Instead of fixing the prefetch queue and recent chunk queue as
different sizes, allow these to share the same limit but be scaled
based on the work being performed.

During walks about 20% of the space will be given to the prefetcher,
and the other 80% will be used by the recent chunks cache. This
should improve cases where there is bad locality between chunks.

During writing of a pack stream, 90-100% of the space should be
made available to the prefetcher, as the prefetch plan is usually
very accurate about the order chunks will be needed in.

Change-Id: I1ca7acb4518e66eb9d4138fb753df38e7254704d
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>

The RecentChunks cache assumes there is always at least one recent
chunk in the maxSize that it receives from the DhtReaderOptions.
Ensure that is true by requiring the size to be at least 1.

Running with 0 recent chunk cache is very a bad idea, often
during commit walking the parents of a commit will be found
on the same chunk as the commit that was just accessed. In
these cases its a good idea to keep that last chunk around
so the parents can be quickly accessed.

Change-Id: I33b65286e8a4cbf6ef4ced28c547837f173e065d
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>

jgit.storage.dht is a storage provider implementation for JGit that
permits storing the Git repository in a distributed hashtable, NoSQL
system, or other database.  The actual underlying storage system is
undefined, and can be plugged in by implementing 7 small interfaces:

  *  Database
  *  RepositoryIndexTable
  *  RepositoryTable
  *  RefTable
  *  ChunkTable
  *  ObjectIndexTable
  *  WriteBuffer

The storage provider interface tries to assume very little about the
underlying storage system, and requires only three key features:

  *  key -> value lookup (a hashtable is suitable)
  *  atomic updates on single rows
  *  asynchronous operations (Java's ExecutorService is easy to use)

Most NoSQL database products offer all 3 of these features in their
clients, and so does any decent network based cache system like the
open source memcache product.  Relying only on key equality for data
retrevial makes it simple for the storage engine to distribute across
multiple machines.  Traditional SQL systems could also be used with a
JDBC based spi implementation.

Before submitting this change I have implemented six storage systems
for the spi layer:

  * Apache HBase[1]
  * Apache Cassandra[2]
  * Google Bigtable[3]
  * an in-memory implementation for unit testing
  * a JDBC implementation for SQL
  * a generic cache provider that can ride on top of memcache

All six systems came in with an spi layer around 1000 lines of code to
implement the above 7 interfaces.  This is a huge reduction in size
compared to prior attempts to implement a new JGit storage layer.  As
this package shows, a complete JGit storage implementation is more
than 17,000 lines of fairly complex code.

A simple cache is provided in storage.dht.spi.cache.  Implementers can
use CacheDatabase to wrap any other type of Database and perform fast
reads against a network based cache service, such as the open source
memcached[4].  An implementation of CacheService must be provided to
glue this spi onto the network cache.

[1] https://github.com/spearce/jgit_hbase
[2] https://github.com/spearce/jgit_cassandra
[3] http://labs.google.com/papers/bigtable.html
[4] http://memcached.org/

Change-Id: I0aa4072781f5ccc019ca421c036adff2c40c4295
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>