OpenMetadata/ingestion/src/metadata/utils/s3_utils.py

#  Copyright 2021 Collate
#  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.

"""
Utils module to convert different file types from s3 buckets into a dataframe
"""

import json
import os
from typing import Any

import pandas as pd
from pandas import DataFrame
from pyarrow import fs
from pyarrow.parquet import ParquetFile


def read_csv_from_s3(
    client: Any, key: str, bucket_name: str, sep: str = ",", sample_size: int = 100
) -> DataFrame:
    """
    Read the csv file from the s3 bucket and return a dataframe
    """

    stream = client.get_object(Bucket=bucket_name, Key=key)["Body"]
    return pd.read_csv(stream, sep=sep, nrows=sample_size + 1)


def read_tsv_from_s3(
    client, key: str, bucket_name: str, sample_size: int = 100
) -> DataFrame:
    """
    Read the tsv file from the s3 bucket and return a dataframe
    """

    return read_csv_from_s3(client, key, bucket_name, sep="\t", sample_size=sample_size)


def read_json_from_s3(
    client: Any, key: str, bucket_name: str, sample_size=100
) -> DataFrame:
    """
    Read the json file from the s3 bucket and return a dataframe
    """
    obj = client.get_object(Bucket=bucket_name, Key=key)
    json_text = obj["Body"].read().decode("utf-8")
    data = json.loads(json_text)
    if isinstance(data, list):
        return pd.DataFrame.from_dict(data[:sample_size])
    return pd.DataFrame.from_dict(
        {key: pd.Series(value) for key, value in data.items()}
    )


def read_parquet_from_s3(client: Any, key: str, bucket_name: str) -> DataFrame:
    """
    Read the parquet file from the s3 bucket and return a dataframe
    """

    s3_file = fs.S3FileSystem(region=client.meta.region_name)
    return (
        ParquetFile(s3_file.open_input_file(os.path.join(bucket_name, key)))
        .schema.to_arrow_schema()
        .empty_table()
        .to_pandas()
    )