def create_merged_stripe(files, used_idxs, s3_file_name, n_x, y, trace_span):
with tracing.start_span('parallel stripe loading', parent=trace_span):
with concurrent.futures.ThreadPoolExecutor(10) as executor:
contents = list(
executor.map(partial(load_stripe, used_idxs, y, n_x), files))
with tracing.start_span('merged stripe save', parent=trace_span):
d = numpy.concatenate(contents, axis=1).tobytes()
s3_put(f"{y}/{s3_file_name}", d)
def ingest_from_queue():
q = get_queue()
for ingest_req in q:
# Queue is empty for now
if ingest_req is None:
logger.info("Empty queue")
break
ingest_req = ingest_req.data
# Expire out anything whose run time is very old (probably a bad request/URL)
if datetime.utcfromtimestamp(
ingest_req['run_time']) < datetime.utcnow() - timedelta(
hours=12):
logger.info("Expiring old request %s", ingest_req)
continue
# If this URL doesn't exist, try again in a few minutes
if not (url_exists(ingest_req['url'])
and url_exists(ingest_req['idx_url'])):
logger.info("Rescheduling request %s", ingest_req)
q.put(ingest_req, '5m')
continue
with tracing.start_span('ingest item') as span:
for k, v in ingest_req.items():
span.set_attribute(k, v)
try:
source = Source.query.filter_by(
short_name=ingest_req['source']).first()
with tempfile.NamedTemporaryFile() as reduced:
with tracing.start_span('download'):
logging.info(
f"Downloading and reducing {ingest_req['url']} from {ingest_req['run_time']} {source.short_name}"
)
reduce_grib(ingest_req['url'], ingest_req['idx_url'],
source.fields, reduced)
with tracing.start_span('ingest'):
logging.info("Ingesting all")
ingest_grib_file(reduced.name, source)
source.last_updated = datetime.utcnow()
db.session.commit()
except KeyboardInterrupt:
raise
except Exception:
logger.exception("Exception while ingesting %s. Will retry",
ingest_req)
q.put(ingest_req, '5m')
def ingest_grib_file(file_path, source):
"""
Ingests a given GRIB file into the backend.
:param file_path: Path to the GRIB file
:param source: Source object which denotes which source this data is from
:return: None
"""
logger.info("Processing GRIB file '%s'", file_path)
grib = pygrib.open(file_path)
# Keeps all data points that we'll be inserting at the end.
# Map of proj_id to map of {(field_id, valid_time, run_time) -> [msg, ...]}
data_by_projection = collections.defaultdict(lambda: collections.defaultdict(list))
for field in SourceField.query.filter(SourceField.source_id == source.id, SourceField.metric.has(Metric.intermediate == False)).all():
try:
msgs = grib.select(**field.selectors)
except ValueError:
logger.warning("Could not find message(s) in grib matching selectors %s", field.selectors)
continue
for msg in msgs:
with tracing.start_span('parse message') as span:
span.set_attribute('message', str(msg))
if field.projection is None or field.projection.params != msg.projparams:
projection = get_or_create_projection(msg)
field.projection_id = projection.id
db.session.commit()
valid_date = get_end_valid_time(msg)
data_by_projection[field.projection.id][(field.id, valid_date, msg.analDate)].append(msg.values)
with tracing.start_span('generate derived'):
logger.info("Generating derived fields")
for proj_id, fields in get_source_module(source.short_name).generate_derived(grib).items():
for k, v in fields.items():
data_by_projection[proj_id][k].extend(v)
with tracing.start_span('save denormalized'):
logger.info("Saving denormalized location/time data for all messages")
for proj_id, fields in data_by_projection.items():
create_files(proj_id, fields)
logger.info("Done saving denormalized data")
def merge():
"""
Merge all (small) files into larger files to reduce the number of S3 requests each query needs to do.
"""
all_files = FileMeta.query.filter(
FileMeta.file_name.in_(
FileBandMeta.query.filter(
FileBandMeta.valid_time > datetime.utcnow()).with_entities(
FileBandMeta.file_name)), ).order_by(
FileMeta.loc_size.asc(), ).all()
proj_files = collections.defaultdict(list)
for f in all_files:
proj_files[f.projection].append(f)
# Pull from the projection with the most backlog first
for proj, proj_files in sorted(proj_files.items(),
key=lambda pair: len(pair[1]),
reverse=True):
# Don't waste time if we don't really have that many files
if len(proj_files) < 8:
continue
# Merge in smaller batches (10 <= batch_size <= 50) to more quickly reduce S3 load per query.
batch_size = min(ceil(len(proj_files) / 4), 50)
if len(proj_files) < 40:
batch_size = len(proj_files)
for files in chunk(proj_files, batch_size):
# This next part is all about figuring out what items are still used in
# each file so that the merge process can effectively garbage collect
# unused data.
# Dict of FileMeta -> list of float32 item indexes still used by some band
used_idxs = collections.defaultdict(list)
offset = 0
# Dict of FileBandMeta -> offset
new_offsets = {}
for f in files:
for band in f.bands:
# Don't bother merging old data. Prevents racing with the cleaner,
# and probably won't be queried anyways.
if band.valid_time < datetime.utcnow():
continue
new_offsets[band] = offset
offset += 4 * band.vals_per_loc
start_idx = band.offset // 4
used_idxs[f].extend(
range(start_idx, start_idx + band.vals_per_loc))
s3_file_name = hashlib.md5(
('-'.join(f.file_name
for f in files)).encode('utf-8')).hexdigest()
merged_meta = FileMeta(
file_name=s3_file_name,
projection_id=proj.id,
loc_size=offset,
)
db.session.add(merged_meta)
logger.info("Merging %s into %s",
','.join(f.file_name for f in files), s3_file_name)
n_y, n_x = proj.shape()
# If we fail to create any merged stripe, don't commit the changes to
# band offset/file name, but _do_ commit the FileMeta to the DB.
# This way the normal cleaning process will remove any orphaned bands.
commit_merged = True
# max workers = 10 to limit mem utilization
# Approximate worst case, we'll have
# (5 sources * 70 runs * 2000 units wide * 20 metrics/unit * 4 bytes per metric) per row
# or ~50MB/row in memory.
# 10 rows keeps us well under 1GB which is what this should be provisioned for.
with tracing.start_span('parallel stripe creation') as span:
span.set_attribute("s3_file_name", s3_file_name)
span.set_attribute("num_files", len(files))
with concurrent.futures.ThreadPoolExecutor(10) as executor:
futures = concurrent.futures.wait([
executor.submit(create_merged_stripe, files, used_idxs,
s3_file_name, n_x, y, span)
for y in range(n_y)
])
for fut in futures.done:
if fut.exception() is not None:
logger.error("Exception merging: %s",
fut.exception())
commit_merged = False
span.set_attribute("commit", commit_merged)
if commit_merged:
for band, offset in new_offsets.items():
band.offset = offset
band.file_name = merged_meta.file_name
logger.info("Updated file band meta")
db.session.commit()
# We know we won't need this projection again, so clear it
clear_proj_cache()
s3_file_name, n_x, y, span)
for y in range(n_y)
])
for fut in futures.done:
if fut.exception() is not None:
logger.error("Exception merging: %s",
fut.exception())
commit_merged = False
span.set_attribute("commit", commit_merged)
if commit_merged:
for band, offset in new_offsets.items():
band.offset = offset
band.file_name = merged_meta.file_name
logger.info("Updated file band meta")
db.session.commit()
# We know we won't need this projection again, so clear it
clear_proj_cache()
if __name__ == "__main__":
init_sentry()
logging.basicConfig(level=logging.INFO)
init_tracing('merge')
with tracing.start_span('merge'):
merge()
short_name=ingest_req['source']).first()
with tempfile.NamedTemporaryFile() as reduced:
with tracing.start_span('download'):
logging.info(
f"Downloading and reducing {ingest_req['url']} from {ingest_req['run_time']} {source.short_name}"
)
reduce_grib(ingest_req['url'], ingest_req['idx_url'],
source.fields, reduced)
with tracing.start_span('ingest'):
logging.info("Ingesting all")
ingest_grib_file(reduced.name, source)
source.last_updated = datetime.utcnow()
db.session.commit()
except KeyboardInterrupt:
raise
except Exception:
logger.exception("Exception while ingesting %s. Will retry",
ingest_req)
q.put(ingest_req, '5m')
if __name__ == "__main__":
init_sentry()
logging.basicConfig(level=logging.INFO)
init_tracing('queue_worker')
with tracing.start_span('queue worker'):
ingest_from_queue()
def summarize():
"""
Summarizes the weather in a natural way.
Returns a list of objects describing a summary of the weather (one per day).
"""
lat = float(request.args['lat'])
lon = float(request.args['lon'])
start = request.args.get('start', type=int)
days = int(request.args['days'])
if lat > 90 or lat < -90 or lon > 180 or lon < -180:
abort(400)
if days > 10:
abort(400)
# TODO: This should be done relative to the location's local TZ
now = datetime.utcnow()
if start is None:
start = now
else:
start = datetime.utcfromtimestamp(start)
if not app.debug:
if start < now - timedelta(days=1):
start = now - timedelta(days=1)
source_fields = SourceField.query.filter(
or_(
SourceField.metric == metrics.temp,
SourceField.metric == metrics.raining,
SourceField.metric == metrics.snowing,
SourceField.metric_id.in_([metrics.wind_speed.id, metrics.wind_direction.id, metrics.gust_speed.id]),
SourceField.metric == metrics.cloud_cover,
SourceField.metric == metrics.composite_reflectivity,
),
SourceField.projection_id != None,
).all()
with tracing.start_span("load_data_points") as span:
end = start + timedelta(days=days)
span.set_attribute("start", str(start))
span.set_attribute("end", str(end))
span.set_attribute("source_fields", str(source_fields))
data_points = load_data_points((lat, lon), start, end, source_fields)
with tracing.start_span("combine_models") as span:
combined_data_points = combine_models(data_points)
time_ranges = [(start, start.replace(hour=0, minute=0, second=0, microsecond=0) + timedelta(days=1))]
for d in range(1, days):
last_end = time_ranges[-1][1]
time_ranges.append((last_end, last_end + timedelta(days=1)))
summarizations = []
with tracing.start_span("summarizations") as span:
for dstart, dend in time_ranges:
summary = SummarizedData(dstart, dend, combined_data_points)
summarizations.append(summary.dict())
return jsonify(summarizations)
def wx_for_location():
"""
Gets the weather for a specific location, optionally limiting by metric and time.
at that time.
"""
lat = float(request.args['lat'])
lon = float(request.args['lon'])
if lat > 90 or lat < -90 or lon > 180 or lon < -180:
abort(400)
requested_metrics = request.args.getlist('metrics', int)
if requested_metrics:
metric_ids = set(requested_metrics)
else:
metric_ids = Metric.query.with_entities(Metric.id)
now = datetime.utcnow()
start = request.args.get('start', type=int)
end = request.args.get('end', type=int)
if start is None:
start = now - timedelta(hours=1)
else:
start = datetime.utcfromtimestamp(start)
if not app.debug:
if start < now - timedelta(days=1):
start = now - timedelta(days=1)
if end is None:
end = now + timedelta(hours=12)
else:
end = datetime.utcfromtimestamp(end)
if not app.debug:
if end > now + timedelta(days=7):
end = now + timedelta(days=7)
requested_source_fields = SourceField.query.filter(
SourceField.metric_id.in_(metric_ids),
SourceField.projection_id != None, # noqa: E711
).all()
with tracing.start_span("load_data_points") as span:
span.set_attribute("start", str(start))
span.set_attribute("end", str(end))
span.set_attribute("source_fields", str(requested_source_fields))
data_points = load_data_points((lat, lon), start, end, requested_source_fields)
# valid time -> data points
datas = collections.defaultdict(list)
for dp in data_points:
datas[datetime2unix(dp.valid_time)].append({
'run_time': datetime2unix(dp.run_time),
'src_field_id': dp.source_field_id,
'value': dp.median(),
'raw_values': dp.values,
})
wx = {
'data': datas,
'ordered_times': sorted(datas.keys()),
}
return jsonify(wx)
def load_data_points(
coords: Tuple[float, float],
start: datetime.datetime,
end: datetime.datetime,
source_fields: Optional[Iterable[SourceField]] = None
) -> List[DataPointSet]:
if source_fields is None:
source_fields = SourceField.query.all()
# Determine all valid source fields (fields in source_fields which cover the given coords),
# and the x,y for projection used in any valid source field.
valid_source_fields = []
locs: Dict[int, Tuple[float, float]] = {}
for sf in source_fields:
if sf.projection_id in locs and locs[sf.projection_id] is None:
continue
if sf.projection_id not in locs:
with tracing.start_span("get_xy_for_coord") as span:
span.set_attribute("projection_id", sf.projection_id)
loc = get_xy_for_coord(sf.projection, coords)
# Skip if given projection does not cover coords
if loc is None:
continue
locs[sf.projection_id] = loc
valid_source_fields.append(sf)
with tracing.start_span("load file band metas") as span:
fbms: List[FileBandMeta] = FileBandMeta.query.filter(
FileBandMeta.source_field_id.in_(
[sf.id for sf in valid_source_fields]),
FileBandMeta.valid_time >= start,
FileBandMeta.valid_time < end,
).all()
# Gather all files we need data from
file_metas = set(fbm.file_meta for fbm in fbms)
file_contents = {}
# Read them in (in parallel)
# TODO: use asyncio here instead once everything else is ported?
with tracing.start_span("load file chunks") as span:
span.set_attribute("num_files", len(file_metas))
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = {
executor.submit(load_file_chunk, fm, locs[fm.projection_id]):
fm
for fm in file_metas
}
for future in concurrent.futures.as_completed(futures):
fm = futures[future]
file_contents[fm.file_name] = future.result()
# filebandmeta -> values
data_points = []
for fbm in fbms:
raw = file_contents[fbm.file_name][fbm.offset:fbm.offset +
(4 * fbm.vals_per_loc)]
data_values: List[float] = array.array("f", raw).tolist()
data_point = DataPointSet(
values=data_values,
metric_id=fbm.source_field.metric.id,
valid_time=fbm.valid_time,
source_field_id=fbm.source_field_id,
run_time=fbm.run_time,
)
data_points.append(data_point)
return data_points
