Files (CSV, JSON, Excel, Feather, Parquet)

Availability:

Airbyte Cloud Airbyte OSS PyAirbyte

Support Level:

certified

Connector Version:

0.5.2(Last updated 3 days ago)

CDK Version:

0.86.3

Sync Success Rate:

Usage Rate:

This page contains the setup guide and reference information for the Files source connector.

Prerequisites

• A file hosted on AWS S3, GCS, HTTPS, or an SFTP server

Setup guide

For Airbyte Cloud users: Please note that locally stored files cannot be used as a source in Airbyte Cloud.

Step 1: Set up the connector in Airbyte

1. From the Airbyte UI, click the Sources tab, then click + New source and select Files (CSV, JSON, Excel, Feather, Parquet) from the list of available sources.
2. Enter a Source name of your choosing.
3. For Dataset Name, enter the name of the final table to replicate this file into (should include letters, numbers, dashes and underscores only).
4. For File Format, select the format of the file to replicate from the dropdown menu (Warning: some formats may be experimental. Please refer to the table of supported formats).

Step 2: Select the provider and set provider-specific configurations:

1. For Storage Provider, use the dropdown menu to select the Storage Provider or Location of the file(s) which should be replicated, then configure the provider-specific fields as needed:

HTTPS: Public Web [Default]

• User-Agent (Optional)

Set this to active if you want to add the User-Agent header to requests (inactive by default).

GCS: Google Cloud Storage

• Service Account JSON (Required for private buckets)

To access private buckets stored on Google Cloud, this connector requires a service account JSON credentials file with the appropriate permissions. A detailed breakdown of this topic can be found at the Google Cloud service accounts page. Please generate the "credentials.json" file and copy its content to this field, ensuring it is in JSON format. If you are accessing publicly available data, this field is not required.

S3: Amazon Web Services

• AWS Access Key ID (Required for private buckets)
• AWS Secret Access Key (Required for private buckets)

To access private buckets stored on AWS S3, this connector requires valid credentials with the necessary permissions. To access these keys, refer to the AWS IAM documentation. More information on setting permissions in AWS can be found here. If you are accessing publicly available data, these fields are not required.

AzBlob: Azure Blob Storage

• Storage Account (Required)

This is the globally unique name of the storage account that the desired blob sits within. See the Azure documentation for more details.

If you are accessing private storage, you must also provide one of the following security credentials with the necessary permissions:

• SAS Token: Find more information here.
• Shared Key: Find more information here.

SSH: Secure Shell / SCP: Secure Copy Protocol / SFTP: Secure File Transfer Protocol

• Host (Required)

Enter the hostname or IP address of the remote server where the file trasfer will take place.

• User (Required)

Enter the username associated with your account on the remote server.

• Password (Optional)

If required by the remote server, enter the password associated with your user account. Otherwise, leave this field blank.

• Port (Optional)

Specify the port number to use for the connection. The default port is usually 22. However, if your remote server uses a non-standard port, you can enter the appropriate port number here.

Local Filesystem (Airbyte Open Source only)

• Storage

caution

Currently, the local storage URL for reading must start with the local mount "/local/".

Please note that if you are replicating data from a locally stored file on Windows OS, you will need to open the .env file in your local Airbyte root folder and change the values for:

• LOCAL_ROOT
• LOCAL_DOCKER_MOUNT
• HACK_LOCAL_ROOT_PARENT

Please set these to an existing absolute path on your machine. Colons in the path need to be replaced with a double forward slash, //. LOCAL_ROOT & LOCAL_DOCKER_MOUNT should be set to the same value, and HACK_LOCAL_ROOT_PARENT should be set to their parent directory.

Step 3: Complete the connector setup

1. For URL, enter the URL path of the file to be replicated.

note

When connecting to a file located in Google Drive, please note that you need to utilize the Download URL format: https://drive.google.com/uc?export=download&id=[DRIVE_FILE_ID]. [DRIVE_FILE_ID] should be replaced with the unique string found in the Share URL specific to Google Drive. You can find the Share URL by visiting https://drive.google.com/file/d/[DRIVE_FILE_ID]/view?usp=sharing.

When connecting to a file using Azure Blob Storage, please note that we account for the base URL. Therefore, you should only need to include the path to your specific file (eg container/file.csv).

2. For Reader Options (Optional), you may choose to enter a string in JSON format. Depending on the file format of your source, this will provide additional options and tune the Reader's behavior. Please refer to the next section for a breakdown of the possible inputs. This field may be left blank if you do not wish to configure custom Reader options.
3. Click Set up source and wait for the tests to complete.

Reader Options

The Reader in charge of loading the file format is currently based on Pandas IO Tools. It is possible to customize how to load the file into a Pandas DataFrame as part of this Source Connector. This is doable in the reader_options that should be in JSON format and depends on the chosen file format. See pandas' documentation, depending on the format:

For example, if the format CSV is selected, then options from the read_csv functions are available.

• It is therefore possible to customize the delimiter (or sep) to in case of tab separated files.
• Header line can be ignored with header=0 and customized with names
• If a file has no header, it is required to set header=null; otherwise, the first record will be missing
• Parse dates for in specified columns
• etc

We would therefore provide in the reader_options the following json:

{ "sep" : "\t", "header" : null, "names": ["column1", "column2"], "parse_dates": ["column2"]}

In case you select JSON format, then options from the read_json reader are available.

For example, you can use the {"orient" : "records"} to change how orientation of data is loaded (if data is [{column -> value}, … , {column -> value}])

If you need to read Excel Binary Workbook, please specify excel_binary format in File Format select.

caution

This connector does not support syncing unstructured data files such as raw text, audio, or videos.

Supported sync modes

Feature	Supported?
Full Refresh Sync	Yes
Incremental Sync	No
Replicate Incremental Deletes	No
Replicate Folders (multiple Files)	No
Replicate Glob Patterns (multiple Files)	No

note

This source produces a single table for the target file as it replicates only one file at a time for the moment. Note that you should provide the dataset_name which dictates how the table will be identified in the destination (since URL can be made of complex characters).

File / Stream Compression

Compression	Supported?
Gzip	Yes
Zip	Yes
Bzip2	No
Lzma	No
Xz	No
Snappy	No

Storage Providers

Storage Providers	Supported?
HTTPS	Yes
Google Cloud Storage	Yes
Amazon Web Services S3	Yes
SFTP	Yes
SSH / SCP	Yes
local filesystem	Local use only (inaccessible for Airbyte Cloud)

File Formats

Format	Supported?
CSV	Yes
JSON/JSONL	Yes
HTML	No
XML	No
Excel	Yes
Excel Binary Workbook	Yes
Fixed Width File	Yes
Feather	Yes
Parquet	Yes
Pickle	No
YAML	Yes

Changing data types of source columns

Normally, Airbyte tries to infer the data type from the source, but you can use reader_options to force specific data types. If you input {"dtype":"string"}, all columns will be forced to be parsed as strings. If you only want a specific column to be parsed as a string, simply use {"dtype" : {"column name": "string"}}.

Examples

Here are a list of examples of possible file inputs:

Dataset Name	Storage	URL	Reader Impl	Service Account	Description
epidemiology	HTTPS	https://storage.googleapis.com/covid19-open-data/v2/latest/epidemiology.csv			COVID-19 Public dataset on BigQuery
hr_and_financials	GCS	gs://airbyte-vault/financial.csv	smart_open or gcfs	{"type": "service_account", "private_key_id": "XXXXXXXX", ...}	data from a private bucket, a service account is necessary
landsat_index	GCS	gcp-public-data-landsat/index.csv.gz	smart_open		Using smart_open, we don't need to specify the compression (note the gs:// is optional too, same for other providers)

Examples with reader options:

Dataset Name	Storage	URL	Reader Impl	Reader Options	Description
landsat_index	GCS	gs://gcp-public-data-landsat/index.csv.gz	GCFS	{"compression": "gzip"}	Additional reader options to specify a compression option to read_csv
GDELT	S3	s3://gdelt-open-data/events/20190914.export.csv		{"sep": "\t", "header": null}	Here is TSV data separated by tabs without header row from AWS Open Data
server_logs	local	/local/logs.log		{"sep": ";"}	After making sure a local text file exists at /tmp/airbyte_local/logs.log with logs file from some server that are delimited by ';' delimiters

Example for SFTP:

Dataset Name	Storage	User	Password	Host	URL	Reader Options	Description
Test Rebext	SFTP	demo	password	test.rebext.net	/pub/example/readme.txt	{"sep": "\r\n", "header": null, "names": \["text"], "engine": "python"}	We use python engine for read_csv in order to handle delimiter of more than 1 character while providing our own column names.

Please see (or add) more at airbyte-integrations/connectors/source-file/integration_tests/integration_source_test.py for further usages examples.

Performance Considerations and Notes

In order to read large files from a remote location, this connector uses the smart_open library. However, it is possible to switch to either GCSFS or S3FS implementations as it is natively supported by the pandas library. This choice is made possible through the optional reader_impl parameter.

• Note that for local filesystem, the file probably have to be stored somewhere in the /tmp/airbyte_local folder with the same limitations as the CSV Destination so the URL should also starts with /local/.
• Please make sure that Docker Desktop has access to /tmp (and /private on a MacOS, as /tmp has a symlink that points to /private. It will not work otherwise). You allow it with "File sharing" in Settings -> Resources -> File sharing -> add the one or two above folder and hit the "Apply & restart" button.
• The JSON implementation needs to be tweaked in order to produce more complex catalog and is still in an experimental state: Simple JSON schemas should work at this point but may not be well handled when there are multiple layers of nesting.

Reference

Config fields reference

Field

Type

Property name

string

dataset_name

string

format

string

url

object

provider

string

reader_options

Changelog

Expand to review

Version	Date	Pull Request	Subject
0.5.2	2024-06-06	39192	[autopull] Upgrade base image to v1.2.2
0.5.1	2024-05-03	37799	Add fastparquet engine for parquet file reader.
0.5.0	2024-03-19	36267	Pin airbyte-cdk version to ^0
0.4.1	2024-03-04	35800	Add PyAirbyte support on Python 3.11
0.4.0	2024-02-15	32354	Add Zip File Support
0.3.17	2024-02-13	34678	Add Fixed-Width File Support
0.3.16	2024-02-12	35186	Manage dependencies with Poetry
0.3.15	2023-10-19	31599	Upgrade to airbyte/python-connector-base:1.0.1
0.3.14	2023-10-13	30984	Prevent local file usage on cloud
0.3.13	2023-10-12	31341	Build from airbyte/python-connector-base:1.0.0
0.3.12	2023-09-19	30579	Add ParserError handling for discovery
0.3.11	2023-06-08	27157	Force smart open log level to ERROR
0.3.10	2023-06-07	27107	Make source-file testable in our new airbyte-ci pipelines
0.3.9	2023-05-18	26275	Add ParserError handling
0.3.8	2023-05-17	26210	Bugfix for https://github.com/airbytehq/airbyte/pull/26115
0.3.7	2023-05-16	26131	Re-release source-file to be in sync with source-file-secure
0.3.6	2023-05-16	26115	Add retry on SSHException('Error reading SSH protocol banner')
0.3.5	2023-05-16	26117	Check if reader options is a valid JSON object
0.3.4	2023-05-10	25965	fix Pandas date-time parsing to airbyte type
0.3.3	2023-05-04	25819	GCP service_account_json is a secret
0.3.2	2023-05-01	25641	Handle network errors
0.3.1	2023-04-27	25575	Fix OOM; read Excel files in chunks using openpyxl
0.3.0	2023-04-24	25445	Add datatime format parsing support for csv files
0.2.38	2023-04-12	23759	Fix column data types for numerical values
0.2.37	2023-04-06	24525	Fix examples in spec
0.2.36	2023-03-27	24588	Remove traceback from user messages.
0.2.35	2023-03-03	24278	Read only file header when checking connectivity; read only a single chunk when discovering the schema.
0.2.34	2023-03-03	23723	Update description in spec, make user-friendly error messages and docs.
0.2.33	2023-01-04	21012	Fix special characters bug
0.2.32	2022-12-21	20740	Source File: increase SSH timeout to 60s
0.2.31	2022-11-17	19567	Source File: bump 0.2.31
0.2.30	2022-11-10	19222	Use AirbyteConnectionStatus for "check" command
0.2.29	2022-11-08	18587	Fix pandas read_csv header none issue.
0.2.28	2022-10-27	18428	Add retry logic for Connection reset error - 104
0.2.27	2022-10-26	18481	Fix check for wrong format
0.2.26	2022-10-18	18116	Transform Dropbox shared link
0.2.25	2022-10-14	17994	Handle UnicodeDecodeError during discover step.
0.2.24	2022-10-03	17504	Validate data for HTTPS while check_connection
0.2.23	2022-09-28	17304	Migrate to per-stream state.
0.2.22	2022-09-15	16772	Fix schema generation for JSON files containing arrays
0.2.21	2022-08-26	15568	Specify pyxlsb library for Excel Binary Workbook files
0.2.20	2022-08-23	15870	Fix CSV schema discovery
0.2.19	2022-08-19	15768	Convert 'nan' to 'null'
0.2.18	2022-08-16	15698	Cache binary stream to file for discover
0.2.17	2022-08-11	15501	Cache binary stream to file
0.2.16	2022-08-10	15293	Add support for encoding reader option
0.2.15	2022-08-05	15269	Bump smart-open version to 6.0.0
0.2.12	2022-07-12	14535	Fix invalid schema generation for JSON files
0.2.11	2022-07-12	9974	Add support to YAML format
0.2.9	2022-02-01	9974	Update airbyte-cdk 0.1.47
0.2.8	2021-12-06	8524	Update connector fields title/description
0.2.7	2021-10-28	7387	Migrate source to CDK structure, add SAT testing.
0.2.6	2021-08-26	5613	Add support to xlsb format
0.2.5	2021-07-26	4953	Allow non-default port for SFTP type
0.2.4	2021-06-09	3973	Add AIRBYTE_ENTRYPOINT for Kubernetes support
0.2.3	2021-06-01	3771	Add Azure Storage Blob Files option
0.2.2	2021-04-16	2883	Fix CSV discovery memory consumption
0.2.1	2021-04-03	2726	Fix base connector versioning
0.2.0	2021-03-09	2238	Protocol allows future/unknown properties
0.1.10	2021-02-18	2118	Support JSONL format
0.1.9	2021-02-02	1768	Add test cases for all formats
0.1.8	2021-01-27	1738	Adopt connector best practices
0.1.7	2020-12-16	1331	Refactor Python base connector
0.1.6	2020-12-08	1249	Handle NaN values
0.1.5	2020-11-30	1046	Add connectors using an index YAML file