是照着Google的文档翻译了一遍，并没有列举出遇到的问题。有的博文，明明其验证提示是错误的，还说如果输出XXX，说明安装成功了。费了老大的劲，终于安装成功了，现将详细步骤及遇到的问题，一一列出，只希望接下来需要研究及安装的朋友，看到这篇博文，能够一气呵成！本篇文章主要包括： 1. Snappy 压缩算法介绍及集中压缩算法比较 2. Snappy 安装过程及验证 3. Hadoop Snappy 源码编译过程及问题解决方案 4. Hadoop上Hadoop Snappy 安装配置过程及验证 5. HBase 配置Snappy及验证 6.如何在集群中所有节点部署 废话不多说，现在开始： 1. Snappy 压缩算法介绍及几种压缩算法比较 这一部分可以参考我的上一篇博文： Hadoop压缩-SNAPPY算法 ，或者直接参看Google文档： http://code.google.com/p/snappy/ 及 http://code.google.com/p/hadoop-snappy/ 。我的 Hadoop压缩-SNAPPY算法 这篇博文中，不仅简介了Google Snappy

I am running a few tests on the storage formats available with Hive and using Parquet and ORC as major options. I included ORC once with default compression and once with Snappy. I have read many a documents that state Parquet to be better in time/space complexity as compared to ORC but my tests are opposite to the documents I went through. Follows some details of my data. Table A- Text File Format- 2.5GB Table B - ORC - 652MB Table C - ORC with Snappy - 802MB Table D - Parquet - 1.9 GB Parquet was worst as far as compression for my table is concerned. My tests with the above tables yielded