Source code for mlrun.datastore.base

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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
from urllib.parse import urlparse

import fsspec
import orjson
import pandas as pd
import pyarrow
import pytz
import requests
import urllib3
from deprecated import deprecated

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:

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:
    using_bucket = False

    def __init__(self, parent, name, kind, endpoint="", secrets: dict = None):
        self._parent = parent
        self.kind = kind = name
        self.endpoint = endpoint
        self.subpath = ""
        self.secret_pfx = ""
        self.options = {}
        self.from_spec = False
        self._filesystem = None
        self._secrets = secrets or {}

    def is_structured(self):
        return False

    def is_unstructured(self):
        return True

    def _sanitize_storage_options(options):
        if not options:
            return {}
        options = {k: v for k, v in options.items() if v is not None and v != ""}
        return options

    def _sanitize_url(url):
        Extract only the schema, netloc, and path from an input URL if they exist,
        excluding parameters, query, or fragments.
        if not url:
            raise mlrun.errors.MLRunInvalidArgumentError("Cannot parse an empty URL")
        parsed_url = urllib.parse.urlparse(url)
        netloc = f"//{parsed_url.netloc}" if parsed_url.netloc else "//"
        return f"{parsed_url.scheme}:{netloc}{parsed_url.path}"

    def uri_to_kfp(endpoint, subpath):
        raise ValueError("data store doesnt support KFP URLs")

    def uri_to_ipython(endpoint, subpath):
        return ""

    # TODO: remove in 1.8.0
        reason="'get_filesystem()' will be removed in 1.8.0, use "
        "'filesystem' property instead",
    def get_filesystem(self):
        return self.filesystem

    def filesystem(self) -> 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, prefix=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, prefix=prefix

    def get_storage_options(self):
        """get fsspec storage options"""
        return self._sanitize_storage_options(None)

    def open(self, filepath, mode):
        file_system = self.filesystem
        return, 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)

    def url(self):
        return f"{self.kind}://{self.endpoint}"

    def get(self, key, size=None, offset=0):

    def query(self, key, query="", **kwargs):
        raise ValueError("data store doesnt support structured queries")

    def put(self, key, data, append=False):

    def stat(self, key):

    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:

    def upload(self, key, src_path):

    def get_spark_options(self):
        return {}

    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 = []
            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)
                    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)
                        start_time_inner = start_time.replace(tzinfo=pytz.utc)
                        end_time_inner = end_time.replace(tzinfo=pytz.utc)

                    return df_module.read_parquet(*args, **kwargs)
                return df_module.read_parquet(*args, **kwargs)

        return reader

    def as_df(
        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.filesystem
        if file_url.endswith(".csv") or format == "csv":
            is_csv = True
            drop_time_column = False
            if columns:
                if (
                    and (start_time or end_time)
                    and time_column not in columns
                    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 (
                                and file_entry["size"] > 0
                                and file_entry["type"] == "file"
                                filename = file_entry["name"]
                                filename = filename.split("/")[-1]
                        dfs = []
                        if df_module is pd:
                            kwargs.pop("filesystem", None)
                            kwargs.pop("storage_options", None)
                            for filename in filenames:
                                fullpath = f"{base_path}/{filename}"
                                with as fhandle:
                                    updated_args = [fhandle]
                                        df_module.read_csv(*updated_args, **kwargs)
                            for filename in filenames:
                                updated_args = [f"{base_path}/{filename}"]
                                dfs.append(df_module.read_csv(*updated_args, **kwargs))
                        return df_module.concat(dfs)

        elif (
            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

            raise Exception(f"File type unhandled {url}")

        if file_system:
            if (
                and file_system.isdir(file_url)
                or self._is_dd(df_module)
                storage_options = self.get_storage_options()
                if url.startswith("ds://"):
                    parsed_url = urllib.parse.urlparse(url)
                    url = parsed_url.path
                    if self.using_bucket:
                        url = url[1:]
                    # Pass the underlying file system
                    kwargs["filesystem"] = file_system
                elif storage_options:
                    kwargs["storage_options"] = storage_options
                df = reader(url, **kwargs)
                file = url
                # Workaround for ARROW-12472 affecting pyarrow 3.x and 4.x.
                if file_system.protocol != "file":
                    # If not dir, use to avoid regression when pandas < 1.2 and does not
                    # support the storage_options parameter.
                    file =

                df = reader(file, **kwargs)
            temp_file = tempfile.NamedTemporaryFile(delete=False)
            df = reader(, **kwargs)

        if is_json or is_csv:
            # for parquet file the time filtering is executed in `reader`
            df = filter_df_start_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 {
            "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.filesystem.rm(path=path, recursive=recursive, maxdepth=maxdepth)

    def _is_dd(df_module):
            import dask.dataframe as dd

            return df_module == dd
        except ImportError:
            return False

[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 ={'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 """, 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, 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 ="downloading {self.url} to local temp file") 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, session=None): try: put_api = session.put if session else requests.put response = put_api( 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)}") from 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() @property def filesystem(self): """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." ) # This wrapper class is designed to extract the 'ds' schema and profile name from URL-formatted paths. # Within fsspec, the AbstractFileSystem::_strip_protocol() internal method is used to handle complete URL paths. # As an example, it converts an S3 URL 's3://s3bucket/path' to just 's3bucket/path'. # Since 'ds' schemas are not inherently processed by fsspec, we have adapted the _strip_protocol() # method specifically to strip away the 'ds' schema as required. def makeDatastoreSchemaSanitizer(cls, using_bucket=False, *args, **kwargs): if not issubclass(cls, fsspec.AbstractFileSystem): raise ValueError("Class must be a subclass of fsspec.AbstractFileSystem") class DatastoreSchemaSanitizer(cls): @classmethod def _strip_protocol(cls, url): if url.startswith("ds://"): parsed_url = urlparse(url) url = parsed_url.path if using_bucket: url = url[1:] return super()._strip_protocol(url) return DatastoreSchemaSanitizer(*args, **kwargs)