# Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import urllib.parse
from base64 import b64encode
from os import path, remove
from typing import Optional, Union
import dask.dataframe as dd
import fsspec
import orjson
import pandas as pd
import pyarrow
import pytz
import requests
import urllib3
import mlrun.errors
from mlrun.errors import err_to_str
from mlrun.utils import StorePrefix, is_ipython, logger
from .store_resources import is_store_uri, parse_store_uri
from .utils import filter_df_start_end_time, select_columns_from_df
verify_ssl = False
if not verify_ssl:
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
class FileStats:
def __init__(self, size, modified, content_type=None):
self.size = size
self.modified = modified
self.content_type = content_type
def __repr__(self):
return f"FileStats(size={self.size}, modified={self.modified}, type={self.content_type})"
class DataStore:
def __init__(self, parent, name, kind, endpoint="", secrets: dict = None):
self._parent = parent
self.kind = kind
self.name = name
self.endpoint = endpoint
self.subpath = ""
self.secret_pfx = ""
self.options = {}
self.from_spec = False
self._filesystem = None
self._secrets = secrets or {}
@property
def is_structured(self):
return False
@property
def is_unstructured(self):
return True
@staticmethod
def _sanitize_url(url):
"""
Extract only the schema, netloc, and path from an input URL if they exist,
excluding parameters, query, or fragments.
"""
parsed_url = urllib.parse.urlparse(url)
scheme = f"{parsed_url.scheme}:" if parsed_url.scheme else ""
netloc = f"//{parsed_url.netloc}" if parsed_url.netloc else "//"
return f"{scheme}{netloc}{parsed_url.path}"
@staticmethod
def uri_to_kfp(endpoint, subpath):
raise ValueError("data store doesnt support KFP URLs")
@staticmethod
def uri_to_ipython(endpoint, subpath):
return ""
def get_filesystem(self, silent=True) -> Optional[fsspec.AbstractFileSystem]:
"""return fsspec file system object, if supported"""
return None
def supports_isdir(self):
"""Whether the data store supports isdir"""
return True
def _get_secret_or_env(self, key, default=None):
# Project-secrets are mounted as env variables whose name can be retrieved from SecretsStore
return mlrun.get_secret_or_env(
key, secret_provider=self._get_secret, default=default
)
def get_storage_options(self):
"""get fsspec storage options"""
return None
def open(self, filepath, mode):
file_system = self.get_filesystem(False)
return file_system.open(filepath, mode)
def _join(self, key):
if self.subpath:
return f"{self.subpath}/{key}"
return key
def _get_parent_secret(self, key):
return self._parent.secret(self.secret_pfx + key)
def _get_secret(self, key: str, default=None):
return self._secrets.get(key, default) or self._get_parent_secret(key)
@property
def url(self):
return f"{self.kind}://{self.endpoint}"
def get(self, key, size=None, offset=0):
pass
def query(self, key, query="", **kwargs):
raise ValueError("data store doesnt support structured queries")
def put(self, key, data, append=False):
pass
def stat(self, key):
pass
def listdir(self, key):
raise ValueError("data store doesnt support listdir")
def download(self, key, target_path):
data = self.get(key)
mode = "wb"
if isinstance(data, str):
mode = "w"
with open(target_path, mode) as fp:
fp.write(data)
fp.close()
def upload(self, key, src_path):
pass
@staticmethod
def _parquet_reader(df_module, url, file_system, time_column, start_time, end_time):
from storey.utils import find_filters, find_partitions
def set_filters(
partitions_time_attributes, start_time_inner, end_time_inner, kwargs
):
filters = []
find_filters(
partitions_time_attributes,
start_time_inner,
end_time_inner,
filters,
time_column,
)
kwargs["filters"] = filters
def reader(*args, **kwargs):
if start_time or end_time:
if time_column is None:
raise mlrun.errors.MLRunInvalidArgumentError(
"When providing start_time or end_time, must provide time_column"
)
partitions_time_attributes = find_partitions(url, file_system)
set_filters(
partitions_time_attributes,
start_time,
end_time,
kwargs,
)
try:
return df_module.read_parquet(*args, **kwargs)
except pyarrow.lib.ArrowInvalid as ex:
if not str(ex).startswith(
"Cannot compare timestamp with timezone to timestamp without timezone"
):
raise ex
if start_time.tzinfo:
start_time_inner = start_time.replace(tzinfo=None)
end_time_inner = end_time.replace(tzinfo=None)
else:
start_time_inner = start_time.replace(tzinfo=pytz.utc)
end_time_inner = end_time.replace(tzinfo=pytz.utc)
set_filters(
partitions_time_attributes,
start_time_inner,
end_time_inner,
kwargs,
)
return df_module.read_parquet(*args, **kwargs)
else:
return df_module.read_parquet(*args, **kwargs)
return reader
def as_df(
self,
url,
subpath,
columns=None,
df_module=None,
format="",
start_time=None,
end_time=None,
time_column=None,
**kwargs,
):
df_module = df_module or pd
file_url = self._sanitize_url(url)
is_csv, is_json, drop_time_column = False, False, False
file_system = self.get_filesystem()
if file_url.endswith(".csv") or format == "csv":
is_csv = True
drop_time_column = False
if columns:
if (
time_column
and (start_time or end_time)
and time_column not in columns
):
columns.append(time_column)
drop_time_column = True
kwargs["usecols"] = columns
reader = df_module.read_csv
if file_system:
if file_system.isdir(file_url):
def reader(*args, **kwargs):
base_path = args[0]
file_entries = file_system.listdir(base_path)
filenames = []
for file_entry in file_entries:
if (
file_entry["name"].endswith(".csv")
and file_entry["size"] > 0
and file_entry["type"] == "file"
):
filename = file_entry["name"]
filename = filename.split("/")[-1]
filenames.append(filename)
dfs = []
for filename in filenames:
updated_args = [f"{base_path}/{filename}"]
updated_args.extend(args[1:])
dfs.append(df_module.read_csv(*updated_args, **kwargs))
return pd.concat(dfs)
elif (
file_url.endswith(".parquet")
or file_url.endswith(".pq")
or format == "parquet"
):
if columns:
kwargs["columns"] = columns
reader = self._parquet_reader(
df_module, url, file_system, time_column, start_time, end_time
)
elif file_url.endswith(".json") or format == "json":
is_json = True
reader = df_module.read_json
else:
raise Exception(f"file type unhandled {url}")
if file_system:
if self.supports_isdir() and file_system.isdir(file_url) or df_module == dd:
storage_options = self.get_storage_options()
if storage_options:
kwargs["storage_options"] = storage_options
df = reader(url, **kwargs)
else:
file = url
# Workaround for ARROW-12472 affecting pyarrow 3.x and 4.x.
if file_system.protocol != "file":
# If not dir, use file_system.open() to avoid regression when pandas < 1.2 and does not
# support the storage_options parameter.
file = file_system.open(url)
df = reader(file, **kwargs)
else:
temp_file = tempfile.NamedTemporaryFile(delete=False)
self.download(self._join(subpath), temp_file.name)
df = reader(temp_file.name, **kwargs)
remove(temp_file.name)
if is_json or is_csv:
# for parquet file the time filtering is executed in `reader`
df = filter_df_start_end_time(
df,
time_column=time_column,
start_time=start_time,
end_time=end_time,
)
if drop_time_column:
df.drop(columns=[time_column], inplace=True)
if is_json:
# for csv and parquet files the columns select is executed in `reader`.
df = select_columns_from_df(df, columns=columns)
return df
def to_dict(self):
return {
"name": self.name,
"url": f"{self.kind}://{self.endpoint}/{self.subpath}",
"secret_pfx": self.secret_pfx,
"options": self.options,
}
def rm(self, path, recursive=False, maxdepth=None):
self.get_filesystem().rm(path=path, recursive=recursive, maxdepth=maxdepth)
[docs]class DataItem:
"""Data input/output class abstracting access to various local/remote data sources
DataItem objects are passed into functions and can be used inside the function, when a function run completes
users can access the run data via the run.artifact(key) which returns a DataItem object.
users can also convert a data url (e.g. s3://bucket/key.csv) to a DataItem using `mlrun.get_dataitem(url)`.
Example::
# using data item inside a function
def my_func(context, data: DataItem):
df = data.as_df()
# reading run results using DataItem (run.artifact())
train_run = train_iris_func.run(inputs={'dataset': dataset},
params={'label_column': 'label'})
train_run.artifact('confusion-matrix').show()
test_set = train_run.artifact('test_set').as_df()
# create and use DataItem from uri
data = mlrun.get_dataitem('http://xyz/data.json').get()
"""
def __init__(
self,
key: str,
store: DataStore,
subpath: str,
url: str = "",
meta=None,
artifact_url=None,
):
self._store = store
self._key = key
self._url = url
self._path = subpath
self._meta = meta
self._artifact_url = artifact_url
self._local_path = ""
@property
def key(self):
"""DataItem key"""
return self._key
@property
def suffix(self):
"""DataItem suffix (file extension) e.g. '.png'"""
_, file_ext = path.splitext(self._path)
return file_ext
@property
def store(self):
"""DataItem store object"""
return self._store
@property
def kind(self):
"""DataItem store kind (file, s3, v3io, ..)"""
return self._store.kind
@property
def meta(self):
"""Artifact Metadata, when the DataItem is read from the artifacts store"""
return self._meta
@property
def artifact_url(self):
"""DataItem artifact url (when its an artifact) or url for simple dataitems"""
return self._artifact_url or self._url
@property
def url(self):
"""DataItem url e.g. /dir/path, s3://bucket/path"""
return self._url
[docs] def get(self, size=None, offset=0, encoding=None):
"""read all or a byte range and return the content
:param size: number of bytes to get
:param offset: fetch from offset (in bytes)
:param encoding: encoding (e.g. "utf-8") for converting bytes to str
"""
body = self._store.get(self._path, size=size, offset=offset)
if encoding and isinstance(body, bytes):
body = body.decode(encoding)
return body
[docs] def download(self, target_path):
"""download to the target dir/path
:param target_path: local target path for the downloaded item
"""
self._store.download(self._path, target_path)
[docs] def put(self, data, append=False):
"""write/upload the data, append is only supported by some datastores
:param data: data (bytes/str) to write
:param append: append data to the end of the object, NOT SUPPORTED BY SOME OBJECT STORES!
"""
self._store.put(self._path, data, append=append)
[docs] def delete(self):
"""delete the object from the datastore"""
self._store.rm(self._path)
[docs] def upload(self, src_path):
"""upload the source file (src_path)
:param src_path: source file path to read from and upload
"""
self._store.upload(self._path, src_path)
[docs] def stat(self):
"""return FileStats class (size, modified, content_type)"""
return self._store.stat(self._path)
[docs] def open(self, mode):
"""return fsspec file handler, if supported"""
return self._store.open(self._url, mode)
[docs] def ls(self):
"""return a list of child file names"""
return self._store.listdir(self._path)
[docs] def listdir(self):
"""return a list of child file names"""
return self._store.listdir(self._path)
[docs] def local(self):
"""get the local path of the file, download to tmp first if it's a remote object"""
if self.kind == "file":
return self._path
if self._local_path:
return self._local_path
dot = self._path.rfind(".")
suffix = "" if dot == -1 else self._path[dot:]
temp_file = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
self._local_path = temp_file.name
logger.info(f"downloading {self.url} to local temp file")
self.download(self._local_path)
return self._local_path
[docs] def remove_local(self):
"""remove the local file if it exists and was downloaded from a remote object"""
if self.kind == "file":
return
if self._local_path:
remove(self._local_path)
self._local_path = ""
[docs] def as_df(
self,
columns=None,
df_module=None,
format="",
time_column=None,
start_time=None,
end_time=None,
**kwargs,
):
"""return a dataframe object (generated from the dataitem).
:param columns: optional, list of columns to select
:param df_module: optional, py module used to create the DataFrame (e.g. pd, dd, cudf, ..)
:param format: file format, if not specified it will be deducted from the suffix
:param start_time: filters out data before this time
:param end_time: filters out data after this time
:param time_column: Store timestamp_key will be used if None.
The results will be filtered by this column and start_time & end_time.
"""
df = self._store.as_df(
self._url,
self._path,
columns=columns,
df_module=df_module,
format=format,
time_column=time_column,
start_time=start_time,
end_time=end_time,
**kwargs,
)
return df
[docs] def show(self, format=None):
"""show the data object content in Jupyter
:param format: format to use (when there is no/wrong suffix), e.g. 'png'
"""
if not is_ipython:
logger.warning(
"Jupyter/IPython was not detected, .show() will only display inside Jupyter"
)
return
from IPython import display
suffix = self.suffix.lower()
if format:
suffix = "." + format
if suffix in [".jpg", ".png", ".gif"]:
display.display(display.Image(self.get(), format=suffix[1:]))
elif suffix in [".htm", ".html"]:
display.display(display.HTML(self.get(encoding="utf-8")))
elif suffix in [".csv", ".pq", ".parquet"]:
display.display(self.as_df())
elif suffix in [".yaml", ".txt", ".py"]:
display.display(display.Pretty(self.get(encoding="utf-8")))
elif suffix == ".json":
display.display(display.JSON(orjson.loads(self.get())))
elif suffix == ".md":
display.display(display.Markdown(self.get(encoding="utf-8")))
else:
logger.error(f"unsupported show() format {suffix} for {self.url}")
[docs] def get_artifact_type(self) -> Union[str, None]:
"""
Check if the data item represents an Artifact (one of Artifact, DatasetArtifact and ModelArtifact). If it does
it return the store uri prefix (artifacts, datasets or models), otherwise None.
:return: The store prefix of the artifact if it is an artifact data item and None if not.
"""
if self.artifact_url and is_store_uri(url=self.artifact_url):
store_uri_prefix = parse_store_uri(self.artifact_url)[0]
if StorePrefix.is_artifact(prefix=store_uri_prefix):
return store_uri_prefix
return None
def __str__(self):
return self.url
def __repr__(self):
return f"'{self.url}'"
def get_range(size, offset):
byterange = f"bytes={offset}-"
if size:
byterange += str(offset + size)
return byterange
def basic_auth_header(user, password):
username = user.encode("latin1")
password = password.encode("latin1")
base = b64encode(b":".join((username, password))).strip()
authstr = "Basic " + base.decode("ascii")
return {"Authorization": authstr}
def http_get(url, headers=None, auth=None):
try:
response = requests.get(url, headers=headers, auth=auth, verify=verify_ssl)
except OSError as exc:
raise OSError(f"error: cannot connect to {url}: {err_to_str(exc)}")
mlrun.errors.raise_for_status(response)
return response.content
def http_head(url, headers=None, auth=None):
try:
response = requests.head(url, headers=headers, auth=auth, verify=verify_ssl)
except OSError as exc:
raise OSError(f"error: cannot connect to {url}: {err_to_str(exc)}")
mlrun.errors.raise_for_status(response)
return response.headers
def http_put(url, data, headers=None, auth=None):
try:
response = requests.put(
url, data=data, headers=headers, auth=auth, verify=verify_ssl
)
except OSError as exc:
raise OSError(f"error: cannot connect to {url}: {err_to_str(exc)}")
mlrun.errors.raise_for_status(response)
def http_upload(url, file_path, headers=None, auth=None):
with open(file_path, "rb") as data:
http_put(url, data, headers, auth)
class HttpStore(DataStore):
def __init__(self, parent, schema, name, endpoint="", secrets: dict = None):
super().__init__(parent, name, schema, endpoint, secrets)
self._https_auth_token = None
self._schema = schema
self.auth = None
self._headers = {}
self._enrich_https_token()
self._validate_https_token()
def get_filesystem(self, silent=True):
"""return fsspec file system object, if supported"""
if not self._filesystem:
self._filesystem = fsspec.filesystem("http")
return self._filesystem
def supports_isdir(self):
return False
def upload(self, key, src_path):
raise ValueError("unimplemented")
def put(self, key, data, append=False):
raise ValueError("unimplemented")
def get(self, key, size=None, offset=0):
data = http_get(self.url + self._join(key), self._headers, self.auth)
if offset:
data = data[offset:]
if size:
data = data[:size]
return data
def _enrich_https_token(self):
token = self._get_secret_or_env("HTTPS_AUTH_TOKEN")
if token:
self._https_auth_token = token
self._headers.setdefault("Authorization", f"token {token}")
def _validate_https_token(self):
if self._https_auth_token and self._schema in ["http"]:
logger.warn(
f"A AUTH TOKEN should not be provided while using {self._schema} "
f"schema as it is not secure and is not recommended."
)
