When I synchronize around 5GB of data from S3 to S3, I'm seeing it split into many small files at the S3 destination.

Based on the information provided in the knowledge sources, it's normal for Airbyte to split large amounts of data into multiple smaller files when synchronizing to an S3 destination. This behavior is by design and serves several purposes: 1. File partitioning: Airbyte partitions the output files by size, targeting a size of approximately 200MB compressed or lower per file. This is mentioned in the [Google Cloud Storage (GCS) documentation](https://docs.airbyte.com/integrations/destinations/gcs#configuration), which follows a similar pattern to S3:

A data sync may create multiple files as the output files can be partitioned by size (targeting a size of 200MB compressed or lower).

2. Parallel stream syncs: The option to control the part size (

part_size_mb

) was deprecated to achieve parallel stream syncs to S3 and other bucket services, as mentioned in this [forum post](https://discuss.airbyte.io/t/2292):

The option of `part_size_mb` was deprecated in <https://github.com/airbytehq/airbyte/pull/13753> this was necessary to achieve parallel stream syncs to S3 and other bucket services.

3. File naming pattern: Each file follows a specific naming pattern that includes the stream name, upload date, and a unique identifier. This allows for easy sorting and identification of files, as explained in the [GCS documentation](https://docs.airbyte.com/integrations/destinations/gcs#configuration). While there isn't currently a way to directly control the chunk size or number of output files, this splitting behavior is intentional and designed to optimize the sync process for large datasets. If you need to work with a single consolidated file after the sync, you may need to implement a post-processing step to combine these files on the destination S3 bucket.

@kapa.ai Now I want to synchronize the data into a CSV file format. Is there a way to do that?

Yes, Airbyte provides the capability to synchronize data into CSV file format. Here's how you can do it: 1. You can use Airbyte to export data to CSV files in various cloud storage destinations. For example, you can set up an S3 destination to store your data as CSV files. When configuring the S3 destination, you can choose CSV as the output format [Export Postgres data to CSV, JSON, Parquet and Avro files in S3](https://airbyte.com/tutorials/postgres-to-csv-json-parquet-avro-in-s3). 2. The CSV output in Airbyte typically includes three columns: - `_airbyte_ab_id`: A UUID assigned by Airbyte to each processed record. - `_airbyte_emitted_at`: A timestamp representing when the event was pulled from the data source. - `_airbyte_data`: Contains all the data as a JSON blob when no normalization is applied. 3. Airbyte also offers the option to normalize (flatten) the data, which can expand root-level fields into separate columns in the CSV file [Google Cloud Storage (GCS)](https://docs.airbyte.com/integrations/destinations/gcs#csv). 4. You can choose to compress the output CSV files. By default, GZIP compression is available, which will add a

.gz

extension to the filename [Azure Blob Storage](https://docs.airbyte.com/integrations/destinations/azure-blob-storage#csv). 5. For some destinations like Azure Blob Storage, you can optionally enable the "File Extension" field to save the output files with a

.csv

extension [Azure Blob Storage](https://docs.airbyte.com/integrations/destinations/azure-blob-storage#csv). Remember that the exact steps might vary depending on your specific source and destination. You'll need to configure both your source connector and a destination connector that supports CSV output (like S3, GCS, or Azure Blob Storage) to complete the synchronization process.

@kapa.ai "Tôi tự hỏi liệu có cách nào để tránh phân mảnh tệp khi xử lý các tệp lớn sang định dạng CSV hay không.

A message from kapa.ai

Xin lỗi, nhưng dựa trên các nguồn thông tin mà tôi có, không có cách nào để tránh việc phân mảnh tệp khi xử lý các tệp lớn sang định dạng CSV trong Airbyte. Khi Airbyte xử lý các tệp lớn, nó thường chia nhỏ chúng thành nhiều phần nhỏ hơn để tối ưu hóa quá trình xử lý. Tuy nhiên, bạn có thể muốn kiểm tra cấu hình của kết nối của mình hoặc liên hệ với cộng đồng Airbyte để biết thêm thông tin chi tiết hoặc hỗ trợ cụ thể hơn [Forum Post: S3 destination makes multiple parts of a parquet file](https://discuss.airbyte.io/t/2292).

@kapa.ai "I've modified the code in my S3 source and I want to rebuild the Docker image.

A message from kapa.ai

Sure, you can rebuild the Docker image for your modified S3 source by following these steps: 1. Navigate to the directory of your S3 source connector in your terminal. 2. Run the Docker build command to rebuild the image. Replace

<name>

with the name of your connector:

bash docker build . -t airbyte/source-<name>:dev

This command builds a Docker image from your current directory (

.

) and tags it as

airbyte/source-<name>:dev

[source](https://docs.airbyte.com/connector-development/tutorials/the-hard-way/build-a-connector-the-hard-way#3-package-the-connector-in-a-docker-image). 3. Verify that the new image has been created by running:

bash docker images

You should see your new image listed in the output [source](https://airbyte.com/tutorials/extract-data-from-the-webflow-api#step-4). Remember, every time you make changes to your connector, you need to rebuild the Docker image for those changes to take effect.

@kapa.ai then , can you write Dockerfile for me

A message from kapa.ai

Sure, here's a basic example of a Dockerfile for a Python-based connector:

Dockerfile FROM python:3.9-slim  WORKDIR /airbyte/integration_code  # Install any needed Python dependencies RUN pip install requests  # Copy source files COPY source.py . COPY spec.json .  # When this container is invoked, append the input arguments to `python source.py` ENTRYPOINT ["python", "/airbyte/integration_code/source.py"]  # Airbyte's build system uses these labels to know what to name and tag the docker images produced by this Dockerfile. LABEL io.airbyte.name=airbyte/source-<name> LABEL io.airbyte.version=0.1.0  # In order to launch a source on Kubernetes in a pod, we need to be able to wrap the entrypoint. # The source connector must specify its entrypoint in the AIRBYTE_ENTRYPOINT variable. ENV AIRBYTE_ENTRYPOINT='python /airbyte/integration_code/source.py'

Replace

<name>

with the name of your connector. This Dockerfile assumes that your connector's code is in a file named

source.py

and that your connector's specification is in a file named

spec.json

. If your files have different names, you should replace

source.py

and

spec.json

with your actual file names [source](https://docs.airbyte.com/connector-development/tutorials/the-hard-way/build-a-connector-the-hard-way#3-package-the-connector-in-a-docker-image). Please note that this is a basic example and might need to be adjusted based on your specific needs. For example, you might need to install additional Python packages with the

pip install

command.

@kapa.ai then please write s3 destination for Dockerfile

A message from kapa.ai

Sure, here's a basic example of a Dockerfile for a Python-based S3 destination connector:

Dockerfile FROM python:3.9-slim  WORKDIR /airbyte/integration_code  # Install any needed Python dependencies RUN pip install boto3  # Copy destination files COPY destination.py . COPY spec.json .  # When this container is invoked, append the input arguments to `python destination.py` ENTRYPOINT ["python", "/airbyte/integration_code/destination.py"]  # Airbyte's build system uses these labels to know what to name and tag the docker images produced by this Dockerfile. LABEL io.airbyte.name=airbyte/destination-s3 LABEL io.airbyte.version=0.1.0  # In order to launch a destination on Kubernetes in a pod, we need to be able to wrap the entrypoint. # The destination connector must specify its entrypoint in the AIRBYTE_ENTRYPOINT variable. ENV AIRBYTE_ENTRYPOINT='python /airbyte/integration_code/destination.py'

This Dockerfile assumes that your connector's code is in a file named

destination.py

and that your connector's specification is in a file named

spec.json

. If your files have different names, you should replace

destination.py

and

spec.json

with your actual file names. Please note that this is a basic example and might need to be adjusted based on your specific needs. For example, you might need to install additional Python packages with the

pip install

command. The

boto3

package is commonly used for interacting with Amazon S3, but your actual dependencies might be different.

@kapa.ai please help me write function check s3 with destination python code

A message from kapa.ai

Sure, here's a basic example of how you might implement a

check

function for an S3 destination in Python. This function would be part of a larger class or script that implements the destination logic.

python import boto3 from botocore.exceptions import NoCredentialsError  def check(self, logger, config):     """     Tests if the input configuration can be used to successfully connect to the S3 bucket      :param logger: Logging object to display debug/info/error to the logs     :param config: Json object containing the configuration of this destination      :return: A tuple (boolean, error). If boolean is true, then the connection was successful and error should be None.              Otherwise, the input configuration could not connect to the S3 bucket, and error should describe what went wrong.     """     s3 = boto3.client(         's3',         aws_access_key_id=config.get('aws_access_key_id'),         aws_secret_access_key=config.get('aws_secret_access_key'),         region_name=config.get('region_name')     )      try:         s3.head_bucket(Bucket=config.get('bucket_name'))         return True, None     except NoCredentialsError:         return False, "No AWS credentials found"     except Exception as e:         return False, f"Unable to connect to the S3 bucket with the provided configuration - {str(e)}"

This function uses the

boto3

library to interact with S3. It attempts to connect to the S3 bucket specified in the

config

parameter. If the connection is successful, it returns

True, None

. If the connection fails due to missing AWS credentials, it returns

False, "No AWS credentials found"

. If the connection fails due to any other reason, it returns

False

along with a description of the error. Please note that this is a basic example and might need to be adjusted based on your specific needs. For example, your actual configuration might include different or additional parameters, and you might need to handle additional types of errors.

@kapa.ai please example config.json for s3 destination

A message from kapa.ai

Sure, here's an example of a

config.json

file for an S3 destination:

json {   "access_key_id": "YOUR_ACCESS_KEY_ID",   "secret_access_key": "YOUR_SECRET_ACCESS_KEY",   "bucket_name": "YOUR_BUCKET_NAME",   "bucket_path": "YOUR_BUCKET_PATH",   "bucket_region": "YOUR_BUCKET_REGION",   "role_arn": "YOUR_ROLE_ARN",   "s3_endpoint": "YOUR_S3_ENDPOINT",   "s3_path_format": "YOUR_S3_PATH_FORMAT",   "s3_filename_pattern": "YOUR_S3_FILENAME_PATTERN" }

You should replace the placeholders (

YOUR_ACCESS_KEY_ID

,

YOUR_SECRET_ACCESS_KEY

, etc.) with your actual values. Here's what each field means: - `access_key_id`: Your AWS access key ID. - `secret_access_key`: Your AWS secret access key. - `bucket_name`: The name of your S3 bucket. - `bucket_path`: The path within your S3 bucket where you want to store the data. - `bucket_region`: The AWS region where your S3 bucket is located. - `role_arn`: The ARN of the AWS role to assume (if you're using STS Assume Role). - `s3_endpoint`: The endpoint URL of your S3 service (leave empty if using AWS S3, fill in S3 URL if using Minio S3). - `s3_path_format`: The format for how to store data under S3 Bucket Path. Default value is

${NAMESPACE}/${STREAM_NAME}/${YEAR}_${MONTH}_${DAY}_${EPOCH}_

. - `s3_filename_pattern`: The pattern for the S3 staging file(s) filename. Supported placeholders are

{date}

,

{date:yyyy_MM}

,

{timestamp}

,

{timestamp:millis}

,

{timestamp:micros}

,

{part_number}

,

{sync_id}

,

{format_extension}

. Please note that not all fields may be required for your specific use case. For example, if you're using AWS credentials, you don't need to provide a

role_arn

. If you're using an instance profile, you can omit the

access_key_id

and

secret_access_key

[source](https://docs.airbyte.com/integrations/destinations/s3#prerequisites).

@kapa.ai hiện tại airbyte của thằng s3 destination đang check test như thế nào , check test bằng cái gì

A message from kapa.ai

Airbyte thực hiện kiểm tra kết nối (check connection) với S3 destination bằng cách sử dụng thông tin cấu hình được cung cấp. Trong quá trình kiểm tra, Airbyte sẽ cố gắng kết nối đến S3 bucket bằng cách sử dụng các thông tin như

access_key_id

,

secret_access_key

, và

bucket_name

. Dưới đây là một ví dụ về cách thức hoạt động của hàm

check

trong mã nguồn Python:

python import boto3 from botocore.exceptions import NoCredentialsError  def check(self, logger, config):     s3 = boto3.client(         's3',         aws_access_key_id=config.get('aws_access_key_id'),         aws_secret_access_key=config.get('aws_secret_access_key'),         region_name=config.get('region_name')     )      try:         s3.head_bucket(Bucket=config.get('bucket_name'))         return True, None     except NoCredentialsError:         return False, "No AWS credentials found"     except Exception as e:         return False, f"Unable to connect to the S3 bucket with the provided configuration - {str(e)}"

Trong đoạn mã trên, Airbyte sử dụng thư viện

boto3

để tạo một kết nối đến S3. Nếu kết nối thành công, hàm

check

sẽ trả về

True, None

. Nếu không thể kết nối do thiếu thông tin xác thực AWS, hàm sẽ trả về

False, "No AWS credentials found"

. Nếu có lỗi khác xảy ra, hàm sẽ trả về

False

cùng với mô tả lỗi [source](https://github.com/airbytehq/airbyte/issues/18277). Lưu ý rằng đây chỉ là một ví dụ cơ bản và có thể cần được điều chỉnh dựa trên yêu cầu cụ thể của bạn.

nó đang lỗi này python3 main.py check --config integration_tests/config.json /usr/lib/python3/dist-packages/requests/__init__.py87 RequestsDependencyWarning: urllib3 (2.2.3) or chardet (4.0.0) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported " {"type":"LOG","log":{"level":"FATAL","message":"'tuple' object has no attribute 'status'\nTraceback (most recent call last):\n File \"/mnt/d/dft/airbyte/airbyte-integrations/connectors/destination-destination-s3-v3/main.py\", line 11, in <module>\n DestinationDestinationS3V3().run(sys.argv[1:])\n File \"/home/hungpm/.local/lib/python3.10/site-packages/airbyte_cdk/destinations/destination.py\", line 120, in run\n print(orjson.dumps(AirbyteMessageSerializer.dump(message)).decode())\n File \"/home/hungpm/.local/lib/python3.10/site-packages/serpyco_rs/_main.py\", line 72, in dump\n return self._encoder.dump(value)\nAttributeError: 'tuple' object has no attribute 'status'"}} {"type":"TRACE","trace":{"type":"ERROR","emitted_at":1729851750465,"error":{"message":"Something went wrong in the connector. See the logs for more details.","internal_message":"'tuple' object has no attribute 'status'","stack_trace":"Traceback (most recent call last):\n File \"/mnt/d/dft/airbyte/airbyte-integrations/connectors/destination-destination-s3-v3/main.py\", line 11, in <module>\n DestinationDestinationS3V3().run(sys.argv[1:])\n File \"/home/hungpm/.local/lib/python3.10/site-packages/airbyte_cdk/destinations/destination.py\", line 120, in run\n print(orjson.dumps(AirbyteMessageSerializer.dump(message)).decode())\n File \"/home/hungpm/.local/lib/python3.10/site-packages/serpyco_rs/_main.py\", line 72, in dump\n return self._encoder.dump(value)\nAttributeError: 'tuple' object has no attribute 'status'\n","failure_type":"system_error"}}}

A message from kapa.ai

Dựa trên thông báo lỗi bạn đã cung cấp, có vẻ như có một vấn đề với cách mã nguồn của bạn đang xử lý các thông điệp từ Airbyte. Cụ thể, mã nguồn đang cố gắng truy cập thuộc tính

status

từ một đối tượng

tuple

, nhưng

tuple

không có thuộc tính này. Lỗi này có thể xuất phát từ việc mã nguồn đang cố gắng xử lý một thông điệp không hợp lệ hoặc không đúng định dạng từ Airbyte. Bạn nên kiểm tra lại cách mã nguồn của bạn xử lý các thông điệp từ Airbyte. Ngoài ra, bạn đang sử dụng Python 3.10, trong khi hầu hết các connector Python của Airbyte đều chạy trên Python 3.9. Điều này có thể gây ra một số vấn đề tương thích. Bạn nên cân nhắc việc chuyển sang sử dụng Python 3.9 [source](https://discuss.airbyte.io/t/1023). Cuối cùng, bạn nên kiểm tra lại cấu hình của mình bằng cách chạy lệnh sau:

bash python main.py check --config secrets/config.json

Nếu bạn gặp vấn đề, hãy chia sẻ thông tin lỗi tại đây để có thể nhận được hỗ trợ tốt hơn [source](https://discuss.airbyte.io/t/943).