Some background reading..

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

On 26 Jul, 2010,at 04:01 PM, Michael Widmann <> 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!


2010/7/26 Aaron Morton <>
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
* 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.


On 24 Jul, 2010,at 06:51 PM, Michael Widmann <> 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...



2010/7/23 Jonathan Shook <>
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
<> 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

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