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https://issues.apache.org/jira/browse/TUBEMQ-124?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17128999#comment-17128999
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Jeff Zhou commented on TUBEMQ-124:
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I think there are several major issues directly from the initial plan, which
contains (but not limited to):
1. Bloom Filter, as a probability prediction set, does **not** support a
**remove** operation, so we have to choose an *appropriate* time to rebuild it
after a long run with massive topics in and out, which is such an expensive
thing just like the GC STP (stop-the-world);
2. Fine-grained control might be introduced to scatter the impact while exploit
the negative-prediction efficiency, like we could do something like Java 8
revised ConcurrentHashMap, we could setup Bloom Filter on Segment (even
available for memory prediction), and apply something like URA (unbalanced
resource allocation) to better concentrate messages of the same topic into the
same segment.
Please review the prototype about the overall architecture of how Bloom Filter
(my opinion) could behave, and let's discuss something in details about above
factors, if any or more.
Do you have any input or idea about above topics?
> 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
> Assignee: Jeff Zhou
> Priority: Major
> Attachments: screenshot-1.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:
> !screenshot-1.png!
> The first add 2 bytes of version information at the end of the segment file,
> then, 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. 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.
> If the design needs to be implemented, I think the following points need to
> be considered:
> 1. Due to the addition of a bitmap index, the checkpoint file needs to be
> added to the index store, so, when the system is restarted we can know the
> starting checkpoint of the index file;
> 2. Due to the change in file structure, before releasing the version of this
> feature, we need to first release a historical version compatible with this
> feature to solve the system rollback problem after this feature version is
> upgraded abnormally. I think that this is a one-time operation, the price is
> worth it.
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