Limit number of partitions that data chunks of a "large" blob can reside on

Nobody has claimed this yet.

Currently Ambry sprays the data chunks of a given "large" blob across all writable partitions (i.e. any partition is eligible to receive a data chunk). This can create some problems
1. If one chunk is lost, the whole blob is considered lost. Spraying it across multiple partitions spread over multiple nodes increases the area of failure
2. One disk being slow/unavailable can have a multiplying effect if it stores a single chunk of many large blobs

In order to restrict these effects, it may be useful to limit the number of partitions the data chunks of a particular "large" blob can reside on. At current time, GET operations request 4 chunks in parallel and maintain the number until the blob is currently served. It may be enough to have 4 * x partitions receive the data chunks (4,8,12 etc) to serve any parallelism requirements.

The partition picking can be random to start with and can be made more intelligent based on requirements (which need to be evaluated)
1. Pick from distinct disks and nodes
2. Pick from distinct disks but limit number of nodes across which these disks are spread (to lessen impact of node failures)
3. ...
Each of the options comes with its own upsides and downsides.

Start by tracing how large-blob data chunks are assigned to writable partitions and how GET operations maintain four chunks in parallel. Evaluate the proposed partition limits and placement options against disk, node, and failure considerations. Done would require a decided design and an implemented, tested restriction on the partitions eligible for each large blob.

Written by the indexing model from the issue text.