# SPDX-License-Identifier: Apache-2.0
# The OpenSearch Contributors require contributions made to
# this file be licensed under the Apache-2.0 license or a
# compatible open source license.
# Any modifications Copyright OpenSearch Contributors. See
# GitHub history for details.
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# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you 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 csv
from collections import deque
from typing import Any, Dict, Generator, List, Mapping, Optional, Tuple, Union
import pandas as pd # type: ignore
from opensearchpy import OpenSearch
from opensearchpy.helpers import parallel_bulk
from opensearch_py_ml import DataFrame
from opensearch_py_ml.common import DEFAULT_CHUNK_SIZE, PANDAS_VERSION
from opensearch_py_ml.field_mappings import FieldMappings, verify_mapping_compatibility
try:
from pandas.io.parsers import _c_parser_defaults # type: ignore
except ImportError:
from pandas.io.parsers.readers import _c_parser_defaults # type: ignore
_DEFAULT_LOW_MEMORY: bool = _c_parser_defaults["low_memory"]
def pandas_to_opensearch(
pd_df: pd.DataFrame,
os_client: Union[str, List[str], Tuple[str, ...], OpenSearch],
os_dest_index: str,
os_if_exists: str = "fail",
os_refresh: bool = False,
os_dropna: bool = False,
os_type_overrides: Optional[Mapping[str, str]] = None,
os_verify_mapping_compatibility: bool = True,
thread_count: int = 4,
chunksize: Optional[int] = None,
use_pandas_index_for_os_ids: bool = True,
) -> DataFrame:
"""
Append a pandas DataFrame to an OpenSearch index.
Mainly used in testing.
Modifies the OpenSearch destination index
Parameters
----------
os_client: OpenSearch client
os_dest_index: str
Name of OpenSearch index to be appended to
os_if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the index already exists.
- fail: Raise a ValueError.
- replace: Delete the index before inserting new values.
- append: Insert new values to the existing index. Create if does not exist.
os_refresh: bool, default 'False'
Refresh os_dest_index after bulk index
os_dropna: bool, default 'False'
* True: Remove missing values (see pandas.Series.dropna)
* False: Include missing values - may cause bulk to fail
os_type_overrides: dict, default None
Dict of field_name: es_data_type that overrides default os data types
os_verify_mapping_compatibility: bool, default 'True'
* True: Verify that the dataframe schema matches the OpenSearch index schema
* False: Do not verify schema
thread_count: int
number of the threads to use for the bulk requests
chunksize: int, default None
Number of pandas.DataFrame rows to read before bulk index into OpenSearch
use_pandas_index_for_os_ids: bool, default 'True'
* True: pandas.DataFrame.index fields will be used to populate OpenSearch '_id' fields.
* False: Ignore pandas.DataFrame.index when indexing into OpenSearch
Returns
-------
opensearch_py_ml.Dataframe
opensearch_py_ml.DataFrame referencing data in destination_index
Examples
--------
>>> from tests import OPENSEARCH_TEST_CLIENT
>>> pd_df = pd.DataFrame(data={'A': 3.141,
... 'B': 1,
... 'C': 'foo',
... 'D': pd.Timestamp('20190102'),
... 'E': [1.0, 2.0, 3.0],
... 'F': False,
... 'G': [1, 2, 3],
... 'H': 'Long text - to be indexed as os type text'},
... index=['0', '1', '2'])
>>> type(pd_df)
<class 'pandas.core.frame.DataFrame'>
>>> pd_df
A B ... G H
0 3.141 1 ... 1 Long text - to be indexed as os type text
1 3.141 1 ... 2 Long text - to be indexed as os type text
2 3.141 1 ... 3 Long text - to be indexed as os type text
<BLANKLINE>
[3 rows x 8 columns]
>>> pd_df.dtypes
A float64
B int64
C object
D datetime64[ns]
E float64
F bool
G int64
H object
dtype: object
Convert `pandas.DataFrame` to `opensearch_py_ml.DataFrame` - this creates an OpenSearch index called
`pandas_to_opensearch`. Overwrite existing OpenSearch index if it exists `if_exists="replace"`, and sync index, so
it is readable on return `refresh=True`
>>> from tests import OPENSEARCH_TEST_CLIENT
>>> oml_df = oml.pandas_to_opensearch(pd_df,
... OPENSEARCH_TEST_CLIENT,
... 'pandas_to_opensearch',
... os_if_exists="replace",
... os_refresh=True,
... os_type_overrides={'H':'text'}) # index field 'H' as text not keyword
>>> type(oml_df)
<class 'opensearch_py_ml.dataframe.DataFrame'>
>>> oml_df
A B ... G H
0 3.141 1 ... 1 Long text - to be indexed as os type text
1 3.141 1 ... 2 Long text - to be indexed as os type text
2 3.141 1 ... 3 Long text - to be indexed as os type text
<BLANKLINE>
[3 rows x 8 columns]
>>> oml_df.dtypes
A float64
B int64
C object
D datetime64[ns]
E float64
F bool
G int64
H object
dtype: object
See Also
--------
opensearch_py_ml.opensearch_to_pandas: Create a pandas.Dataframe from opensearch_py_ml.DataFrame
"""
if chunksize is None:
chunksize = DEFAULT_CHUNK_SIZE
mapping = FieldMappings._generate_os_mappings(pd_df, os_type_overrides)
# If table exists, check if_exists parameter
if os_client.indices.exists(index=os_dest_index): # type: ignore
if os_if_exists == "fail":
raise ValueError(
f"Could not create the index [{os_dest_index}] because it "
f"already exists. "
f"Change the 'os_if_exists' parameter to "
f"'append' or 'replace' data."
)
elif os_if_exists == "replace":
os_client.indices.delete(index=os_dest_index) # type: ignore
os_client.indices.create( # type: ignore
index=os_dest_index, body={"mappings": mapping["mappings"]}
)
elif os_if_exists == "append" and os_verify_mapping_compatibility:
dest_mapping = os_client.indices.get_mapping(index=os_dest_index)[ # type: ignore
os_dest_index
]
verify_mapping_compatibility(
oml_mapping=mapping,
os_mapping=dest_mapping,
os_type_overrides=os_type_overrides,
)
else:
os_client.indices.create( # type: ignore
index=os_dest_index, body={"mappings": mapping["mappings"]}
)
def action_generator(
pd_df: pd.DataFrame,
os_dropna: bool,
use_pandas_index_for_os_ids: bool,
os_dest_index: str,
) -> Generator[Dict[str, Any], None, None]:
for row in pd_df.iterrows():
if os_dropna:
values = row[1].dropna().to_dict()
else:
values = row[1].to_dict()
if use_pandas_index_for_os_ids:
# Use index as _id
id = row[0]
action = {"_index": os_dest_index, "_source": values, "_id": str(id)}
else:
action = {"_index": os_dest_index, "_source": values}
yield action
# parallel_bulk is lazy generator so use deque to consume them immediately
# maxlen = 0 because don't need results of parallel_bulk
deque(
parallel_bulk(
client=os_client, # type: ignore
actions=action_generator(
pd_df, os_dropna, use_pandas_index_for_os_ids, os_dest_index
),
thread_count=thread_count,
chunk_size=int(chunksize / thread_count),
),
maxlen=0,
)
if os_refresh:
os_client.indices.refresh(index=os_dest_index) # type: ignore
return DataFrame(os_client, os_dest_index)
def opensearch_to_pandas(
oml_df: DataFrame, show_progress: bool = False
) -> pd.DataFrame:
"""
Convert an opensearch_py_ml.Dataframe to a pandas.DataFrame
**Note: this loads the entire OpenSearch index into in core pandas.DataFrame structures. For large
indices this can create significant load on the OpenSearch cluster and require signficant memory**
Parameters
----------
oml_df: opensearch_py_ml.DataFrame
The source opensearch_py_ml.Dataframe referencing the OpenSearch index
show_progress: bool
Output progress of option to stdout? By default, False.
Returns
-------
pandas.Dataframe
pandas.DataFrame contains all rows and columns in opensearch_py_ml.DataFrame
Examples
--------
>>> from tests import OPENSEARCH_TEST_CLIENT
>>> oml_df = oml.DataFrame(OPENSEARCH_TEST_CLIENT, 'flights').head()
>>> type(oml_df)
<class 'opensearch_py_ml.dataframe.DataFrame'>
>>> oml_df
AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False ... 0 2018-01-01 05:13:00
<BLANKLINE>
[5 rows x 27 columns]
Convert `opensearch_py_ml.DataFrame` to `pandas.DataFrame` (Note: this loads entire OpenSearch index into core memory)
>>> pd_df = oml.opensearch_to_pandas(oml_df)
>>> type(pd_df)
<class 'pandas.core.frame.DataFrame'>
>>> pd_df
AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False ... 0 2018-01-01 05:13:00
<BLANKLINE>
[5 rows x 27 columns]
Convert `opensearch_py_ml.DataFrame` to `pandas.DataFrame` and show progress every 10000 rows
>>> pd_df = oml.opensearch_to_pandas(oml.DataFrame(OPENSEARCH_TEST_CLIENT, 'flights'), show_progress=True) # doctest: +SKIP
2020-01-29 12:43:36.572395: read 10000 rows
2020-01-29 12:43:37.309031: read 13059 rows
See Also
--------
opensearch_py_ml.pandas_to_opensearch: Create an opensearch_py_ml.Dataframe from pandas.DataFrame
"""
return oml_df.to_pandas(show_progress=show_progress)
[docs]
def csv_to_opensearch( # type: ignore
filepath_or_buffer,
os_client: Union[str, List[str], Tuple[str, ...], OpenSearch],
os_dest_index: str,
os_if_exists: str = "fail",
os_refresh: bool = False,
os_dropna: bool = False,
os_type_overrides: Optional[Mapping[str, str]] = None,
sep=",",
delimiter=None,
# Column and Index Locations and Names
header="infer",
names=None,
index_col=None,
usecols=None,
prefix=None,
# General Parsing Configuration
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
# Iteration
# iterator=False,
chunksize=None,
# NA and Missing Data Handling
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
# Datetime Handling
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
# Quoting, Compression, and File Format
compression="infer",
thousands=None,
decimal=b".",
lineterminator=None,
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
# Error Handling
warn_bad_lines: bool = True,
error_bad_lines: bool = True,
on_bad_lines: str = "error",
# Internal
delim_whitespace=False,
low_memory: bool = _DEFAULT_LOW_MEMORY,
memory_map=False,
float_precision=None,
) -> "DataFrame":
"""
Read a comma-separated values (csv) file into opensearch_py_ml.DataFrame (i.e. an OpenSearch index).
**Modifies an OpenSearch index**
**Note pandas iteration options not supported**
Parameters
----------
os_client: OpenSearch client
os_dest_index: str
Name of OpenSearch index to be appended to
os_if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the index already exists.
- fail: Raise a ValueError.
- replace: Delete the index before inserting new values.
- append: Insert new values to the existing index. Create if does not exist.
os_dropna: bool, default 'False'
* True: Remove missing values (see pandas.Series.dropna)
* False: Include missing values - may cause bulk to fail
os_type_overrides: dict, default None
Dict of columns: es_type to override default os datatype mappings
chunksize
number of csv rows to read before bulk index into OpenSearch
Other Parameters
----------------
Parameters derived from :pandas_api_docs:`pandas.read_csv`.
See Also
--------
:pandas_api_docs:`pandas.read_csv`
Notes
-----
iterator not supported
Examples
--------
See if 'churn' index exists in OpenSearch
>>> from opensearchpy import OpenSearch # doctest: +SKIP
>>> osclient = OpenSearch() # doctest: +SKIP
>>> osclient.indices.exists(index="churn") # doctest: +SKIP
False
Read 'churn.csv' and use first column as _id (and opensearch_py_ml.DataFrame index)
::
# churn.csv
,state,account length,area code,phone number,international plan,voice mail plan,number vmail messages,total day minutes,total day calls,total day charge,total eve minutes,total eve calls,total eve charge,total night minutes,total night calls,total night charge,total intl minutes,total intl calls,total intl charge,customer service calls,churn
0,KS,128,415,382-4657,no,yes,25,265.1,110,45.07,197.4,99,16.78,244.7,91,11.01,10.0,3,2.7,1,0
1,OH,107,415,371-7191,no,yes,26,161.6,123,27.47,195.5,103,16.62,254.4,103,11.45,13.7,3,3.7,1,0
...
>>> oml.csv_to_opensearch(
... "churn.csv",
... os_client=OPENSEARCH_TEST_CLIENT,
... os_dest_index='churn',
... os_refresh=True,
... index_col=0
... ) # doctest: +SKIP
account length area code churn customer service calls ... total night calls total night charge total night minutes voice mail plan
0 128 415 0 1 ... 91 11.01 244.7 yes
1 107 415 0 1 ... 103 11.45 254.4 yes
2 137 415 0 0 ... 104 7.32 162.6 no
3 84 408 0 2 ... 89 8.86 196.9 no
4 75 415 0 3 ... 121 8.41 186.9 no
... ... ... ... ... ... ... ... ... ...
3328 192 415 0 2 ... 83 12.56 279.1 yes
3329 68 415 0 3 ... 123 8.61 191.3 no
3330 28 510 0 2 ... 91 8.64 191.9 no
3331 184 510 0 2 ... 137 6.26 139.2 no
3332 74 415 0 0 ... 77 10.86 241.4 yes
<BLANKLINE>
[3333 rows x 21 columns]
Validate data now exists in 'churn' index:
>>> oml.search(index="churn", size=1) # doctest: +SKIP
{'took': 1, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 3333, 'relation': 'eq'}, 'max_score': 1.0, 'hits': [{'_index': 'churn', '_id': '0', '_score': 1.0, '_source': {'state': 'KS', 'account length': 128, 'area code': 415, 'phone number': '382-4657', 'international plan': 'no', 'voice mail plan': 'yes', 'number vmail messages': 25, 'total day minutes': 265.1, 'total day calls': 110, 'total day charge': 45.07, 'total eve minutes': 197.4, 'total eve calls': 99, 'total eve charge': 16.78, 'total night minutes': 244.7, 'total night calls': 91, 'total night charge': 11.01, 'total intl minutes': 10.0, 'total intl calls': 3, 'total intl charge': 2.7, 'customer service calls': 1, 'churn': 0}}]}}
TODO - currently the opensearch_py_ml.DataFrame may not retain the order of the data in the csv.
"""
kwargs: Dict[str, Any] = {
"sep": sep,
"delimiter": delimiter,
"engine": engine,
"dialect": dialect,
"compression": compression,
# "engine_specified": engine_specified,
"doublequote": doublequote,
"escapechar": escapechar,
"quotechar": quotechar,
"quoting": quoting,
"skipinitialspace": skipinitialspace,
"lineterminator": lineterminator,
"header": header,
"index_col": index_col,
"names": names,
"prefix": prefix,
"skiprows": skiprows,
"skipfooter": skipfooter,
"na_values": na_values,
"true_values": true_values,
"false_values": false_values,
"keep_default_na": keep_default_na,
"thousands": thousands,
"comment": comment,
"decimal": decimal,
"parse_dates": parse_dates,
"keep_date_col": keep_date_col,
"dayfirst": dayfirst,
"date_parser": date_parser,
"cache_dates": cache_dates,
"nrows": nrows,
# "iterator": iterator,
"chunksize": chunksize,
"converters": converters,
"dtype": dtype,
"usecols": usecols,
"verbose": verbose,
"encoding": encoding,
"memory_map": memory_map,
"float_precision": float_precision,
"na_filter": na_filter,
"delim_whitespace": delim_whitespace,
"warn_bad_lines": warn_bad_lines,
"error_bad_lines": error_bad_lines,
"on_bad_lines": on_bad_lines,
"low_memory": low_memory,
"infer_datetime_format": infer_datetime_format,
"skip_blank_lines": skip_blank_lines,
}
if chunksize is None:
kwargs["chunksize"] = DEFAULT_CHUNK_SIZE
if PANDAS_VERSION >= (1, 3):
# Bug in Pandas v1.3.0
# If names and prefix both passed as None, it's considering them as specified values and throwing ValueError
# Ref: https://github.com/pandas-dev/pandas/issues/42387
if kwargs["names"] is None and kwargs["prefix"] is None:
kwargs.pop("prefix")
if kwargs["warn_bad_lines"] is True:
kwargs["on_bad_lines"] = "warn"
if kwargs["error_bad_lines"] is True:
kwargs["on_bad_lines"] = "error"
kwargs.pop("warn_bad_lines")
kwargs.pop("error_bad_lines")
else:
if on_bad_lines == "warn":
kwargs["warn_bad_lines"] = True
if on_bad_lines == "error":
kwargs["error_bad_lines"] = True
kwargs.pop("on_bad_lines")
# read csv in chunks to pandas DataFrame and dump to opensearch_py_ml DataFrame (and OpenSearch)
reader = pd.read_csv(filepath_or_buffer, **kwargs)
first_write = True
for chunk in reader:
pandas_to_opensearch(
chunk,
os_client,
os_dest_index,
chunksize=chunksize,
os_refresh=os_refresh,
os_dropna=os_dropna,
os_type_overrides=os_type_overrides,
# es_if_exists should be 'append' except on the first call to pandas_to_opensearch()
os_if_exists=(os_if_exists if first_write else "append"),
)
first_write = False
# Now create an opensearch_py_ml.DataFrame that references the new index
return DataFrame(os_client, os_index_pattern=os_dest_index)