The issue was that at startup, or after nodes are back, the previous code
prevented full load balancing until some data are written.

It was like this to limit the number of connections, which does not matter
anymore (see commit 77132157).

# Previous status

The issue was that we had extreme storage fragmentation from the point of view
of the replication algorithm, which processes one partition at a time.

By using an autoincrement for the 'data' table, rows were ordered by the time
at which they were added:
- parts may be the result of replication -> ordered by partition, tid, oid
- other rows are globally sorted by tid

Which means that when scanning a given partition, many rows were skipped all
the time:
- if readahead is bigger enough, the efficiency is 1/N for a node with N
  partitions assigned
- else, it is worse because it seeks all the time

For huge databases, the replication was horribly slow, in particular from HDD.

# Chosen solution

This commit changes how ids are generated to somehow split 'data'
per partition. The backend tracks 1 last id per assigned partition, where the
16 higher bits contains the partition. Keep in mind that the value of id has no
meaning and it's only chosen for performance reasons. IOW, a row can be
referred by an oid of a partition different than the 16 higher bits of id:
- there's no migration needed and the 16 higher bits of all existing rows are 0
- in case of deduplication, a row can still be shared by different partitions

Due to https://jira.mariadb.org/browse/MDEV-12836, we leave the autoincrement
on existing databases.

## Downsides

On insertion, increasing the number of partitions now slows down significantly:
for 2 nodes using TokuDB, 4% for 180 partitions, 40% for 2000. For 12
partitions, the difference remains negligible. The solution for this issue will
be to enable to increase the number of partitions efficiently, so that nodes
can keep a small number of them, even for DB that are expected to grow so much
that many nodes are added over time: such feature was already considered so
that users don't have to worry anymore about this obscure setting at database
creation.

Read performance is only slowed down for applications that read a lot of data
that were written contiguously, but split in small blocks. A solution is to
extend ZODB so that the application tells it to chose new oids that will end up
in the same partition. Like for insertion, there should not be too many
partitions.

With RocksDB (MariaDB 10.2.10), it takes a significant amount of time to
collect all last ids at startup when there are many partitions.

## Other advantages

- The storage layout of data is now always the same and does not depend on
  whether rows came from replication or commits.
- Efficient deletion of partition to free space in-place will be possible.

# Considered alternative

The only serious alternative was to replicate as many partitions as possible at
the same time, ideally all assigned partitions, but it's not always possible.
For best performance, it would often require to synchronize new nodes, or even
all of them, so that thesource nodes don't have to scan 'data' several times.

If existing nodes are kept, all data that aren't copied to the newly added
nodes have to be skipped. If the number of nodes is multiplied by N, the
efficiency is 1-1/N at best (synchronized nodes), else it's even worse
because partitions are somehow shuffled.

Checking/replacing a single node would remain slow when there are several
source nodes.

At last, such an algorithm would be much more complex and we would not have the
other advantages listed above.

For existing DB, altering the table may be doable with schema editing and
clean up of sqlite_sequence.

getObject becomes faster because it does not use secondary index anymore.
Only the primary one. This frees RAM during normal operation. For MySQL,
DatabaseManager._getObject is sped up by ~3% for in-memory loads.
An improvement of ~1% from ERP5 was also mesured for IO-bound loads.

On insertion, the fast index is (`partition`, tid, oid) because we almost
always insert lines with increasing tid, whereas oid values are more random.
Although the value (data_id+value_tid) is moved from the fast to the slow index,
this should have little impact on performance because the value size is quite
small compared to the key.

The impact on replication should also be negligible:
- a little faster when there's no oid to replicate: only the secondary index,
  smaller, is scanned
- otherwise: the (slightly) biggest index is scanned randomly

On disk usage, an increase of ~4% was observed for TokuDB.
Less compressibility ? Any link with https://jira.percona.com/browse/TDB-86 ?

This information is handy to see how well cache performs.

Amended by Julien Muchembled:
- do not abbreviate some existing field names in repr result (asking the
  user to look at the source code in order to decipher logs is not nice)
- hit: change from %.1f to %.3g
- hit: hide it completely if nload is 0
- use __future__.division instead of adding more casts to float

INFO Z2 Log files reopened successfully
INFO SignalHandler Caught signal SIGTERM
INFO Z2 Shutting down fast
INFO ZServer closing HTTP to new connections
ERROR ZODB.Connection Couldn't load state for BTrees.LOBTree.LOBucket 0xc12e29
Traceback (most recent call last):
  File "ZODB/Connection.py", line 909, in setstate
    self._setstate(obj, oid)
  File "ZODB/Connection.py", line 953, in _setstate
    p, serial = self._storage.load(oid, '')
  File "neo/client/Storage.py", line 81, in load
    return self.app.load(oid)[:2]
  File "neo/client/app.py", line 355, in load
    data, tid, next_tid, _ = self._loadFromStorage(oid, tid, before_tid)
  File "neo/client/app.py", line 387, in _loadFromStorage
    askStorage)
  File "neo/client/app.py", line 297, in _askStorageForRead
    self.sync()
  File "neo/client/app.py", line 898, in sync
    self._askPrimary(Packets.Ping())
  File "neo/client/app.py", line 163, in _askPrimary
    return self._ask(self._getMasterConnection(), packet,
  File "neo/client/app.py", line 177, in _getMasterConnection
    result = self.master_conn = self._connectToPrimaryNode()
  File "neo/client/app.py", line 202, in _connectToPrimaryNode
    index = (index + 1) % len(master_list)
ZeroDivisionError: integer division or modulo by zero

It's not possible yet to replicate a node that is importing data.
One must wait that the migration is finished.

This reverts commit d3c22487 partially
and fixes the bug in a much simpler way.

Traceback (most recent call last):
  [...]
  File "neo/storage/handlers/client.py", line 115, in askStoreObject
    *e.args)
  File "neo/lib/handler.py", line 333, in queueEvent
    self.sortQueuedEvents()
  File "neo/lib/handler.py", line 326, in <lambda>
    self._event_queue.sort(key=key))()
  File "neo/storage/transactions.py", line 67, in __lt__
    return self.locking_tid < other.locking_tid
AttributeError: 'NoneType' object has no attribute 'locking_tid'

Pending events:
  (None, <askFetchTransactions: ...>)
  (<Transaction(C13, locking_tid=03c266508a058388, tid=None, age=0.21s) at 0x7f086bbc3d50>, <_askStoreObject: ...>)

It was out-of-date since commit 23b6a66a.

This time we had issues with _getObject and getReplicationObjectList
on production.
It seems related to the upgrade of MariaDB, from 10.0.21 to 10.1.25.

See also commits eaa00a88
             and 13911ca3

The explanation became wrong during a git-rebase, when it was decided
to keep the old code that drop partitions. The new one needs more work
and it is kept in a branch.