RefData now uses a sequence number as part of the field, ensuring
that updates always increase the sequence number by one whenever
a reference is modified.

Attaching a sequence number to RefData will help with storing
reference log entries during updates. As the sequence number should
be unique within the reference name space, log entries can be keyed
by the sequence number and remain unique.  Making this work over
reference delete-create cycles will require an additional RefTable
API to return the oldest sequence number previously used in the
reference log to seed the recreated reference.

Change-Id: I11cfff2a96ef962e57f29925a3eef41bdbf9f9bb
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
Signed-off-by: Chris Aniszczyk <caniszczyk@gmail.com>

The standard Google distribution of Protocol Buffers in Java is better
maintained than TinyProtobuf, and should be faster for most uses.  It
does use slightly more memory due to many of our key types being
stored as strings in protobuf messages, but this is probably worth the
small hit to memory in exchange for better maintained code that is
easier to reuse in other applications.

Exposing all of our data members to the underlying implementation
makes it easier to develop reporting and data mining tools, or to
expand out a nested structure like RefData into a flat format in a SQL
database table.

Since the C++ `protoc` tool is necessary to convert the protobuf
script into Java code, the generated files are committed as part of
the source repository to make it easier for developers who do not have
this tool installed to still build the overall JGit package and make
use of it.  Reviewers will need to be careful to ensure that any edits
made to a *.proto file come in a commit that also updates the
generated code to match.

CQ: 5135
Change-Id: I53e11e82c186b9cf0d7b368e0276519e6a0b2893
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
Signed-off-by: Chris Aniszczyk <caniszczyk@gmail.com>

Performance testing has indicated the per-process ChunkCache isn't
very effective for the DHT storage implementation.  If a server is
using the DHT storage backend, it is most likely part of a larger
cluster where requests are distributed in a round-robin fashion
between the member servers.

In such a scenario there is insufficient data locality between
requests to get a good hit ratio on the per-process ChunkCache.  A low
hit ratio means the cache is actually hurting performance by eating up
memory that could otherwise be used for transient request data, and
increasing pressure on the GC when it needs to find free space.

Remove all of the ChunkCache code.  Installations that want to cache
(to reduce database usage) should wrap their Database with a
CacheDatabase and use a network based CacheServer.

I left the ChunkCache in the original DHT storage commit because I
wanted to document in the history of the project that its probably
worth *not* having, but leave open a door for someone to revert this
change if they find otherwise at a later date.

Change-Id: I364d0725c46c5a19f7443642a40c89ba4d3fdd29
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
Signed-off-by: Chris Aniszczyk <caniszczyk@gmail.com>

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>