In the next patch we are going to expose access to BTree/Bucket entries
as public API. This will turn _BTreeItem into Entry and will also add
BucketEntry data type. Before doing that rearrange the order in which
the data structure go:

	- BTree,
	- Entry (_BTreeItem for now),
	- Bucket
	- BucketEntry (not present for now).

Only code movement - no other change.

Very brief and incomplete.

They are all either BTree or all Buckets.

See https://github.com/zopefoundation/ZODB/blob/3.10.7-4-gb8d7a8567/src/BTrees/Development.txt#L231
for details

This is one of BTree invariants - check it on load.

2dba8607 (go/zodb/btree: New package to work with ZODB BTrees (draft))
added btree module with btree.BTree essentially being LOBTree (int64 key
-> object). Since for wendelin.core we also need IOBTree (int32 key ->
object), which is used in ZBlk1

	https://lab.nexedi.com/nexedi/wendelin.core/blob/v0.12-6-g318efce/bigfile/file_zodb.py#L267
	https://lab.nexedi.com/nexedi/wendelin.core/blob/v0.12-6-g318efce/bigfile/file_zodb.py#L374

let's turn btree module into template internally and generate code for
both LOBTree and IOBTree.

For the reference BTree/py takes similar approach with respect to
templating.

To know database state corresponding to the connection.

The format of tid assumes ~ ns precision, and it is only formatted to µs
precision by default. So don't truncate TimeStamp value when computing
it from Tid, and perform the µs-rounding only on formatting.

The float numbers are not always exactly as in python. For example the
following program

	tidv = [
	    0x0000000000000000,
	    0x0285cbac258bf266,
	    0x0285cbad27ae14e6,
	    0x037969f722a53488,
	    0x03b84285d71c57dd,
	    0x03caa84275fc1166,
	]

	for tid in tidv:
	    t = TimeStamp.TimeStamp(p64(tid))
	    print '0x%016x %s %.9f\t%.9f' % (tid, t, t.timeTime(), t.second())

prints:

	0x0000000000000000 1900-01-01 00:00:00.000000 -2208988800.000000000     0.000000000
	0x0285cbac258bf266 1979-01-03 21:00:08.800000 284245208.800000191       8.800000185
	0x0285cbad27ae14e6 1979-01-03 21:01:09.300001 284245269.300001621       9.300001496	<-- ex here
	0x037969f722a53488 2008-10-24 05:11:08.120000 1224825068.119999886      8.119999878
	0x03b84285d71c57dd 2016-07-01 09:41:50.416574 1467366110.416574001      50.416573989
	0x03caa84275fc1166 2018-10-01 16:34:27.652650 1538411667.652649879      27.652650112

the difference is due to floating point operation ordering, because
TimeStamp.timeTime() looses precision - e.g. for marked case:

	In [8]: '%.10f' % (281566860.000000000 + 9.300001496)
	Out[8]: '281566869.3000015020'

We don't try to mimic float64 behaviour to Python exactly - because it is even
different for PURE_PYTHON=y or C TimeStamp implementations. However we don't
limit due to that our timestamp precision to only 1µs.

In other words we keep on maintaining exact compatibility with Python on
printing, but timestamp values itself are now ~ ns precision.

As https://github.com/kisielk/og-rek/pull/57 maybe shows []byte was
pickling as string only unintentionally and that might change.

We are already explicitly checking for string in corresponding index
load place:

	https://lab.nexedi.com/kirr/neo/blob/2dba8607/go/zodb/storage/fs1/index.go#L282

so it is better we also explicitly save the bits as string.

If we don't and https://github.com/kisielk/og-rek/pull/57 gets accepted,
tests will fail:

	--- FAIL: TestIndexSaveLoad (0.00s)
	    index_test.go:176: index load: /tmp/t-index893650059/458967662/1.fs.index: pickle @6: invalid oidPrefix: type []uint8
	Traceback (most recent call last):
	  File "./py/indexcmp", line 41, in <module>
	    main()
	  File "./py/indexcmp", line 29, in main
	    d2 = fsIndex.load(path2)
	  File "/home/kirr/src/wendelin/z/ZODB/src/ZODB/fsIndex.py", line 138, in load
	    data[ensure_bytes(k)] = fsBucket().fromString(ensure_bytes(v))
	  File "/home/kirr/src/wendelin/z/ZODB/src/ZODB/fsIndex.py", line 71, in ensure_bytes
	    return s.encode('ascii') if not isinstance(s, bytes) else s
	AttributeError: 'bytearray' object has no attribute 'encode'
	--- FAIL: TestIndexSaveToPy (0.04s)
	    index_test.go:218: zodb/py read/compare index: exit status 1

Provide minimal support for BTrees.LOBTree Get for now.

DB represents a handle to database at application level and contains pool
of connections. DB.Open opens database connection. The connection will be
automatically put back into DB pool for future reuse after corresponding
transaction is complete. DB thus provides service to maintain live objects
cache and reuse live objects from transaction to transaction.

Note that it is possible to have several DB handles to the same database.
This might be useful if application accesses distinctly different sets of
objects in different transactions and knows beforehand which set it will be
next time. Then, to avoid huge cache misses, it makes sense to keep DB
handles opened for every possible case of application access.

TODO handle invalidations.

For example Wendelin.core wcfs will need to keep some types of objects
(e.g. BigFile index) always in RAM for efficiency.

Provide corresponding interface that application could use to install
such live-cache eviction decision-making tuning.

Connection represents an application-level view of a ZODB database.
It has groups of in-RAM application-level objects associated with it.
The objects are isolated from both changes in further database
transactions and from changes to in-RAM objects in other connections.

Connection, as objects group manager, is responsible for handling
object -> object database references. For this to work it keeps

	{} oid -> obj

dict and uses it to find already loaded object when another object
persistently references particular oid. Since it related pydata handling
of persistent references is correspondingly implemented in this patch.

The dict must keep weak references on objects. The following text
explains the rationale:

	if Connection keeps strong link to obj, just
	obj.PDeactivate will not fully release obj if there are no
	references to it from other objects:

	     - deactivate will release obj state (ok)
	     - but there will be still reference from connection `oid -> obj` map to this object,
	       which means the object won't be garbage-collected.

	-> we can solve it by using "weak" pointers in the map.

	NOTE we cannot use regular map and arbitrarily manually "gc" entries
	there periodically: since for an obj we don't know whether other
	objects are referencing it, we can't just remove obj's oid from
	the map - if we do so and there are other live objects that
	reference obj, user code can still reach obj via those
	references. On the other hand, if another, not yet loaded, object
	also references obj and gets loaded, traversing reference from
	that loaded object will load second copy of obj, thus breaking 1
	object in db <-> 1 live object invariant:

	     A  →  B  →  C
	     ↓           |
	     D <--------- - - -> D2 (wrong)

	- A activate
	- D activate
	- B activate
	- D gc, A still keeps link on D
	- C activate -> it needs to get to D, but D was removed from objtab
	  -> new D2 is wrongly created

	that's why we have to depend on Go's GC to know whether there are
	still live references left or not. And that in turn means finalizers
	and thus weak references.

	some link on the subject:
	https://groups.google.com/forum/#!topic/golang-nuts/PYWxjT2v6ps

We will need weak references to handle {} oid -> obj inside zodb.Connection .

In Go world they often say that weak references are not needed at all.
Please see however the next patch for detailed rationale for why weak
references (finalizers and cooperation from Go's GC in other words) are
_required_ in that case.

As promised in 354e0e51 (go/zodb: Persistent - the base type to
implement IPersistent objects) add support to persistency machinery to
set object state from python pickles serialized by ZODB/py.

Persistent references are not yet handled.

As promised add some very minimal persistent tests.

Currently we handle many ways ZODB could serialize a Python class in
PyData.ClassName. Since we'll be using this functionality in other
places soon - extract it into dedicated function.

Since will be also frequently using

	class.__module__ + "." + class.__name__

don't inline it in ClassName and instead put it into pyclassPath() right
away.

Add the base type, that types which want to implement persistency
could embed, and this way inherit persistent functionality. For example

	type MyObject struct {
		Persistent
		...
	}

	type myObjectState MyObject

	func (o *myObjectState) DropState() { ... }
	func (o *myObjectState) SetState(state *mem.Buf) error { ... }

Here state management methods (DropState and SetState) will be
automatically used by persistency machinery on activation and
deactivation.

For this to work MyObject class has to be registered to ZODB

	func init() {
		t := reflect.TypeOf
		zodb.RegisterClass("mymodule.MyObject", t(MyObject{}), t(myObjectState))
	}

and new instances of MyObject has to be created via zodb.NewPersistent:

	obj := zodb.NewPersistent(reflect.TypeOf(MyObject{}), jar).(*MyObject)

SetState corresponds to __setstate__ in Python. However in Go version it
is explicitly separated from class's public API - as it is the contract
between a class and persistency machinery, not between the class and its
user. Notice that SetState takes raw buffer as its argument. In the
following patch we'll add SetState cousing (PySetState) that will be
taking unpickled objects as the state - exactly how __setstate__
operates in Python. Classes will be able to choose whether to accept
state as raw bytes or as a python object.

The activation/deactivation is implemented via reference counting.

Tests are pending (for PySetState).

Add to ZODB/go IPersistent - the interface that is used to represent
in-RAM application-level objects that are mirroring objects in database.

The interface is modelled after Python's IPersistent

	https://github.com/zopefoundation/ZODB/blob/3.10.7-4-gb8d7a8567/src/persistent/interfaces.py#L22

but is not exactly equal to it. In particular we support concurrent
access to an object from multiple goroutines simultaneously.

Due to concurrency support there is no STICKY state, because STICKY is
used in CPython version to temporarily pin object in RAM briefly and is
not safe to use from multiple threads there. Correspondingly the
semantic of PActivate is a bit different from _p_activate - in Go, after
an object has been activated, it is guaranteed that it will remain
present in RAM until it is explicitly deactivated by user.

Please see details of the activation protocol in IPersistent
documentation.

ZODB/py uses interface (IDataManager) for a persistent-object's jar, but
in Go I decided, at least for now, to go without explicit interface at
that level. For this reason a concrete type - Connection - will be used,
and so its stub is also introduced in the patch, since IPersistent wants
to return the connection via PJar.

We already have the functionality, just add an overview on how to
implement drivers and use the most common ones.

There will be many text added to pkgdoc with new sections and
per-section footnotes, and this way it is better to use a dedicated
section for references instead of global footnote whose context might
become unclear.

As we are going to add another - "Application layer" to zodb package,
turn previous text overviewing IStorage & friends into "Storage layer"
section.

We are too used to have this for granted with ZODB, but this property of
object databases is not generally universally available in other databases.

- change "types, interfaces and errors" to API in the header.
- it is not only data model, but also API that is tried to be reasonable
  compatible with ZODB/py.
- an article before "the" transaction is better.

Since 0 is valid Oid that is used for root database object, zero Oid
value cannot be used as invalid oid. -> Provide explicit invalid Oid
constant.

For symmetry do similarly to Tid, even though zero Tid value is invalid Tid.

This should be in 1f92a4e2 (go/zodb: Allow to open a storage in "direct"
mode - without local cache).

Add Go counterpart of Python transaction package[1,2]. The Go version is
not complete - in particular Transaction.Commit is not yet implemented.
However even in this state it is useful to have transaction around for
read-only transaction cases.

The synchronization logic is more well-thought - in particular there is
no dance around "new transaction", because in Go, contrary to ZODB/py,
ZODB connections will be always opened under already started
transaction. See "Synchronization" section in package documentation for
details on this topic.

[1] http://transaction.readthedocs.org
[2] https://github.com/zopefoundation/transaction

Previously TxnComplete was typed, but TxnPacked and TxnInprogress were
untyped constants. Make all constants have TxnStatus type.

The reason for this is that the buffer can be shared with other loaders
via cache.

There are situations when we want to work with, or emulate, only subset
of whole IStorage functionality, with one already established example
being Cache. For this reason split IStorage interface into finer-grained
ones. For now:

	- Loader
	- Prefetcher
	- Iterator
	- Committer
	- Notifier

Oid(0) represents root database object. We cannot change this.

Missed few places in 8685b742.

While at it add draft overview of data model & friends to package
documentation.

We send output from tested process to master. We also print it to
stdout,stderr so it appears in testnode logs.

However till now it was like, whole output first read, and only then
emitted to log as a whole, thus not allowing to oversee current test
progress by watching testnode log tail.

Fix it by implementing the teeing process manually.

Some draft history related to this patch:

	lab.nexedi.com/kirr/neo/commit/aa370ca3        fixup! X neotest/runTestSuite: Tee tested process stdout,stderr to testnode logs incrementally
	lab.nexedi.com/kirr/neo/commit/096550b1        fixup! X neotest/runTestSuite: Tee tested process stdout,stderr to testnode logs incrementally
	lab.nexedi.com/kirr/neo/commit/63956f43        fixup! X neotest/runTestSuite: Tee tested process stdout,stderr to testnode logs incrementally
	lab.nexedi.com/kirr/neo/commit/b9819d0e        X neotest/runTestSuite: Tee tested process stdout,stderr to testnode logs incrementally

The wrapper runs `neotest bench-local` in neotest SlapOS instance:

	https://lab.nexedi.com/nexedi/slapos/blob/ab8705f4/software/neotest/instance.cfg.in

Extracted from nexedi/slapos!282

Add the program that reads results from either bench-local or bench-cluster
neotest output and visualizes it. It uses benchlib.py module to read data
in Go benchmark format(*), processes them and plots scalability and other
graphs via matplotlib.

There are lots of hacks and rough edges, and in particular callout coordinate
calculation is completely wrong. However even in this state benchplot was used
to prepare the graphs in http://navytux.spb.ru/~kirr/neo.html and
http://navytux.spb.ru/~kirr/misc/neo·P4.html .

Some draft history related to this patch:

	lab.nexedi.com/kirr/neo/commit/078c9ac3        X move benchlib to -> https://lab.nexedi.com/kirr/pygolang
	lab.nexedi.com/kirr/neo/commit/0edd5129        X benchplot: Teach it to understand benchmark names for partitioned NEO clusters
	lab.nexedi.com/kirr/neo/commit/a1dde3c9        X deco-rio timings
	lab.nexedi.com/kirr/neo/commit/916782b6        X normalize/convert units, so that disk and ping/tcp latencies could be plotted too
	lab.nexedi.com/kirr/neo/commit/f5fec740        X switch node info to labels; start adding that to plot
	lab.nexedi.com/kirr/neo/commit/906462a3        X neotest: Move cluster / node out fro benchmark name to label in environment
	lab.nexedi.com/kirr/neo/commit/cceca65f        X benchplot: Start of automated plotting for neotest benchmark data
	lab.nexedi.com/kirr/neo/commit/a9b10a45        X benchlib/benchstat: Emit label:value info for several labels on one line, similary to go version
	lab.nexedi.com/kirr/neo/commit/502d9477        X benchlib: Python module to read & work with data in Go benchmark format

(*) benchlib.py is now part of pygolang: https://pypi.org/project/pygolang .

These commands do full benchmarking for localhost and networked cases:

- show system info
- do server & client cpu benchmarks
- do server disk benchmarks
- for networked case: do network benchmarks
- tail to either zbench-local or zbench-cluster

It was full `neotest bench-local` that was used to prepare benchmarks
for http://navytux.spb.ru/~kirr/neo.html and http://navytux.spb.ru/~kirr/misc/neo·P4.html

name                 old time/op    new time/op    delta
unzlib/py/wczdata      20.8µs ± 2%    20.7µs ± 1%     ~     (p=0.421 n=5+5)
unzlib/go/wczdata      64.4µs ± 1%    21.3µs ± 0%  -66.89%  (p=0.008 n=5+5)
unzlib/py/prod1-avg    4.00µs ± 1%    4.02µs ± 1%     ~     (p=0.421 n=5+5)
unzlib/go/prod1-avg    10.4µs ± 1%     4.3µs ± 1%  -58.72%  (p=0.008 n=5+5)

There is also unsafe interface with czlib.UnsafeDecompress & friends which I
had not tried because even using safe interface brings ~ 3x speedup.

name                 old time/op    new time/op    delta
unzlib/py/wczdata      20.7µs ± 2%    20.8µs ± 2%     ~     (p=0.548 n=5+5)
unzlib/go/wczdata      70.6µs ± 0%    64.4µs ± 1%   -8.85%  (p=0.008 n=5+5)
unzlib/py/prod1-avg    4.02µs ± 1%    4.00µs ± 1%     ~     (p=0.167 n=5+5)
unzlib/go/prod1-avg    15.2µs ± 0%    10.4µs ± 1%  -31.59%  (p=0.008 n=5+5)

still on wczdata and prod1 much slower compared to py/c zlib.

NEO uses zlib compression for data, and this way client has to spend
time decompressing it. Benchmark how much time zlib decompression takes.
With stdlib zlib decompressor out of the box it looks like:

	name                 time/op
	unzlib/py/wczdata    20.7µs ± 2%
	unzlib/go/wczdata    70.6µs ± 0%
	unzlib/py/prod1-avg  4.02µs ± 1%
	unzlib/go/prod1-avg  15.2µs ± 0%

i.e. much not in favour of Go.

We'll be fixing that in the following patches.

With zwrk for ZODB being similar to what wrk is for HTTP.

Rationale: simulating multiple clients is:

1. noisy - the timings from run to run are changing sometimes up to 50%
2. with significant additional overhead - there are constant OS-level
   process switches in between client processes and this prevents to
   actually create the load.
3. the above load from "2" actually takes resources from the server in
   localhost case.

So let's switch to simulating many requests in lightweight way similarly
to how it is done in wrk - in one process and not so many threads (it
can be just 1) with many connections opened to server and epolly way to
load it with Go providing epoll-goroutine matching.

Example summarized zbench-local output:

	x/src/lab.nexedi.com/kirr/neo/go/neo/t$ benchstat -split node,cluster,dataset x.txt
	name                             time/object
	cluster:rio dataset:wczblk1-8
	fs1-zhash.py                             23.7µs ± 5%
	fs1-zhash.go                             5.68µs ± 8%
	fs1-zhash.go+prefetch128                 6.44µs ±16%
	zeo/py/fs1-zhash.py                       376µs ± 4%
	zeo/py/fs1-zhash.go                       130µs ± 3%
	zeo/py/fs1-zhash.go+prefetch128          72.3µs ± 4%
	neo/py(!log)/sqlite·P1-zhash.py           565µs ± 4%
	neo/py(!log)/sql·P1-zhash.py              491µs ± 8%
	cluster:rio dataset:prod1-1024
	fs1-zhash.py                             19.5µs ± 2%
	fs1-zhash.go                             3.92µs ±12%
	fs1-zhash.go+prefetch128                 4.42µs ± 6%
	zeo/py/fs1-zhash.py                       365µs ± 9%
	zeo/py/fs1-zhash.go                       120µs ± 1%
	zeo/py/fs1-zhash.go+prefetch128          68.4µs ± 3%
	neo/py(!log)/sqlite·P1-zhash.py           560µs ± 5%
	neo/py(!log)/sql·P1-zhash.py              482µs ± 8%

	name                             req/s
	cluster:rio dataset:wczblk1-8
	fs1-zwrk.go·1                              380k ± 2%
	fs1-zwrk.go·2                              666k ± 3%
	fs1-zwrk.go·3                              948k ± 1%
	fs1-zwrk.go·4                             1.24M ± 1%
	fs1-zwrk.go·8                             1.62M ± 0%
	fs1-zwrk.go·12                            1.70M ± 0%
	fs1-zwrk.go·16                            1.71M ± 0%
	zeo/py/fs1-zwrk.go·1                      8.29k ± 1%
	zeo/py/fs1-zwrk.go·2                      10.4k ± 2%
	zeo/py/fs1-zwrk.go·3                      11.2k ± 1%
	zeo/py/fs1-zwrk.go·4                      11.7k ± 1%
	zeo/py/fs1-zwrk.go·8                      12.1k ± 2%
	zeo/py/fs1-zwrk.go·12                     12.3k ± 1%
	zeo/py/fs1-zwrk.go·16                     12.3k ± 2%
	cluster:rio dataset:prod1-1024
	fs1-zwrk.go·1                              594k ± 7%
	fs1-zwrk.go·2                             1.14M ± 4%
	fs1-zwrk.go·3                             1.60M ± 2%
	fs1-zwrk.go·4                             2.09M ± 1%
	fs1-zwrk.go·8                             2.74M ± 1%
	fs1-zwrk.go·12                            2.76M ± 0%
	fs1-zwrk.go·16                            2.76M ± 1%
	zeo/py/fs1-zwrk.go·1                      9.42k ± 9%
	zeo/py/fs1-zwrk.go·2                      10.4k ± 1%
	zeo/py/fs1-zwrk.go·3                      11.4k ± 1%
	zeo/py/fs1-zwrk.go·4                      11.7k ± 2%
	zeo/py/fs1-zwrk.go·8                      12.4k ± 1%
	zeo/py/fs1-zwrk.go·12                     12.5k ± 1%
	zeo/py/fs1-zwrk.go·16                     13.4k ±11%

	name                             latency-time/object
	cluster:rio dataset:wczblk1-8
	fs1-zwrk.go·1                            2.63µs ± 2%
	fs1-zwrk.go·2                            3.00µs ± 3%
	fs1-zwrk.go·3                            3.16µs ± 1%
	fs1-zwrk.go·4                            3.23µs ± 1%
	fs1-zwrk.go·8                            4.94µs ± 0%
	fs1-zwrk.go·12                           7.06µs ± 0%
	fs1-zwrk.go·16                           9.36µs ± 0%
	zeo/py/fs1-zwrk.go·1                      121µs ± 1%
	zeo/py/fs1-zwrk.go·2                      192µs ± 2%
	zeo/py/fs1-zwrk.go·3                      267µs ± 1%
	zeo/py/fs1-zwrk.go·4                      343µs ± 1%
	zeo/py/fs1-zwrk.go·8                      660µs ± 2%
	zeo/py/fs1-zwrk.go·12                     977µs ± 1%
	zeo/py/fs1-zwrk.go·16                    1.30ms ± 2%
	cluster:rio dataset:prod1-1024
	fs1-zwrk.go·1                            1.69µs ± 7%
	fs1-zwrk.go·2                            1.76µs ± 4%
	fs1-zwrk.go·3                            1.88µs ± 2%
	fs1-zwrk.go·4                            1.91µs ± 1%
	fs1-zwrk.go·8                            2.92µs ± 1%
	fs1-zwrk.go·12                           4.34µs ± 0%
	fs1-zwrk.go·16                           5.80µs ± 1%
	zeo/py/fs1-zwrk.go·1                      107µs ± 9%
	zeo/py/fs1-zwrk.go·2                      192µs ± 1%
	zeo/py/fs1-zwrk.go·3                      263µs ± 1%
	zeo/py/fs1-zwrk.go·4                      342µs ± 2%
	zeo/py/fs1-zwrk.go·8                      648µs ± 1%
	zeo/py/fs1-zwrk.go·12                     957µs ± 1%
	zeo/py/fs1-zwrk.go·16                    1.20ms ±10%

The scalability graphs in http://navytux.spb.ru/~kirr/neo.html were
made with simulating client load by zwrk, not many client OS processes.
http://navytux.spb.ru/~kirr/neo.html#performance-tests has some
additional notes on zwrk.

Some draft history related to this patch:

	lab.nexedi.com/kirr/neo/commit/ca0d828b	X neotest: Tzwrk1 - place to control running time of 1 zwrk iteration
	lab.nexedi.com/kirr/neo/commit/bbfb5006	X zwrk: Make sure we warm up connections to all NEO storages when cluster is partitioned
	lab.nexedi.com/kirr/neo/commit/7f22bba6	X zwrk: New tool to simulate paralell load from multiple clients