nilmdb/design.md
2012-12-14 16:57:02 -05:00

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Structure
---------
nilmdb.nilmdb is the NILM database interface. It tracks a PyTables
database holds actual rows of data, and a SQL database tracks metadata
and ranges.
Access to the nilmdb must be single-threaded. This is handled with
the nilmdb.serializer class.
nilmdb.server is a HTTP server that provides an interface to talk,
thorugh the serialization layer, to the nilmdb object.
nilmdb.client is a HTTP client that connects to this.
Sqlite performance
------------------
Committing a transaction in the default sync mode (PRAGMA synchronous=FULL)
takes about 125msec. sqlite3 will commit transactions at 3 times:
1: explicit con.commit()
2: between a series of DML commands and non-DML commands, e.g.
after a series of INSERT, SELECT, but before a CREATE TABLE or
PRAGMA.
3: at the end of an explicit transaction, e.g. "with self.con as con:"
To speed up testing, or if this transaction speed becomes an issue,
the sync=False option to NilmDB will set PRAGMA synchronous=OFF.
Inserting streams
-----------------
We need to send the contents of "data" as POST. Do we need chunked
transfer?
- Don't know the size in advance, so we would need to use chunked if
we send the entire thing in one request.
- But we shouldn't send one chunk per line, so we need to buffer some
anyway; why not just make new requests?
- Consider the infinite-streaming case, we might want to send it
immediately? Not really -- server still should do explicit inserts
of fixed-size chunks.
- Even chunked encoding needs the size of each chunk beforehand, so
everything still gets buffered. Just a tradeoff of buffer size.
Before timestamps are added:
- Raw data is about 440 kB/s (9 channels)
- Prep data is about 12.5 kB/s (1 phase)
- How do we know how much data to send?
- Remember that we can only do maybe 8-50 transactions per second on
the sqlite database. So if one block of inserted data is one
transaction, we'd need the raw case to be around 64kB per request,
ideally more.
- Maybe use a range, based on how long it's taking to read the data
- If no more data, send it
- If data > 1 MB, send it
- If more than 10 seconds have elapsed, send it
- Should those numbers come from the server?
Converting from ASCII to PyTables:
- For each row getting added, we need to set attributes on a PyTables
Row object and call table.append(). This means that there isn't a
particularly efficient way of converting from ascii.
- Could create a function like nilmdb.layout.Layout("foo".fillRow(asciiline)
- But this means we're doing parsing on the serialized side
- Let's keep parsing on the threaded server side so we can detect
errors better, and not block the serialized nilmdb for a slow
parsing process.
- Client sends ASCII data
- Server converts this ACSII data to a list of values
- Maybe:
# threaded side creates this object
parser = nilmdb.layout.Parser("layout_name")
# threaded side parses and fills it with data
parser.parse(textdata)
# serialized side pulls out rows
for n in xrange(parser.nrows):
parser.fill_row(rowinstance, n)
table.append()
Inserting streams, inside nilmdb
--------------------------------
- First check that the new stream doesn't overlap.
- Get minimum timestamp, maximum timestamp from data parser.
- (extend parser to verify monotonicity and track extents)
- Get all intervals for this stream in the database
- See if new interval overlaps any existing ones
- If so, bail
- Question: should we cache intervals inside NilmDB?
- Assume database is fast for now, and always rebuild fom DB.
- Can add a caching layer later if we need to.
- `stream_get_ranges(path)` -> return IntervalSet?
Speed
-----
- First approach was quadratic. Adding four hours of data:
$ time zcat /home/jim/bpnilm-data/snapshot-1-20110513-110002.raw.gz | ./nilmtool.py insert -s 20110513-110000 /bpnilm/1/raw
real 24m31.093s
$ time zcat /home/jim/bpnilm-data/snapshot-1-20110513-110002.raw.gz | ./nilmtool.py insert -s 20110513-120001 /bpnilm/1/raw
real 43m44.528s
$ time zcat /home/jim/bpnilm-data/snapshot-1-20110513-110002.raw.gz | ./nilmtool.py insert -s 20110513-130002 /bpnilm/1/raw
real 93m29.713s
$ time zcat /home/jim/bpnilm-data/snapshot-1-20110513-110002.raw.gz | ./nilmtool.py insert -s 20110513-140003 /bpnilm/1/raw
real 166m53.007s
- Disabling pytables indexing didn't help:
real 31m21.492s
real 52m51.963s
real 102m8.151s
real 176m12.469s
- Server RAM usage is constant.
- Speed problems were due to IntervalSet speed, of parsing intervals
from the database and adding the new one each time.
- First optimization is to cache result of `nilmdb:_get_intervals`,
which gives the best speedup.
- Also switched to internally using bxInterval from bx-python package.
Speed of `tests/test_interval:TestIntervalSpeed` is pretty decent
and seems to be growing logarithmically now. About 85μs per insertion
for inserting 131k entries.
- Storing the interval data in SQL might be better, with a scheme like:
http://www.logarithmic.net/pfh/blog/01235197474
- Next slowdown target is nilmdb.layout.Parser.parse().
- Rewrote parsers using cython and sscanf
- Stats (rev 10831), with _add_interval disabled
layout.pyx.Parser.parse:128 6303 sec, 262k calls
layout.pyx.parse:63 13913 sec, 5.1g calls
numpy:records.py.fromrecords:569 7410 sec, 262k calls
- Probably OK for now.
- After all updates, now takes about 8.5 minutes to insert an hour of
data, constant after adding 171 hours (4.9 billion data points)
- Data set size: 98 gigs = 20 bytes per data point.
6 uint16 data + 1 uint32 timestamp = 16 bytes per point
So compression must be off -- will retry with compression forced on.
IntervalSet speed
-----------------
- Initial implementation was pretty slow, even with binary search in
sorted list
- Replaced with bxInterval; now takes about log n time for an insertion
- TestIntervalSpeed with range(17,18) and profiling
- 85 μs each
- 131072 calls to `__iadd__`
- 131072 to bx.insert_interval
- 131072 to bx.insert:395
- 2355835 to bx.insert:106 (18x as many?)
- Tried blist too, worse than bxinterval.
- Might be algorithmic improvements to be made in Interval.py,
like in `__and__`
- Replaced again with rbtree. Seems decent. Numbers are time per
insert for 2**17 insertions, followed by total wall time and RAM
usage for running "make test" with `test_rbtree` and `test_interval`
with range(5,20):
- old values with bxinterval:
20.2 μS, total 20 s, 177 MB RAM
- rbtree, plain python:
97 μS, total 105 s, 846 MB RAM
- rbtree converted to cython:
26 μS, total 29 s, 320 MB RAM
- rbtree and interval converted to cython:
8.4 μS, total 12 s, 134 MB RAM
Layouts
-------
Current/old design has specific layouts: RawData, PrepData, RawNotchedData.
Let's get rid of this entirely and switch to simpler data types that are
just collections and counts of a single type. We'll still use strings
to describe them, with format:
type_count
where type is "uint16", "float32", or "float64", and count is an integer.
nilmdb.layout.named() will parse these strings into the appropriate
handlers. For compatibility:
"RawData" == "uint16_6"
"RawNotchedData" == "uint16_9"
"PrepData" == "float32_8"