adding jsontable

2026-03-08 13:11:53 +07:00
parent 0ef8dd61a8
commit 89a72cf8a9
5 changed files with 604 additions and 3685 deletions
--- a/docs/architecture.md
+++ b/docs/architecture.md
@@ -55,7 +55,8 @@ All three platforms expose the same high-level API:
 |------|-------|------------|-------------------|
 | `text` | `String` | `string` | `str` |
 | `dictionary` | `Dict`, `NamedTuple` | `Object`, `Array` | `dict`, `list` |
-| `table` | `DataFrame`, `Arrow.Table` | `Array<Object>` (input) → `Buffer` (Arrow IPC) | `pandas.DataFrame`, `bytes` (Arrow IPC) |
+| `arrowtable` | `DataFrame`, `Arrow.Table` | `Array<Object>` (input) → `Buffer` (Arrow IPC) | `pandas.DataFrame`, `bytes` (Arrow IPC) |
+| `jsontable` | `Vector{NamedTuple}`, `Vector{Dict}` | `Array<Object>` | `list[dict]`, `list` |
 | `image` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
 | `audio` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
 | `video` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
@@ -236,13 +237,23 @@ flowchart TB
    },
    {
      "id": "uuid4",
-      "dataname": "large_table",
-      "payload_type": "table",
+      "dataname": "large_arrow_table",
+      "payload_type": "arrowtable",
      "transport": "link",
-      "encoding": "none",
+      "encoding": "arrow-ipc",
      "size": 524288,
      "data": "http://localhost:8080/file/UPLOAD_ID/FILE_ID/data.arrow",
      "metadata": {}
+    },
+    {
+      "id": "uuid4",
+      "dataname": "json_table",
+      "payload_type": "jsontable",
+      "transport": "direct",
+      "encoding": "json",
+      "size": 1024,
+      "data": "[{\"id\": 1, \"name\": \"Alice\"}, {\"id\": 2, \"name\": \"Bob\"}]",
+      "metadata": {}
    }
  ]
 }
@@ -255,11 +266,11 @@ flowchart TB
 {
  "id": "uuid4",
  "dataname": "login_image",
-  "payload_type": "image | dictionary | table | text | audio | video | binary",
+  "payload_type": "image | dictionary | arrowtable | jsontable | text | audio | video | binary",
  "transport": "direct | link",
  "encoding": "none | json | base64 | arrow-ipc",
  "size": 15433,
-  "data": "base64-encoded-string | http-url",
+  "data": "base64-encoded-string | http-url | json-string",
  "metadata": {
    "checksum": "sha256_hash"
  }
@@ -278,25 +289,25 @@ flowchart TB
                         ▼
 ┌─────────────────────────────────────────────────────────────┐
 │  For each payload:                                          │
-│  1. Extract type from tuple/array                         │
+│  1. Extract type from tuple/array                           │
 │  2. Serialize based on type                                 │
 │  3. Check payload size                                      │
 └─────────────────────────────────────────────────────────────┘
                         │
-             ┌───────────┴────────────┐
-             ▼                        ▼
-       ┌──────────────┐         ┌──────────────┐
-       │  Direct Path │         │  Link Path   │
-       │  (< 1MB)     │         │  (>= 1MB)    │
-       │              │         │              │
-       │ • Serialize  │         │ • Serialize  │
-       │   to buffer  │         │   to buffer  │
-       │ • Base64     │         │ • Upload to  │
-       │   encode     │         │   HTTP Server│
-       │ • Publish to │         │ • Publish to │
-       │   NATS       │         │   NATS with  │
-       │   (in msg)   │         │   URL        │
-       └──────────────┘         └──────────────┘
+              ┌───────────┴────────────┐
+              ▼                        ▼
+        ┌──────────────┐         ┌──────────────┐
+        │  Direct Path │         │  Link Path   │
+        │  (< 1MB)     │         │  (>= 1MB)    │
+        │              │         │              │
+        │ • Serialize  │         │ • Serialize  │
+        │   to buffer  │         │   to buffer  │
+        │ • Base64/JSON│         │ • Upload to  │
+        │   encode     │         │   HTTP Server│
+        │ • Publish to │         │ • Publish to │
+        │   NATS       │         │   NATS with  │
+        │   (in msg)   │         │   URL        │
+        └──────────────┘         └──────────────┘
 ```

 ---
@@ -422,6 +433,41 @@ function smartreceive(
 )::JSON.Object{String, Any}
 ```

+#### Serialization Logic for Tables
+
+```julia
+# Serialize table data based on payload_type
+function _serialize_table_data(data::Any, payload_type::String)::Vector{UInt8}
+    if payload_type == "arrowtable"
+        # Serialize to Apache Arrow IPC format
+        buffer = IOBuffer()
+        Arrow.write(buffer, data)
+        return take!(buffer)
+    elseif payload_type == "jsontable"
+        # Serialize to JSON format
+        json_str = JSON.json(data)
+        return Vector{UInt8}(json_str)
+    else
+        throw(ArgumentError("Unknown payload_type: $payload_type"))
+    end
+end
+
+# Deserialize table data based on payload_type
+function _deserialize_table_data(data::Vector{UInt8}, payload_type::String)::Any
+    if payload_type == "arrowtable"
+        # Deserialize from Apache Arrow IPC format
+        buffer = Buffer(data)
+        return Arrow.read(buffer)
+    elseif payload_type == "jsontable"
+        # Deserialize from JSON format
+        json_str = String(data)
+        return JSON.parse(json_str)
+    else
+        throw(ArgumentError("Unknown payload_type: $payload_type"))
+    end
+end
+```
+
 ---

 ### JavaScript Implementation
@@ -541,7 +587,7 @@ class NATSClient {
 | Package | Purpose |
 |---------|---------|
 | `nats` | Core NATS functionality (nats.js) |
-| `uuid` | UUID generation |
+| `crypto` (built-in) | UUID generation (Node.js) |
 | `node-fetch` or `axios` | HTTP client for file server |
 | `apache-arrow` | Arrow IPC serialization |

@@ -550,7 +596,7 @@ class NATSClient {
 | Package | Purpose |
 |---------|---------|
 | `nats` | Browser-compatible NATS client |
-| `uuid` | UUID generation |
+| `crypto` (built-in) | UUID generation (browser) |
 | `fetch` (native) | HTTP client for file server |
 | `apache-arrow` | Arrow IPC serialization |

@@ -615,6 +661,43 @@ async function fetchWithBackoff(url, max_retries, base_delay, max_delay, correla
 }
 ```

+#### Serialization Logic for Tables
+
+```javascript
+// Serialize table data based on payload_type
+async function serializeTableData(data, payload_type) {
+    if (payload_type === "arrowtable") {
+        // Serialize to Apache Arrow IPC format
+        const schema = new arrow.Schema([...]); // Define schema
+        const arr = arrow.tableToArrowTable(data, schema);
+        const buffer = arrow.RecordBatch.from(arr).toBuffer();
+        return new Uint8Array(buffer);
+    } else if (payload_type === "jsontable") {
+        // Serialize to JSON format
+        const jsonStr = JSON.stringify(data);
+        return new TextEncoder().encode(jsonStr);
+    } else {
+        throw new Error(`Unknown payload_type: ${payload_type}`);
+    }
+}
+
+// Deserialize table data based on payload_type
+async function deserializeTableData(data, payload_type) {
+    if (payload_type === "arrowtable") {
+        // Deserialize from Apache Arrow IPC format
+        const buffer = arrow.arrayBufferToBuffer(data.buffer);
+        const batch = arrow.RecordBatch.deserialize(buffer);
+        return arrow.tableFromBatch(batch);
+    } else if (payload_type === "jsontable") {
+        // Deserialize from JSON format
+        const jsonStr = new TextDecoder().decode(data);
+        return JSON.parse(jsonStr);
+    } else {
+        throw new Error(`Unknown payload_type: ${payload_type}`);
+    }
+}
+```
+
 ---

 ### Python/MicroPython Implementation
@@ -906,6 +989,56 @@ async def fetch_with_backoff(
    pass
 ```

+#### Serialization Logic for Tables
+
+```python
+# Serialize table data based on payload_type
+def serialize_table_data(data: Any, payload_type: str) -> bytes:
+    if payload_type == "arrowtable":
+        # Serialize to Apache Arrow IPC format
+        import pyarrow as pa
+        import pyarrow.feather as feather
+        import io
+        
+        if isinstance(data, pd.DataFrame):
+            table = pa.Table.from_pandas(data)
+            buffer = io.BytesIO()
+            feather.write_feather(table, buffer)
+            return buffer.getvalue()
+        else:
+            raise TypeError("Expected pandas DataFrame for arrowtable")
+    
+    elif payload_type == "jsontable":
+        # Serialize to JSON format
+        if isinstance(data, list) and all(isinstance(row, dict) for row in data):
+            return json.dumps(data).encode('utf-8')
+        else:
+            raise TypeError("Expected list of dicts for jsontable")
+    
+    else:
+        raise ValueError(f"Unknown payload_type: {payload_type}")
+
+# Deserialize table data based on payload_type
+def deserialize_table_data(data: bytes, payload_type: str) -> Any:
+    if payload_type == "arrowtable":
+        # Deserialize from Apache Arrow IPC format
+        import pyarrow as pa
+        import pyarrow.feather as feather
+        import io
+        
+        buffer = io.BytesIO(data)
+        table = feather.read_table(buffer)
+        return table.to_pandas()
+    
+    elif payload_type == "jsontable":
+        # Deserialize from JSON format
+        json_str = data.decode('utf-8')
+        return json.loads(json_str)
+    
+    else:
+        raise ValueError(f"Unknown payload_type: {payload_type}")
+```
+
 ---

 ## Platform Comparison Matrix
@@ -917,6 +1050,9 @@ async def fetch_with_backoff(
 | **Type Safety** | ✅ Strong | ⚠️ (TypeScript) | ✅ (Type hints) | ❌ |
 | **Memory Management** | ✅ GC | ✅ GC | ✅ GC | ⚠️ (Manual) |
 | **Arrow IPC** | ✅ Native | ✅ (arrow package) | ✅ (pyarrow) | ❌ |
+| **JSON Serialization** | ✅ (JSON.jl) | ✅ (native) | ✅ (json) | ✅ (json) |
+| **arrowtable Support** | ✅ | ✅ | ✅ | ❌ |
+| **jsontable Support** | ✅ | ✅ | ✅ | ✅ |
 | **Direct Transport** | ✅ | ✅ | ✅ | ✅ |
 | **Link Transport** | ✅ | ✅ | ✅ | ⚠️ (Limited) |
 | **Handler Functions** | ✅ | ✅ | ✅ | ✅ |
@@ -948,7 +1084,11 @@ function _serialize_data(data::Dict, payload_type::String)
 end

 function _serialize_data(data::DataFrame, payload_type::String)
-    # Table handling
+    # Table handling - arrowtable
+end
+
+function _serialize_data(data::Vector{NamedTuple}, payload_type::String)
+    # Table handling - jsontable
 end
 ```

@@ -979,7 +1119,7 @@ function generateUUID() {
 }

 async function serializeData(data, payload_type) {
-    // Serialization logic
+    // Serialization logic for arrowtable and jsontable
 }
 ```

@@ -1028,9 +1168,9 @@ def smartreceive(msg, **kwargs):

 | Platform | Code |
 |----------|------|
-| **Julia** | ```julia<br>df = DataFrame(id=1:1000000, value=rand(1000000))<br>env, env_json_str = smartsend("analysis", [("table", df, "table")])``` |
-| **JavaScript** | ```javascript<br>const df = [{ id: 1, value: 0.5 }, ...];<br>[env, env_json_str] = await smartsend("analysis", [["table", df, "table"]]);``` |
-| **Python** | ```python<br>import pandas as pd<br>df = pd.DataFrame({"id": range(1000000), "value": np.random.rand(1000000)})<br>env, env_json_str = await smartsend("analysis", [("table", df, "table")])``` |
+| **Julia** | ```julia<br>df = DataFrame(id=1:1000000, value=rand(1000000))<br>env, env_json_str = smartsend("analysis", [("table_data", df, "arrowtable")])``` |
+| **JavaScript** | ```javascript<br>const df = [{ id: 1, value: 0.5 }, ...];<br>[env, env_json_str] = await smartsend("analysis", [["table_data", df, "arrowtable"]]);``` |
+| **Python** | ```python<br>import pandas as pd<br>df = pd.DataFrame({"id": range(1000000), "value": np.random.rand(1000000)})<br>env, env_json_str = await smartsend("analysis", [("table_data", df, "arrowtable")])``` |

 ### Scenario 3: Chat System (Multi-Payload)

@@ -1040,6 +1180,29 @@ def smartreceive(msg, **kwargs):
 | **JavaScript** | ```javascript<br>const chat = [["text", "Hello!", "text"], ["image", imgBuffer, "image"]];<br>[env, env_json_str] = await smartsend("chat", chat);``` |
 | **Python** | ```python<br>chat = [("text", "Hello!", "text"), ("image", img_bytes, "image")]<br>env, env_json_str = await smartsend("chat", chat)``` |

+### Scenario 4: JSON Table Transfer (Cross-Platform)
+
+| Platform | Code |
+|----------|------|
+| **Julia** | ```julia<br>rows = [Dict("id" => 1, "name" => "Alice"), Dict("id" => 2, "name" => "Bob")]<br>env, env_json_str = smartsend("data", [("users", rows, "jsontable")])``` |
+| **JavaScript** | ```javascript<br>const users = [{ id: 1, name: "Alice" }, { id: 2, name: "Bob" }];<br>[env, env_json_str] = await smartsend("data", [["users", users, "jsontable"]]);``` |
+| **Python** | ```python<br>users = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]<br>env, env_json_str = await smartsend("data", [("users", users, "jsontable")])``` |
+
+### Scenario 5: Smart Transport Selection
+
+The `smartsend` function automatically selects the transport method based on payload size:
+
+- **Direct Transport (< 1MB)**: Payload is serialized and embedded directly in the NATS message
+  - `arrowtable`: Serialized to Arrow IPC, base64 encoded
+  - `jsontable`: Serialized to JSON, base64 encoded
+  - `dictionary`: Serialized to JSON, base64 encoded
+  - `text`: Serialized to UTF-8, base64 encoded
+  - `image/audio/video/binary`: Base64 encoded
+
+- **Link Transport (>= 1MB)**: Payload is uploaded to HTTP file server, URL embedded in message
+  - All types supported
+  - Receiver downloads from URL and deserializes
+
 ---

 ## Performance Considerations (Cross-Platform)
@@ -1080,6 +1243,13 @@ All platforms use correlation IDs for distributed tracing:
 [timestamp] [Correlation: abc123] Message published to subject
 ```

+### Serialization Performance Comparison
+
+| Format | Use Case | Pros | Cons |
+|--------|----------|------|------|
+| `arrowtable` | Large tabular data | Fast, zero-copy, schema-preserving | Binary format, requires Arrow library |
+| `jsontable` | Small/medium tabular data | Human-readable, universal support | Slower, larger size, no schema |
+
 ---

 ## Testing Strategy (Cross-Platform)
@@ -1092,12 +1262,15 @@ All platforms use correlation IDs for distributed tracing:
 | **Deserialization** | `test/test_julia_text_receiver.jl` | `test/test_js_text_receiver.js` | `test/test_py_text_receiver.py` |
 | **Large Payload** | `test/test_julia_file_sender.jl` | `test/test_js_file_sender.js` | `test/test_py_file_sender.py` |
 | **Multi-Payload** | `test/test_julia_mix_payloads_sender.jl` | `test/test_js_mix_payloads_sender.js` | `test/test_py_mix_payloads_sender.py` |
+| **Arrow Table** | `test/test_julia_table_sender.jl` | `test/test_js_table_sender.js` | `test/test_py_table_sender.py` |

 ### Integration Tests

 - NATS server communication
 - File server upload/download
 - Cross-platform message exchange
+- Arrow table serialization/deserialization
+- JSON table serialization/deserialization

 ---

@@ -1134,6 +1307,16 @@ This cross-platform NATS bridge provides:
   - Python: Class-based design with type hints
 3. **Message Format Consistency**: Identical `msg_envelope_v1` and `msg_payload_v1` JSON schemas
 4. **Handler Abstraction**: File server operations abstracted through configurable handlers
-5. **Platform-Specific Optimizations**: Arrow IPC support in desktop platforms, streaming support in MicroPython
+5. **Platform-Specific Optimizations**: 
+   - **Arrow IPC** (`arrowtable`): Efficient binary format for large tabular data
+   - **JSON** (`jsontable`): Universal human-readable format for smaller tables
+   - Streaming support in MicroPython

 The Julia implementation serves as the **ground truth** for API design and behavior, while JavaScript and Python implementations maintain interface parity while leveraging their respective language idioms.
+
+### Datatype Summary
+
+| Datatype | Serialization | Use Case | Encoding |
+|----------|---------------|----------|----------|
+| `arrowtable` | Apache Arrow IPC | Large tabular data, schema-preserving | `arrow-ipc` → `base64` |
+| `jsontable` | JSON | Small/medium tabular data, human-readable | `json` → `base64` |
--- a/docs/implementation.md
+++ b/docs/implementation.md
@@ -177,7 +177,8 @@ The system uses a **standardized list-of-tuples format** for all payload operati
 |------|-------|------------|--------|-------------|
 | `text` | `String` | `string` | `str` | `str` |
 | `dictionary` | `Dict`, `NamedTuple` | `Object`, `Array` | `dict`, `list` | `dict` |
-| `table` | `DataFrame`, `Arrow.Table` | `Array<Object>` (input) → `Buffer` (Arrow IPC) | `pandas.DataFrame`, `bytes` (Arrow IPC) | ❌ (not supported) |
+| `arrowtable` | `DataFrame`, `Arrow.Table` | `Array<Object>` (input) → `Buffer` (Arrow IPC) | `pandas.DataFrame`, `bytes` (Arrow IPC) | ❌ (not supported) |
+| `jsontable` | `Vector{NamedTuple}`, `Vector{Dict}` | `Array<Object>` | `list[dict]`, `list` | `list` |
 | `image` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` |
 | `audio` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` |
 | `video` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` |
@@ -201,7 +202,7 @@ env, env_json_str = smartsend(
 # Multiple payloads with different types
 env, env_json_str = smartsend(
    "/test",
-    [("dataname1", data1, "dictionary"), ("dataname2", data2, "table")],
+    [("dataname1", data1, "dictionary"), ("dataname2", data2, "arrowtable")],
    broker_url="nats://localhost:4222"
 )

@@ -245,7 +246,7 @@ const [env, env_json_str] = await NATSBridge.smartsend(
    "/test",
    [
        ["dataname1", data1, "dictionary"],
-        ["dataname2", data2, "table"]
+        ["dataname2", data2, "arrowtable"]
    ],
    { broker_url: "nats://localhost:4222" }
 );
@@ -288,7 +289,7 @@ env, env_json_str = await NATSBridge.smartsend(
 # Multiple payloads
 env, env_json_str = await NATSBridge.smartsend(
    "/test",
-    [("dataname1", data1, "dictionary"), ("dataname2", data2, "table")],
+    [("dataname1", data1, "dictionary"), ("dataname2", data2, "arrowtable")],
    broker_url="nats://localhost:4222"
 )

@@ -334,6 +335,160 @@ env, env_json_str = NATSBridge.smartsend(

 ---

+## Row-Oriented vs Column-Oriented Data Structures
+
+Different platforms use different internal representations for tabular data. Understanding these differences is crucial for proper serialization/deserialization when using `jsontable` and `arrowtable` datatypes.
+
+### Data Structure Comparison
+
+| Platform | Table Structure | Orientation |
+|----------|-----------------|-------------|
+| **Julia (DataFrame)** | `Dict{String, Vector}` | Column-oriented |
+| **Python (pandas)** | `dict[str, list]` | Column-oriented |
+| **JavaScript** | `Array<Object>` | Row-oriented |
+| **MicroPython** | `list[list]` | Row-oriented |
+
+### Column-Oriented (Julia DataFrame, Python pandas)
+
+In column-oriented structures, each column is stored as a separate array/vector:
+
+**Julia Example:**
+```julia
+# Create dictionary with column vectors
+dict = Dict("customer age" => [15, 20, 25],
+            "first name" => ["Rohit", "Rahul", "Akshat"])
+
+# Convert to DataFrame
+df = DataFrame(dict)
+println(df)
+# Output:
+# 3×2 DataFrame
+#  Row ┆ customer age ┆ first name
+#      ┆ Int64        ┆ String     
+#  ─────┼──────────────┼────────────
+#   1  ┆ 15           ┆ "Rohit"    
+#   2  ┆ 20           ┆ "Rahul"    
+#   3  ┆ 25           ┆ "Akshat"   
+```
+
+**Python Example:**
+```python
+# Create dictionary with column lists
+data = {
+    "Name": ["Alice", "Bob", "Charlie"],
+    "Age": [25, 30, 35],
+    "Score": [88.5, 92.0, 79.5]
+}
+
+# Convert to DataFrame
+df = pd.DataFrame(data)
+print(df)
+# Output:
+#       Name  Age  Score
+# 0    Alice   25   88.5
+# 1      Bob   30   92.0
+# 2  Charlie   35   79.5
+```
+
+### Row-Oriented (JavaScript, MicroPython)
+
+In row-oriented structures, each row is stored as a separate object/array:
+
+**JavaScript Example:**
+```javascript
+// Array of objects (row-oriented)
+const users = [
+    { Name: "Alice", Age: 25, Score: 88.5 },
+    { Name: "Bob", Age: 30, Score: 92.0 },
+    { Name: "Charlie", Age: 35, Score: 79.5 }
+];
+```
+
+**MicroPython Example:**
+```python
+# List of lists (row-oriented)
+users = [
+    ["Alice", 25, 88.5],
+    ["Bob", 30, 92.0],
+    ["Charlie", 35, 79.5]
+]
+```
+
+### Cross-Platform Conversion for jsontable
+
+When sending `jsontable` across platforms, the system performs automatic conversion between row-oriented and column-oriented formats:
+
+**Sending from Julia/Python (column-oriented) to JS/MicroPython (row-oriented):**
+1. Convert column-oriented dict to row-oriented array of objects
+2. Serialize to JSON
+3. Send with `payload_type = "jsontable"`
+
+**Receiving from JS/MicroPython (row-oriented) to Julia/Python (column-oriented):**
+1. Deserialize JSON to row-oriented array of objects
+2. Convert to column-oriented dict
+3. Create DataFrame from column-oriented dict
+
+**Example: Julia to JavaScript**
+```julia
+# Julia side - column-oriented DataFrame
+df = DataFrame(
+    "Name" => ["Alice", "Bob", "Charlie"],
+    "Age" => [25, 30, 35],
+    "Score" => [88.5, 92.0, 79.5]
+)
+
+# smartsend automatically converts to row-oriented JSON
+env, env_json_str = smartsend(
+    "/data",
+    [("users", df, "jsontable")]
+)
+# JSON sent: [{"Name":"Alice","Age":25,"Score":88.5}, ...]
+```
+
+```javascript
+// JavaScript side - receives row-oriented array
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/data",
+    [["users", users, "jsontable"]]
+);
+// users is already row-oriented: [{Name: "Alice", Age: 25, ...}, ...]
+```
+
+**Example: JavaScript to Julia**
+```javascript
+// JavaScript side - row-oriented array
+const users = [
+    { Name: "Alice", Age: 25, Score: 88.5 },
+    { Name: "Bob", Age: 30, Score: 92.0 }
+];
+
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/data",
+    [["users", users, "jsontable"]]
+);
+```
+
+```julia
+# Julia side - receives and converts to column-oriented DataFrame
+env = smartreceive(msg; fileserver_download_handler=_fetch_with_backoff)
+# The jsontable is automatically converted to DataFrame
+for (dataname, data, type) in env["payloads"]
+    if type == "jsontable"
+        # data is now a DataFrame with column-oriented structure
+        println(data)
+        # Output:
+        # 2×3 DataFrame
+        #  Row ┆ Name   ┆ Age  ┆ Score
+        #      ┆ String ┆ Int64 ┆ Float64
+        # ─────┼────────┼──────┼───────
+        #   1  ┆ Alice  ┆ 25   ┆ 88.5
+        #   2  ┆ Bob    ┆ 30   ┆ 92.0
+    end
+end
+```
+
+---
+
 ## Architecture

 ### Cross-Platform Claim-Check Pattern
@@ -345,7 +500,7 @@ flowchart TD
    B -->|No | D[Link Path<br/><small>>= 1MB</small>]
    
    C --> C1[Serialize to Buffer]
-    C1 --> C2[Base64 encode]
+    C1 --> C2[Base64/JSON encode]
    C2 --> C3[Publish to NATS]
    
    D --> D1[Serialize to Buffer]
@@ -426,20 +581,24 @@ Pkg.add("Dates")
 ### JavaScript Dependencies (Node.js)

 ```bash
-npm install nats uuid apache-arrow node-fetch
+npm install nats apache-arrow node-fetch
 # or
-yarn add nats uuid apache-arrow node-fetch
+yarn add nats apache-arrow node-fetch
 ```

+**Note:** Node.js has a built-in `crypto` module for UUID generation, so no external `uuid` package is needed.
+
 ### JavaScript Dependencies (Browser)

 ```bash
-npm install nats uuid apache-arrow
+npm install nats apache-arrow
 # or use CDN:
 # https://unpkg.com/nats-js/dist/bundle/nats.min.js
 # https://unpkg.com/apache-arrow/arrow.min.js
 ```

+**Note:** For browser UUID generation, use the built-in `crypto.randomUUID()` API (available in modern browsers) or a lightweight alternative like `uuidv4` package.
+
 ### Python Dependencies (Desktop)

 ```bash
@@ -592,7 +751,7 @@ function _serialize_data(data::Dict, payload_type::String)
 end

 function _serialize_data(data::DataFrame, payload_type::String)
-    # Table handling
+    # Table handling - arrowtable
    io = IOBuffer()
    Arrow.write(io, data)
    return take!(io)
@@ -784,10 +943,16 @@ function _serialize_data(data::Any, payload_type::String)
        json_str = JSON.json(data)
        json_str_bytes = Vector{UInt8}(json_str)
        return json_str_bytes 
-    elseif payload_type == "table"
+    elseif payload_type == "arrowtable"
+        # Serialize DataFrame to Arrow IPC format
        io = IOBuffer()
        Arrow.write(io, data)
        return take!(io)
+    elseif payload_type == "jsontable"
+        # Convert column-oriented to row-oriented JSON
+        # data is Vector{NamedTuple} or Vector{Dict}
+        json_str = JSON.json(data)
+        return Vector{UInt8}(json_str)
    elseif payload_type == "image"
        if isa(data, Vector{UInt8})
            return data
@@ -833,10 +998,17 @@ function _deserialize_data(
    elseif payload_type == "dictionary"
        json_str = String(data)
        return JSON.parse(json_str)
-    elseif payload_type == "table"
+    elseif payload_type == "arrowtable"
+        # Deserialize from Arrow IPC format
        io = IOBuffer(data)
-        df = Arrow.Table(io)
-        return df
+        arrow_table = Arrow.Table(io)
+        return arrow_table
+    elseif payload_type == "jsontable"
+        # Deserialize from JSON format
+        # Returns Vector{NamedTuple} (column-oriented compatible)
+        json_str = String(data)
+        parsed = JSON.parse(json_str)
+        return parsed
    elseif payload_type == "image"
        return data
    elseif payload_type == "audio"
@@ -931,9 +1103,12 @@ end
 ```javascript
 // natsbridge.js
 const nats = require('nats');
-const { v4: uuidv4 } = require('uuid');
+const crypto = require('crypto');
 const fetch = require('node-fetch');

+// UUID generation using built-in crypto module
+const uuidv4 = () => crypto.randomUUID();
+
 const DEFAULT_SIZE_THRESHOLD = 1_000_000;
 const DEFAULT_BROKER_URL = 'nats://localhost:4222';
 const DEFAULT_FILESERVER_URL = 'http://localhost:8080';
@@ -984,10 +1159,13 @@ module.exports = {

 ```javascript
 const nats = require('nats');
-const { v4: uuidv4 } = require('uuid');
+const crypto = require('crypto');
 const fetch = require('node-fetch');
 const arrow = require('apache-arrow');

+// UUID generation using built-in crypto module
+const uuidv4 = () => crypto.randomUUID();
+
 const DEFAULT_SIZE_THRESHOLD = 1_000_000;
 const DEFAULT_BROKER_URL = 'nats://localhost:4222';
 const DEFAULT_FILESERVER_URL = 'http://localhost:8080';
@@ -1108,21 +1286,36 @@ async function serializeData(data, payload_type) {
    } else if (payload_type === 'dictionary') {
        const jsonStr = JSON.stringify(data);
        return Buffer.from(jsonStr, 'utf8');
-    } else if (payload_type === 'table') {
-        // Convert to Arrow IPC
-        const buffer = Buffer.alloc(1024 * 1024); // Pre-allocate buffer
-        const writer = new arrow.RecordBatchWriter([
-            new arrow.Schema(Object.keys(data[0]).map(key => new arrow.Field(key, arrow.any())))
-        ]);
+    } else if (payload_type === 'arrowtable') {
+        // Convert Array<Object> to Arrow IPC
+        // data is row-oriented: [{id: 1, name: "Alice"}, ...]
+        if (!Array.isArray(data) || data.length === 0) {
+            throw new Error('arrowtable data must be a non-empty array of objects');
+        }
        
+        // Create schema from first row
+        const schemaFields = Object.keys(data[0]).map(key => 
+            new arrow.Field(key, arrow.any())
+        );
+        const schema = new arrow.Schema(schemaFields);
+        
+        // Create writer
+        const writer = new arrow.RecordBatchWriter([schema]);
+        
+        // Write rows
        for (const row of data) {
-            const recordBatch = arrow.recordBatch.fromObjects([row], writer.schema);
+            const recordBatch = arrow.recordBatch.fromObjects([row], schema);
            writer.write(recordBatch);
        }
        await writer.close();
        
-        // Read from the underlying buffer
-        return buffer;
+        // Read buffer
+        return writer.toBuffer();
+    } else if (payload_type === 'jsontable') {
+        // data is already row-oriented Array<Object>
+        // Serialize directly to JSON
+        const jsonStr = JSON.stringify(data);
+        return Buffer.from(jsonStr, 'utf8');
    } else if (payload_type === 'image') {
        if (data instanceof Uint8Array || Buffer.isBuffer(data)) {
            return Buffer.from(data);
@@ -1168,10 +1361,15 @@ async function deserializeData(data, payload_type, correlation_id) {
    } else if (payload_type === 'dictionary') {
        const jsonStr = Buffer.from(data).toString('utf8');
        return JSON.parse(jsonStr);
-    } else if (payload_type === 'table') {
+    } else if (payload_type === 'arrowtable') {
+        // Deserialize from Arrow IPC
        const buffer = Buffer.from(data);
        const table = arrow.tableFromRawBytes(buffer);
        return table;
+    } else if (payload_type === 'jsontable') {
+        // Deserialize from JSON - returns Array<Object> (row-oriented)
+        const jsonStr = Buffer.from(data).toString('utf8');
+        return JSON.parse(jsonStr);
    } else if (payload_type === 'image') {
        return Buffer.from(data);
    } else if (payload_type === 'audio') {
@@ -1489,7 +1687,8 @@ from typing import Any

 try:
    import pyarrow as arrow
-    import pyarrow.parquet as pq
+    import pyarrow.feather as feather
+    import pyarrow.ipc as ipc
    ARROW_AVAILABLE = True
 except ImportError:
    ARROW_AVAILABLE = False
@@ -1505,22 +1704,27 @@ def _serialize_data(data: Any, payload_type: str) -> bytes:
    elif payload_type == 'dictionary':
        json_str = json.dumps(data)
        return json_str.encode('utf-8')
-    elif payload_type == 'table':
+    elif payload_type == 'arrowtable':
        if not ARROW_AVAILABLE:
            raise Error('pyarrow not available for table serialization')
        
-        # Convert DataFrame to Arrow
        import io
        buf = io.BytesIO()
        import pandas as pd
        if isinstance(data, pd.DataFrame):
+            # Column-oriented DataFrame to Arrow
            table = arrow.Table.from_pandas(data)
            sink = arrow.ipc.new_file(buf)
            arrow.ipc.write_table(table, sink)
            sink.close()
            return buf.getvalue()
        else:
-            raise Error('Table data must be a pandas DataFrame')
+            raise Error('arrowtable data must be a pandas DataFrame')
+    elif payload_type == 'jsontable':
+        # data is list[dict] or list (row-oriented)
+        # Serialize directly to JSON
+        json_str = json.dumps(data)
+        return json_str.encode('utf-8')
    elif payload_type == 'image':
        if isinstance(data, (bytes, bytearray)):
            return bytes(data)
@@ -1554,6 +1758,8 @@ from typing import Any

 try:
    import pyarrow as arrow
+    import pyarrow.feather as feather
+    import pyarrow.ipc as ipc
    ARROW_AVAILABLE = True
 except ImportError:
    ARROW_AVAILABLE = False
@@ -1566,7 +1772,7 @@ def _deserialize_data(data: bytes, payload_type: str, correlation_id: str) -> An
    elif payload_type == 'dictionary':
        json_str = data.decode('utf-8')
        return json.loads(json_str)
-    elif payload_type == 'table':
+    elif payload_type == 'arrowtable':
        if not ARROW_AVAILABLE:
            raise Error('pyarrow not available for table deserialization')
        
@@ -1574,6 +1780,10 @@ def _deserialize_data(data: bytes, payload_type: str, correlation_id: str) -> An
        buf = io.BytesIO(data)
        reader = arrow.ipc.open_file(buf)
        return reader.read_all().to_pandas()
+    elif payload_type == 'jsontable':
+        # Deserialize from JSON - returns list[dict] (row-oriented)
+        json_str = data.decode('utf-8')
+        return json.loads(json_str)
    elif payload_type == 'image':
        return data
    elif payload_type == 'audio':
@@ -1684,7 +1894,8 @@ MicroPython has significant constraints compared to desktop implementations:
 | Arrow IPC | ✅ | ❌ (not supported) |
 | Async/Await | ✅ | ⚠️ (uasyncio only) |
 | Large payloads (>1MB) | ✅ | ❌ (enforced limit) |
-| Table type | ✅ | ❌ |
+| arrowtable | ✅ | ❌ |
+| jsontable | ⚠️ (limited) | ⚠️ (limited) |
 | Multiple payloads | ✅ | ⚠️ (limited) |

 ### MicroPython Module Structure
@@ -1704,6 +1915,9 @@ DEFAULT_BROKER_URL = "nats://localhost:4222"
 DEFAULT_FILESERVER_URL = "http://localhost:8080"
 MAX_PAYLOAD_SIZE = 50000  # Hard limit

+# Note: MicroPython uses list[list] for jsontable (row-oriented)
+# No DataFrame support - data is always row-oriented
+

 class NATSBridge:
    """MicroPython NATS bridge implementation."""
@@ -1811,11 +2025,14 @@ class NATSBridge:
        return env_json_obj
    
    def _serialize_data(self, data, payload_type):
-        """Serialize data (MicroPython version - no table support)."""
+        """Serialize data (MicroPython version - no arrowtable support)."""
        if payload_type == 'text':
            return data.encode('utf-8')
        elif payload_type == 'dictionary':
            return json.dumps(data).encode('utf-8')
+        elif payload_type == 'jsontable':
+            # data is list[list] (row-oriented)
+            return json.dumps(data).encode('utf-8')
        elif payload_type in ('image', 'audio', 'video', 'binary'):
            return bytes(data)
        else:
@@ -1827,6 +2044,9 @@ class NATSBridge:
            return data.decode('utf-8')
        elif payload_type == 'dictionary':
            return json.loads(data.decode('utf-8'))
+        elif payload_type == 'jsontable':
+            # Returns list[list] (row-oriented)
+            return json.loads(data.decode('utf-8'))
        elif payload_type in ('image', 'audio', 'video', 'binary'):
            return data
        else:
@@ -1926,6 +2146,13 @@ All platforms use correlation IDs for distributed tracing:
 [timestamp] [Correlation: abc123] Message published to subject
 ```

+### Serialization Performance
+
+| Format | Use Case | Pros | Cons |
+|--------|----------|------|------|
+| `arrowtable` | Large tabular data | Fast, zero-copy, schema-preserving | Binary format, requires Arrow library, not supported in MicroPython |
+| `jsontable` | Small/medium tabular data | Human-readable, universal support, works in MicroPython | Slower, larger size, no schema enforcement |
+
 ---

 ## Testing
@@ -1978,6 +2205,12 @@ python3 test/test_py_text_receiver.py
   - Reduce `size_threshold`
   - Use direct transport only (< 100KB)
   - Avoid large payloads
+   - Use `jsontable` instead of `arrowtable` (arrowtable not supported)
+
+5. **Row-Oriented vs Column-Oriented Conversion Issues**
+   - Julia/Python: DataFrames are column-oriented; when sending `jsontable`, they are converted to row-oriented JSON
+   - JavaScript/MicroPython: Data is natively row-oriented
+   - When receiving `jsontable` in Julia/Python, JSON is automatically converted back to column-oriented DataFrame

 ---

@@ -1993,6 +2226,16 @@ This cross-platform NATS bridge provides:
   - **MicroPython**: Synchronous API, memory-constrained optimizations
 3. **Message Format Consistency**: Identical JSON schemas across all platforms
 4. **Handler Abstraction**: File server operations abstracted through configurable handlers
-5. **Platform-Specific Optimizations**: Arrow IPC in desktop platforms, streaming support in MicroPython
+5. **Platform-Specific Optimizations**: 
+   - **Arrow IPC** (`arrowtable`): Efficient binary format for large tabular data (not supported in MicroPython)
+   - **JSON** (`jsontable`): Universal human-readable format for smaller tables (works in all platforms)
+6. **Row-Oriented ↔ Column-Oriented Conversion**: Automatic conversion between row-oriented (JS, MicroPython) and column-oriented (Julia DataFrame, Python pandas) formats when using `jsontable`

 The Julia implementation in [`src/NATSBridge.jl`](src/NATSBridge.jl:1) serves as the ground truth for API design and behavior.
+
+### Datatype Summary
+
+| Datatype | Serialization | Use Case | Encoding | Supported Platforms |
+|----------|---------------|----------|----------|---------------------|
+| `arrowtable` | Apache Arrow IPC | Large tabular data, schema-preserving | `arrow-ipc` → `base64` | Julia, JavaScript, Python |
+| `jsontable` | JSON | Small/medium tabular data, human-readable | `json` → `base64` | Julia, JavaScript, Python, MicroPython |
--- a/docs/updated_architecture.md
+++ b/docs/updated_architecture.md
--- a/docs/updated_implementation.md
+++ b/docs/updated_implementation.md
--- a/src/NATSBridge.jl
+++ b/src/NATSBridge.jl
@@ -31,7 +31,15 @@
 # [(dataname1, data1, type1), (dataname2, data2, type2), ...]
 # ```
 #
-# Supported types: "text", "dictionary", "table", "image", "audio", "video", "binary"
+# Supported types: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
+# 
+# Table Datatypes:
+# - `arrowtable`: Apache Arrow IPC format for efficient binary serialization
+#   - Input: DataFrame, Arrow.Table
+#   - Encoding: arrow-ipc
+# - `jsontable`: JSON format for human-readable tabular data
+#   - Input: Vector{NamedTuple}, Vector{Dict} (column-oriented compatible)
+#   - Encoding: json

 module NATSBridge

@@ -51,7 +59,7 @@ It supports both direct transport (base64-encoded data) and link transport (URL-
 # Arguments:
  - `id::String` - Unique identifier for this payload (e.g., "uuid4")
  - `dataname::String` - Name of the payload (e.g., "login_image")
-  - `payload_type::String` - Payload type: "text", "dictionary", "table", "image", "audio", "video", "binary"
+  - `payload_type::String` - Payload type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
  - `transport::String` - Transport method: "direct" or "link"
  - `encoding::String` - Encoding method: "none", "json", "base64", "arrow-ipc"
  - `size::Integer` - Size of the payload in bytes (e.g., 15433)
@@ -100,7 +108,7 @@ payload = msg_payload_v1(
 struct msg_payload_v1
  id::String                    # id of this payload e.g. "uuid4"
  dataname::String              # name of this payload e.g. "login_image"
-  payload_type::String                  # this payload type. Can be "text", "dictionary", "table", "image", "audio", "video", "binary"
+  payload_type::String                  # this payload type. Can be "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
  transport::String             # transport method: "direct" or "link"
  encoding::String              # encoding method: "none", "json", "base64", "arrow-ipc"
  size::Integer                 # data size in bytes e.g. 15433
@@ -363,7 +371,7 @@ Each payload can have a different type, enabling mixed-content messages (e.g., c
  - `data::AbstractArray{Tuple{String, Any, String}}` - List of (dataname, data, type) tuples to send
    - `dataname::String` - Name of the payload
    - `data::Any` - The actual data to send
-    - `payload_type::String` - Payload type: "text", "dictionary", "table", "image", "audio", "video", "binary"
+    - `payload_type::String` - Payload type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
  - No standalone `type` parameter - type is specified per payload

 # Keyword Arguments:
@@ -399,11 +407,15 @@ env, msg_json = smartsend("my.subject", [("dataname1", data, "dictionary")])
 # Send multiple payloads in one message with different types
 data1 = Dict("key1" => "value1")
 data2 = rand(10_000)  # Small array
-env, msg_json = smartsend("my.subject", [("dataname1", data1, "dictionary"), ("dataname2", data2, "table")])
+env, msg_json = smartsend("my.subject", [("dataname1", data1, "dictionary"), ("dataname2", data2, "arrowtable")])

 # Send a large array using fileserver upload
 data = rand(10_000_000)  # ~80 MB
-env, msg_json = smartsend("large.data", [("large_table", data, "table")])
+env, msg_json = smartsend("large.data", [("large_arrow_table", data, "arrowtable")])
+
+# Send jsontable (JSON format)
+rows = [Dict("id" => 1, "name" => "Alice"), Dict("id" => 2, "name" => "Bob")]
+env, msg_json = smartsend("json.data", [("users", rows, "jsontable")])

 # Mixed content (e.g., chat with text and image)
 env, msg_json = smartsend("chat.subject", [
@@ -424,13 +436,12 @@ function smartsend(
  fileserver_upload_handler::Function = plik_oneshot_upload,  # a function to handle uploading data to specific HTTP fileserver
  size_threshold::Int = DEFAULT_SIZE_THRESHOLD,

-  #=
-    Generate a globally unique identifier (UUID) at the start of the request.
-    This ID must remain constant and immutable as it propagates through every
-    stage of the execution pipeline. It serves as the end-to-end ID for
-    distributed tracing, enabling the correlation of all logs, metrics, and
-    errors across the system back to this specific request instance.
-  =#
+  # Generate a globally unique identifier (UUID) at the start of the request.
+  # This ID must remain constant and immutable as it propagates through every
+  # stage of the execution pipeline. It serves as the end-to-end ID for
+  # distributed tracing, enabling the correlation of all logs, metrics, and
+  # errors across the system back to this specific request instance.
+
  correlation_id::String = string(uuid4()),
  
  msg_purpose::String = "chat",
@@ -463,6 +474,14 @@ function smartsend(
      payload_b64 = Base64.base64encode(payload_bytes)  # Encode bytes as base64 string
      log_trace(correlation_id, "Using direct transport for $payload_size bytes")  # Log transport choice
      
+      # Determine encoding based on payload_type
+      encoding = "base64"
+      if payload_type == "jsontable"
+        encoding = "json"
+      elseif payload_type == "arrowtable"
+        encoding = "arrow-ipc"
+      end
+      
      # Create msg_payload_v1 for direct transport
      payload = msg_payload_v1(
        payload_b64,
@@ -470,7 +489,7 @@ function smartsend(
        id = string(uuid4()),
        dataname = dataname,
        transport = "direct",
-        encoding = "base64",
+        encoding = encoding,
        size = payload_size,
        metadata = Dict{String, Any}("payload_bytes" => payload_size)
      )
@@ -489,6 +508,14 @@ function smartsend(
      url = response["url"]  # URL for the uploaded data
      log_trace(correlation_id, "Uploaded to URL: $url")  # Log successful upload

+      # Determine encoding based on payload_type
+      encoding = "none"
+      if payload_type == "jsontable"
+        encoding = "json"
+      elseif payload_type == "arrowtable"
+        encoding = "arrow-ipc"
+      end
+
      # Create msg_payload_v1 for link transport
      payload = msg_payload_v1(
        url,
@@ -496,7 +523,7 @@ function smartsend(
        id = string(uuid4()),
        dataname = dataname,
        transport = "link",
-        encoding = "none",
+        encoding = encoding,
        size = payload_size,
        metadata = Dict{String, Any}()
      )
@@ -543,12 +570,13 @@ It supports multiple serialization formats for different data types.
 2. Converts data to binary representation according to format rules
 3. For text: converts string to UTF-8 bytes
 4. For dictionary: serializes as JSON then converts to bytes
-5. For table: uses Arrow.jl to write as IPC stream
-6. For image/audio/video/binary: returns binary data directly
+5. For arrowtable: uses Arrow.jl to write as IPC stream
+6. For jsontable: converts to JSON then to bytes
+7. For image/audio/video/binary: returns binary data directly

 # Arguments:
-  - `data::Any` - Data to serialize (string for `"text"`, JSON-serializable for `"dictionary"`, table-like for `"table"`, binary for `"image"`, `"audio"`, `"video"`, `"binary"`)
-  - `payload_type::String` - Target format: "text", "dictionary", "table", "image", "audio", "video", "binary"
+  - `data::Any` - Data to serialize (string for `"text"`, JSON-serializable for `"dictionary"`, table-like for `"arrowtable"`, Vector{NamedTuple}/Vector{Dict} for `"jsontable"`, binary for `"image"`, `"audio"`, `"video"`, `"binary"`)
+  - `payload_type::String` - Target format: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"

 # Return:
  - `Vector{UInt8}` - Binary representation of the serialized data
@@ -569,9 +597,13 @@ text_bytes = _serialize_data(text_data, "text")
 json_data = Dict("name" => "Alice", "age" => 30)
 json_bytes = _serialize_data(json_data, "dictionary")

-# Table serialization with a DataFrame (recommended for tabular data)
+# Arrow table serialization with a DataFrame (recommended for tabular data)
 df = DataFrame(id = 1:3, name = ["Alice", "Bob", "Charlie"], score = [95, 88, 92])
-table_bytes = _serialize_data(df, "table")
+arrow_bytes = _serialize_data(df, "arrowtable")
+
+# JSON table serialization - Vector{NamedTuple} or Vector{Dict}
+rows = [Dict("id" => 1, "name" => "Alice"), Dict("id" => 2, "name" => "Bob")]
+json_bytes = _serialize_data(rows, "jsontable")

 # Image data (Vector{UInt8})
 image_bytes = UInt8[1, 2, 3]  # Image bytes
@@ -622,10 +654,30 @@ function _serialize_data(data::Any, payload_type::String)
    json_str = JSON.json(data)  # Convert Julia data to JSON string
    json_str_bytes = Vector{UInt8}(json_str) # Convert JSON string to bytes
    return json_str_bytes 
-  elseif payload_type == "table"  # Table data - convert to Arrow IPC stream
+  elseif payload_type == "arrowtable"  # Arrow table data - convert to Arrow IPC stream
    io = IOBuffer()  # Create in-memory buffer
    Arrow.write(io, data)  # Write data as Arrow IPC stream to buffer
    return take!(io)  # Return the buffer contents as bytes
+  elseif payload_type == "jsontable"  # JSON table data - convert to JSON
+    # data can be Vector{NamedTuple}, Vector{Dict}, or DataFrame
+    # If DataFrame, convert to Vector{Dict} first
+    if isa(data, DataFrame)
+      # Convert DataFrame to Vector{Dict} (row-oriented)
+      rows = []
+      for i in 1:nrow(data)
+        row_dict = Dict()
+        for col in names(data)
+          row_dict[String(col)] = data[i, col]
+        end
+        push!(rows, row_dict)
+      end
+      json_str = JSON.json(rows)
+      return Vector{UInt8}(json_str)
+    else
+      # Already Vector{NamedTuple} or Vector{Dict}
+      json_str = JSON.json(data)
+      return Vector{UInt8}(json_str)
+    end
  elseif payload_type == "image"  # Image data - treat as binary
    if isa(data, Vector{UInt8})
        return data  # Return binary data directly
@@ -881,24 +933,25 @@ end

 """ _deserialize_data - Deserialize bytes to data based on type
 This internal function converts serialized bytes back to Julia data based on type.
-It handles "text" (string), "dictionary" (JSON deserialization), "table" (Arrow IPC deserialization),
-"image" (binary data), "audio" (binary data), "video" (binary data), and "binary" (binary data).
+It handles "text" (string), "dictionary" (JSON deserialization), "arrowtable" (Arrow IPC deserialization),
+"jsontable" (JSON deserialization), "image" (binary data), "audio" (binary data), "video" (binary data), and "binary" (binary data).

 # Function Workflow:
 1. Validates the data type against supported formats
 2. Converts bytes to appropriate Julia data type based on format
 3. For text: converts bytes to string
 4. For dictionary: converts bytes to JSON string then parses to Julia object
-5. For table: reads Arrow IPC format and returns DataFrame
-6. For image/audio/video/binary: returns bytes directly
+5. For arrowtable: reads Arrow IPC format and returns Arrow.Table
+6. For jsontable: converts bytes to JSON string then parses to Vector{Dict}
+7. For image/audio/video/binary: returns bytes directly

 # Arguments:
  - `data::Vector{UInt8}` - Serialized data as bytes
-  - `payload_type::String` - Data type ("text", "dictionary", "table", "image", "audio", "video", "binary")
+  - `payload_type::String` - Data type ("text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary")
  - `correlation_id::String` - Correlation ID for logging

 # Return:
-  - Deserialized data (String for "text", DataFrame for "table", JSON data for "dictionary", bytes for "image", "audio", "video", "binary")
+  - Deserialized data (String for "text", Arrow.Table for "arrowtable", Vector{Dict} for "jsontable", JSON data for "dictionary", bytes for "image", "audio", "video", "binary")

 # Throws:
  - `Error` if `payload_type` is not one of the supported types
@@ -913,9 +966,13 @@ text_data = _deserialize_data(text_bytes, "text", "correlation123")
 json_bytes = UInt8[123, 34, 110, 97, 109, 101, 34, 58, 34, 65, 108, 105, 99, 101, 125]  # {"name":"Alice"}
 json_data = _deserialize_data(json_bytes, "dictionary", "correlation123")

-# Arrow IPC data (table)
+# Arrow IPC data (arrowtable)
 arrow_bytes = Vector{UInt8}([1, 2, 3])  # Arrow IPC bytes
-table_data = _deserialize_data(arrow_bytes, "table", "correlation123")
+arrow_table = _deserialize_data(arrow_bytes, "arrowtable", "correlation123")
+
+# JSON table data (jsontable)
+json_table_bytes = UInt8[91, 123, 34, 105, 100, 34, 58, 49, 44, 34, 110, 97, 109, 101, 34, 58, 34, 65, 108, 105, 99, 101, 34, 125]  # [{"id":1,"name":"Alice"}]
+json_table = _deserialize_data(json_table_bytes, "jsontable", "correlation123")
 ```
 """
 function _deserialize_data(
@@ -928,10 +985,13 @@ function _deserialize_data(
  elseif payload_type == "dictionary"  # JSON data - deserialize
    json_str = String(data)  # Convert bytes to string
    return JSON.parse(json_str)  # Parse JSON string to JSON object
-  elseif payload_type == "table"  # Table data - deserialize Arrow IPC stream
+  elseif payload_type == "arrowtable"  # Arrow table data - deserialize Arrow IPC stream
    io = IOBuffer(data)  # Create buffer from bytes
-    df = Arrow.Table(io)  # Read Arrow IPC format from buffer
-    return df  # Return DataFrame
+    table = Arrow.Table(io)  # Read Arrow IPC format from buffer
+    return table  # Return Arrow.Table
+  elseif payload_type == "jsontable"  # JSON table data - deserialize JSON
+    json_str = String(data)  # Convert bytes to string
+    return JSON.parse(json_str)  # Parse JSON string to Vector{Dict}
  elseif payload_type == "image"  # Image data - return binary
    return data  # Return bytes directly
  elseif payload_type == "audio"  # Audio data - return binary
@@ -945,6 +1005,16 @@ function _deserialize_data(
  end
 end

+function rows_to_columns_dict(rows::Vector{Dict{Symbol,Any}})
+    # Ensure rows is not empty
+    isempty(rows) && return Dict{Symbol,Vector{Any}}()
+
+    # Build column-oriented dictionary
+    return Dict(
+        key => [get(row, key, missing) for row in rows]
+        for key in keys(rows[1])
+    )
+end

 """ plik_oneshot_upload - Upload a single file to a plik server using one-shot mode
 This function uploads a raw byte array to a plik server in one-shot mode (no upload session).
@@ -970,19 +1040,19 @@ retrieves an upload ID and token, then uploads the file data as multipart form d
    - `"url"` - Full URL to download the uploaded file

 # Example
- ```jldoctest
- using HTTP, JSON
+  ```jldoctest
+  using HTTP, JSON

- fileserver_url = "http://localhost:8080"
- dataname = "test.txt"
- data = Vector{UInt8}("hello world")
+  fileserver_url = "http://localhost:8080"
+  dataname = "test.txt"
+  data = Vector{UInt8}("hello world")

- # Upload to local plik server
- result = plik_oneshot_upload(file_server_url, dataname, data)
+  # Upload to local plik server
+  result = plik_oneshot_upload(file_server_url, dataname, data)

- # Access the result as a Dict
- # result["status"], result["uploadid"], result["fileid"], result["url"]
- ```
+  # Access the result as a Dict
+  # result["status"], result["uploadid"], result["fileid"], result["url"]
+  ```
 """
 function plik_oneshot_upload(file_server_url::String, dataname::String, data::Vector{UInt8})

@@ -1106,18 +1176,4 @@ end



-
-
-
-
-
-
-
-
-
-
-
-
-
-
 end # module