[1180]clickhouse查看数据库和表的容量大小

周小董

发布于 2023-10-10 08:28:09

7760

发布于 2023-10-10 08:28:09

文章被收录于专栏：python前行者python前行者

在mysql中information_schema这个数据库中保存了mysql服务器所有数据库的信息，而在clickhouse，我们可以通过system.parts查看clickhouse数据库和表的容量大小、行数、压缩率以及分区信息。

在此通过测试数据库来说明。

1.查看数据库容量、行数、压缩率

SELECT 
    sum(rows) AS `总行数`,
    formatReadableSize(sum(data_uncompressed_bytes)) AS `原始大小`,
    formatReadableSize(sum(data_compressed_bytes)) AS `压缩大小`,
    round((sum(data_compressed_bytes) / sum(data_uncompressed_bytes)) * 100, 0) AS `压缩率`
FROM system.parts

┌────总行数─┬─原始大小──┬─压缩大小─┬─压缩率─┐
│ 326819026 │ 77.15 GiB │ 5.75 GiB │      7 │
└───────────┴───────────┴──────────┴────────┘

1 rows in set. Elapsed: 0.047 sec. Processed 1.04 thousand rows, 520.93 KB (21.95 thousand rows/s., 
11.02 MB/s.)

2.查看数据表容量、行数、压缩率

--在此查询一张临时表的信息
SELECT 
    table AS `表名`,
    sum(rows) AS `总行数`,
    formatReadableSize(sum(data_uncompressed_bytes)) AS `原始大小`,
    formatReadableSize(sum(data_compressed_bytes)) AS `压缩大小`,
    round((sum(data_compressed_bytes) / sum(data_uncompressed_bytes)) * 100, 0) AS `压缩率`
FROM system.parts
WHERE table IN ('temp_1')
GROUP BY table

┌─表名───┬──总行数─┬─原始大小───┬─压缩大小──┬─压缩率─┐
│ temp_1 │ 3127523 │ 838.21 MiB │ 60.04 MiB │      7 │
└────────┴─────────┴────────────┴───────────┴────────┘

1 rows in set. Elapsed: 0.008 sec.

3.查看数据表分区信息

--查看测试表在19年12月的分区信息
SELECT 
    partition AS `分区`,
    sum(rows) AS `总行数`,
    formatReadableSize(sum(data_uncompressed_bytes)) AS `原始大小`,
    formatReadableSize(sum(data_compressed_bytes)) AS `压缩大小`,
    round((sum(data_compressed_bytes) / sum(data_uncompressed_bytes)) * 100, 0) AS `压缩率`
FROM system.parts
WHERE (database IN ('default')) AND (table IN ('temp_1')) AND (partition LIKE '2019-12-%')
GROUP BY partition
ORDER BY partition ASC

┌─分区───────┬─总行数─┬─原始大小──┬─压缩大小───┬─压缩率─┐
│ 2019-12-01 │     24 │ 6.17 KiB  │ 2.51 KiB   │     41 │
│ 2019-12-02 │   9215 │ 2.45 MiB  │ 209.74 KiB │      8 │
│ 2019-12-03 │  17265 │ 4.46 MiB  │ 453.78 KiB │     10 │
│ 2019-12-04 │  27741 │ 7.34 MiB  │ 677.25 KiB │      9 │
│ 2019-12-05 │  31500 │ 8.98 MiB  │ 469.30 KiB │      5 │
│ 2019-12-06 │    157 │ 37.50 KiB │ 4.95 KiB   │     13 │
│ 2019-12-07 │    110 │ 32.75 KiB │ 3.86 KiB   │     12 │
└────────────┴────────┴───────────┴────────────┴────────┘

7 rows in set. Elapsed: 0.005 sec.

4.查看数据表字段的信息

SELECT 
    column AS `字段名`,
    any(type) AS `类型`,
    formatReadableSize(sum(column_data_uncompressed_bytes)) AS `原始大小`,
    formatReadableSize(sum(column_data_compressed_bytes)) AS `压缩大小`,
    sum(rows) AS `行数`
FROM system.parts_columns
WHERE (database = 'default') AND (table = 'temp_1')
GROUP BY column
ORDER BY column ASC

┌─字段名───────────┬─类型─────┬─原始大小───┬─压缩大小───┬────行数─┐
│ a                │ String   │ 23.83 MiB  │ 134.13 KiB │ 3127523 │
│ b                │ String   │ 19.02 MiB  │ 127.72 KiB │ 3127523 │
│ c                │ String   │ 5.97 MiB   │ 49.09 KiB  │ 3127523 │
│ d        		   │ String   │ 3.95 MiB   │ 532.86 KiB │ 3127523 │
│ e                │ String   │ 5.17 MiB   │ 49.47 KiB  │ 3127523 │
│ totalDate        │ DateTime │ 11.93 MiB  │ 1.26 MiB   │ 3127523 │
└──────────────────┴──────────┴────────────┴────────────┴─────────┘
————————————————

5. 查看表的各个指标

select database,
       table,
       sum(bytes) as size,
       sum(rows) as rows,
       min(min_date) as min_date,
       max(max_date) as max_date,
       sum(bytes_on_disk) as bytes_on_disk,
       sum(data_uncompressed_bytes) as data_uncompressed_bytes,
       sum(data_compressed_bytes) as data_compressed_bytes,
       (data_compressed_bytes / data_uncompressed_bytes) * 100 as compress_rate,
       max_date - min_date as days,
       size / (max_date - min_date) as avgDaySize
  from system.parts
 where active
   and database = 'database'
   and table = 'tablename'
 group by database, table

结果为：这种结果显示的大小size是字节，我们如何转换为常见的MB和GB呢？

select
    database,
    table,
    formatReadableSize(size) as size,
    formatReadableSize(bytes_on_disk) as bytes_on_disk,
    formatReadableSize(data_uncompressed_bytes) as data_uncompressed_bytes,
    formatReadableSize(data_compressed_bytes) as data_compressed_bytes,
    compress_rate,
    rows,
    days,
    formatReadableSize(avgDaySize) as avgDaySize
from
(
    select
        database,
        table,
        sum(bytes) as size,
        sum(rows) as rows,
        min(min_date) as min_date,
        max(max_date) as max_date,
        sum(bytes_on_disk) as bytes_on_disk,
        sum(data_uncompressed_bytes) as data_uncompressed_bytes,
        sum(data_compressed_bytes) as data_compressed_bytes,
        (data_compressed_bytes / data_uncompressed_bytes) * 100 as compress_rate,
        max_date - min_date as days,
        size / (max_date - min_date) as avgDaySize
    from system.parts
    where active 
     and database = 'database'
     and table = 'tablename'
    group by
        database,
        table
)

结果：这就转换为常见的单位了。

上面过程可以看到，最终都用表进行了聚合，为什么会这样呢？

以一个简单的例子来看，我们最常见的是查看表分区，下面来看下不进行聚合的结果：

select partition
  from system.parts
 where active
   and database = 'database'
   and table = 'tablename'

结果为：这是因为在CH中，和我们hive表不一样，hive表一个分区只会有一条记录，但CH不是，每个分区分为了不同的marks

因此，我们要实现和hive一样查分区的功能时，要对表进行聚合查看。

6.跟踪分区

SELECT database,
       table,
       count() AS parts,
       uniq(partition) AS partitions,
       sum(marks) AS marks,
       sum(rows) AS rows,
       formatReadableSize(sum(data_compressed_bytes)) AS compressed,
       formatReadableSize(sum(data_uncompressed_bytes)) AS uncompressed,
       round((sum(data_compressed_bytes) / sum(data_uncompressed_bytes)) * 100.,2) AS percentage
  FROM system.parts
 WHERE active
   and database = 'database'
   and table = 'tablename'
 GROUP BY database, table

7.检查数据大小

SELECT table,
       formatReadableSize(sum(data_compressed_bytes)) AS tc,
       formatReadableSize(sum(data_uncompressed_bytes)) AS tu,
       round((sum(data_compressed_bytes) / sum(data_uncompressed_bytes)) * 100,2) AS ratio
  FROM system.columns
 WHERE database = 'database'
   and table = 'table'
 GROUP BY table
 ORDER BY sum(data_compressed_bytes) ASC

参考：https://blog.csdn.net/weixin_39025362/article/details/109051723 https://blog.csdn.net/qq_21383435/article/details/115679147

本文参与腾讯云自媒体分享计划，分享自作者个人站点/博客。

原始发表：2023-10-10，如有侵权请联系 cloudcommunity@tencent.com 删除

数据库