Setting up a data warehousing mining project on a Linux cloud server. The primary language is Python .
Would like to use this pattern for querying on data and storing data:
- SQL Database – SQL database is used to query on data. However, the SQL database stores only fields that need to be searched on, it does NOT store the “blob” of data itself. Instead it stores a key that references that full “blob” of data in the a key-value Blobstore.
- Blobstore – A key-value Blobstore is used to store actual “documents” or “blobs” of data.
The issue that we are having is that we would like more frequently accessed blobs of data to be automatically stored in RAM. We were planning to use Redis for this. However, we would like a solution that automatically tries to get the data out of RAM first, if it can’t find it there, then it goes to the blobstore.
Is there a good library or ready-made solution for this that we can use without rolling our own? Also, any comments and criticisms about the proposed architecture would also be appreciated.
Thanks so much!
Rather than using Redis or Memcached for caching, plus a “blobstore” package to store things on disk, I would suggest to have a look at Couchbase Server which does exactly what you want (i.e. serving hot blobs from memory, but still storing them to disk).
In the company I work for, we commonly use the pattern you described (i.e. indexing in a relational database, plus blob storage) for our archiving servers (terabytes of data). It works well when the I/O done to write the blobs are kept sequential. The blobs are never rewritten, but simply appended at the end of a file (it is fine for an archiving application).
The same approach has been also used by others. For instance: