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Home/ Questions/Q 7800881
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Editorial Team
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Editorial Team
Asked: June 2, 20262026-06-02T00:46:46+00:00 2026-06-02T00:46:46+00:00

This is in conjunction with my previous question Click here . We are using

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This is in conjunction with my previous question Click here.
We are using berkeley DB for temporary storage before it is processed and stored into a relational DB.The problem arises when the size increases beyond a certain point.Now we have to either split the files into smaller ones or compress the existing files.In this question I want to ask the compression part,whether berkeley DB has any built in compression utility or do we have to do it programatically.If it is built in,then it will always be faster.

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  1. Editorial Team
    Editorial Team
    2026-06-02T00:46:48+00:00Added an answer on June 2, 2026 at 12:46 am

    From here:

    According to the Berkeley FAQ there are two ways of optimizing it (before compression):

    1. Compact
    2. Vacuum

    You can also implement your own compression algorithm as shown here.

    How different is the Berkeley DB VACUUM from SQLite’s?

    SQLite
    implements the VACUUM command as a database dump followed by a
    complete reload from that dump. It is an expensive operation, locking
    the entire database for the duration of the operation. It is also an
    all or nothing operation. Either it works, or it fails and you have to
    try again sometime. When SQLite finishes, the database is frequently
    smaller in size (file size is smaller) and the btree is better
    organized (shallower) than before due to in-order key insertion of the
    data from the dump file. SQLite, when it works and when you can afford
    locking everyone out of the database, does a good job of VACUUM.
    Berkeley DB approaches this in a completely different way. For many
    releases now Berkeley DB’s B-Tree implementation has had the ability
    to compact while other oprations are in-flight. Compacting is a
    process wherein the B-Tree nodes are examined and, when less than
    optimal, they are re-organized (reverse split, etc.). The more shallow
    your B-Tree, the fewer lookups required to find the data at a leaf
    node. Berkeley DB can compact sections of the tree, or the whole tree
    at once. For 7x24x365 (five-nines) operation this is critical. The BDB
    version of compact won’t adversly impact ongoing database operations
    whereas SQLite’s approach does. But compaction doesn’t address empty
    sections of the database (segments of the database file where deleted
    data once lived). Berkeley DB also supports compression of database
    files by moving data within the file, then truncating the file
    returning that space to the filesystem. As of release 5.1 of Berkeley
    DB, the VACUUM command will compact and compress the database file(s).
    This operation takes more time than the dump/load approach of SQLite
    because it is doing more work to allow for the database to remain
    operational. We believe this is the right trade-off, but if you
    disagree you can always dump/load the database in your code.

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