json-crud

n8n community node for easy JSON CRUD operations

Package Information

Downloads: 135 weekly / 531 monthly

Latest Version: 1.1.3

Author: Hueyan Chen

Available Nodes

JSON CRUD

Perform CRUD operations on JSON data (perfect for Excel data)

Documentation

n8n-nodes-json-crud

This is an n8n community node that allows you to easily perform CRUD (Create, Read, Update, Delete) operations on JSON data.

Especially suitable for handling JSON data converted from Excel!

🎯 Why Do You Need This Node?

When processing Excel files with n8n, you typically need to:

Use Extract from File to convert Excel to JSON
Combine multiple nodes (Filter, Edit Fields, Code, etc.) to process data
Use Convert to File to convert back to Excel

This node simplifies Step 2, integrating multiple operations into a single node, making it more beginner-friendly!

✨ Features

📝 CREATE

Add single or multiple records
Choose to prepend or append
Support JSON format input

🔍 READ (Filter/Query)

Filter: 12 comparison operators with AND/OR logic support
Sort: Ascending or descending order
Search: Full-text or field-specific search
Limit: Pagination with offset support
By Cell Position: Excel-like precise reading
- Single row: 0 (row 1)
- Range: 0-5 (rows 1-6)
- Multiple rows: 0,2,4 (rows 1,3,5)
- Combined: 0-2,5,7-9 (rows 1-3,6,8-10)
- Select specific fields or read all fields

✏️ UPDATE

Conditional Update: Batch update multiple fields based on conditions
Cell Update: Excel-like A1/B2 positioning to update specific cells
- Single row: 0 (row 1)
- Range: 0-5 (rows 1-6)
- Multiple rows: 0,2,4 (rows 1,3,5)
- Combined: 0-2,5,7-9 (rows 1-3,6,8-10)
Support expression calculations

🗑️ DELETE

Conditional Delete: Batch delete records based on conditions
Row Index Delete: Excel-like precise deletion of specific rows
- Single row: 0 (row 1)
- Range: 0-5 (rows 1-6)
- Multiple rows: 0,2,4 (rows 1,3,5)
- Combined: 0-2,5,7-9 (rows 1-3,6,8-10)
Support multiple condition combinations
Preserve records that don't match conditions

🔄 REMOVE DUPLICATES

Remove duplicates based on specific fields
Or compare all fields for deduplication

📊 STATISTICS

Calculate count, sum, avg, min, max
Support group statistics
Quickly generate report data

📦 Installation

Method 1: Install from npm (After publication)

npm install n8n-nodes-json-crud

Method 2: Install from GitHub

cd ~/.n8n/nodes
npm install git+https://github.com/fchart/n8n-nodes-json-crud.git

Method 3: Manual Installation (For development)

# 1. Clone the project
git clone https://github.com/fchart/n8n-nodes-json-crud.git
cd n8n-nodes-json-crud

# 2. Install dependencies
npm install

# 3. Build
npm run build

# 4. Link to n8n
npm link
cd ~/.n8n
npm link n8n-nodes-json-crud

# 5. Restart n8n

🚀 Usage Examples

Example 1: Processing Employee Excel File

┌─────────────────┐
│ Read Binary File│  Read employees.xlsx
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Extract from    │  Convert to JSON
│ File (XLSX)     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ JSON CRUD       │  Operation: Filter
│ Filter Tech Dept│  Condition: Department = "Tech"
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ JSON CRUD       │  Operation: Update
│ 10% Raise       │  Update: Salary = Salary * 1.1
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ JSON CRUD       │  Operation: Sort
│ Sort by Salary  │  Field: Salary (Descending)
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Convert to File │  Convert back to Excel
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Write Binary    │  Save file
│ File            │
└─────────────────┘

Example 2: Add Data

Operation Settings:

Operation: Create
Position: Append
Data to Add:

{
  "EmployeeID": "E0099",
  "Name": "John Doe",
  "Department": "Tech",
  "Salary": 50000
}

Or batch add:

[
  {"Name": "Alice", "Department": "Sales", "Salary": 45000},
  {"Name": "Bob", "Department": "Admin", "Salary": 40000}
]

Example 3: Filter Data

Operation Settings:

Operation: Read
Read Mode: Filter
Conditions:
- Condition 1: Department equals "Tech"
- Condition 2: Salary greater than 45000
Condition Logic: AND

Result: Returns only Tech department employees with salary > 45000

Example 3-1: ⭐ Cell Position Read (Excel-like)

Scenario 1: Read all fields of a single row

Operation Settings:

Operation: Read
Read Mode: By Cell Position
Row Index: 0 (row 1)
Field Names: Leave empty (read all fields)

Result: Returns complete data of row 1

Scenario 2: Read specific fields of a range

Operation Settings:

Operation: Read
Read Mode: By Cell Position
Row Index: 0-4 (rows 1-5)
Field Names: Name,Salary

Result: Returns rows 1-5, but each row only contains Name and Salary fields

Scenario 3: Read multiple non-contiguous rows

Operation Settings:

Operation: Read
Read Mode: By Cell Position
Row Index: 0,5,10 (rows 1,6,11)
Field Names: Leave empty

Result: Returns complete data of rows 1, 6, and 11

Scenario 4: Extract header and data range

Operation Settings:

Operation: Read
Read Mode: By Cell Position
Row Index: 0-2,10-12 (rows 1-3 and 11-13)
Field Names: Name,Department,Salary

Result: Returns rows 1-3 and 11-13, containing only the specified three fields

Example 4: Conditional Update

Operation Settings:

Operation: Update
Update Mode: By Condition
Update Conditions:
- Department equals "Tech"
Fields to Update:
- Field: Salary, Value: {{ $json.Salary * 1.15 }}
- Field: UpdateDate, Value: {{ $now }}

Result: Tech department employee salaries increased by 15%

Example 5: ⭐ Cell Update (Excel-like)

Scenario 1: Update single cell

Operation Settings:

Operation: Update
Update Mode: By Cell Position
Row Index: 0 (row 1, 0-based)
Field Name: Salary
New Value: 60000

Result: Updates only the Salary field of row 1 to 60000

Scenario 2: Update range cells

Operation Settings:

Operation: Update
Update Mode: By Cell Position
Row Index: 0-4 (rows 1-5)
Field Name: Department
New Value: Tech

Result: Updates Department field of rows 1-5 to "Tech"

Scenario 3: Update multiple non-contiguous cells

Operation Settings:

Operation: Update
Update Mode: By Cell Position
Row Index: 0,2,4,6 (rows 1,3,5,7)
Field Name: Status
New Value: Approved

Result: Updates Status field of rows 1,3,5,7 only

Scenario 4: Combined range and single cells

Operation Settings:

Operation: Update
Update Mode: By Cell Position
Row Index: 0-2,5,8-10 (rows 1-3,6,9-11)
Field Name: Flag
New Value: Important

Result: Updates Flag field of rows 1-3,6,9-11

Scenario 5: Use expression to update specific rows

Operation Settings:

Operation: Update
Update Mode: By Cell Position
Row Index: 0
Field Name: Salary
New Value: {{ $json.Salary * 1.2 }}

Result: Row 1 salary increased by 20%

Example 6: Conditional Delete

Operation Settings:

Operation: Delete
Delete Mode: By Condition
Delete Conditions:
- Status equals "Resigned"

Result: Removes all resigned employee records

Example 6-1: ⭐ Row Index Delete (Excel-like)

Scenario 1: Delete single row

Operation Settings:

Operation: Delete
Delete Mode: By Row Index
Row Index: 0 (row 1)

Result: Deletes row 1 data (usually test data or header)

Scenario 2: Delete range rows

Operation Settings:

Operation: Delete
Delete Mode: By Row Index
Row Index: 5-9 (rows 6-10)

Result: Deletes rows 6-10 data

Scenario 3: Delete multiple non-contiguous rows

Operation Settings:

Operation: Delete
Delete Mode: By Row Index
Row Index: 0,5,10,15 (rows 1,6,11,16)

Result: Deletes only the specified 4 rows, preserves other data

Scenario 4: Combined delete

Operation Settings:

Operation: Delete
Delete Mode: By Row Index
Row Index: 0-2,10-12,20 (rows 1-3,11-13,21)

Result: Deletes rows 1-3, rows 11-13, and row 21

Example 7: Search Functionality

Operation Settings:

Operation: Read
Read Mode: Search
Search Field: Leave empty (search all fields)
Search Value: "Engineer"
Case Sensitive: false

Result: Returns records containing "Engineer" in any field

Example 8: Group Statistics

Operation Settings:

Operation: Statistics
Statistics Field: Salary
Group By Field: Department

Result:

[
  {
    "group": "Tech",
    "count": 15,
    "sum": 750000,
    "avg": 50000,
    "min": 40000,
    "max": 70000
  },
  {
    "group": "Sales",
    "count": 10,
    "sum": 450000,
    "avg": 45000,
    "min": 38000,
    "max": 55000
  }
]

📚 Detailed Feature Descriptions

Read Modes

Mode 1: Filter

Filter records based on conditions
Support multiple operators and logic combinations
Suitable for batch filtering data matching specific conditions

Mode 2: Sort

Sort by specified field
Support ascending and descending order
Suitable for generating rankings or ordered lists

Mode 3: Search

Full-text or field-specific search
Support case-sensitive option
Suitable for keyword queries

Mode 4: Limit

Pagination with offset support
Suitable for batch reading when handling large datasets

Mode 5: By Cell Position

Excel-like precise positioning
Use row index (0-based) + optional field names
Support flexible range selection

Row Index Formats:

Format	Description	Example
Single row	`0`	Read row 1 only
Range	`0-5`	Read rows 1-6
Multiple rows	`0,2,4`	Read rows 1,3,5
Combined	`0-2,5,7-9`	Read rows 1-3,6,8-10

Field Selection:

Leave empty: Read all fields
Specify: Read only specified fields (comma-separated), e.g., Name,Salary,Department

Use Cases:

Extract Excel header row
Read specific data range for analysis
Extract non-contiguous rows
Extract only needed fields to reduce data volume

Filter Supported Operators

Operator	Description	Example
Equals	Equals	Department = "Tech"
Not Equals	Not equals	Status ≠ "Resigned"
Contains	Contains	Name contains "Wang"
Not Contains	Not contains	Position not contains "Intern"
Greater Than	Greater than	Salary > 40000
Greater or Equal	Greater or equal	Salary >= 45000
Less Than	Less than	Age < 30
Less or Equal	Less or equal	Age <= 35
Starts With	Starts with	EmployeeID starts with "E"
Ends With	Ends with	Email ends with "@company.com"
Is Empty	Is empty	Notes is empty
Is Not Empty	Is not empty	Phone is not empty

Update Two Modes

Mode 1: By Condition (Conditional Update)

Filter records to update based on conditions
Can update multiple fields simultaneously
Support complex condition combinations
Suitable for batch updating data matching specific conditions

Use Cases:

Increase salary by 10% for all Tech department employees
Update status for all active employees
Standardize data format for matching conditions

Mode 2: By Cell Position

Excel-like A1/B2 positioning
Use row index (0-based) + field name
Support flexible range selection

Row Index Formats:

Format	Description	Example
Single row	`0`	Update row 1 only
Range	`0-5`	Update rows 1-6
Multiple rows	`0,2,4`	Update rows 1,3,5
Combined	`0-2,5,7-9`	Update rows 1-3,6,8-10

Note: Row index is 0-based, i.e., row 1 is 0, row 2 is 1, and so on.

Use Cases:

Fix data errors in specific rows
Update header or total rows in reports
Mark data at specific positions
Batch update contiguous or non-contiguous cells

Delete Two Modes

Mode 1: By Condition (Conditional Delete)

Filter records to delete based on conditions
Support complex condition combinations (AND/OR)
Suitable for batch deleting data matching specific conditions

Use Cases:

Remove all resigned employee records
Delete expired or invalid records
Clear test data matching specific conditions

Mode 2: By Row Index

Excel-like precise row deletion
Use row index (0-based) to specify rows to delete
Support flexible range selection

Row Index Formats:

Format	Description	Example
Single row	`0`	Delete row 1 only
Range	`0-5`	Delete rows 1-6
Multiple rows	`0,2,4`	Delete rows 1,3,5
Combined	`0-2,5,7-9`	Delete rows 1-3,6,8-10

Note: Row index is 0-based, i.e., row 1 is 0, row 2 is 1, and so on.

Use Cases:

Remove Excel imported header row
Delete error data at specific positions
Remove subtotal or note rows in reports
Clear non-contiguous test data or anomalous records

Update Supports Expressions

When updating field values, you can use n8n expressions:

Important: Expressions are evaluated for each matching record separately, ensuring each record uses its own values for calculation!

// Numeric calculations
{{ $json.Salary * 1.1 }}                    // 10% raise (each record uses its own salary)
{{ $json.Price - 100 }}                     // Subtract 100
{{ $json.Quantity + 5 }}                    // Add 5

// String operations
{{ $json.Name + " (Updated)" }}             // String concatenation
{{ $json.Email.toLowerCase() }}             // Convert to lowercase
{{ $json.Code.toUpperCase() }}              // Convert to uppercase

// Date and time
{{ $now }}                                  // Current time
{{ $now.format('YYYY-MM-DD') }}             // Format date

// Conditional expressions
{{ $json.Salary > 50000 ? "High" : "Normal" }} // Ternary operator (each record evaluated independently)
{{ $json.Age >= 60 ? "Retired" : "Active" }}   // Age check

// Math functions
{{ Math.round($json.Salary * 1.1) }}        // Round
{{ Math.ceil($json.Price * 0.9) }}          // Ceiling
{{ Math.floor($json.Amount / 100) * 100 }}  // Floor to hundreds

Example: Tech department 15% raise

Update Mode: By Condition
Condition: Department = "Tech"
Field: Salary
Value: {{ $json.Salary * 1.15 }}

Result:
- Employee A (Tech, 50000) → 57500
- Employee B (Tech, 60000) → 69000  ✅ Each record uses its own salary for calculation
- Employee C (Sales, 45000) → 45000  (unchanged)

🔧 Comparison with Other Nodes

Feature	Traditional Way	JSON CRUD Node
Filter data	Filter node	✅ Single node
Update data	Edit Fields + IF	✅ Single node
Cell update	Code node + complex logic	✅ Visual configuration
Delete data	Filter (reverse)	✅ More intuitive deletion
Search	Code node	✅ Built-in search
Statistics	Aggregate	✅ Simpler configuration
Combined operations	Requires 3-5 nodes	✅ One node chain

💡 Best Practices

1. Complete Excel Processing Workflow

Read Binary File 
→ Extract from File 
→ JSON CRUD (Remove Duplicates)
→ JSON CRUD (Filter)
→ JSON CRUD (Update)
→ JSON CRUD (Sort)
→ Convert to File
→ Write Binary File

2. Data Validation Workflow

Extract from File
→ JSON CRUD (Filter to remove invalid data)
→ JSON CRUD (Update to standardize format)
→ JSON CRUD (Statistics to check data quality)
→ Convert to File

3. Report Generation Workflow

Extract from File
→ JSON CRUD (Filter time range)
→ JSON CRUD (Statistics group analysis)
→ JSON CRUD (Sort)
→ Convert to File

4. ⭐ Cell Correction Workflow

Extract from File
→ JSON CRUD (Update by Cell - Fix header row)
→ JSON CRUD (Update by Cell - Update specific error data)
→ JSON CRUD (Filter valid data)
→ Convert to File

5. ⭐ Cell Precise Processing Workflow (For complex Excel processing)

Extract from File
→ JSON CRUD (Read by Cell - Extract header)
→ JSON CRUD (Read by Cell - Extract data range)
→ JSON CRUD (Update by Cell - Fix specific errors)
→ JSON CRUD (Filter - Filter valid data)
→ JSON CRUD (Statistics - Statistical analysis)
→ Convert to File

6. Mixed Mode Workflow

Extract from File
→ JSON CRUD (Read by Cell - Read only needed rows and columns)
→ JSON CRUD (Filter - Further conditional filtering)
→ JSON CRUD (Update by Condition - Batch update)
→ JSON CRUD (Update by Cell - Fix special cases)
→ JSON CRUD (Sort - Sort)
→ Convert to File

🎯 Real-World Cases

Case 1: Extract and Analyze Specific Excel Range

Scenario: From a large Excel file, only need to extract rows 10-50 with Name, Department, and Salary fields for analysis

Solution:

Step 1: Extract from File
- Convert Excel to JSON

Step 2: JSON CRUD (Read by Cell)
- Row Index: 9-49  (rows 10-50, 0-based)
- Field Names: Name,Department,Salary

Step 3: JSON CRUD (Statistics)
- Perform statistical analysis on extracted data

Benefits:

Significantly reduce memory usage
Improve processing speed
Process only needed data

Case 2: Excel Report Data Reorganization

Scenario: In Excel report, row 1 is header, rows 2-10 are current month data, rows 50-60 are last year's same period data, need to extract separately for comparison

Solution:

Step 1: JSON CRUD (Read by Cell) - Extract current month data
- Row Index: 1-9  (rows 2-10)
- Field Names: Leave empty (read all fields)
- Output to next step

Step 2: JSON CRUD (Read by Cell) - Extract last year data
- Row Index: 49-59  (rows 50-60)
- Field Names: Leave empty
- Output to next step

Step 3: Compare and analyze two datasets

Case 3: Fix Excel Import Error Data

Scenario: In imported Excel file, row 1 (header) Department field is wrong, and rows 5-8 salaries need uniform update

Solution:

Step 1: JSON CRUD (Update by Cell)
- Row Index: 0
- Field Name: Department
- New Value: Department

Step 2: JSON CRUD (Update by Cell)
- Row Index: 4-7  (rows 5-8, 0-based)
- Field Name: Salary
- New Value: {{ $json.Salary * 1.05 }}

Case 3-1: Clean Excel Import Redundant Data

Scenario: In imported Excel file, row 1 is header to be deleted, last 3 rows (rows 98-100) are notes also to be deleted

Solution:

Step 1: JSON CRUD (Delete by Row Index)
- Row Index: 0  (delete header)

Step 2: JSON CRUD (Delete by Row Index)  
- Row Index: 97-99  (delete last 3 rows, note index changed after deleting first row)

Or delete at once:
JSON CRUD (Delete by Row Index)
- Row Index: 0,97-99

Case 3-2: Remove Test and Anomalous Data

Scenario: In data, rows 5, 10, 15 are test data, rows 50-55 are import anomalous data, all need to be removed

Solution:

JSON CRUD (Delete by Row Index)
- Row Index: 4,9,14,49-54  (note 0-based index)

Result: Precisely delete all specified rows at once

Case 4: Batch Process Customer Data

Scenario: Need to mark specific customers' status as "VIP" and update their discount rate

Solution:

Step 1: JSON CRUD (Filter)
- Filter target customers

Step 2: JSON CRUD (Update by Condition)
- Condition: CustomerLevel = "Platinum"
- Update: Status = "VIP", DiscountRate = 0.15

⚠️ Notes

Data Backup
- Always backup original files before processing important data
Condition Logic
- Multiple conditions in Filter default to AND logic
- Can switch to OR logic
Row Index Rules
- Row index starts from 0 (0 = row 1)
- Range is inclusive (0-5 means rows 1-6)
- Out-of-range indexes are automatically ignored
Performance Considerations
- When processing large datasets (> 10,000 records), consider batch processing
- Use Filter first to reduce data volume
- Cell Update is suitable for precise modifications, not large-scale batch updates
Data Types
- Numeric comparisons automatically convert types
- String comparisons are case-sensitive (unless using Search's Case Sensitive option)

🐛 Troubleshooting

Issue: Node doesn't appear in n8n

Solution:

# Confirm environment variables
export N8N_COMMUNITY_PACKAGES_ENABLED=true

# Restart n8n
n8n stop
n8n start

Issue: Filter returns no results

Solution:

Check if field names are correct (case-sensitive)
Check comparison value format
Use Search functionality to test data

Issue: Update doesn't take effect

Solution:

Verify Update Conditions are correct
Check expression syntax
Test with no conditions to see if all updates work

Issue: Cell Update updates wrong rows

Solution:

Confirm row index is 0-based (row 1 = 0)
Check index format is correct (single: 0, range: 0-5, multiple: 0,2,4)
Use Table view to check actual row numbers in data

Issue: Range update exceeds expectations

Solution:

Check total row count in input data
Range is automatically limited to valid range
Use Limit operation to confirm record count first

Issue: Cell Read returns incomplete fields

Solution:

Check Field Names spelling is correct (case-sensitive)
Confirm field names match actual data field names
Leave Field Names empty to read all fields for inspection

Issue: Cell Read returns empty results

Solution:

Confirm row index range is within data range
Check actual row count in data (can use Limit mode to view)
Verify row index format is correct (0-based)

Issue: How to read multiple non-contiguous ranges simultaneously?

Solution:

Use combined format: 0-5,10-15,20-25
Or use multiple JSON CRUD nodes to read separately, then merge results

Issue: Delete by Row Index - do indexes change after deletion?

Solution:

Yes, delete operation immediately changes subsequent row indexes
Recommend specifying all rows to delete at once (using combined format)
If deleting in steps, delete from bottom to top (delete larger indexes first)

Issue: How to delete Excel header row?

Solution:

JSON CRUD (Delete by Row Index)
- Delete Mode: By Row Index
- Row Index: 0

This deletes row 1 (usually the header)

Issue: How to choose between Delete by Row Index and Delete by Condition?

Solution:

Use Row Index: When you know exact row positions to delete
- E.g., delete header, delete error data at specific positions
Use Condition: When you need to determine based on data content
- E.g., delete all resigned employees, delete expired records

🤝 Contributing

Issues and Pull Requests are welcome!

📄 License

MIT License

🔗 Related Resources

📧 Contact

For questions or suggestions:

Open an Issue: https://github.com/code4Copilot/n8n-nodes-json-crud/issues
Email: hueyan.chen@gmail.com

Make Excel data processing simple! 🚀

json-crudInstall

Package Information

Available Nodes

Documentation

n8n-nodes-json-crud

🎯 Why Do You Need This Node?

✨ Features

📝 CREATE

🔍 READ (Filter/Query)

✏️ UPDATE

🗑️ DELETE

🔄 REMOVE DUPLICATES

📊 STATISTICS

📦 Installation

Method 1: Install from npm (After publication)

Method 2: Install from GitHub

Method 3: Manual Installation (For development)

🚀 Usage Examples

Example 1: Processing Employee Excel File

Example 2: Add Data

Example 3: Filter Data

Example 3-1: ⭐ Cell Position Read (Excel-like)

Scenario 1: Read all fields of a single row

Scenario 2: Read specific fields of a range

Scenario 3: Read multiple non-contiguous rows

Scenario 4: Extract header and data range

Example 4: Conditional Update

Example 5: ⭐ Cell Update (Excel-like)

Scenario 1: Update single cell

Scenario 2: Update range cells

Scenario 3: Update multiple non-contiguous cells

Scenario 4: Combined range and single cells

Scenario 5: Use expression to update specific rows

Example 6: Conditional Delete

Example 6-1: ⭐ Row Index Delete (Excel-like)

Scenario 1: Delete single row

Scenario 2: Delete range rows

Scenario 3: Delete multiple non-contiguous rows

Scenario 4: Combined delete

Example 7: Search Functionality

Example 8: Group Statistics

📚 Detailed Feature Descriptions

Read Modes

Mode 1: Filter

Mode 2: Sort

Mode 3: Search

Mode 4: Limit

Mode 5: By Cell Position

Filter Supported Operators

Update Two Modes

Mode 1: By Condition (Conditional Update)

Mode 2: By Cell Position

Delete Two Modes

Mode 1: By Condition (Conditional Delete)

Mode 2: By Row Index

Update Supports Expressions

🔧 Comparison with Other Nodes

💡 Best Practices

1. Complete Excel Processing Workflow

2. Data Validation Workflow

3. Report Generation Workflow

4. ⭐ Cell Correction Workflow

5. ⭐ Cell Precise Processing Workflow (For complex Excel processing)

6. Mixed Mode Workflow

🎯 Real-World Cases

Case 1: Extract and Analyze Specific Excel Range

Case 2: Excel Report Data Reorganization

Case 3: Fix Excel Import Error Data

Case 3-1: Clean Excel Import Redundant Data

Case 3-2: Remove Test and Anomalous Data

Case 4: Batch Process Customer Data

⚠️ Notes

🐛 Troubleshooting

Issue: Node doesn't appear in n8n

Issue: Filter returns no results

Issue: Update doesn't take effect

Issue: Cell Update updates wrong rows

Issue: Range update exceeds expectations

Issue: Cell Read returns incomplete fields

Issue: Cell Read returns empty results

json-crud