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chore:add readme
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rulego-team
@@ -237,152 +237,6 @@ func main() {
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}
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```
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### 🔍 Pattern Matching Notes
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**Note**: StreamSQL currently does not support standard SQL `LIKE` syntax, but provides more powerful alternatives:
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- **Prefix matching**: `STARTSWITH(field, 'prefix')` replaces `field LIKE 'prefix%'`
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- **Suffix matching**: `ENDSWITH(field, 'suffix')` replaces `field LIKE '%suffix'`
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- **Contains matching**: `INDEXOF(field, 'substring') >= 0` replaces `field LIKE '%substring%'`
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- **Regex matching**: `REGEXP_MATCHES(field, '^pattern$')` supports complex pattern matching
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**Examples**：
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```sql
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-- Replace: deviceId LIKE 'sensor%'
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WHERE STARTSWITH(deviceId, 'sensor')
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-- Replace: message LIKE '%error%'
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WHERE INDEXOF(message, 'error') >= 0
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-- Replace complex pattern: deviceId LIKE 'sensor[0-9]+'
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WHERE REGEXP_MATCHES(deviceId, '^sensor[0-9]+$')
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```
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### Non-Aggregation Scenarios
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StreamSQL supports real-time data transformation and filtering without aggregation operations. This mode provides immediate processing and output for each input record, making it ideal for data cleaning, enrichment, and real-time filtering.
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```go
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// Real-time data transformation and filtering example
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package main
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import (
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"fmt"
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"time"
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"github.com/rulego/streamsql"
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)
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func main() {
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ssql := streamsql.New()
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defer ssql.Stop()
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// Non-aggregation SQL - immediate data transformation
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rsql := `SELECT deviceId,
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upper(deviceType) as device_type,
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temperature * 1.8 + 32 as temp_fahrenheit,
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concat(location, '-', deviceId) as full_location,
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now() as processed_time
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FROM stream
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WHERE temperature > 0 AND deviceId LIKE 'sensor%'`
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err := ssql.Execute(rsql)
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if err != nil {
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panic(err)
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}
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// Handle real-time transformation results
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ssql.Stream().AddSink(func(result interface{}) {
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fmt.Printf("Transformed data: %+v\n", result)
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})
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// Input raw data
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rawData := []map[string]interface{}{
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{
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"deviceId": "sensor001",
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"deviceType": "temperature",
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"temperature": 25.0,
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"humidity": 60,
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"location": "warehouse-A",
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},
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{
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"deviceId": "sensor002",
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"deviceType": "humidity",
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"temperature": 22.5,
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"humidity": 55,
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"location": "warehouse-B",
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},
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{
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"deviceId": "pump001", // Will be filtered out
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"deviceType": "actuator",
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"temperature": 30.0,
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"location": "factory",
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},
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}
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// Each data record is processed immediately
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for _, data := range rawData {
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ssql.Stream().AddData(data)
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time.Sleep(100 * time.Millisecond) // Simulate real-time arrival
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}
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time.Sleep(500 * time.Millisecond) // Wait for processing
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}
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```
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#### Use Cases for Non-Aggregation Scenarios
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**1. Real-time Data Cleaning and Validation**
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```sql
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-- Filter invalid records and normalize data formats
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SELECT deviceId,
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CAST(temperature AS FLOAT) as temperature,
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LOWER(status) as status,
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COALESCE(location, 'unknown') as location
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FROM stream
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WHERE temperature IS NOT NULL AND deviceId != ''
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```
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**2. Data Enrichment and Transformation**
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```sql
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-- Add calculated fields and enrichment
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SELECT *,
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temperature * 1.8 + 32 as temp_fahrenheit,
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CASE WHEN temperature > 30 THEN 'hot'
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WHEN temperature < 10 THEN 'cold'
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ELSE 'normal' END as temp_category,
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FORMAT(humidity, 2) as formatted_humidity
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FROM stream
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```
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**3. Real-time Alerting and Monitoring**
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```sql
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-- Filter critical events for immediate alerting
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SELECT deviceId, temperature, humidity, now() as alert_time
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FROM stream
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WHERE temperature > 50 OR humidity < 10
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```
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**4. Data Format Conversion**
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```sql
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-- Convert data format for downstream systems
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SELECT TO_JSON(MAP(
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'id', deviceId,
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'metrics', MAP('temp', temperature, 'hum', humidity),
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'meta', MAP('location', location, 'type', deviceType)
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)) as json_output
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FROM stream
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```
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**5. Real-time Data Routing**
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```sql
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-- Route data based on conditions
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SELECT *,
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CASE WHEN deviceType = 'sensor' THEN 'sensor_topic'
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WHEN deviceType = 'actuator' THEN 'actuator_topic'
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ELSE 'default_topic' END as routing_key
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FROM stream
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```
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### Nested Field Access
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StreamSQL supports querying nested structured data using dot notation (`.`) syntax to access nested fields:
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@@ -253,39 +253,6 @@ func main() {
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}
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```
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### 🔍 模式匹配功能
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StreamSQL 支持标准 SQL 的 `LIKE` 语法进行模式匹配：
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- **前缀匹配**: `field LIKE 'prefix%'` - 匹配以指定前缀开头的字符串
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- **后缀匹配**: `field LIKE '%suffix'` - 匹配以指定后缀结尾的字符串
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- **包含匹配**: `field LIKE '%substring%'` - 匹配包含指定子字符串的字符串
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- **单字符通配符**: `field LIKE 'patte_n'` - `_` 匹配任意单个字符
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- **复杂模式**: `field LIKE 'prefix%suffix'` - 组合前缀和后缀匹配
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**示例**：
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```sql
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-- 前缀匹配：查找以'sensor'开头的设备ID
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WHERE deviceId LIKE 'sensor%'
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-- 后缀匹配：查找以'error'结尾的消息
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WHERE message LIKE '%error'
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-- 包含匹配：查找包含'alert'的日志
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WHERE logMessage LIKE '%alert%'
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-- 单字符通配符：匹配三位数错误代码如E01, E02等
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WHERE errorCode LIKE 'E_0'
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-- 复杂模式：匹配log_开头.log结尾的文件
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WHERE filename LIKE 'log_%.log'
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```
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**兼容的字符串函数**：
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- `STARTSWITH(field, 'prefix')` - 等价于 `field LIKE 'prefix%'`
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- `ENDSWITH(field, 'suffix')` - 等价于 `field LIKE '%suffix'`
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- `REGEXP_MATCHES(field, '^pattern$')` - 支持更复杂的正则表达式匹配
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### 嵌套字段访问
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StreamSQL 还支持对嵌套结构数据进行查询，可以使用点号（`.`）语法访问嵌套字段：
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