[jira] [Updated] (TUBEMQ-124) Structured index storage

["Guocheng Zhang (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/TUBEMQ-124?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Guocheng Zhang updated TUBEMQ-124:
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    Description: 
1. Structured index storage: optimize the current index storage, for example, increase the structured index storage, which can be quickly retrieved through the index when in use to quickly locate the data; the increase in the index structure may make the write request slower, At the same time, it takes more time to check and restore the index when the system restarts

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To solve this problem, I plan to implement it like this:
 !image-2020-05-17-10-42-15-736.png! 

Divide the datas to bucket in index segment file, and use the Bloom filter algorithm to save the position of the filter item for each data in the bucket, and finally add 2 bytes of version information at the end of the segment file. After this improvement, there are level 2 indexes in the index segment file, the Bloom filter bitmap is the first level, and the index bucket with message index information is the second level. 

When filtering consumption, the system first searches whether the filter item exists in the corresponding data bucket from the first level. If it does not exist, it continues to search for the existence of the next data bucket until the index segment file is completed and the filter is switched to the next index segment file; if the filter item is in a data bucket, the data in the corresponding data bucket will be read according to the current index file retrieval method. 

Implementation effect estimation: The results of using the Bloom filter algorithm to locate the results are not guaranteed to be unique, but they should be improved compared to the current item-by-item inspection, at least in the worst case, the filtering effect is consistent; and it will be a very good help if the sparse and non-colliding index item collection. The impact is that we need additional index storage space, and index file recovery requires special attention.



  was:
1. Structured index storage: optimize the current index storage, for example, increase the structured index storage, which can be quickly retrieved through the index when in use to quickly locate the data; the increase in the index structure may make the write request slower, At the same time, it takes more time to check and restore the index when the system restarts

--------------------------------------------------------------------------

To solve this problem, I plan to implement it like this:
 !image-2020-05-17-10-42-15-736.png! 


> Structured index storage
> ------------------------
>
>                 Key: TUBEMQ-124
>                 URL: https://issues.apache.org/jira/browse/TUBEMQ-124
>             Project: Apache TubeMQ
>          Issue Type: Sub-task
>            Reporter: Guocheng Zhang
>            Priority: Major
>         Attachments: image-2020-05-17-10-42-15-736.png
>
>
> 1. Structured index storage: optimize the current index storage, for example, increase the structured index storage, which can be quickly retrieved through the index when in use to quickly locate the data; the increase in the index structure may make the write request slower, At the same time, it takes more time to check and restore the index when the system restarts
> --------------------------------------------------------------------------
> To solve this problem, I plan to implement it like this:
>  !image-2020-05-17-10-42-15-736.png! 
> Divide the datas to bucket in index segment file, and use the Bloom filter algorithm to save the position of the filter item for each data in the bucket, and finally add 2 bytes of version information at the end of the segment file. After this improvement, there are level 2 indexes in the index segment file, the Bloom filter bitmap is the first level, and the index bucket with message index information is the second level. 
> When filtering consumption, the system first searches whether the filter item exists in the corresponding data bucket from the first level. If it does not exist, it continues to search for the existence of the next data bucket until the index segment file is completed and the filter is switched to the next index segment file; if the filter item is in a data bucket, the data in the corresponding data bucket will be read according to the current index file retrieval method. 
> Implementation effect estimation: The results of using the Bloom filter algorithm to locate the results are not guaranteed to be unique, but they should be improved compared to the current item-by-item inspection, at least in the worst case, the filtering effect is consistent; and it will be a very good help if the sparse and non-colliding index item collection. The impact is that we need additional index storage space, and index file recovery requires special attention.



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