Files

Overview

File are often exchanged or published in various remote locations. This source aims to support an expanding range of file formats and storage providers. The File source supports Full Refresh syncs. That is, every time a sync is run, Airbyte will copy all rows in the file and columns you set up for replication into the destination in a new table.

Output schema

At this time, this source produces only a single stream for the target file as it replicates only one file at a time for the moment. We'll be considering to improve this behavior by globing folders or use patterns to capture more files in the next iterations as well as more file formats and storage providers. 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)

Features

Feature

Supported?

Full Refresh Sync

Yes

Incremental Sync

No

Replicate Incremental Deletes

No

Replicate Folders (multiple Files)

No

Replicate Glob Patterns (multiple Files)

No

How do we rate the functionalities below?

  • Yes, means we verified and have Automated Integration Tests for it.

  • Verified, means we don't have Automated Tests but were able to successfully manually test and use it with Airbyte.

  • Experimental, means we tried to verify but we may have ran into edge cases that still need to be addressed to be usable, please use with cautions.

  • Untested, means the library we are using claims to support such configurations in theory but we haven't tested or verified that's it's working in Airbyte yet.

  • Hidden, means that we haven't tested or even hooked up the options all the way to the UI yet.

Please, don't hesitate to get in touch with us and/or provide usage feedbacks if you are able to report issues, verify, contribute some testing or even suggest an option that is not part of these list, thanks!

Storage Providers

Storage Providers are mostly enabled (and further tested) thanks to other open-source libraries that we are using under the hood such as:

Storage Providers

Supported?

HTTPS

Yes

Google Cloud Storage

Yes

Amazon Web Services S3

Yes

SFTP

Yes

SSH / SCP

Yes

local filesystem

Experimental

File / Stream Compression

Compression

Supported?

Gzip

Yes

Zip

No

Bzip2

No

Lzma

No

Xz

No

Snappy

No

File Formats

File Formats are mostly enabled (and further tested) thanks to other open-source libraries that we are using under the hood such as:

Format

Supported?

CSV

Yes

JSON

Yes

HTML

No

XML

No

Excel

Yes

Feather

Yes

Parquet

Yes

Pickle

No

Performance considerations

In order to read large files from a remote location, we are leveraging the capabilities of smart_open. 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.

Limitations / Experimentation notes

  • 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/. This may not be ideal as a Source but will probably evolve later.

  • 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.

Getting started

  • Once the File Source is selected, you should define both the storage provider along its URL and format of the file.

  • Depending on the choice made previously, more options may be necessary, especially when accessing private data.

  • 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_id and aws_secret_access_key is necessary to access private S3 buckets.

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 panda's 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 \t in case of tab separated files.

  • Header line can be ignored with header=0 and customized with names

  • 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}])

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.