Json uploads (#647)

* Upload json or json-list files, in addition to CSV and SQLite
* Improvements to user uploads; refactoring, test coverage, better UI, bug fixes, logging
* Clicking a field name in schema explorer copies it to clipboard
* Limit charts to 10 numerical series for performance (past 10 it's incomprehensible anyway)
* Limit sampling of uploaded files to 5k rows for the purposes of type inference

docs/features.rst

@@ -1,22 +1,20 @@
 Features
 ========
 
-Easy to get started
--------------------
-- Built on Django's ORM, so works with MySQL, Postgres, Oracle,
-  SQLite, Snowflake, MS SQL Server, RedShift, and MariaDB.
-- If you want to use Snowflake or SQL Server, you will need to install the relevant package
-  (e.g. https://pypi.org/project/django-snowflake/, https://github.com/microsoft/mssql-django)
-- Small number of dependencies.
-- MIT licensed (except for functionality in the /ee/ directory,
-  which is still free for commercial use, but can't be resold).
-
 SQL Assistant
 -------------
 - Built in integration with OpenAI (or the LLM of your choosing)
   to quickly get help with your query, with relevant schema
   automatically injected into the prompt. Simple, effective.
 
+Database Support
+----------------
+- Supports MySql, postgres (and, by extension, pg-connection-compatible DBs like Redshift), SQLite,
+  Oracle, MS SQL Server, MariaDB, and Snowflake
+- Note for Snowflake or SQL Server, you will need to install the relevant Django connection package
+  (e.g. https://pypi.org/project/django-snowflake/, https://github.com/microsoft/mssql-django)
+- Also supports ad-hoc data sources by uploading JSON, CSV, or SQLite files directly.
+
 Snapshots
 ---------
 - Tick the 'snapshot' box on a query, and Explorer will upload a
@@ -120,7 +118,8 @@ Displaying query results as charts
 ----------------------------------
 
 If the results table has numeric columns, they can be displayed in a bar chart. The first column will always be used
-as the x-axis labels. This is quite basic, but can be useful for quick visualization.
+as the x-axis labels. This is quite basic, but can be useful for quick visualization. Charts (if enabled) will render
+for query results with ten or fewer numeric columns. With more series than that, the charts become a hot mess quickly.
 
 To enable this feature, set ``EXPLORER_CHARTS_ENABLED`` setting to ``True`` and install the plotting library
 ``matplotlib`` with:
@@ -169,7 +168,7 @@ Multiple Connections
   way. See connections.py for more documentation on
   multi-connection setup.
 - SQL Explorer also supports user-provided connections in the form
-  of standard database connection details, or uploading CSV or SQLite
+  of standard database connection details, or uploading CSV, JSON or SQLite
   files. See the 'User uploads' section of :doc:`settings`.
 
 Power tips

explorer/charts.py

@@ -25,7 +25,8 @@ def get_chart(result: QueryResult, chart_type: str) -> Optional[str]:
         c for c in range(1, len(result.data[0]))
         if all([isinstance(col[c], (int, float)) or col[c] is None for col in result.data])
     ]
-    if len(numeric_columns) < 1:
+    # Don't create charts for > 10 series. This is a lightweight visualization.
+    if len(numeric_columns) < 1 or len(numeric_columns) > 10:
         return None
     labels = [row[0] for row in result.data]
     fig, ax = plt.subplots(figsize=(10, 3.8))

explorer/ee/db_connections/create_sqlite.py

@@ -0,0 +1,24 @@
+import os
+from io import BytesIO
+
+from explorer.ee.db_connections.type_infer import get_parser
+from explorer.ee.db_connections.utils import pandas_to_sqlite
+
+
+def parse_to_sqlite(file) -> (BytesIO, str):
+    f_name = file.name
+    f_bytes = file.read()
+    df_parser = get_parser(file)
+    if df_parser:
+        df = df_parser(f_bytes)
+        try:
+            f_bytes = pandas_to_sqlite(df, local_path=f"{f_name}_tmp_local.db")
+        except Exception as e:  # noqa
+            raise ValueError(f"Error while parsing {f_name}: {e}") from e
+        # replace the previous extension with .db, as it is now a sqlite file
+        name, _ = os.path.splitext(f_name)
+        f_name = f"{name}.db"
+    else:
+        return BytesIO(f_bytes), f_name  # if it's a SQLite file already, simply cough it up as a BytesIO object
+    return f_bytes, f_name
+

explorer/ee/db_connections/mime.py

@@ -0,0 +1,54 @@
+import csv
+import json
+
+# These are 'shallow' checks. They are just to understand if the upload appears valid at surface-level.
+# A deeper check will happen when pandas tries to parse the file.
+# This is designed to be quick, and simply assigned the right (full) parsing function to the uploaded file.
+
+
+def is_csv(file):
+    if file.content_type != "text/csv":
+        return False
+    try:
+        # Check if the file content can be read as a CSV
+        file.seek(0)
+        sample = file.read(1024).decode("utf-8")
+        csv.Sniffer().sniff(sample)
+        file.seek(0)
+        return True
+    except csv.Error:
+        return False
+
+
+def is_json(file):
+    if file.content_type != "application/json":
+        return False
+    if not file.name.lower().endswith(".json"):
+        return False
+    return True
+
+
+def is_json_list(file):
+    if not file.name.lower().endswith(".json"):
+        return False
+    file.seek(0)
+    first_line = file.readline()
+    file.seek(0)
+    try:
+        json.loads(first_line.decode("utf-8"))
+        return True
+    except ValueError:
+        return False
+
+
+def is_sqlite(file):
+    if file.content_type != "application/x-sqlite3":
+        return False
+    try:
+        # Check if the file starts with the SQLite file header
+        file.seek(0)
+        header = file.read(16)
+        file.seek(0)
+        return header == b"SQLite format 3\x00"
+    except Exception as e:  # noqa
+        return False

explorer/ee/db_connections/type_infer.py

@@ -0,0 +1,132 @@
+import io
+import json
+from explorer.ee.db_connections.mime import is_csv, is_json, is_sqlite, is_json_list
+
+
+MAX_TYPING_SAMPLE_SIZE = 5000
+SHORTEST_PLAUSIBLE_DATE_STRING = 5
+
+
+def get_parser(file):
+    if is_csv(file):
+        return csv_to_typed_df
+    if is_json_list(file):
+        return json_list_to_typed_df
+    if is_json(file):
+        return json_to_typed_df
+    if is_sqlite(file):
+        return None
+    raise ValueError(f"File {file.content_type} not supported.")
+
+
+def csv_to_typed_df(csv_bytes, delimiter=",", has_headers=True):
+    import pandas as pd
+    csv_file = io.BytesIO(csv_bytes)
+    df = pd.read_csv(csv_file, sep=delimiter, header=0 if has_headers else None)
+    return df_to_typed_df(df)
+
+
+def json_list_to_typed_df(json_bytes):
+    import pandas as pd
+    data = []
+    for line in io.BytesIO(json_bytes).readlines():
+        data.append(json.loads(line.decode("utf-8")))
+
+    df = pd.json_normalize(data)
+    return df_to_typed_df(df)
+
+
+def json_to_typed_df(json_bytes):
+    import pandas as pd
+    json_file = io.BytesIO(json_bytes)
+    json_content = json.load(json_file)
+    df = pd.json_normalize(json_content)
+    return df_to_typed_df(df)
+
+
+def atof_custom(value):
+    # Remove any thousands separators and convert the decimal point
+    if "," in value and "." in value:
+        if value.index(",") < value.index("."):
+            # 0,000.00 format
+            value = value.replace(",", "")
+        else:
+            # 0.000,00 format
+            value = value.replace(".", "").replace(",", ".")
+    elif "," in value:
+        # No decimal point, only thousands separator
+        value = value.replace(",", "")
+    return float(value)
+
+
+
+def df_to_typed_df(df):  # noqa
+    import pandas as pd
+    from dateutil import parser
+    try:
+
+        for column in df.columns:
+
+            # If we somehow have an array within a field (e.g. from a json object) then convert it to a string
+            df[column] = df[column].apply(lambda x: str(x) if isinstance(x, list) else x)
+
+            values = df[column].dropna().unique()
+            if len(values) > MAX_TYPING_SAMPLE_SIZE:
+                values = pd.Series(values).sample(MAX_TYPING_SAMPLE_SIZE, random_state=42).to_numpy()
+
+            is_date = False
+            is_integer = True
+            is_float = True
+
+            for value in values:
+                try:
+                    float_val = atof_custom(str(value))
+                    if float_val == int(float_val):
+                        continue  # This is effectively an integer
+                    else:
+                        is_integer = False
+                except ValueError:
+                    is_integer = False
+                    is_float = False
+                    break
+
+            if is_integer:
+                is_float = False
+
+            if not is_integer and not is_float:
+                is_date = True
+
+                # The dateutil parser is very aggressive and will interpret many short strings as dates.
+                # For example "12a" will be interpreted as 12:00 AM on the current date.
+                # That is not the behavior anyone wants. The shortest plausible date string is e.g. 1-1-23
+                try_parse = [v for v in values if len(str(v)) > SHORTEST_PLAUSIBLE_DATE_STRING]
+                if len(try_parse) > 0:
+                    for value in try_parse:
+                        try:
+                            parser.parse(str(value))
+                        except (ValueError, TypeError, OverflowError):
+                            is_date = False
+                            break
+                else:
+                    is_date = False
+
+            if is_date:
+                df[column] = pd.to_datetime(df[column], errors="coerce", utc=True)
+            elif is_integer:
+                df[column] = df[column].apply(lambda x: int(atof_custom(str(x))) if pd.notna(x) else x)
+                # If there are NaN / blank values, the column will be converted to float
+                # Convert it back to integer
+                df[column] = df[column].astype("Int64")
+            elif is_float:
+                df[column] = df[column].apply(lambda x: atof_custom(str(x)) if pd.notna(x) else x)
+            else:
+                inferred_type = pd.api.types.infer_dtype(values)
+                if inferred_type == "integer":
+                    df[column] = pd.to_numeric(df[column], errors="coerce", downcast="integer")
+                elif inferred_type == "floating":
+                    df[column] = pd.to_numeric(df[column], errors="coerce")
+
+        return df
+
+    except pd.errors.ParserError as e:
+        return str(e)

explorer/ee/db_connections/utils.py

@@ -108,96 +108,3 @@ def pandas_to_sqlite(df, local_path="local_database.db"):
         # Delete the local SQLite database file
         # Finally block to ensure we don't litter files around
         os.remove(local_path)
-
-
-MAX_TYPING_SAMPLE_SIZE = 10000
-SHORTEST_PLAUSIBLE_DATE_STRING = 5
-
-
-def atof_custom(value):
-    # Remove any thousands separators and convert the decimal point
-    if "," in value and "." in value:
-        if value.index(",") < value.index("."):
-            # 0,000.00 format
-            value = value.replace(",", "")
-        else:
-            # 0.000,00 format
-            value = value.replace(".", "").replace(",", ".")
-    elif "," in value:
-        # No decimal point, only thousands separator
-        value = value.replace(",", "")
-    return float(value)
-
-def csv_to_typed_df(csv_bytes, delimiter=",", has_headers=True):  # noqa
-    import pandas as pd
-    from dateutil import parser
-    try:
-
-        csv_file = io.BytesIO(csv_bytes)
-        df = pd.read_csv(csv_file, sep=delimiter, header=0 if has_headers else None)
-
-        for column in df.columns:
-            values = df[column].dropna().unique()
-            if len(values) > MAX_TYPING_SAMPLE_SIZE:
-                values = pd.Series(values).sample(MAX_TYPING_SAMPLE_SIZE, random_state=42).to_numpy()
-
-            is_date = False
-            is_integer = True
-            is_float = True
-
-            for value in values:
-                try:
-                    float_val = atof_custom(str(value))
-                    if float_val == int(float_val):
-                        continue  # This is effectively an integer
-                    else:
-                        is_integer = False
-                except ValueError:
-                    is_integer = False
-                    is_float = False
-                    break
-
-            if is_integer:
-                is_float = False
-
-            if not is_integer and not is_float:
-                is_date = True
-
-                # The dateutil parser is very aggressive and will interpret many short strings as dates.
-                # For example "12a" will be interpreted as 12:00 AM on the current date.
-                # That is not the behavior anyone wants. The shortest plausible date string is e.g. 1-1-23
-                try_parse = [v for v in values if len(str(v)) > SHORTEST_PLAUSIBLE_DATE_STRING]
-                if len(try_parse) > 0:
-                    for value in try_parse:
-                        try:
-                            parser.parse(str(value))
-                        except (ValueError, TypeError, OverflowError):
-                            is_date = False
-                            break
-                else:
-                    is_date = False
-
-            if is_date:
-                df[column] = pd.to_datetime(df[column], errors="coerce", utc=True)
-            elif is_integer:
-                df[column] = df[column].apply(lambda x: int(atof_custom(str(x))) if pd.notna(x) else x)
-                # If there are NaN / blank values, the column will be converted to float
-                # Convert it back to integer
-                df[column] = df[column].astype("Int64")
-            elif is_float:
-                df[column] = df[column].apply(lambda x: atof_custom(str(x)) if pd.notna(x) else x)
-            else:
-                inferred_type = pd.api.types.infer_dtype(values)
-                if inferred_type == "integer":
-                    df[column] = pd.to_numeric(df[column], errors="coerce", downcast="integer")
-                elif inferred_type == "floating":
-                    df[column] = pd.to_numeric(df[column], errors="coerce")
-
-        return df
-
-    except pd.errors.ParserError as e:
-        return str(e)
-
-
-def is_csv(file):
-    return file.content_type == "text/csv"