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https://issues.apache.org/jira/browse/OAK-3090?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14622214#comment-14622214
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Chetan Mehrotra commented on OAK-3090:
--------------------------------------
Given we already use Guava in Oak it might be better to just make use of them
(or LIRSCache if it supports removal listener) and have a simple
CachingDataStore impl. Have a look at {{DataStoreBlobStore#getInputStream}}
where we have some caching done for small binaries on heap.
Extrapolating that design in following way would allow us to implement a simple
FS based caching layer
# Have a new cache where the cached value is File (or some instance which keeps
a reference to File)
# Provide support for Weight via a simple
[Weigher|http://docs.guava-libraries.googlecode.com/git-history/release/javadoc/com/google/common/cache/Weigher.html]
which is based on File size
# Register a
[RemovalListener|http://docs.guava-libraries.googlecode.com/git-history/release/javadoc/com/google/common/cache/RemovalListener.html]
which removes the file from file system upon eviction
# Provide a
[CacheLoader|http://docs.guava-libraries.googlecode.com/git-history/release/javadoc/com/google/common/cache/CacheLoader.html]
which spools the remote binary to local filesystem
This should be a small logic and would provide you all benefits of Guava cache
including cache stats. And this would transparently work for any DataStore.
May be we implement it at {{BlobStore}} level itself and then it would be
useful for other BlobStore also. Doing it at BlobStore level would require some
support from {{BlobStore}} to determine the blob length from blobId itself.
[1] https://code.google.com/p/guava-libraries/wiki/CachesExplained
> Caching BlobStore implementation
> ---------------------------------
>
> Key: OAK-3090
> URL: https://issues.apache.org/jira/browse/OAK-3090
> Project: Jackrabbit Oak
> Issue Type: New Feature
> Components: blob
> Reporter: Chetan Mehrotra
> Fix For: 1.3.4
>
>
> Storing binaries in Mongo puts lots of pressure on the MongoDB for reads. To
> reduce the read load it would be useful to have a filesystem based cache of
> frequently used binaries.
> This would be similar to CachingFDS (OAK-3005) but would be implemented on
> top of BlobStore API.
> Requirements
> * Specify the max binary size which can be cached on file system
> * Limit the size of all binary content present in the cache
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