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  • 3.4-3.6 Hive Storage Format

    一、file format

    ORCFile在HDP 2:更好的压缩,更好的性能:

    https://zh.hortonworks.com/blog/orcfile-in-hdp-2-better-compression-better-performance/


    官方ORCfile介绍:

    https://cwiki.apache.org/confluence/display/Hive/LanguageManual+ORC


    官方PARQUET介绍:

    https://cwiki.apache.org/confluence/display/Hive/Parquet


    file_format:
      : SEQUENCEFILE
      | TEXTFILE    -- (Default, depending on hive.default.fileformat configuration)
      | RCFILE      -- (Note: Available in Hive 0.6.0 and later)
      | ORC         -- (Note: Available in Hive 0.11.0 and later)
      | PARQUET     -- (Note: Available in Hive 0.13.0 and later)
      | AVRO        -- (Note: Available in Hive 0.14.0 and later)
      | JSONFILE    -- (Note: Available in Hive 4.0.0 and later)
      | INPUTFORMAT input_format_classname OUTPUTFORMAT output_format_classname
    
    
    
    数据存储:
        按行存储
        按列存储
    
    
    #TEXTFILE :行式存储
    #其他的都是列存储,ORC是对RCFILE的优化;ORC和PARQUET用的较多;


    二、测试

    我们建三张表导入同样的数据,看数据存储的大小;

    1、TEXTFILE

    #建表
    hive (default)> create table page_views(
                  > 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 '	'
                  > STORED AS TEXTFILE;
    OK
    Time taken: 0.089 seconds
    
    
    #加载数据
    hive (default)> load data local inpath '/opt/datas/page_views.data' into table page_views;
    Copying data from file:/opt/datas/page_views.data
    Copying file: file:/opt/datas/page_views.data
    Loading data to table default.page_views
    Table default.page_views stats: [numFiles=1, numRows=0, totalSize=19014993, rawDataSize=0]
    OK
    Time taken: 0.326 seconds


    2、ORCFILE

    hive (default)> create table page_views_orc(
                  > 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 '	'
                  > STORED AS orc;
    OK
    Time taken: 0.04 seconds
    
    hive (default)> insert into table page_views_orc select * from page_views;


    3、PARQUET

    hive (default)> create table page_views_parquet(
                  > 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 '	'
                  > STORED AS parquet;
    OK
    Time taken: 0.037 seconds
    
    hive (default)> insert into table page_views_parquet select * from page_views;


    4、查看各个表文件大小

    hive (default)> dfs -du -h /user/hive/warehouse/page_views/;
    18.1 M  /user/hive/warehouse/page_views/page_views.data
    
    hive (default)> dfs -du -h /user/hive/warehouse/page_views_orc/;              
    2.6 M  /user/hive/warehouse/page_views_orc/000000_0
    
    hive (default)> dfs -du -h /user/hive/warehouse/page_views_parquet/;
    13.1 M  /user/hive/warehouse/page_views_parquet/000000_0
    
    
    #明显的可以看出,数据相同时,存储为不同的格式:
    TEXTFILE格式的表:18.1M
    ORCFILE格式的表:2.6M
    PARQUET格式的表:13.1M


    5、查询测试

    #TEXTFILE表查询
    hive (default)> select session_id, count(*) cnt from page_views group by session_id order by cnt desc limit 30;
    ......
    Time taken: 39.427 seconds, Fetched: 30 row(s)        #39.427秒
    
    
    #ORCFILE表查询
    hive (default)> select session_id, count(*) cnt from page_views_orc group by session_id order by cnt desc limit 30;
    ......
    Time taken: 38.319 seconds, Fetched: 30 row(s)        #38.319秒
    
    
    #可见orc格式的表,不仅数据体积小很多,查询也有优势


    三、ORCFILE优化

    创建ORC表时,可以使用许多表属性进一步优化ORC的工作方式:

    image

    1、例如,假设您想使用snappy压缩而不是zlib压缩。方法如下:

    #建表
    hive (default)> create table page_views_orc_snappy(
                  > 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 '	'
                  > STORED AS orc tblproperties ("orc.compress"="SNAPPY");
    OK
    Time taken: 0.033 seconds
    
    #插入数据
    hive (default)> insert into table page_views_orc_snappy select * from page_views;
    
    #查询
    hive (default)> dfs -du -h /user/hive/warehouse/page_views_orc_snappy/;
    3.8 M  /user/hive/warehouse/page_views_orc_snappy/000000_0
    
    
    #此时发现,用snappy压缩完,数据反而比orcfile表数据还大了,怎么回事呢?
    因为建ORCFILE表时,不指定压缩,默认使用ZLIB压缩,但是指定了snappy压缩就会使用snappy,
    而且ZLIB的压缩比大于snappy,所以此时的snappy压缩稍微大一些;

    总结:

    在实际的项目开发当中,hive表的数据
        *存储格式I orcfile/qarquet    #推荐
        *数据压缩
            snappy        #推荐
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  • 原文地址:https://www.cnblogs.com/weiyiming007/p/10774049.html
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