谷歌云bigtable列版本不会被删除 [英] google cloud bigtable column versions are not deleted
问题描述
我们创建了一个包含两列系列的云大表格。一列有30个版本,另一个有1个版本。但是,当我们查询表时,我们得到了多个版本的列,我们已经将最大版本数设置为1。
表创建语句:
创建'myTable',{NAME => 'cf1',VERSIONS => '30'},{NAME => 'cf2',VERSIONS => '1'}
描述'myTable':
{NAME => 'cf2',BLOOMFILTER => 'ROW',版本=> '** 1 **',IN_MEMORY => 'false',KEEP_DELETED_CELLS => 'FALSE',DATA_BLOCK_ENCODING => 'NONE',TTL => 'FOREVER',COMPRESSION => 'NONE',MIN_VERSIONS => '0',BLOCKCACHE => 'true',BLOCKSIZE => '65536',REPLICATION_SCOPE
=> '0'}
{NAME => 'cf1',BLOOMFILTER => 'ROW',版本=> '** 30 **',IN_MEMORY => 'false',KEEP_DELETED_CELLS => 'FALSE',DATA_BLOCK_ENCODING => 'NONE',TTL => 'FOREVER',COMPRESSION => 'NONE',MIN_VERSIONS => '0',BLOCKCACHE => 'true',BLOCKSIZE => '65536',REPLICATION_SCOPE
=> '0'}
bigtable垃圾回收是如何工作的?它多久删除旧版本?或者我们是否在创建表时丢失了一些东西? 解决方案
从Bigtable Docs中删除: ,因此您可能仍然能够在几天之后读取数据。
We have created a table in cloud bigtable with two column families. One column family with 30 versions and the other with 1 version. However, when we query the table we are getting multiple versions of the columns for which we have set max number of versions to 1.
Table create statement:
create 'myTable', {NAME => 'cf1', VERSIONS => '30'}, {NAME => 'cf2', VERSIONS => '1'}
Describe 'myTable':
{NAME => ‘cf2’, BLOOMFILTER => ‘ROW’, VERSIONS => ‘**1**’, IN_MEMORY => ‘false’, KEEP_DELETED_CELLS => ‘FALSE’, DATA_BLOCK_ENCODING => ‘NONE’, TTL => ‘FOREVER’, COMPRESSION => ‘NONE’, MIN_VERSIONS => ‘0’, BLOCKCACHE => ‘true’, BLOCKSIZE => ‘65536’, REPLICATION_SCOPE
=> ‘0’}
{NAME => ‘cf1’, BLOOMFILTER => ‘ROW’, VERSIONS => ‘**30**’, IN_MEMORY => ‘false’, KEEP_DELETED_CELLS => ‘FALSE’, DATA_BLOCK_ENCODING => ‘NONE’, TTL => ‘FOREVER’, COMPRESSION => ‘NONE’, MIN_VERSIONS => ‘0’, BLOCKCACHE => ‘true’, BLOCKSIZE => ‘65536’, REPLICATION_SCOPE
=> ‘0’}
How does the bigtable garbage collection work? How frequently does it delete the older versions? or are we missing something while creating the table ?
From Bigtable Docs: Deletion of values happens opportunistically in the background, so you might still be able to read the data for several days after it has expired.
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