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From Aaron Morton <aa...@thelastpickle.com>
Subject Re: Cassandra to store 1 billion small 64KB Blobs
Date Mon, 26 Jul 2010 05:00:21 GMT
Some background reading.. http://ria101.wordpress.com/2010/02/22/cassandra-randompartitioner-vs-orderpreservingpartitioner/

Not sure on your follow up question, so I'll just wildly blather on about things :)

My assumption of your data is you have 64K chunks that are identified by a hash, which can
somehow be grouped together into larger files (so there is a "file name" of sorts).

One possible storage design (assuming the Random Partitioner) is....

A Chunks CF, each row in this CF uses the hash of the chunk as it's key and has is a single
column with the chunk data. You could use more columns to store meta here.

A ChunkIndex CF, each row uses the file name (from above) as the key and has one column for
each chunk in the file. The column name *could* be an offset for the chunk and the column
value could be the hash for the chunk. Or you could use the chunk hash as the col name and
the offset as the col value if needed.

To rebuild the file read the entire row from the ChunkIndex, then make a series of multi gets
to read all the chunks. Or you could lazy populate the ones you needed.

This is all assuming that the 1000's comment below means you could want to combine the chunks
 60+ MB chunks. It would be easier to keep all the chunks together in one row, if you are
going to have large (unbounded) file size this may not be appropriate.

You could also think about using the order preserving partitioner, and using a compound key
for each row such as "file_name_hash.offset" . Then by using the get_range_slices to scan
the range of chunks for a file you would not need to maintain a secondary index. Some drawbacks
to that approach, read the article above.

Hope the helps
Aaron


On 26 Jul, 2010,at 04:01 PM, Michael Widmann <michael.widmann@gmail.com> wrote:

> Thanks for this detailed description ...
>
> You mentioned the secondary index in a standard column, would it be better to build several
indizes?
> Is that even possible to build a index on for example 32 columns?
>
> The hint with the smaller boxes is very valuable!
>
> Mike
>
> 2010/7/26 Aaron Morton <aaron@thelastpickle.com>
>
>     For what it's worth...
>
>     * Many smaller boxes with local disk storage are preferable to 2 with huge NAS storage.
>     * To cache the hash values look at the KeysCached setting in the storage-config
>     * There are some row size limits see http://wiki.apache.org/cassandra/CassandraLimitations
>     * If you wanted to get 1000 blobs, rather then group them in a single row using a
super column consider building a secondary index in a standard column. One CF for the blobs
using your hash, one CF that uses whatever they grouping key is with a col for every blobs
hash value. Read from the index first, then from the blobs themselves.
>
>     Aaron
>
>
>
>     On 24 Jul, 2010,at 06:51 PM, Michael Widmann <michael.widmann@gmail.com> wrote:
>
>>     Hi Jonathan
>>
>>     Thanks for your very valuable input on this.
>>
>>     I maybe didn't enough explanation - so I'll try to clarify
>>
>>     Here are some thoughts:
>>
>>         * binary data will not be indexed - only stored. 
>>         * The file name to the binary data (a hash) should be indexed for search
>>         * We could group the hashes in 62 "entry" points for search retrieving ->
i think suprcolumns (If I'm right in terms) (a-z,A_Z,0-9)
>>         * the 64k Blobs meta data (which one belong to which file) should be stored
separate in cassandra
>>         * For Hardware we rely on solaris / opensolaris with ZFS in the backend
>>         * Write operations occur much more often than reads
>>         * Memory should hold the hash values mainly for fast search (not the binary
data)
>>         * Read Operations (restore from cassandra) may be async - (get about 1000
Blobs) - group them restore
>>
>>     So my question is too: 
>>
>>     2 or 3 Big boxes or 10 till 20 small boxes for storage...
>>     Could we separate "caching" - hash values CFs cashed and indexed - binary data
CFs not ...
>>     Writes happens around the clock - on not that tremor speed but constantly
>>     Would compaction of the database need really much disk space
>>     Is it reliable on this size (more my fear)
>>
>>     thx for thinking and answers...
>>
>>     greetings
>>
>>     Mike
>>
>>     2010/7/23 Jonathan Shook <jshook@gmail.com>
>>
>>         There are two scaling factors to consider here. In general the worst
>>         case growth of operations in Cassandra is kept near to O(log2(N)). Any
>>         worse growth would be considered a design problem, or at least a high
>>         priority target for improvement.  This is important for considering
>>         the load generated by very large column families, as binary search is
>>         used when the bloom filter doesn't exclude rows from a query.
>>         O(log2(N)) is basically the best achievable growth for this type of
>>         data, but the bloom filter improves on it in some cases by paying a
>>         lower cost every time.
>>
>>         The other factor to be aware of is the reduction of binary search
>>         performance for datasets which can put disk seek times into high
>>         ranges. This is mostly a direct consideration for those installations
>>         which will be doing lots of cold reads (not cached data) against large
>>         sets. Disk seek times are much more limited (low) for adjacent or near
>>         tracks, and generally much higher when tracks are sufficiently far
>>         apart (as in a very large data set). This can compound with other
>>         factors when session times are longer, but that is to be expected with
>>         any system. Your storage system may have completely different
>>         characteristics depending on caching, etc.
>>
>>         The read performance is still quite high relative to other systems for
>>         a similar data set size, but the drop-off in performance may be much
>>         worse than expected if you are wanting it to be linear. Again, this is
>>         not unique to Cassandra. It's just an important consideration when
>>         dealing with extremely large sets of data, when memory is not likely
>>         to be able to hold enough hot data for the specific application.
>>
>>         As always, the real questions have lots more to do with your specific
>>         access patterns, storage system, etc I would look at the benchmarking
>>         info available on the lists as a good starting point.
>>
>>
>>         On Fri, Jul 23, 2010 at 11:51 AM, Michael Widmann
>>         <michaelwidmann@gmail.com> wrote:
>>         > Hi
>>         >
>>         > We plan to use cassandra as a data storage on at least 2 nodes with
RF=2
>>         > for about 1 billion small files.
>>         > We do have about 48TB discspace behind for each node.
>>         >
>>         > now my question is - is this possible with cassandra - reliable - means
>>         > (every blob is stored on 2 jbods)..
>>         >
>>         > we may grow up to nearly 40TB or more on cassandra "storage" data ...
>>         >
>>         > anyone out did something similar?
>>         >
>>         > for retrieval of the blobs we are going to index them with an hashvalue
>>         > (means hashes are used to store the blob) ...
>>         > so we can search fast for the entry in the database and combine the
blobs to
>>         > a normal file again ...
>>         >
>>         > thanks for answer
>>         >
>>         > michael
>>         >
>>
>>
>>
>>
>>     -- 
>>     bayoda.com - Professional Online Backup Solutions for Small and Medium Sized
Companies
>
>
>
>
> -- 
> bayoda.com - Professional Online Backup Solutions for Small and Medium Sized Companies

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