# FlareDB SQL Layer Design

## Overview

This document outlines the design for a SQL-compatible layer built on top of FlareDB's KVS foundation. The goal is to enable SQL queries (DDL/DML) while leveraging FlareDB's existing distributed KVS capabilities.

## Architecture Principles

1. **KVS Foundation**: All SQL data stored as KVS key-value pairs
2. **Simple First**: Start with core SQL subset (no JOINs, no transactions initially)
3. **Efficient Encoding**: Optimize key encoding for range scans
4. **Namespace Isolation**: Use FlareDB namespaces for multi-tenancy

## Key Design Decisions

### 1. SQL Parser

**Choice**: Use `sqlparser-rs` crate
- Mature, well-tested SQL parser
- Supports MySQL/PostgreSQL/ANSI SQL dialects
- Easy to extend for custom syntax

### 2. Table Metadata Schema

Table metadata stored in KVS with special prefix:

```
Key:   __sql_meta:tables:{table_name}
Value: TableMetadata {
  table_id: u32,
  table_name: String,
  columns: Vec<ColumnDef>,
  primary_key: Vec<String>,
  created_at: u64,
}

ColumnDef {
  name: String,
  data_type: DataType,
  nullable: bool,
  default_value: Option<Value>,
}

DataType enum:
  - Integer
  - BigInt
  - Text
  - Boolean
  - Timestamp
```

Table ID allocation:
```
Key:   __sql_meta:next_table_id
Value: u32 (monotonic counter)
```

### 3. Row Key Encoding

Efficient key encoding for table rows:

```
Format: __sql_data:{table_id}:{primary_key_encoded}

Example:
  Table: users (table_id=1)
  Primary key: id=42
  Key: __sql_data:1:42
```

For composite primary keys:
```
Format: __sql_data:{table_id}:{pk1}:{pk2}:...

Example:
  Table: order_items (table_id=2)
  Primary key: (order_id=100, item_id=5)
  Key: __sql_data:2:100:5
```

### 4. Row Value Encoding

Row values stored as serialized structs:

```
Value: RowData {
  columns: HashMap<String, Value>,
  version: u64,  // For optimistic concurrency
}

Value enum:
  - Null
  - Integer(i64)
  - Text(String)
  - Boolean(bool)
  - Timestamp(u64)
```

Serialization: Use `bincode` for efficient binary encoding

### 5. Query Execution Engine

Simple query execution pipeline:

```
SQL String
  ↓
[Parser]
  ↓
Abstract Syntax Tree (AST)
  ↓
[Planner]
  ↓
Execution Plan
  ↓
[Executor]
  ↓
Result Set
```

**Supported Operations (v1):**

DDL:
- CREATE TABLE
- DROP TABLE

DML:
- INSERT INTO ... VALUES (...)
- SELECT * FROM table WHERE ...
- SELECT col1, col2 FROM table WHERE ...
- UPDATE table SET ... WHERE ...
- DELETE FROM table WHERE ...

**WHERE Clause Support:**
- Simple comparisons: =, !=, <, >, <=, >=
- Logical operators: AND, OR, NOT
- Primary key lookups (optimized)
- Full table scans (for non-PK queries)

**Query Optimization:**
- Primary key point lookups → raw_get()
- Primary key range queries → raw_scan()
- Non-indexed queries → full table scan

### 6. API Surface

New gRPC service: `SqlService`

```protobuf
service SqlService {
  rpc Execute(SqlRequest) returns (SqlResponse);
  rpc Query(SqlRequest) returns (stream RowBatch);
}

message SqlRequest {
  string namespace = 1;
  string sql = 2;
}

message SqlResponse {
  oneof result {
    DdlResult ddl_result = 1;
    DmlResult dml_result = 2;
    QueryResult query_result = 3;
    ErrorResult error = 4;
  }
}

message DdlResult {
  string message = 1;  // "Table created", "Table dropped"
}

message DmlResult {
  uint64 rows_affected = 1;
}

message QueryResult {
  repeated string columns = 1;
  repeated Row rows = 2;
}

message Row {
  repeated Value values = 1;
}

message Value {
  oneof value {
    int64 int_value = 1;
    string text_value = 2;
    bool bool_value = 3;
    uint64 timestamp_value = 4;
  }
  bool is_null = 5;
}
```

### 7. Namespace Integration

SQL layer respects FlareDB namespaces:
- Each namespace has isolated SQL tables
- Table IDs are namespace-scoped
- Metadata keys include namespace prefix

```
Key format with namespace:
  {namespace_id}:__sql_meta:tables:{table_name}
  {namespace_id}:__sql_data:{table_id}:{primary_key}
```

## Implementation Plan

### Phase 1: Core Infrastructure (S2)
- Table metadata storage
- CREATE TABLE / DROP TABLE
- Table ID allocation

### Phase 2: Row Storage (S3)
- Row key/value encoding
- INSERT statement
- UPDATE statement
- DELETE statement

### Phase 3: Query Engine (S4)
- SELECT parser
- WHERE clause evaluator
- Result set builder
- Table scan implementation

### Phase 4: Integration (S5)
- E2E tests
- Example application
- Performance benchmarks

## Performance Considerations

1. **Primary Key Lookups**: O(1) via raw_get()
2. **Range Scans**: O(log N) via raw_scan() with key encoding
3. **Full Table Scans**: O(N) - unavoidable without indexes
4. **Metadata Access**: Cached in memory for frequently accessed tables

## Future Enhancements (Out of Scope)

1. **Secondary Indexes**: Additional KVS entries for non-PK queries
2. **JOINs**: Multi-table query support
3. **Transactions**: ACID guarantees across multiple operations
4. **Query Optimizer**: Cost-based query planning
5. **SQL Standard Compliance**: More data types, functions, etc.

## Testing Strategy

1. **Unit Tests**: Parser, executor, encoding/decoding
2. **Integration Tests**: Full SQL operations via gRPC
3. **E2E Tests**: Real-world application scenarios
4. **Performance Tests**: Benchmark vs PostgreSQL/SQLite baseline

## Example Usage

```rust
// Create connection
let client = SqlServiceClient::connect("http://127.0.0.1:8001").await?;

// Create table
client.execute(SqlRequest {
    namespace: "default".to_string(),
    sql: "CREATE TABLE users (
        id INTEGER PRIMARY KEY,
        name TEXT NOT NULL,
        email TEXT,
        created_at TIMESTAMP
    )".to_string(),
}).await?;

// Insert data
client.execute(SqlRequest {
    namespace: "default".to_string(),
    sql: "INSERT INTO users (id, name, email) VALUES (1, 'Alice', 'alice@example.com')".to_string(),
}).await?;

// Query data
let response = client.query(SqlRequest {
    namespace: "default".to_string(),
    sql: "SELECT * FROM users WHERE id = 1".to_string(),
}).await?;
```

## Success Criteria

✓ CREATE/DROP TABLE working
✓ INSERT/UPDATE/DELETE working
✓ SELECT with WHERE clause working
✓ Primary key lookups optimized
✓ Integration tests passing
✓ Example application demonstrating CRUD

## References

- sqlparser-rs: https://github.com/sqlparser-rs/sqlparser-rs
- FlareDB KVS API: flaredb/proto/kvrpc.proto
- RocksDB encoding: https://github.com/facebook/rocksdb/wiki