[
https://issues.apache.org/jira/browse/HAWQ-1546?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16248427#comment-16248427
]
Hongxu Ma commented on HAWQ-1546:
---------------------------------
Hi ~lynn
Hawq is not good at dealing with real-time data, and call insert statement
frequently can cause big data files (and also cause select slowly) is a known
issue of hawq (on parquet format).
As a walkaround, you can reduce the load frequency, every hour maybe a good
choice, but obviously it loses the real-time ability...
Maybe AO table is better for your scenario, I will check it later, but
obviously using AO table can cause some performance lose.
> The data files on hdfs after hawq load data were too large!!!
> -------------------------------------------------------------
>
> Key: HAWQ-1546
> URL: https://issues.apache.org/jira/browse/HAWQ-1546
> Project: Apache HAWQ
> Issue Type: Bug
> Components: libhdfs
> Reporter: lynn
> Assignee: Radar Lei
>
> create table person_l1 (id int, name varchar(20), age int, sex
> char(1))with(appendonly=true,orientation=parquet,compresstype=snappy);
> create table person_l2 (id int, name varchar(20), age int, sex
> char(1))with(appendonly=true,orientation=parquet,compresstype=snappy);
> 执行480次插入语句：
> sh insert.sh
> 脚本内容：
> i4.sql:
> insert into person_l1 values(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3,
> 'lynn', 28, '1'),(4, 'lynn', 28, '1');
> insert.sh:
> #!/bin/bash
> num=480
> for ((i=0; i<$num; i=$[$i+1])) do
> psql -d test -f i4.sql
> done
> 执行1次插入语句：
> 1920条数据
> psql -d test -f i1.sql
> 脚本内容：
> i1.sql:
> SET hawq_rm_stmt_nvseg=10;
> SET hawq_rm_stmt_vseg_memory='512mb';
> insert into person_l2 values(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3,
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> 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28,
> '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3,
> 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28,
> '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2,
> 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28,
> '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1,
> 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28,
> '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4,
> 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28,
> '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3,
> 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28,
> '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2,
> 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28,
> '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1,
> 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28,
> '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4,
> 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28,
> '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28, '1'),(3,
> 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2, 'lynn', 28,
> '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28, '1'),(2,
> 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1'),(1, 'lynn', 28,
> '1'),(2, 'lynn', 28, '1'),(3, 'lynn', 28, '1'),(4, 'lynn', 28, '1');
> 查询数据条目数：
> test=# select count(*) from person_l1;
> count
> -------
> 1920
> (1 row)
> test=# select count(*) from person_l2;
> count
> -------
> 1920
> (1 row)
> select sotdtablename, sotdsize from hawq_toolkit.hawq_size_of_table_disk
> where sotdtablename like 'person_%';
> test=# select sotdtablename, sotdsize from
> hawq_toolkit.hawq_size_of_table_disk where sotdtablename like 'person_%';
> sotdtablename | sotdsize
> ---------------+----------
> person_l2 | 2525
> person_l1 | 16421795
> In our product environment, the source data was csv format, the file size is
> 500MB per day, we call hawq load every minute, the file on hdfs is greater
> than 500GB！！！
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