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  1. docs/bucket/lifecycle/README.md

    other MinIO clusters via the ILM transition feature. This will allow transitioning of older objects to a different cluster or the public cloud by setting up transition rules in the bucket lifecycle configuration. This feature enables applications to optimize storage costs by moving less frequently accessed data to a cheaper storage without compromising accessibility of data.
    
    To transition objects in a bucket to a destination bucket on a different cluster, applications need to specify a transition...
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  2. docs/bucket/versioning/README.md

    ### Motivation
    
    **PLEASE READ: This feature is meant for advanced usecases only where the setup is using bucket versioning or with replicated buckets, use this feature to optimize versioning behavior for some specific applications. MinIO experts will evaluate and guide on the benefits for your application, please reach out to us on <https://subnet.min.io>.**
    
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  3. docs/erasure/storage-class/README.md

    saving the object in specific number of data and parity drives.
    
    ## Storage usage
    
    The selection of varying data and parity drives has a direct impact on the drive space usage. With storage class, you can optimize for high
    redundancy or better drive space utilization.
    
    To get an idea of how various combinations of data and parity drives affect the storage usage, let’s take an example of a 100 MiB file stored
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  4. README.md

    * Transactions, Nested Transactions, Save Point, RollbackTo to Saved Point
    * Context, Prepared Statement Mode, DryRun Mode
    * Batch Insert, FindInBatches, Find To Map
    * SQL Builder, Upsert, Locking, Optimizer/Index/Comment Hints, NamedArg, Search/Update/Create with SQL Expr
    * Composite Primary Key
    * Auto Migrations
    * Logger
    * Extendable, flexible plugin API: Database Resolver (Multiple Databases, Read/Write Splitting) / Prometheus…
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  5. docs/compression/README.md

    MinIO uses [`klauspost/compress/s2`](https://github.com/klauspost/compress/tree/master/s2)
    streaming compression due to its stability and performance.
    
    This algorithm is specifically optimized for machine generated content.
    Write throughput is typically at least 500MB/s per CPU core,
    and scales with the number of available CPU cores.
    Decompression speed is typically at least 1GB/s.
    
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