| Feature | Supported? |
|---|---|
| Full Refresh Sync | Yes |
| Incremental Sync | No |
| Replicate Incremental Deletes | No |
| Replicate Folders (multiple Files) | No |
| Replicate Glob Patterns (multiple Files) | No |
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).
| 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) |
| Compression | Supported? |
|---|---|
| Gzip | Yes |
| Zip | No |
| Bzip2 | No |
| Lzma | No |
| Xz | No |
| Snappy | No |
| Format | Supported? |
|---|---|
| CSV | Yes |
| JSON | Yes |
| HTML | No |
| XML | No |
| Excel | Yes |
| Excel Binary Workbook | Yes |
| Feather | Yes |
| Parquet | Yes |
| Pickle | No |
| YAML | Yes |
This connector does not support syncing unstructured data files such as raw text, audio, or videos.
Setup through Airbyte Cloud will be exactly the same as the open-source setup, except for the fact that local files are disabled.
- Once the File Source is selected, you should define both the storage provider along its URL and format of the file.
- Depending on the provider choice and privacy of the data, you will have to configure more options.
- In case of GCS, it is necessary to provide the content of the service account keyfile to access private buckets. See settings of BigQuery Destination
- In case of AWS S3, the pair of
aws_access_key_idandaws_secret_access_keyis necessary to access private S3 buckets. - In case of AzBlob, it is necessary to provide the
storage_accountin which the blob you want to access resides. Eithersas_token(info) orshared_key(info) is necessary to access private blobs.
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(orsep) to in case of tab separated files. - Header line can be ignored with
header=0and customized withnames - etc
We would therefore provide in the reader_options the following json:
{ "sep" : "\t", "header" : 0, "names": "column1, 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}])
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"}}.
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.
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_localfolder with the same limitations as the CSV Destination so theURLshould also starts with/local/. - 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.
| Version | Date | Pull Request | Subject |
|---|---|---|---|
| 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 |