Github：https://github.com/aceld/kis-flow
文件：https://github.com/aceld/kis-flow/wiki
第 1 部分-概覽
Part2.1-專案建置/基礎模組
Part2.2-專案建置/基礎模組
第三部分-資料流
Part4-功能調度
第5部-連接器
Part6-配置導入導出
Part7-KisFlow 動作
Part8-Cache/Params 資料快取和資料參數
Part9-流程多份
Part10-Prometheus Metrics 統計量
Part11-基於反射的FaaS參數類型自適應註冊
案例1-快速入門
Case2-Flow並行操作
Case3-KisFlow在多Goroutine中的應用
案例4-訊息佇列（MQ）應用中的KisFlow
下載 KisFlow 原始碼
$go get github.com/aceld/kis-flow
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KisFlow 開發者文件
原始碼範例
https://github.com/aceld/kis-flow-usage/tree/main/8-connector
KisFlow 可以透過 Connector 實現兩個流的組合
結合以下兩個流程，本介紹將涵蓋 Connector 的介面和使用方法。
數據流程圖
案例介紹
假設一個學生有四個屬性：
Student ID: stu_id
Credit 1: score_1
Credit 2: score_2
Credit 3: score_3
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定義 Flow1：CalStuAvgScore-1-2，計算學生學分 1 (score_1) 與學分 2 (score_2) 的平均分數 (avg_score_1_2)。
定義 Flow2：CalStuAvgScore-3，計算學生的學分 3 的平均分數 (score_3) 和 avg_score_1_2，即學分 1、學分 2 和學分 3 的平均值。學分 1 和學分 2 的平均值由 Flow1 提供。
流程1
Flow1 由 4 個函數組成：
V（函數：VerifyStu）驗證StuId的有效性
C（函數：AvgStuScore12）計算學分1和學分2的平均分數
S（函數：SaveScoreAvg12）將 avg_score_1_2 儲存在 Redis
E（函數：PrintStuAvgScore）列印學分1和學分2的平均分數。
流程2
Flow2 由 4 個函數組成：
V（函數：VerifyStu）驗證StuId的有效性
L（函數：LoadScoreAvg12）讀取Flow1計算出的目前學生學分1和學分2的平均分數（avg_score_1_2）
C（函數：AvgStuScore3）計算學分3的平均分數以及學分1和學分2的平均分數
E（函數：PrintStuAvgScore）列印學分1、學分2、學分3的平均分數。
conf/func/func-AvgStuScore-3.yml
kistype: func
fname: AvgStuScore3
fmode: Calculate
source:
name: SourceStuScore
must:
- stu_id
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conf/func/func-LoadScoreAvg-1-2.yml
kistype: func
fname: LoadScoreAvg12
fmode: Load
source:
name: SourceStuScore
must:
- stu_id
option:
cname: Score12Cache
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基本數據協議
stu_proto.go
package main
type StuScore1_2 struct {
StuId int `json:"stu_id"`
Score1 int `json:"score_1"`
Score2 int `json:"score_2"`
}
type StuScoreAvg struct {
StuId int `json:"stu_id"`
AvgScore float64 `json:"avg_score"`
}
type StuScore3 struct {
StuId int `json:"stu_id"`
AvgScore12 float64 `json:"avg_score_1_2"` // score_1, score_2 avg
Score3 int `json:"score_3"`
}
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連接器初始化
本專案中定義的Connector Score12Cache是與Redis關聯的連結資源。此連接器需要一個初始化方法，用於在 KisFlow 啟動時建立連線。
conn_init.go
package main
import (
"context"
"fmt"
"github.com/aceld/kis-flow/kis"
"github.com/aceld/kis-flow/log"
"github.com/go-redis/redis/v8"
)
// type ConnInit func(conn Connector) error
func InitScore12Cache(connector kis.Connector) error {
fmt.Println("===> Call Connector InitScore12Cache")
// init Redis Conn Client
rdb := redis.NewClient(&redis.Options{
Addr: connector.GetConfig().AddrString, // Redis-Server address
Password: "", // password
DB: 0, // select db
})
// Ping test
pong, err := rdb.Ping(context.Background()).Result()
if err != nil {
log.Logger().ErrorF("Failed to connect to Redis: %v", err)
return err
}
fmt.Println("Connected to Redis:", pong)
// set rdb to connector
connector.SetMetaData("rdb", rdb)
return nil
}
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這裡，成功連線的Redis實例儲存在連接器的快取變數「rdb」中。
// set rdb to connector
connector.SetMetaData("rdb", rdb)
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FaaS 實施
功能(五)：VerifyStu
faas_stu_verify.go
package main
import (
"context"
"fmt"
"github.com/aceld/kis-flow/kis"
"github.com/aceld/kis-flow/serialize"
)
type VerifyStuIn struct {
serialize.DefaultSerialize
StuId int `json:"stu_id"`
}
func VerifyStu(ctx context.Context, flow kis.Flow, rows []*VerifyStuIn) error {
fmt.Printf("->Call Func VerifyStu\n")
for _, stu := range rows {
// Filter out invalid data
if stu.StuId < 0 || stu.StuId > 999 {
// Terminate the current Flow process, subsequent functions of the current Flow will not be executed
return flow.Next(kis.ActionAbort)
}
}
return flow.Next(kis.ActionDataReuse)
}
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VerifyStu() 用於驗證資料。如果資料不符合要求，則終止目前資料流。最後，資料被重複使用，透過flow.Next(kis.ActionDataReuse)傳遞到下一層。
函數(C)：AvgStuScore12
faas_avg_score_1_2.go
package main
import (
"context"
"fmt"
"github.com/aceld/kis-flow/kis"
"github.com/aceld/kis-flow/serialize"
)
type AvgStuScoreIn_1_2 struct {
serialize.DefaultSerialize
StuScore1_2
}
type AvgStuScoreOut_1_2 struct {
serialize.DefaultSerialize
StuScoreAvg
}
func AvgStuScore12(ctx context.Context, flow kis.Flow, rows []*AvgStuScoreIn_1_2) error {
fmt.Printf("->Call Func AvgStuScore12\n")
for _, row := range rows {
out := AvgStuScoreOut_1_2{
StuScoreAvg: StuScoreAvg{
StuId: row.StuId,
AvgScore: float64(row.Score1+row.Score2) / 2,
},
}
// Submit result data
_ = flow.CommitRow(out)
}
return flow.Next()
}
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AvgStuScore12() 計算score_1 和score_2 的平均分數，得到avg_score。
函數(S)：儲存ScoreAvg12
faas_save_score_avg_1_2.go
package main
import (
"context"
"fmt"
"github.com/aceld/kis-flow/kis"
"github.com/aceld/kis-flow/serialize"
"github.com/go-redis/redis/v8"
"strconv"
)
type SaveStuScoreIn struct {
serialize.DefaultSerialize
StuScoreAvg
}
func BatchSetStuScores(ctx context.Context, conn kis.Connector, rows []*SaveStuScoreIn) error {
var rdb *redis.Client
// Get Redis Client
rdb = conn.GetMetaData("rdb").(*redis.Client)
// Set data to redis
pipe := rdb.Pipeline()
for _, score := range rows {
// make key
key := conn.GetConfig().Key + strconv.Itoa(score.StuId)
pipe.HMSet(context.Background(), key, map[string]interface{}{
"avg_score": score.AvgScore,
})
}
_, err := pipe.Exec(ctx)
if err != nil {
return err
}
return nil
}
func SaveScoreAvg12(ctx context.Context, flow kis.Flow, rows []*SaveStuScoreIn) error {
fmt.Printf("->Call Func SaveScoreAvg12\n")
conn, err := flow.GetConnector()
if err != nil {
fmt.Printf("SaveScoreAvg12(): GetConnector err = %s\n", err.Error())
return err
}
if BatchSetStuScores(ctx, conn, rows) != nil {
fmt.Printf("SaveScoreAvg12(): BatchSetStuScores err = %s\n", err.Error())
return err
}
return flow.Next(kis.ActionDataReuse)
}
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SaveScoreAvg12()透過綁定的Connector將資料儲存到Redis中，使用的是Connector中設定的key。最後將來源資料透傳給下一個函數。
函數(E)：PrintStuAvgScore
faas_stu_score_avg_print.go
package main
import (
"context"
"fmt"
"github.com/aceld/kis-flow/kis"
"github.com/aceld/kis-flow/serialize"
)
type PrintStuAvgScoreIn struct {
serialize.DefaultSerialize
StuId int `json:"stu_id"`
AvgScore float64 `json:"avg_score"`
}
func PrintStuAvgScore(ctx context.Context, flow kis.Flow, rows []*PrintStuAvgScoreIn) error {
fmt.Printf("->Call Func PrintStuAvgScore, in Flow[%s]\n", flow.GetName())
for _, row := range rows {
fmt.Printf("stuid: [%+v], avg score: [%+v]\n", row.StuId, row.AvgScore)
}
return flow.Next()
}
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PrintStuAvgScore() 列印目前學生的平均分數。
函數(L)：LoadScoreAvg12
faas_load_score_avg_1_2.go
package main
import (
"context"
"fmt"
"github.com/aceld/kis-flow/kis"
"github.com/aceld/kis-flow/serialize"
"github.com/go-redis/redis/v8"
"strconv"
)
type LoadStuScoreIn struct {
serialize.DefaultSerialize
StuScore3
}
type LoadStuScoreOut struct {
serialize.DefaultSerialize
StuScore3
}
func GetStuScoresByStuId(ctx context.Context, conn kis.Connector, stuId int) (float64, error) {
var rdb *redis.Client
// Get Redis Client
rdb = conn.GetMetaData("rdb").(*redis.Client)
// make key
key := conn.GetConfig().Key + strconv.Itoa(stuId)
// get data from redis
result, err := rdb.HGetAll(ctx, key).Result()
if err != nil {
return 0, err
}
// get value
avgScoreStr, ok := result["avg_score"]
if !ok {
return 0, fmt.Errorf("avg_score not found for stuId: %d", stuId)
}
// parse to float64
avgScore, err := strconv.ParseFloat(avgScoreStr, 64)
if err != nil {
return 0, err
}
return avgScore, nil
}
func LoadScoreAvg12(ctx context.Context, flow kis.Flow, rows []*LoadStuScoreIn) error {
fmt.Printf("->Call Func LoadScoreAvg12\n")
conn, err := flow.GetConnector()
if err != nil {
fmt.Printf("LoadScoreAvg12(): GetConnector err = %s\n", err.Error())
return err
}
for _, row := range rows {
stuScoreAvg1_2, err := GetStuScoresByStuId(ctx, conn, row.StuId)
if err != nil {
fmt.Printf("LoadScoreAvg12(): GetStuScoresByStuId err = %s\n", err.Error())
return err
}
out := LoadStuScoreOut{
StuScore3: StuScore3{
StuId: row.StuId,
Score3: row.Score3,
AvgScore12: stuScoreAvg1_2, // avg score of score1 and score2 (load from redis)
},
}
// commit result
_ = flow.CommitRow(out)
}
return flow.Next()
}
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LoadScoreAvg12() reads the average score of score_1 and score_2 from Redis through the linked resource Redis of the bound Connector using the key configured in the Connector. It then sends the source data from upstream, along with the newly read average score of score1 and score2, to the next layer.
Function(C): AvgStuScore3
faas_stu_score_avg_3.go
package main
import (
"context"
"fmt"
"github.com/aceld/kis-flow/kis"
"github.com/aceld/kis-flow/serialize"
)
type AvgStuScore3In struct {
serialize.DefaultSerialize
StuScore3
}
type AvgStuScore3Out struct {
serialize.DefaultSerialize
StuScoreAvg
}
func AvgStuScore3(ctx context.Context, flow kis.Flow, rows []*AvgStuScore3In) error {
fmt.Printf("->Call Func AvgStuScore3\n")
for _, row := range rows {
out := AvgStuScore3Out{
StuScoreAvg: StuScoreAvg{
StuId: row.StuId,
AvgScore: (float64(row.Score3) + row.AvgScore12*2) / 3,
},
}
// Submit result data
_ = flow.CommitRow(out)
}
return flow.Next()
}
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AvgStuScore3() recalculates the average score of three scores by adding score_3 and the average score of score_1 and score_2, resulting in the final average score avg_score.
Register FaaS & CaaSInit/CaaS (Register Function/Connector)
main.go
func init() {
// Register functions
kis.Pool().FaaS("VerifyStu", VerifyStu)
kis.Pool().FaaS("AvgStuScore12", AvgStuScore12)
kis.Pool().FaaS("SaveScoreAvg12", SaveScoreAvg12)
kis.Pool().FaaS("PrintStuAvgScore", PrintStuAvgScore)
kis.Pool().FaaS("LoadScoreAvg12", LoadScoreAvg12)
kis.Pool().FaaS("AvgStuScore3", AvgStuScore3)
// Register connectors
kis.Pool().CaaSInit("Score12Cache", InitScore12Cache)
}
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Main Process
main.go
package main
import (
"context"
"github.com/aceld/kis-flow/file"
"github.com/aceld/kis-flow/kis"
"sync"
)
func RunFlowCalStuAvgScore12(ctx context.Context, flow kis.Flow) error {
// Commit data
_ = flow.CommitRow(`{"stu_id":101, "score_1":100, "score_2":90}`)
_ = flow.CommitRow(`{"stu_id":102, "score_1":100, "score_2":80}`)
// Run the flow
if err := flow.Run(ctx); err != nil {
return err
}
return nil
}
func RunFlowCalStuAvgScore3(ctx context.Context, flow kis.Flow) error {
// Commit data
_ = flow.CommitRow(`{"stu_id":101, "score_3": 80}`)
_ = flow.CommitRow(`{"stu_id":102, "score_3": 70}`)
// Run the flow
if err := flow.Run(ctx); err != nil {
return err
}
return nil
}
func main() {
ctx := context.Background()
// Load Configuration from file
if err := file.ConfigImportYaml("conf/"); err != nil {
panic(err)
}
var wg sync.WaitGroup
wg.Add(2)
go func() {
// Run flow1 concurrently
defer wg.Done()
flow1 := kis.Pool().GetFlow("CalStuAvgScore12")
if flow1 == nil {
panic("flow1 is nil")
}
if err := RunFlowCalStuAvgScore12(ctx, flow1); err != nil {
panic(err)
}
}()
go func() {
// Run flow2 concurrently
defer wg.Done()
flow2 := kis.Pool().GetFlow("CalStuAvgScore3")
if flow2 == nil {
panic("flow2 is nil")
}
if err := RunFlowCalStuAvgScore3(ctx, flow2); err != nil {
panic(err)
}
}()
wg.Wait()
return
}
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Two Goroutines are launched concurrently to execute Flow1 and Flow2, calculating the final average scores for student 101 and student 102.
Execution Results
===> Call Connector InitScore12Cache
Connected to Redis: PONG
Add FlowRouter FlowName=CalStuAvgScore12
===> Call Connector InitScore12Cache
Connected to Redis: PONG
Add FlowRouter FlowName=CalStuAvgScore3
->Call Func VerifyStu
->Call Func VerifyStu
->Call Func AvgStuScore12
->Call Func LoadScoreAvg12
->Call Func SaveScoreAvg12
->Call Func PrintStuAvgScore, in Flow[CalStuAvgScore12]
stuid: [101], avg score: [95]
stuid: [102], avg score: [90]
->Call Func AvgStuScore3
->Call Func PrintStuAvgScore, in Flow[CalStuAvgScore3]
stuid: [101], avg score: [90]
stuid: [102], avg score: [83.33333333333333]
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In Flow[CalStuAvgScore3], we observe the final computed average scores for scores 1, 2, and 3.
Author: Aceld
GitHub: https://github.com/aceld
KisFlow Open Source Project Address: https://github.com/aceld/kis-flow
Document: https://github.com/aceld/kis-flow/wiki
Part1-OverView
Part2.1-Project Construction / Basic Modules
Part2.2-Project Construction / Basic Modules
Part3-Data Stream
Part4-Function Scheduling
Part5-Connector
Part6-Configuration Import and Export
Part7-KisFlow Action
Part8-Cache/Params Data Caching and Data Parameters
Part9-Multiple Copies of Flow
Part10-Prometheus Metrics Statistics
Part11-Adaptive Registration of FaaS Parameter Types Based on Reflection
Case1-Quick Start
Case2-Flow Parallel Operation
Case3-Application of KisFlow in Multi-Goroutines
Case4-KisFlow in Message Queue (MQ) Applications
以上是案例（二）-KisFlow-Golang流即時計算-流並行運算的詳細內容。更多資訊請關注PHP中文網其他相關文章！
