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Streamset filter to use dataframe #44

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a9a39cd
update streamset filter to use DataFrame after initializing a _metada…
jleifnf Aug 11, 2023
8bcb3c1
add dask to requirements and fix missing pattern for unit filter
jleifnf Aug 11, 2023
a2150ca
make streams series to list
jleifnf Aug 11, 2023
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95 changes: 59 additions & 36 deletions btrdb/stream.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,9 @@
from collections.abc import Sequence
from copy import deepcopy

import pandas as pd
import pyarrow as pa
from dask import compute, delayed

from btrdb.exceptions import (
BTrDBError,
Expand Down Expand Up @@ -58,6 +60,7 @@

_arrow_not_impl_str = "The BTrDB server you are using does not support {}."


##########################################################################
## Stream Classes
##########################################################################
Expand Down Expand Up @@ -1433,6 +1436,16 @@ def __repr__(self):
##########################################################################


@delayed
def get_metadata(stream):
columns = ["collection", "tags", "annotations", "stream", "uuid"]
stream.refresh_metadata()
metadata = {
c: (getattr(stream, f"_{c}") if c != "stream" else stream) for c in columns
}
return metadata


class StreamSetBase(Sequence):
"""
A lighweight wrapper around a list of stream objects
Expand All @@ -1455,6 +1468,13 @@ def __init__(self, streams):
self.width = None
self.depth = 0

# create a DataFrame to store the metadata for filtering
_metadata = compute([get_metadata(s) for s in self._streams])[0]
_metadata = pd.DataFrame(_metadata)
self._metadata = _metadata.join(pd.json_normalize(_metadata["tags"])).drop(
columns=["tags", "annotations"]
)

@property
def allow_window(self):
return not bool(self.pointwidth or (self.width and self.depth == 0))
Expand Down Expand Up @@ -1712,69 +1732,72 @@ def filter(
)
)

tf = ~obj._metadata.uuid.isna() # shouldn't contain any None

# filter by collection
if collection is not None:
if isinstance(collection, RE_PATTERN):
obj._streams = [
s
for s in obj._streams
for m in [collection.search(s.collection)]
if m
]
tf = tf & obj._metadata.collection.str.contains(
collection.pattern, case=False, regex=True
)
elif isinstance(collection, str):
obj._streams = [
s
for s in obj._streams
if s.collection.lower() == collection.lower()
]
tf = tf & obj._metadata.collection.str.contains(
collection, case=False, regex=False
)
else:
raise BTRDBTypeError("collection must be string or compiled regex")

# filter by name
if name is not None:
if isinstance(name, RE_PATTERN):
obj._streams = [
s for s in obj._streams for m in [name.search(s.name)] if m
]
tf = tf & obj._metadata.name.str.contains(
name.pattern, case=False, regex=True
)
elif isinstance(name, str):
obj._streams = [
s for s in obj._streams if s.name.lower() == name.lower()
]
tf = tf & obj._metadata.name.str.contains(name, case=False, regex=False)
else:
raise BTRDBTypeError("name must be string or compiled regex")

# filter by unit
if unit is not None:
if isinstance(unit, RE_PATTERN):
obj._streams = [
s
for s in obj._streams
for m in [unit.search(s.tags()["unit"])]
if m
]
tf = tf & obj._metadata.unit.str.contains(
unit.pattern, case=False, regex=True
)
elif isinstance(unit, str):
obj._streams = [
s
for s in obj._streams
if s.tags().get("unit", "").lower() == unit.lower()
]
tf = tf & obj._metadata.name.str.contains(unit, case=False, regex=False)
else:
raise BTRDBTypeError("unit must be string or compiled regex")

# filter by tags
if tags:
# filters if the subset of the tags matches the given tags
obj._streams = [s for s in obj._streams if tags.items() <= s.tags().items()]
tf = tf & obj._metadata.loc[
:, obj._metadata.columns.isin(tags.keys())
].apply(
lambda x: x.str.contains(tags[x.name], case=False, regex=False)
).all(
axis=1
)
obj._metadata = obj._metadata[tf]

# filter by annotations
if annotations:
_annotations = pd.json_normalize(obj._metadata["annotations"])
if not _annotations.columns.isin(annotations.keys()).any():
raise BTRDBValueError("annotations key not found")
obj._metadata = obj._metadata.join(
_annotations, rsuffix="_annotations"
).drop(columns=["annotations"])

_columns = list(annotations.keys()) + list(
map(lambda s: "".join([s, "_annotations"]), annotations.keys())
)
# filters if the subset of the annotations matches the given annotations
obj._streams = [
s
for s in obj._streams
if annotations.items() <= s.annotations()[0].items()
]

tf = tf & obj._metadata.loc[:, obj._metadata.columns.isin(_columns)].apply(
lambda x: x.str.contains(annotations[x.name], case=False, regex=False)
).all(axis=1)
obj._metadata = obj._metadata[tf]
obj._streams = obj._metadata["stream"].to_list()
return obj

def clone(self):
Expand Down
1 change: 1 addition & 0 deletions requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
# pip-compile --output-file=requirements.txt --resolver=backtracking pyproject.toml
#
# This file was modified to remove version pins.
dask
certifi
# via btrdb (pyproject.toml)
grpcio
Expand Down