Source File: Support JsonL format

airbyte-config/init/src/main/resources/config/STANDARD_SOURCE_DEFINITION/778daa7c-feaf-4db6-96f3-70fd645acc77.json

@@ -2,6 +2,6 @@
   "sourceDefinitionId": "778daa7c-feaf-4db6-96f3-70fd645acc77",
   "name": "File",
   "dockerRepository": "airbyte/source-file",
-  "dockerImageTag": "0.1.9",
+  "dockerImageTag": "0.1.10",
   "documentationUrl": "https://hub.docker.com/r/airbyte/source-file"
 }

airbyte-config/init/src/main/resources/seed/source_definitions.yaml

@@ -6,7 +6,7 @@
 - sourceDefinitionId: 778daa7c-feaf-4db6-96f3-70fd645acc77
   name: File
   dockerRepository: airbyte/source-file
-  dockerImageTag: 0.1.9
+  dockerImageTag: 0.1.10
   documentationUrl: https://hub.docker.com/r/airbyte/source-file
 - sourceDefinitionId: fdc8b827-3257-4b33-83cc-106d234c34d4
   name: Google Adwords

airbyte-integrations/connectors/source-file/Dockerfile

@@ -11,5 +11,5 @@ COPY $CODE_PATH ./$CODE_PATH
 COPY setup.py ./
 RUN pip install ".[main]"
 
-LABEL io.airbyte.version=0.1.9
+LABEL io.airbyte.version=0.1.10
 LABEL io.airbyte.name=airbyte/source-file

airbyte-integrations/connectors/source-file/integration_tests/file_formats_test.py

@@ -40,19 +40,21 @@ def check_read(config, expected_columns=10, expected_rows=42):
 
 
 @pytest.mark.parametrize(
-    "file_format, extension, expected_columns, expected_rows",
+    "file_format, extension, expected_columns, expected_rows, filename",
     [
-        ("csv", "csv", 8, 5000),
-        ("json", "json", 2, 1),
-        ("excel", "xls", 8, 50),
-        ("excel", "xlsx", 8, 50),
-        ("feather", "feather", 9, 3),
-        ("parquet", "parquet", 9, 3),
+        ("csv", "csv", 8, 5000, "demo"),
+        ("json", "json", 2, 1, "demo"),
+        ("jsonl", "jsonl", 2, 10, "jsonl_nested"),
+        ("jsonl", "jsonl", 2, 6492, "jsonl"),
+        ("excel", "xls", 8, 50, "demo"),
+        ("excel", "xlsx", 8, 50, "demo"),
+        ("feather", "feather", 9, 3, "demo"),
+        ("parquet", "parquet", 9, 3, "demo"),
     ],
 )
-def test_local_file_read(file_format, extension, expected_columns, expected_rows):
+def test_local_file_read(file_format, extension, expected_columns, expected_rows, filename):
     file_directory = SAMPLE_DIRECTORY.joinpath(file_format)
-    file_path = str(file_directory.joinpath(f"demo.{extension}"))
+    file_path = str(file_directory.joinpath(f"{filename}.{extension}"))
     configs = {"dataset_name": "test", "format": file_format, "url": file_path, "provider": {"storage": "local"}}
     check_read(configs, expected_columns, expected_rows)
 

airbyte-integrations/connectors/source-file/integration_tests/sample_files/formats/jsonl/jsonl_nested.jsonl

@@ -0,0 +1,10 @@
+{"article_id_a":"art14980","recommendations": {"listing":"art10316"}}
+{"article_id_a":"art149801","recommendations": {"listing":"art1031126"}}
+{"article_id_a":"art149802","recommendations": {"listing":"art103126"}}
+{"article_id_a":"art149803","recommendations": {"listing":"art10316"}}
+{"article_id_a":"art149804","recommendations": {"listing":"art10316"}}
+{"article_id_a":"art149805","recommendations": {"listing":"art10316"}}
+{"article_id_a":"art149806","recommendations": {"listing":"art10316"}}
+{"article_id_a":"art1498078","recommendations": {"listing":"art10316"}}
+{"article_id_a":"art149809","recommendations": {"listing":"art10316"}}
+{"article_id_a":"art14980000","recommendations": {"listing":"art10316"}}

airbyte-integrations/connectors/source-file/source_file/client.py

@@ -229,21 +229,31 @@ def stream_name(self) -> str:
             return self._dataset_name
         return f"file_{self._provider['storage']}.{self._reader_format}"
 
-    @staticmethod
-    def load_nested_json_schema(fp) -> dict:
+    def load_nested_json_schema(self, fp) -> dict:
         # Use Genson Library to take JSON objects and generate schemas that describe them,
         builder = SchemaBuilder()
-        builder.add_object(json.load(fp))
+        if self._reader_format == "jsonl":
+            for o in self.read():
+                builder.add_object(o)
+        else:
+            builder.add_object(json.load(fp))
+
         result = builder.to_schema()
         if "items" in result and "properties" in result["items"]:
             result = result["items"]["properties"]
         return result
 
-    @staticmethod
-    def load_nested_json(fp) -> list:
-        result = json.load(fp)
-        if not isinstance(result, list):
-            result = [result]
+    def load_nested_json(self, fp) -> list:
+        if self._reader_format == "jsonl":
+            result = []
+            line = fp.readline()
+            while line:
+                result.append(json.loads(line))
+                line = fp.readline()
+        else:
+            result = json.load(fp)
+            if not isinstance(result, list):
+                result = [result]
         return result
 
     def load_dataframes(self, fp, skip_data=False) -> List:
@@ -309,7 +319,7 @@ def binary_source(self):
     def read(self, fields: Iterable = None) -> Iterable[dict]:
         """Read data from the stream"""
         with self.reader.open(binary=self.binary_source) as fp:
-            if self._reader_format == "json":
+            if self._reader_format == "json" or self._reader_format == "jsonl":
                 yield from self.load_nested_json(fp)
             else:
                 for df in self.load_dataframes(fp):
@@ -319,7 +329,7 @@ def read(self, fields: Iterable = None) -> Iterable[dict]:
 
     def _stream_properties(self):
         with self.reader.open(binary=self.binary_source) as fp:
-            if self._reader_format == "json":
+            if self._reader_format == "json" or self._reader_format == "jsonl":
                 return self.load_nested_json_schema(fp)
 
             df_list = self.load_dataframes(fp, skip_data=False)

airbyte-integrations/connectors/source-file/source_file/spec.json

@@ -14,7 +14,7 @@
       },
       "format": {
         "type": "string",
-        "enum": ["csv", "json", "excel", "feather", "parquet"],
+        "enum": ["csv", "json", "jsonl", "excel", "feather", "parquet"],
         "default": "csv",
         "description": "File Format of the file to be replicated (Warning: some format may be experimental, please refer to docs)."
       },