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依赖Kafka的Go单元测试例解

本文永久链接 – https://tonybai.com/2024/01/08/go-unit-testing-deps-on-kafka

Kafka是Apache基金会开源的一个分布式事件流处理平台,是Java阵营(最初为Scala)中的一款杀手级应用,其提供的高可靠性、高吞吐量和低延迟的数据传输能力,让其到目前为止依旧是现代企业级应用系统以及云原生应用系统中使用的重要中间件。

在日常开发Go程序时,我们经常会遇到一些依赖Kafka的代码,如何对这些代码进行测试,尤其是单测是摆在Go开发者前面的一个现实问题!

有人说用mock,是个路子。但看过我的《单测时尽量用fake object》一文的童鞋估计已经走在了寻找kafka fake object的路上了!Kafka虽好,但身形硕大,不那么灵巧。找到一个合适的fake object不容易。在这篇文章中,我们就来聊聊如何测试那些依赖kafka的代码,再往本质一点说,就是和大家以找找那些合适的kafka fake object。

1. 寻找fake object的策略

在《单测时尽量用fake object》一文中,我们提到过,如果测试的依赖提供了tiny版本或某些简化版,我们可以直接使用这些版本作为fake object的候选,就像etcd提供了用于测试的自身简化版的实现(embed)那样。

但Kafka并没有提供tiny版本,我们也只能选择《单测时尽量用fake object》一文提到的另外一个策略,那就是利用容器来充当fake object,这是目前能搞到任意依赖的fake object的最简单路径了。也许以后WASI(WebAssembly System Interface)成熟了,让wasm脱离浏览器并可以在本地系统上飞起,到时候换用wasm也不迟。

下面我们就按照使用容器的策略来找一找适合的kafka container。

2. testcontainers-go

我们第一站就来到了testcontainers-go。testcontainers-go是一个Go语言开源项目,专门用于简化创建和清理基于容器的依赖项,常用于Go项目的单元测试、自动化集成或冒烟测试中。通过testcontainers-go提供的易于使用的API,开发人员能够以编程方式定义作为测试的一部分而运行的容器,并在测试完成时清理这些资源。

注:testcontainers不仅提供Go API,它还覆盖了主流的编程语言,包括:Java、.NET、Python、Node.js、Rust等。

在几个月之前,testcontainers-go项目还没有提供对Kafka的直接支持,我们需要自己使用testcontainers.GenericContainer来自定义并启动kafka容器。2023年9月,以KRaft模式运行的Kafka容器才被首次引入testcontainers-go项目

目前testcontainers-go使用的kafka镜像版本是confluentinc/confluent-local:7.5.0Confluent是在kafka背后的那家公司,基于kafka提供商业化支持。今年初,Confluent还收购了Immerok,将apache的另外一个明星项目Flink招致麾下。

confluent-local并不是一个流行的kafka镜像,它只是一个使用KRaft模式的零配置的、包含Confluent Community RestProxy的Apache Kafka,并且镜像是实验性的,仅应用于本地开发工作流,不应该用在支持生产工作负载。

生产中最常用的开源kafka镜像是confluentinc/cp-kafka镜像,它是基于开源Kafka项目构建的,但在此基础上添加了一些额外的功能和工具,以提供更丰富的功能和更易于部署和管理的体验。cp-kafka镜像的版本号并非kafka的版本号,其对应关系需要cp-kafka镜像官网查询。

另外一个开发领域常用的kafka镜像是bitnami的kafka镜像。Bitnami是一个提供各种开源软件的预打包镜像和应用程序栈的公司。Bitnami Kafka镜像是基于开源Kafka项目构建的,是一个可用于快速部署和运行Kafka的Docker镜像。Bitnami Kafka镜像与其内部的Kakfa的版本号保持一致。

下面我们就来看看如何使用testcontainers-go的kafka来作为依赖kafka的Go单元测试用例的fake object。

这第一个测试示例改编自testcontainers-go/kafka module的example_test.go:

// testcontainers/kafka_setup/kafka_test.go

package main

import (
    "context"
    "fmt"
    "testing"

    "github.com/testcontainers/testcontainers-go/modules/kafka"
)

func TestKafkaSetup(t *testing.T) {
    ctx := context.Background()

    kafkaContainer, err := kafka.RunContainer(ctx, kafka.WithClusterID("test-cluster"))
    if err != nil {
        panic(err)
    }

    // Clean up the container
    defer func() {
        if err := kafkaContainer.Terminate(ctx); err != nil {
            panic(err)
        }
    }()

    state, err := kafkaContainer.State(ctx)
    if err != nil {
        panic(err)
    }

    if kafkaContainer.ClusterID != "test-cluster" {
        t.Errorf("want test-cluster, actual %s", kafkaContainer.ClusterID)
    }
    if state.Running != true {
        t.Errorf("want true, actual %t", state.Running)
    }
    brokers, _ := kafkaContainer.Brokers(ctx)
    fmt.Printf("%q\n", brokers)
}

在这个例子中,我们直接调用kafka.RunContainer创建了一个名为test-cluster的kafka实例,如果没有通过WithImage向RunContainer传入自定义镜像,那么默认我们将启动一个confluentinc/confluent-local:7.5.0的容器(注意:随着时间变化,该默认容器镜像的版本也会随之改变)。

通过RunContainer返回的kafka.KafkaContainer我们可以获取到关于kafka容器的各种信息,比如上述代码中的ClusterID、kafka Broker地址信息等。有了这些信息,我们后续便可以与以容器形式启动的kafka建立连接并做数据的写入和读取操作了。

我们先来看这个测试的运行结果,与预期一致:

$ go test
2023/12/16 21:45:52 github.com/testcontainers/testcontainers-go - Connected to docker:
  ... ...
  Resolved Docker Host: unix:///var/run/docker.sock
  Resolved Docker Socket Path: /var/run/docker.sock
  Test SessionID: 19e47867b733f4da4f430d78961771ae3a1cc66c5deca083b4f6359c6d4b2468
  Test ProcessID: 41b9ef62-2617-4189-b23a-1bfa4c06dfec
2023/12/16 21:45:52 Creating container for image docker.io/testcontainers/ryuk:0.5.1
2023/12/16 21:45:53 Container created: 8f2240042c27
2023/12/16 21:45:53 Starting container: 8f2240042c27
2023/12/16 21:45:53 Container started: 8f2240042c27
2023/12/16 21:45:53 Waiting for container id 8f2240042c27 image: docker.io/testcontainers/ryuk:0.5.1. Waiting for: &{Port:8080/tcp timeout:<nil> PollInterval:100ms}
2023/12/16 21:45:53 Creating container for image confluentinc/confluent-local:7.5.0
2023/12/16 21:45:53 Container created: a39a495aed0b
2023/12/16 21:45:53 Starting container: a39a495aed0b
2023/12/16 21:45:53 Container started: a39a495aed0b
["localhost:1037"]
2023/12/16 21:45:58 Terminating container: a39a495aed0b
2023/12/16 21:45:58 Container terminated: a39a495aed0b
PASS
ok      demo    6.236s

接下来,在上面用例的基础上,我们再来做一个Kafka连接以及数据读写测试:

// testcontainers/kafka_consumer_and_producer/kafka_test.go

package main

import (
    "bytes"
    "context"
    "errors"
    "net"
    "strconv"
    "testing"
    "time"

    "github.com/testcontainers/testcontainers-go/modules/kafka"

    kc "github.com/segmentio/kafka-go" // kafka client
)

func createTopics(brokers []string, topics ...string) error {
    // to create topics when auto.create.topics.enable='false'
    conn, err := kc.Dial("tcp", brokers[0])
    if err != nil {
        return err
    }
    defer conn.Close()

    controller, err := conn.Controller()
    if err != nil {
        return err
    }
    var controllerConn *kc.Conn
    controllerConn, err = kc.Dial("tcp", net.JoinHostPort(controller.Host, strconv.Itoa(controller.Port)))
    if err != nil {
        return err
    }
    defer controllerConn.Close()

    var topicConfigs []kc.TopicConfig
    for _, topic := range topics {
        topicConfig := kc.TopicConfig{
            Topic:             topic,
            NumPartitions:     1,
            ReplicationFactor: 1,
        }
        topicConfigs = append(topicConfigs, topicConfig)
    }

    err = controllerConn.CreateTopics(topicConfigs...)
    if err != nil {
        return err
    }

    return nil
}

func newWriter(brokers []string, topic string) *kc.Writer {
    return &kc.Writer{
        Addr:                   kc.TCP(brokers...),
        Topic:                  topic,
        Balancer:               &kc.LeastBytes{},
        AllowAutoTopicCreation: true,
        RequiredAcks:           0,
    }
}

func newReader(brokers []string, topic string) *kc.Reader {
    return kc.NewReader(kc.ReaderConfig{
        Brokers:  brokers,
        Topic:    topic,
        GroupID:  "test-group",
        MaxBytes: 10e6, // 10MB
    })
}

func TestProducerAndConsumer(t *testing.T) {
    ctx := context.Background()

    kafkaContainer, err := kafka.RunContainer(ctx, kafka.WithClusterID("test-cluster"))
    if err != nil {
        t.Fatalf("want nil, actual %v\n", err)
    }

    // Clean up the container
    defer func() {
        if err := kafkaContainer.Terminate(ctx); err != nil {
            t.Fatalf("want nil, actual %v\n", err)
        }
    }()

    state, err := kafkaContainer.State(ctx)
    if err != nil {
        t.Fatalf("want nil, actual %v\n", err)
    }

    if state.Running != true {
        t.Errorf("want true, actual %t", state.Running)
    }

    brokers, err := kafkaContainer.Brokers(ctx)
    if err != nil {
        t.Fatalf("want nil, actual %v\n", err)
    }

    topic := "test-topic"
    w := newWriter(brokers, topic)
    defer w.Close()
    r := newReader(brokers, topic)
    defer r.Close()

    err = createTopics(brokers, topic)
    if err != nil {
        t.Fatalf("want nil, actual %v\n", err)
    }
    time.Sleep(5 * time.Second)

    messages := []kc.Message{
        {
            Key:   []byte("Key-A"),
            Value: []byte("Value-A"),
        },
        {
            Key:   []byte("Key-B"),
            Value: []byte("Value-B"),
        },
        {
            Key:   []byte("Key-C"),
            Value: []byte("Value-C"),
        },
        {
            Key:   []byte("Key-D"),
            Value: []byte("Value-D!"),
        },
    }

    const retries = 3
    for i := 0; i < retries; i++ {
        ctx, cancel := context.WithTimeout(context.Background(), 10*time.Second)
        defer cancel()

        // attempt to create topic prior to publishing the message
        err = w.WriteMessages(ctx, messages...)
        if errors.Is(err, kc.LeaderNotAvailable) || errors.Is(err, context.DeadlineExceeded) {
            time.Sleep(time.Millisecond * 250)
            continue
        }

        if err != nil {
            t.Fatalf("want nil, actual %v\n", err)
        }
        break
    }

    var getMessages []kc.Message
    for i := 0; i < len(messages); i++ {
        m, err := r.ReadMessage(context.Background())
        if err != nil {
            t.Fatalf("want nil, actual %v\n", err)
        }
        getMessages = append(getMessages, m)
    }

    for i := 0; i < len(messages); i++ {
        if !bytes.Equal(getMessages[i].Key, messages[i].Key) {
            t.Errorf("want %s, actual %s\n", string(messages[i].Key), string(getMessages[i].Key))
        }
        if !bytes.Equal(getMessages[i].Value, messages[i].Value) {
            t.Errorf("want %s, actual %s\n", string(messages[i].Value), string(getMessages[i].Value))
        }
    }
}

我们使用segmentio/kafka-go这个客户端来实现kafka的读写。关于如何使用segmentio/kafka-go这个客户端,可以参考我之前写的《Go社区主流Kafka客户端简要对比》。

这里我们在TestProducerAndConsumer这个用例中,先通过testcontainers-go的kafka.RunContainer启动一个Kakfa实例,然后创建了一个topic: “test-topic”。我们在写入消息前也可以不单独创建这个“test-topic”,Kafka默认启用topic自动创建,并且segmentio/kafka-go的高级API:Writer也支持AllowAutoTopicCreation的设置。不过topic的创建需要一些时间,如果要在首次写入消息时创建topic,此次写入可能会失败,需要retry。

向topic写入一条消息(实际上是一个批量Message,包括四个key-value pair)后,我们调用ReadMessage从上述topic中读取消息,并将读取的消息与写入的消息做比较。

注:近期发现kafka-go的一个可能导致内存暴涨的问题,在kafka ack返回延迟变大的时候,可能触发该问题。

下面是执行该用例的输出结果:

$ go test
2023/12/17 17:43:54 github.com/testcontainers/testcontainers-go - Connected to docker:
  Server Version: 24.0.7
  API Version: 1.43
  Operating System: CentOS Linux 7 (Core)
  Total Memory: 30984 MB
  Resolved Docker Host: unix:///var/run/docker.sock
  Resolved Docker Socket Path: /var/run/docker.sock
  Test SessionID: f76fe611c753aa4ef1456285503b0935a29795e7c0fab2ea2588029929215a08
  Test ProcessID: 27f531ee-9b5f-4e4f-b5f0-468143871004
2023/12/17 17:43:54 Creating container for image docker.io/testcontainers/ryuk:0.5.1
2023/12/17 17:43:54 Container created: 577309098f4c
2023/12/17 17:43:54 Starting container: 577309098f4c
2023/12/17 17:43:54 Container started: 577309098f4c
2023/12/17 17:43:54 Waiting for container id 577309098f4c image: docker.io/testcontainers/ryuk:0.5.1. Waiting for: &{Port:8080/tcp timeout:<nil> PollInterval:100ms}
2023/12/17 17:43:54 Creating container for image confluentinc/confluent-local:7.5.0
2023/12/17 17:43:55 Container created: 1ee11e11742b
2023/12/17 17:43:55 Starting container: 1ee11e11742b
2023/12/17 17:43:55 Container started: 1ee11e11742b
2023/12/17 17:44:15 Terminating container: 1ee11e11742b
2023/12/17 17:44:15 Container terminated: 1ee11e11742b
PASS
ok      demo    21.505s

我们看到默认情况下,testcontainer能满足与kafka交互的基本需求,并且testcontainer提供了一系列Option(WithXXX)可以对container进行定制,以满足一些扩展性的要求,但是这需要你对testcontainer提供的API有更全面的了解。

除了开箱即用的testcontainer之外,我们还可以使用另外一种方便的基于容器的技术:docker-compose来定制和启停我们需要的kafka image。接下来,我们就来看看如何使用docker-compose建立fake kafka object。

3. 使用docker-compose建立fake kafka

3.1 一个基础的基于docker-compose的fake kafka实例模板

这次我们使用bitnami提供的kafka镜像,我们先建立一个“等价”于上面“testcontainers-go”提供的kafka module的kafka实例,下面是docker-compose.yml:

// docker-compose/bitnami/plaintext/docker-compose.yml

version: "2"

services:
  kafka:
    image: docker.io/bitnami/kafka:3.6
    network_mode: "host"
    volumes:
      - "kafka_data:/bitnami"
    environment:
      # KRaft settings
      - KAFKA_CFG_NODE_ID=0
      - KAFKA_CFG_PROCESS_ROLES=controller,broker
      - KAFKA_CFG_CONTROLLER_QUORUM_VOTERS=0@localhost:9093
      # Listeners
      - KAFKA_CFG_LISTENERS=PLAINTEXT://:9092,CONTROLLER://:9093
      - KAFKA_CFG_ADVERTISED_LISTENERS=PLAINTEXT://:9092
      - KAFKA_CFG_LISTENER_SECURITY_PROTOCOL_MAP=CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT
      - KAFKA_CFG_CONTROLLER_LISTENER_NAMES=CONTROLLER
      - KAFKA_CFG_INTER_BROKER_LISTENER_NAME=PLAINTEXT
      # borrow from testcontainer
      - KAFKA_CFG_BROKER_ID=0
      - KAFKA_CFG_OFFSETS_TOPIC_REPLICATION_FACTOR=1
      - KAFKA_CFG_OFFSETS_TOPIC_NUM_PARTITIONS=1
      - KAFKA_CFG_TRANSACTION_STATE_LOG_MIN_ISR=1
      - KAFKA_CFG_GROUP_INITIAL_REBALANCE_DELAY_MS=0
      - KAFKA_CFG_LOG_FLUSH_INTERVAL_MESSAGES=9223372036854775807
volumes:
  kafka_data:
    driver: local

我们看到其中一些配置“借鉴”了testcontainers-go的kafka module,我们启动一下该容器:

$ docker-compose up -d
[+] Running 2/2
 ✔ Volume "plaintext_kafka_data"  Created                                                                                    0.0s
 ✔ Container plaintext-kafka-1    Started                                                                                    0.1s

依赖该容器的go测试代码与前面的TestProducerAndConsumer差不多,只是在开始处去掉了container的创建过程:

// docker-compose/bitnami/plaintext/kafka_test.go

func TestProducerAndConsumer(t *testing.T) {
    brokers := []string{"localhost:9092"}
    topic := "test-topic"
    w := newWriter(brokers, topic)
    defer w.Close()
    r := newReader(brokers, topic)
    defer r.Close()

    err := createTopics(brokers, topic)
    if err != nil {
        t.Fatalf("want nil, actual %v\n", err)
    }
    time.Sleep(5 * time.Second)
    ... ...
}

运行该测试用例,我们看到预期的结果:

go test
write message ok  Value-A
write message ok  Value-B
write message ok  Value-C
write message ok  Value-D!
PASS
ok      demo    15.143s

不过对于单元测试来说,显然我们不能手动来启动和停止kafka container,我们需要为每个用例填上setup和teardown,这样也能保证用例间的相互隔离,于是我们增加了一个docker_compose_helper.go文件,在这个文件中我们提供了一些帮助testcase启停kafka的helper函数:

// docker-compose/bitnami/plaintext/docker_compose_helper.go

package main

import (
    "fmt"
    "os/exec"
    "strings"
    "time"
)

// helpler function for operating docker container through docker-compose command

const (
    defaultCmd     = "docker-compose"
    defaultCfgFile = "docker-compose.yml"
)

func execCliCommand(cmd string, opts ...string) ([]byte, error) {
    cmds := cmd + " " + strings.Join(opts, " ")
    fmt.Println("exec command:", cmds)
    return exec.Command(cmd, opts...).CombinedOutput()
}

func execDockerComposeCommand(cmd string, cfgFile string, opts ...string) ([]byte, error) {
    var allOpts = []string{"-f", cfgFile}
    allOpts = append(allOpts, opts...)
    return execCliCommand(cmd, allOpts...)
}

func UpKakfa(composeCfgFile string) ([]byte, error) {
    b, err := execDockerComposeCommand(defaultCmd, composeCfgFile, "up", "-d")
    if err != nil {
        return nil, err
    }
    time.Sleep(10 * time.Second)
    return b, nil
}

func UpDefaultKakfa() ([]byte, error) {
    return UpKakfa(defaultCfgFile)
}

func DownKakfa(composeCfgFile string) ([]byte, error) {
    b, err := execDockerComposeCommand(defaultCmd, composeCfgFile, "down", "-v")
    if err != nil {
        return nil, err
    }
    time.Sleep(10 * time.Second)
    return b, nil
}

func DownDefaultKakfa() ([]byte, error) {
    return DownKakfa(defaultCfgFile)
}

眼尖的童鞋可能看到:在UpKakfa和DownKafka函数中我们使用了硬编码的“time.Sleep”来等待10s,通常在镜像已经pull到本地后这是有效的,但却不是最精确地等待方式,testcontainers-go/wait中提供了等待容器内程序启动完毕的多种策略,如果你想用更精确的等待方式,可以了解一下wait包。

基于helper函数,我们改造一下TestProducerAndConsumer用例:

// docker-compose/bitnami/plaintext/kafka_test.go
func TestProducerAndConsumer(t *testing.T) {
    _, err := UpDefaultKakfa()
    if err != nil {
        t.Fatalf("want nil, actual %v\n", err)
    }

    t.Cleanup(func() {
        DownDefaultKakfa()
    })
    ... ...
}

我们在用例开始处通过UpDefaultKakfa使用docker-compose将kafka实例启动起来,然后注册了Cleanup函数,用于在test case执行结束后销毁kafka实例。

下面是新版用例的执行结果:

$ go test
exec command: docker-compose -f docker-compose.yml up -d
write message ok  Value-A
write message ok  Value-B
write message ok  Value-C
write message ok  Value-D!
exec command: docker-compose -f docker-compose.yml down -v
PASS
ok      demo    36.402s

使用docker-compose的最大好处就是可以通过docker-compose.yml文件对要fake的object进行灵活的定制,这种定制与testcontainers-go的差别就是你无需去研究testcontiners-go的API。

下面是使用tls连接与kafka建立连接并实现读写的示例。

3.2 建立一个基于TLS连接的fake kafka实例

Kafka的配置复杂是有目共睹的,为了建立一个基于TLS连接,我也是花了不少时间做“试验”,尤其是listeners以及证书的配置,不下点苦功夫读文档还真是配不出来。

下面是一个基于bitnami/kafka镜像配置出来的基于TLS安全通道上的kafka实例:

// docker-compose/bitnami/tls/docker-compose.yml

# config doc:  https://github.com/bitnami/containers/blob/main/bitnami/kafka/README.md

version: "2"

services:
  kafka:
    image: docker.io/bitnami/kafka:3.6
    network_mode: "host"
    #ports:
      #- "9092:9092"
    environment:
      # KRaft settings
      - KAFKA_CFG_NODE_ID=0
      - KAFKA_CFG_PROCESS_ROLES=controller,broker
      - KAFKA_CFG_CONTROLLER_QUORUM_VOTERS=0@localhost:9094
      # Listeners
      - KAFKA_CFG_LISTENERS=PLAINTEXT://:9092,SECURED://:9093,CONTROLLER://:9094
      - KAFKA_CFG_ADVERTISED_LISTENERS=SECURED://:9093
      - KAFKA_CFG_LISTENER_SECURITY_PROTOCOL_MAP=CONTROLLER:PLAINTEXT,SECURED:SSL,PLAINTEXT:PLAINTEXT
      - KAFKA_CFG_CONTROLLER_LISTENER_NAMES=CONTROLLER
      - KAFKA_CFG_INTER_BROKER_LISTENER_NAME=SECURED
      # SSL settings
      - KAFKA_TLS_TYPE=PEM
      - KAFKA_TLS_CLIENT_AUTH=none
      - KAFKA_CFG_SSL_ENDPOINT_IDENTIFICATION_ALGORITHM=
      # borrow from testcontainer
      - KAFKA_CFG_BROKER_ID=0
      - KAFKA_CFG_OFFSETS_TOPIC_REPLICATION_FACTOR=1
      - KAFKA_CFG_OFFSETS_TOPIC_NUM_PARTITIONS=1
      - KAFKA_CFG_TRANSACTION_STATE_LOG_MIN_ISR=1
      - KAFKA_CFG_GROUP_INITIAL_REBALANCE_DELAY_MS=0
      - KAFKA_CFG_LOG_FLUSH_INTERVAL_MESSAGES=9223372036854775807
    volumes:
      # server.cert, server.key and ca.crt
      - "kafka_data:/bitnami"
      - "./kafka.keystore.pem:/opt/bitnami/kafka/config/certs/kafka.keystore.pem:ro"
      - "./kafka.keystore.key:/opt/bitnami/kafka/config/certs/kafka.keystore.key:ro"
      - "./kafka.truststore.pem:/opt/bitnami/kafka/config/certs/kafka.truststore.pem:ro"
volumes:
  kafka_data:
    driver: local

这里我们使用pem格式的证书和key,在上面配置中,volumes下面挂载的kafka.keystore.pem、kafka.keystore.key和kafka.truststore.pem分别对应了以前在Go中常用的名字:server-cert.pem(服务端证书), server-key.pem(服务端私钥)和ca-cert.pem(CA证书)。

这里整理了一个一键生成的脚本docker-compose/bitnami/tls/kafka-generate-cert.sh,我们执行该脚本生成所有需要的证书并放到指定位置(遇到命令行提示,只需要一路回车即可):

$bash kafka-generate-cert.sh
.........++++++
.............................++++++
You are about to be asked to enter information that will be incorporated
into your certificate request.
What you are about to enter is what is called a Distinguished Name or a DN.
There are quite a few fields but you can leave some blank
For some fields there will be a default value,
If you enter '.', the field will be left blank.
-----
Country Name (2 letter code) [XX]:
State or Province Name (full name) []:
Locality Name (eg, city) [Default City]:
Organization Name (eg, company) [Default Company Ltd]:
Organizational Unit Name (eg, section) []:
Common Name (eg, your name or your server's hostname) []:
Email Address []:

Please enter the following 'extra' attributes
to be sent with your certificate request
A challenge password []:
An optional company name []:
Signature ok
subject=/C=XX/L=Default City/O=Default Company Ltd
Getting Private key
.....................++++++
.........++++++
You are about to be asked to enter information that will be incorporated
into your certificate request.
What you are about to enter is what is called a Distinguished Name or a DN.
There are quite a few fields but you can leave some blank
For some fields there will be a default value,
If you enter '.', the field will be left blank.
-----
Country Name (2 letter code) [XX]:
State or Province Name (full name) []:
Locality Name (eg, city) [Default City]:
Organization Name (eg, company) [Default Company Ltd]:
Organizational Unit Name (eg, section) []:
Common Name (eg, your name or your server's hostname) []:
Email Address []:

Please enter the following 'extra' attributes
to be sent with your certificate request
A challenge password []:
An optional company name []:
Signature ok
subject=/C=XX/L=Default City/O=Default Company Ltd
Getting CA Private Key

接下来,我们来改造用例,使之支持以tls方式建立到kakfa的连接:

//docker-compose/bitnami/tls/kafka_test.go

func createTopics(brokers []string, tlsConfig *tls.Config, topics ...string) error {
    dialer := &kc.Dialer{
        Timeout:   10 * time.Second,
        DualStack: true,
        TLS:       tlsConfig,
    }

    conn, err := dialer.DialContext(context.Background(), "tcp", brokers[0])
    if err != nil {
        fmt.Println("creating topic: dialer dial error:", err)
        return err
    }
    defer conn.Close()
    fmt.Println("creating topic: dialer dial ok")
    ... ...
}

func newWriter(brokers []string, tlsConfig *tls.Config, topic string) *kc.Writer {
    w := &kc.Writer{
        Addr:                   kc.TCP(brokers...),
        Topic:                  topic,
        Balancer:               &kc.LeastBytes{},
        AllowAutoTopicCreation: true,
        Async:                  true,
        //RequiredAcks:           0,
        Completion: func(messages []kc.Message, err error) {
            for _, message := range messages {
                if err != nil {
                    fmt.Println("write message fail", err)
                } else {
                    fmt.Println("write message ok", string(message.Topic), string(message.Value))
                }
            }
        },
    }

    if tlsConfig != nil {
        w.Transport = &kc.Transport{
            TLS: tlsConfig,
        }
    }
    return w
}

func newReader(brokers []string, tlsConfig *tls.Config, topic string) *kc.Reader {
    dialer := &kc.Dialer{
        Timeout:   10 * time.Second,
        DualStack: true,
        TLS:       tlsConfig,
    }

    return kc.NewReader(kc.ReaderConfig{
        Dialer:   dialer,
        Brokers:  brokers,
        Topic:    topic,
        GroupID:  "test-group",
        MaxBytes: 10e6, // 10MB
    })
}

func TestProducerAndConsumer(t *testing.T) {
    var err error
    _, err = UpDefaultKakfa()
    if err != nil {
        t.Fatalf("want nil, actual %v\n", err)
    }

    t.Cleanup(func() {
        DownDefaultKakfa()
    })

    brokers := []string{"localhost:9093"}
    topic := "test-topic"

    tlsConfig, _ := newTLSConfig()
    w := newWriter(brokers, tlsConfig, topic)
    defer w.Close()
    r := newReader(brokers, tlsConfig, topic)
    defer r.Close()
    err = createTopics(brokers, tlsConfig, topic)
    if err != nil {
        fmt.Printf("create topic error: %v, but it may not affect the later action, just ignore it\n", err)
    }
    time.Sleep(5 * time.Second)
    ... ...
}

func newTLSConfig() (*tls.Config, error) {
    /*
       // 加载 CA 证书
       caCert, err := ioutil.ReadFile("/path/to/ca.crt")
       if err != nil {
               return nil, err
       }

       // 加载客户端证书和私钥
       cert, err := tls.LoadX509KeyPair("/path/to/client.crt", "/path/to/client.key")
       if err != nil {
               return nil, err
       }

       // 创建 CertPool 并添加 CA 证书
       caCertPool := x509.NewCertPool()
       caCertPool.AppendCertsFromPEM(caCert)
    */
    // 创建并返回 TLS 配置
    return &tls.Config{
        //RootCAs:      caCertPool,
        //Certificates: []tls.Certificate{cert},
        InsecureSkipVerify: true,
    }, nil
}

在上述代码中,我们按照segmentio/kafka-go为createTopics、newWriter和newReader都加上了tls.Config参数,此外在测试用例中,我们用newTLSConfig创建一个tls.Config的实例,在这里我们一切简化处理,采用InsecureSkipVerify=true的方式与kafka broker服务端进行握手,既不验证服务端证书,也不做双向认证(mutual TLS)。

下面是修改代码后的测试用例执行结果:

$ go test
exec command: docker-compose -f docker-compose.yml up -d
creating topic: dialer dial ok
creating topic: get controller ok
creating topic: dial control listener ok
create topic error: EOF, but it may not affect the later action, just ignore it
write message error: [3] Unknown Topic Or Partition: the request is for a topic or partition that does not exist on this broker
write message ok  Value-A
write message ok  Value-B
write message ok  Value-C
write message ok  Value-D!
exec command: docker-compose -f docker-compose.yml down -v
PASS
ok      demo    38.473s

这里我们看到:createTopics虽然连接kafka的各个listener都ok,但调用topic创建时,返回EOF,但这的确不影响后续action的执行,不确定这是segmentio/kafka-go的问题,还是kafka实例的问题。另外首次写入消息时,也因为topic或partition未建立而失败,retry后消息正常写入。

通过这个例子我们看到,基于docker-compose建立fake object有着更广泛的灵活性,如果做好容器启动和停止的精准wait机制的话,我可能会更多选择这种方式。

4. 小结

本文介绍了如何在Go编程中进行依赖Kafka的单元测试,并探讨了寻找适合的Kafka fake object的策略。

对于Kafka这样的复杂系统来说,找到合适的fake object并不容易。因此,本文推荐使用容器作为fake object的策略,并分别介绍了使用testcontainers-go项目和使用docker-compose作为简化创建和清理基于容器的依赖项的工具。相对于刚刚加入testcontainers-go项目没多久的kafka module而言,使用docker-compose自定义fake object更加灵活一些。但无论哪种方法,开发人员都需要对kafka的配置有一个较为整体和深入的理解。

文中主要聚焦使用testcontainers-go和docker-compose建立fake kafka的过程,而用例并没有建立明确的sut(被测目标),比如针对某个函数的白盒单元测试。

文本涉及的源码可以在这里下载。


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Go语言开发者的Apache Arrow使用指南:读写Parquet文件

本文永久链接 – https://tonybai.com/2023/07/31/a-guide-of-using-apache-arrow-for-gopher-part6

Apache Arrow是一种开放的、与语言无关的列式内存格式,在本系列文章的前几篇中,我们都聚焦于内存表示内存操作

但对于一个数据库系统或大数据分析平台来说,数据不能也无法一直放在内存中,虽说目前内存很大也足够便宜了,但其易失性也决定了我们在特定时刻还是要将数据序列化后存储到磁盘或一些低成本的存储服务上(比如AWS的S3等)。

那么将Arrow序列化成什么存储格式呢?CSV、JSON?显然这些格式都不是为最大限度提高空间效率以及数据检索能力而设计的。在数据分析领域,Apache Parquet是与Arrow相似的一种开放的、面向列的数据存储格式,它被设计用于高效的数据编码和检索并最大限度提高空间效率。

和Arrow是一种内存格式不同,Parquet是一种数据文件格式。此外,Arrow和Parquet在设计上也做出了各自的一些取舍。Arrow旨在由矢量化计算内核对数据进行操作,提供对任何数组索引的 O(1) 随机访问查找能力;而Parquet为了最大限度提高空间效率,采用了可变长度编码方案和块压缩来大幅减小数据大小,这些技术都是以丧失高性能随机存取查找为代价的。

Parquet也是Apache的顶级项目,大多数实现了Arrow的编程语言也都提供了支持Arrow格式与Parquet文件相互转换的库实现,Go也不例外。在本文中,我们就来粗浅看一下如何使用Go实现Parquet文件的读写,即Arrow和Parquet的相互转换。

注:关于Parquet文件的详细格式(也蛮复杂),我可能会在后续文章中说明。

1. Parquet简介

如果不先说一说Parquet文件格式,后面的内容理解起来会略有困难的。下面是一个Parquet文件的结构示意图:


图来自https://www.uber.com/blog/cost-efficiency-big-data

我们看到Parquet格式的文件被分为多个row group,每个row group由每一列的列块(column chunk)组成。考虑到磁盘存储的特点,每个列块又分为若干个页。这个列块中的诸多同构类型的列值可以在编码和压缩后存储在各个页中。下面是Parquet官方文档中Parquet文件中数据存储的具体示意图:

我们看到Parquet按row group顺序向后排列,每个row group中column chunk也是依column次序向后排列的。

注:关于上图中repetion level和definition level这样的高级概念,不会成为理解本文内容的障碍,我们将留到后续文章中系统说明。

2. Arrow Table <-> Parquet

有了上面Parquet文件格式的初步知识后,接下来我们就来看看如何使用Go在Arrow和Parquet之间进行转换。

《高级数据结构》一文中,我们学习了Arrow Table和Record Batch两种高级结构。接下来我们就来看看如何将Table或Record与Parquet进行转换。一旦像Table、Record Batch这样的高级结构的转换搞定了,那Arrow中的那些简单数据类型)也就不在话下了。况且在实际项目中,我们面对更多的也是Arrow的高级数据结构(Table或Record)与Parquet的转换。

我们先来看看Table。

2.1 Table -> Parquet

通过在《高级数据结构》一文,我们知道了Arrow Table的每一列本质上就是Schema+Chunked Array,这和Parquet的文件格式具有较高的适配度。

Arrow Go的parquet实现提供对了Table的良好支持,我们通过一个WriteTable函数就可以将内存中的Arrow Table持久化为Parquet格式的文件,我们来看看下面这个示例:

// flat_table_to_parquet.go

package main

import (
    "os"

    "github.com/apache/arrow/go/v13/arrow"
    "github.com/apache/arrow/go/v13/arrow/array"
    "github.com/apache/arrow/go/v13/arrow/memory"
    "github.com/apache/arrow/go/v13/parquet/pqarrow"
)

func main() {
    schema := arrow.NewSchema(
        []arrow.Field{
            {Name: "col1", Type: arrow.PrimitiveTypes.Int32},
            {Name: "col2", Type: arrow.PrimitiveTypes.Float64},
            {Name: "col3", Type: arrow.BinaryTypes.String},
        },
        nil,
    )

    col1 := func() *arrow.Column {
        chunk := func() *arrow.Chunked {
            ib := array.NewInt32Builder(memory.DefaultAllocator)
            defer ib.Release()

            ib.AppendValues([]int32{1, 2, 3}, nil)
            i1 := ib.NewInt32Array()
            defer i1.Release()

            ib.AppendValues([]int32{4, 5, 6, 7, 8, 9, 10}, nil)
            i2 := ib.NewInt32Array()
            defer i2.Release()

            c := arrow.NewChunked(
                arrow.PrimitiveTypes.Int32,
                []arrow.Array{i1, i2},
            )
            return c
        }()
        defer chunk.Release()

        return arrow.NewColumn(schema.Field(0), chunk)
    }()
    defer col1.Release()

    col2 := func() *arrow.Column {
        chunk := func() *arrow.Chunked {
            fb := array.NewFloat64Builder(memory.DefaultAllocator)
            defer fb.Release()

            fb.AppendValues([]float64{1.1, 2.2, 3.3, 4.4, 5.5}, nil)
            f1 := fb.NewFloat64Array()
            defer f1.Release()

            fb.AppendValues([]float64{6.6, 7.7}, nil)
            f2 := fb.NewFloat64Array()
            defer f2.Release()

            fb.AppendValues([]float64{8.8, 9.9, 10.0}, nil)
            f3 := fb.NewFloat64Array()
            defer f3.Release()

            c := arrow.NewChunked(
                arrow.PrimitiveTypes.Float64,
                []arrow.Array{f1, f2, f3},
            )
            return c
        }()
        defer chunk.Release()

        return arrow.NewColumn(schema.Field(1), chunk)
    }()
    defer col2.Release()

    col3 := func() *arrow.Column {
        chunk := func() *arrow.Chunked {
            sb := array.NewStringBuilder(memory.DefaultAllocator)
            defer sb.Release()

            sb.AppendValues([]string{"s1", "s2"}, nil)
            s1 := sb.NewStringArray()
            defer s1.Release()

            sb.AppendValues([]string{"s3", "s4"}, nil)
            s2 := sb.NewStringArray()
            defer s2.Release()

            sb.AppendValues([]string{"s5", "s6", "s7", "s8", "s9", "s10"}, nil)
            s3 := sb.NewStringArray()
            defer s3.Release()

            c := arrow.NewChunked(
                arrow.BinaryTypes.String,
                []arrow.Array{s1, s2, s3},
            )
            return c
        }()
        defer chunk.Release()

        return arrow.NewColumn(schema.Field(2), chunk)
    }()
    defer col3.Release()

    var tbl arrow.Table
    tbl = array.NewTable(schema, []arrow.Column{*col1, *col2, *col3}, -1)
    defer tbl.Release()

    f, err := os.Create("flat_table.parquet")
    if err != nil {
        panic(err)
    }
    defer f.Close()

    err = pqarrow.WriteTable(tbl, f, 1024, nil, pqarrow.DefaultWriterProps())
    if err != nil {
        panic(err)
    }
}

我们基于arrow的Builder模式以及NewTable创建了一个拥有三个列的Table(该table的创建例子来自于《高级数据结构》一文)。有了table后,我们直接调用pqarrow的WriteTable函数即可将table写成parquet格式的文件。

我们来运行一下上述代码:

$go run flat_table_to_parquet.go

执行完上面命令后,当前目录下会出现一个flat_table.parquet的文件!

我们如何查看该文件内容来验证写入的数据是否与table一致呢?arrow go的parquet实现提供了一个parquet_reader的工具可以帮助我们做到这点,你可以执行如下命令安装这个工具:

$go install github.com/apache/arrow/go/v13/parquet/cmd/parquet_reader@latest

之后我们就可以执行下面命令查看我们刚刚生成的flat_table.parquet文件的内容了:

$parquet_reader flat_table.parquet
File name: flat_table.parquet
Version: v2.6
Created By: parquet-go version 13.0.0-SNAPSHOT
Num Rows: 10
Number of RowGroups: 1
Number of Real Columns: 3
Number of Columns: 3
Number of Selected Columns: 3
Column 0: col1 (INT32/INT_32)
Column 1: col2 (DOUBLE)
Column 2: col3 (BYTE_ARRAY/UTF8)
--- Row Group: 0  ---
--- Total Bytes: 396  ---
--- Rows: 10  ---
Column 0
 Values: 10, Min: 1, Max: 10, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 111, Compressed Size: 111
Column 1
 Values: 10, Min: 1.1, Max: 10, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 169, Compressed Size: 169
Column 2
 Values: 10, Min: [115 49], Max: [115 57], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 116, Compressed Size: 116
--- Values ---
col1              |col2              |col3              |
1                 |1.100000          |s1                |
2                 |2.200000          |s2                |
3                 |3.300000          |s3                |
4                 |4.400000          |s4                |
5                 |5.500000          |s5                |
6                 |6.600000          |s6                |
7                 |7.700000          |s7                |
8                 |8.800000          |s8                |
9                 |9.900000          |s9                |
10                |10.000000         |s10               |

parquet_reader列出了parquet文件的meta数据和每个row group中的column列的值,从输出来看,与我们arrow table的数据是一致的。

我们再回头看一下WriteTable函数,它的原型如下:

func WriteTable(tbl arrow.Table, w io.Writer, chunkSize int64,
                props *parquet.WriterProperties, arrprops ArrowWriterProperties) error

这里说一下WriteTable的前三个参数,第一个是通过NewTable得到的arrow table结构,第二个参数也容易理解,就是一个可写的文件描述符,我们通过os.Create可以轻松拿到,第三个参数为chunkSize,这个chunkSize是什么呢?会对parquet文件的写入结果有影响么?其实这个chunkSize就是每个row group中的行数。同时parquet通过该chunkSize也可以计算出arrow table转parquet文件后有几个row group。

我们示例中的chunkSize值为1024,因此整个parquet文件只有一个row group。下面我们将其值改为5,再来看看输出的parquet文件内容:

$parquet_reader flat_table.parquet
File name: flat_table.parquet
Version: v2.6
Created By: parquet-go version 13.0.0-SNAPSHOT
Num Rows: 10
Number of RowGroups: 2
Number of Real Columns: 3
Number of Columns: 3
Number of Selected Columns: 3
Column 0: col1 (INT32/INT_32)
Column 1: col2 (DOUBLE)
Column 2: col3 (BYTE_ARRAY/UTF8)
--- Row Group: 0  ---
--- Total Bytes: 288  ---
--- Rows: 5  ---
Column 0
 Values: 5, Min: 1, Max: 5, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 86, Compressed Size: 86
Column 1
 Values: 5, Min: 1.1, Max: 5.5, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 122, Compressed Size: 122
Column 2
 Values: 5, Min: [115 49], Max: [115 53], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 80, Compressed Size: 80
--- Values ---
col1              |col2              |col3              |
1                 |1.100000          |s1                |
2                 |2.200000          |s2                |
3                 |3.300000          |s3                |
4                 |4.400000          |s4                |
5                 |5.500000          |s5                |

--- Row Group: 1  ---
--- Total Bytes: 290  ---
--- Rows: 5  ---
Column 0
 Values: 5, Min: 6, Max: 10, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 86, Compressed Size: 86
Column 1
 Values: 5, Min: 6.6, Max: 10, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 122, Compressed Size: 122
Column 2
 Values: 5, Min: [115 49 48], Max: [115 57], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 82, Compressed Size: 82
--- Values ---
col1              |col2              |col3              |
6                 |6.600000          |s6                |
7                 |7.700000          |s7                |
8                 |8.800000          |s8                |
9                 |9.900000          |s9                |
10                |10.000000         |s10               |

当chunkSize值为5后,parquet文件的row group变成了2,然后parquet_reader工具会按照两个row group的格式分别输出它们的meta信息和列值信息。

接下来,我们再来看一下如何从生成的parquet文件中读取数据并转换为arrow table。

2.2 Table <- Parquet

和WriteTable函数对应,arrow提供了ReadTable函数读取parquet文件并转换为内存中的arrow table,下面是代码示例:

// flat_table_from_parquet.go
func main() {
    f, err := os.Open("flat_table.parquet")
    if err != nil {
        panic(err)
    }
    defer f.Close()

    tbl, err := pqarrow.ReadTable(context.Background(), f, parquet.NewReaderProperties(memory.DefaultAllocator),
        pqarrow.ArrowReadProperties{}, memory.DefaultAllocator)
    if err != nil {
        panic(err)
    }

    dumpTable(tbl)
}

func dumpTable(tbl arrow.Table) {
    s := tbl.Schema()
    fmt.Println(s)
    fmt.Println("------")

    fmt.Println("the count of table columns=", tbl.NumCols())
    fmt.Println("the count of table rows=", tbl.NumRows())
    fmt.Println("------")

    for i := 0; i < int(tbl.NumCols()); i++ {
        col := tbl.Column(i)
        fmt.Printf("arrays in column(%s):\n", col.Name())
        chunk := col.Data()
        for _, arr := range chunk.Chunks() {
            fmt.Println(arr)
        }
        fmt.Println("------")
    }
}

我们看到ReadTable使用起来非常简单,由于parquet文件中包含meta信息,我们调用ReadTable时,一些参数使用默认值或零值即可。

我们运行一下上述代码:

$go run flat_table_from_parquet.go
schema:
  fields: 3
    - col1: type=int32
      metadata: ["PARQUET:field_id": "-1"]
    - col2: type=float64
      metadata: ["PARQUET:field_id": "-1"]
    - col3: type=utf8
      metadata: ["PARQUET:field_id": "-1"]
------
the count of table columns= 3
the count of table rows= 10
------
arrays in column(col1):
[1 2 3 4 5 6 7 8 9 10]
------
arrays in column(col2):
[1.1 2.2 3.3 4.4 5.5 6.6 7.7 8.8 9.9 10]
------
arrays in column(col3):
["s1" "s2" "s3" "s4" "s5" "s6" "s7" "s8" "s9" "s10"]
------

2.3 Table -> Parquet(压缩)

前面提到,Parquet文件格式的设计充分考虑了空间利用效率,再加上其是面向列存储的格式,Parquet支持列数据的压缩存储,并支持为不同列选择不同的压缩算法。

前面示例中调用的WriteTable在默认情况下是不对列进行压缩的,这从parquet_reader读取到的列的元信息中也可以看到(比如下面的Compression: UNCOMPRESSED):

Column 0
 Values: 10, Min: 1, Max: 10, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 111, Compressed Size: 111

我们在WriteTable时也可以通过parquet.WriterProperties参数来为每个列指定压缩算法,比如下面示例:

// flat_table_to_parquet_compressed.go

var tbl arrow.Table
tbl = array.NewTable(schema, []arrow.Column{*col1, *col2, *col3}, -1)
defer tbl.Release()

f, err := os.Create("flat_table_compressed.parquet")
if err != nil {
    panic(err)
}
defer f.Close()

wp := parquet.NewWriterProperties(parquet.WithCompression(compress.Codecs.Snappy),
    parquet.WithCompressionFor("col1", compress.Codecs.Brotli))
err = pqarrow.WriteTable(tbl, f, 1024, wp, pqarrow.DefaultWriterProps())
if err != nil {
    panic(err)
}

在这段代码中,我们通过parquet.NewWriterProperties构建了新的WriterProperties,这个新的Properties默认所有列使用Snappy压缩,针对col1列使用Brotli算法压缩。我们将压缩后的数据写入flat_table_compressed.parquet文件。使用go run运行flat_table_to_parquet_compressed.go,然后使用parquet_reader查看文件flat_table_compressed.parquet得到如下结果:

$go run flat_table_to_parquet_compressed.go
$parquet_reader flat_table_compressed.parquet
File name: flat_table_compressed.parquet
Version: v2.6
Created By: parquet-go version 13.0.0-SNAPSHOT
Num Rows: 10
Number of RowGroups: 1
Number of Real Columns: 3
Number of Columns: 3
Number of Selected Columns: 3
Column 0: col1 (INT32/INT_32)
Column 1: col2 (DOUBLE)
Column 2: col3 (BYTE_ARRAY/UTF8)
--- Row Group: 0  ---
--- Total Bytes: 352  ---
--- Rows: 10  ---
Column 0
 Values: 10, Min: 1, Max: 10, Null Values: 0, Distinct Values: 0
 Compression: BROTLI, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 111, Compressed Size: 98
Column 1
 Values: 10, Min: 1.1, Max: 10, Null Values: 0, Distinct Values: 0
 Compression: SNAPPY, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 168, Compressed Size: 148
Column 2
 Values: 10, Min: [115 49], Max: [115 57], Null Values: 0, Distinct Values: 0
 Compression: SNAPPY, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 116, Compressed Size: 106
--- Values ---
col1              |col2              |col3              |
1                 |1.100000          |s1                |
2                 |2.200000          |s2                |
3                 |3.300000          |s3                |
4                 |4.400000          |s4                |
5                 |5.500000          |s5                |
6                 |6.600000          |s6                |
7                 |7.700000          |s7                |
8                 |8.800000          |s8                |
9                 |9.900000          |s9                |
10                |10.000000         |s10               |

从parquet_reader的输出,我们可以看到:各个Column的Compression信息不再是UNCOMPRESSED了,并且三个列在经过压缩后的Size与未压缩对比都有一定的减小:

Column 0:
    Compression: BROTLI, Uncompressed Size: 111, Compressed Size: 98
Column 1:
    Compression: SNAPPY, Uncompressed Size: 168, Compressed Size: 148
Column 2:
    Compression: SNAPPY, Uncompressed Size: 116, Compressed Size: 106

从文件大小对比也能体现出压缩算法的作用:

-rw-r--r--   1 tonybai  staff   786  7 22 08:06 flat_table.parquet
-rw-r--r--   1 tonybai  staff   742  7 20 13:19 flat_table_compressed.parquet

Go的parquet实现支持多种压缩算法:

// github.com/apache/arrow/go/parquet/compress/compress.go

var Codecs = struct {
    Uncompressed Compression
    Snappy       Compression
    Gzip         Compression
    // LZO is unsupported in this library since LZO license is incompatible with Apache License
    Lzo    Compression
    Brotli Compression
    // LZ4 unsupported in this library due to problematic issues between the Hadoop LZ4 spec vs regular lz4
    // see: http://mail-archives.apache.org/mod_mbox/arrow-dev/202007.mbox/%3CCAAri41v24xuA8MGHLDvgSnE+7AAgOhiEukemW_oPNHMvfMmrWw@mail.gmail.com%3E
    Lz4  Compression
    Zstd Compression
}{
    Uncompressed: Compression(parquet.CompressionCodec_UNCOMPRESSED),
    Snappy:       Compression(parquet.CompressionCodec_SNAPPY),
    Gzip:         Compression(parquet.CompressionCodec_GZIP),
    Lzo:          Compression(parquet.CompressionCodec_LZO),
    Brotli:       Compression(parquet.CompressionCodec_BROTLI),
    Lz4:          Compression(parquet.CompressionCodec_LZ4),
    Zstd:         Compression(parquet.CompressionCodec_ZSTD),
}

你只需要根据你的列的类型选择最适合的压缩算法即可。

2.4 Table <- Parquet(压缩)

接下来,我们来读取这个数据经过压缩的Parquet。读取压缩的Parquet是否需要在ReadTable时传入特殊的Properties呢?答案是不需要!因为Parquet文件中存储了元信息(metadata),可以帮助ReadTable使用对应的算法解压缩并提取信息:

// flat_table_from_parquet_compressed.go

func main() {
    f, err := os.Open("flat_table_compressed.parquet")
    if err != nil {
        panic(err)
    }
    defer f.Close()

    tbl, err := pqarrow.ReadTable(context.Background(), f, parquet.NewReaderProperties(memory.DefaultAllocator),
        pqarrow.ArrowReadProperties{}, memory.DefaultAllocator)
    if err != nil {
        panic(err)
    }

    dumpTable(tbl)
}

运行这段程序,我们就可以读取压缩后的parquet文件了:

$go run flat_table_from_parquet_compressed.go
schema:
  fields: 3
    - col1: type=int32
      metadata: ["PARQUET:field_id": "-1"]
    - col2: type=float64
      metadata: ["PARQUET:field_id": "-1"]
    - col3: type=utf8
      metadata: ["PARQUET:field_id": "-1"]
------
the count of table columns= 3
the count of table rows= 10
------
arrays in column(col1):
[1 2 3 4 5 6 7 8 9 10]
------
arrays in column(col2):
[1.1 2.2 3.3 4.4 5.5 6.6 7.7 8.8 9.9 10]
------
arrays in column(col3):
["s1" "s2" "s3" "s4" "s5" "s6" "s7" "s8" "s9" "s10"]
------

接下来,我们来看看Arrow中的另外一种高级数据结构Record Batch如何实现与Parquet文件格式的转换。

3. Arrow Record Batch <-> Parquet

注:大家可以先阅读/温习一下《高级数据结构》一文来了解一下Record Batch的概念。

3.1 Record Batch -> Parquet

Arrow Go实现将一个Record Batch作为一个Row group来对应。下面的程序向Parquet文件中写入了三个record,我们来看一下:

// flat_record_to_parquet.go

func main() {
    var records []arrow.Record
    schema := arrow.NewSchema(
        []arrow.Field{
            {Name: "archer", Type: arrow.BinaryTypes.String},
            {Name: "location", Type: arrow.BinaryTypes.String},
            {Name: "year", Type: arrow.PrimitiveTypes.Int16},
        },
        nil,
    )

    rb := array.NewRecordBuilder(memory.DefaultAllocator, schema)
    defer rb.Release()

    for i := 0; i < 3; i++ {
        postfix := strconv.Itoa(i)
        rb.Field(0).(*array.StringBuilder).AppendValues([]string{"tony" + postfix, "amy" + postfix, "jim" + postfix}, nil)
        rb.Field(1).(*array.StringBuilder).AppendValues([]string{"beijing" + postfix, "shanghai" + postfix, "chengdu" + postfix}, nil)
        rb.Field(2).(*array.Int16Builder).AppendValues([]int16{1992 + int16(i), 1993 + int16(i), 1994 + int16(i)}, nil)
        rec := rb.NewRecord()
        records = append(records, rec)
    }

    // write to parquet
    f, err := os.Create("flat_record.parquet")
    if err != nil {
        panic(err)
    }

    props := parquet.NewWriterProperties()
    writer, err := pqarrow.NewFileWriter(schema, f, props,
        pqarrow.DefaultWriterProps())
    if err != nil {
        panic(err)
    }
    defer writer.Close()

    for _, rec := range records {
        if err := writer.Write(rec); err != nil {
            panic(err)
        }
        rec.Release()
    }
}

和调用WriteTable完成table到parquet文件的写入不同,这里我们创建了一个FileWriter,通过FileWriter将构建出的Record Batch逐个写入。运行上述代码生成flat_record.parquet文件并使用parquet_reader展示该文件的内容:

$go run flat_record_to_parquet.go
$parquet_reader flat_record.parquet
File name: flat_record.parquet
Version: v2.6
Created By: parquet-go version 13.0.0-SNAPSHOT
Num Rows: 9
Number of RowGroups: 3
Number of Real Columns: 3
Number of Columns: 3
Number of Selected Columns: 3
Column 0: archer (BYTE_ARRAY/UTF8)
Column 1: location (BYTE_ARRAY/UTF8)
Column 2: year (INT32/INT_16)
--- Row Group: 0  ---
--- Total Bytes: 255  ---
--- Rows: 3  ---
Column 0
 Values: 3, Min: [97 109 121 48], Max: [116 111 110 121 48], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 79, Compressed Size: 79
Column 1
 Values: 3, Min: [98 101 105 106 105 110 103 48], Max: [115 104 97 110 103 104 97 105 48], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 99, Compressed Size: 99
Column 2
 Values: 3, Min: 1992, Max: 1994, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 77, Compressed Size: 77
--- Values ---
archer            |location          |year              |
tony0             |beijing0          |1992              |
amy0              |shanghai0         |1993              |
jim0              |chengdu0          |1994              |

--- Row Group: 1  ---
--- Total Bytes: 255  ---
--- Rows: 3  ---
Column 0
 Values: 3, Min: [97 109 121 49], Max: [116 111 110 121 49], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 79, Compressed Size: 79
Column 1
 Values: 3, Min: [98 101 105 106 105 110 103 49], Max: [115 104 97 110 103 104 97 105 49], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 99, Compressed Size: 99
Column 2
 Values: 3, Min: 1993, Max: 1995, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 77, Compressed Size: 77
--- Values ---
archer            |location          |year              |
tony1             |beijing1          |1993              |
amy1              |shanghai1         |1994              |
jim1              |chengdu1          |1995              |

--- Row Group: 2  ---
--- Total Bytes: 255  ---
--- Rows: 3  ---
Column 0
 Values: 3, Min: [97 109 121 50], Max: [116 111 110 121 50], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 79, Compressed Size: 79
Column 1
 Values: 3, Min: [98 101 105 106 105 110 103 50], Max: [115 104 97 110 103 104 97 105 50], Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 99, Compressed Size: 99
Column 2
 Values: 3, Min: 1994, Max: 1996, Null Values: 0, Distinct Values: 0
 Compression: UNCOMPRESSED, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 77, Compressed Size: 77
--- Values ---
archer            |location          |year              |
tony2             |beijing2          |1994              |
amy2              |shanghai2         |1995              |
jim2              |chengdu2          |1996              |

我们看到parquet_reader分别输出了三个row group的元数据和列值,每个row group与我们写入的一个record对应。

那读取这样的parquet文件与ReadTable有何不同呢?我们继续往下看。

3.2 Record Batch <- Parquet

下面是用于读取

// flat_record_from_parquet.go
func main() {
    f, err := os.Open("flat_record.parquet")
    if err != nil {
        panic(err)
    }
    defer f.Close()

    rdr, err := file.NewParquetReader(f)
    if err != nil {
        panic(err)
    }
    defer rdr.Close()

    arrRdr, err := pqarrow.NewFileReader(rdr,
        pqarrow.ArrowReadProperties{
            BatchSize: 3,
        }, memory.DefaultAllocator)
    if err != nil {
        panic(err)
    }

    s, _ := arrRdr.Schema()
    fmt.Println(*s)

    rr, err := arrRdr.GetRecordReader(context.Background(), nil, nil)
    if err != nil {
        panic(err)
    }

    for {
        rec, err := rr.Read()
        if err != nil && err != io.EOF {
            panic(err)
        }
        if err == io.EOF {
            break
        }
        fmt.Println(rec)
    }
}

我们看到相对于将parquet转换为table,将parquet转换为record略为复杂一些,这里的一个关键是在调用NewFileReader时传入的ArrowReadProperties中的BatchSize字段,要想正确读取出record,这个BatchSize需适当填写。这个BatchSize会告诉Reader 每个读取的Record Batch的长度,也就是row数量。这里传入的是3,即3个row为一个Recordd batch。

下面是运行上述程序的结果:

$go run flat_record_from_parquet.go
{[{archer 0x26ccc00 false {[PARQUET:field_id] [-1]}} {location 0x26ccc00 false {[PARQUET:field_id] [-1]}} {year 0x26ccc00 false {[PARQUET:field_id] [-1]}}] map[archer:[0] location:[1] year:[2]] {[] []} 0}
record:
  schema:
  fields: 3
    - archer: type=utf8
        metadata: ["PARQUET:field_id": "-1"]
    - location: type=utf8
          metadata: ["PARQUET:field_id": "-1"]
    - year: type=int16
      metadata: ["PARQUET:field_id": "-1"]
  rows: 3
  col[0][archer]: ["tony0" "amy0" "jim0"]
  col[1][location]: ["beijing0" "shanghai0" "chengdu0"]
  col[2][year]: [1992 1993 1994]

record:
  schema:
  fields: 3
    - archer: type=utf8
        metadata: ["PARQUET:field_id": "-1"]
    - location: type=utf8
          metadata: ["PARQUET:field_id": "-1"]
    - year: type=int16
      metadata: ["PARQUET:field_id": "-1"]
  rows: 3
  col[0][archer]: ["tony1" "amy1" "jim1"]
  col[1][location]: ["beijing1" "shanghai1" "chengdu1"]
  col[2][year]: [1993 1994 1995]

record:
  schema:
  fields: 3
    - archer: type=utf8
        metadata: ["PARQUET:field_id": "-1"]
    - location: type=utf8
          metadata: ["PARQUET:field_id": "-1"]
    - year: type=int16
      metadata: ["PARQUET:field_id": "-1"]
  rows: 3
  col[0][archer]: ["tony2" "amy2" "jim2"]
  col[1][location]: ["beijing2" "shanghai2" "chengdu2"]
  col[2][year]: [1994 1995 1996]

我们看到:每3行被作为一个record读取出来了。如果将BatchSize改为5,则输出如下:

$go run flat_record_from_parquet.go
{[{archer 0x26ccc00 false {[PARQUET:field_id] [-1]}} {location 0x26ccc00 false {[PARQUET:field_id] [-1]}} {year 0x26ccc00 false {[PARQUET:field_id] [-1]}}] map[archer:[0] location:[1] year:[2]] {[] []} 0}
record:
  schema:
  fields: 3
    - archer: type=utf8
        metadata: ["PARQUET:field_id": "-1"]
    - location: type=utf8
          metadata: ["PARQUET:field_id": "-1"]
    - year: type=int16
      metadata: ["PARQUET:field_id": "-1"]
  rows: 5
  col[0][archer]: ["tony0" "amy0" "jim0" "tony1" "amy1"]
  col[1][location]: ["beijing0" "shanghai0" "chengdu0" "beijing1" "shanghai1"]
  col[2][year]: [1992 1993 1994 1993 1994]

record:
  schema:
  fields: 3
    - archer: type=utf8
        metadata: ["PARQUET:field_id": "-1"]
    - location: type=utf8
          metadata: ["PARQUET:field_id": "-1"]
    - year: type=int16
      metadata: ["PARQUET:field_id": "-1"]
  rows: 4
  col[0][archer]: ["jim1" "tony2" "amy2" "jim2"]
  col[1][location]: ["chengdu1" "beijing2" "shanghai2" "chengdu2"]
  col[2][year]: [1995 1994 1995 1996]

这次:前5行作为一个record,后4行作为另外一个record。

当然,我们也可以使用flat_table_from_parquet.go中的代码来读取flat_record.parquet(将读取文件名改为flat_record.parquet),只不过由于将parquet数据转换为了table,其输出内容将变为:

$go run flat_table_from_parquet.go
schema:
  fields: 3
    - archer: type=utf8
        metadata: ["PARQUET:field_id": "-1"]
    - location: type=utf8
          metadata: ["PARQUET:field_id": "-1"]
    - year: type=int16
      metadata: ["PARQUET:field_id": "-1"]
------
the count of table columns= 3
the count of table rows= 9
------
arrays in column(archer):
["tony0" "amy0" "jim0" "tony1" "amy1" "jim1" "tony2" "amy2" "jim2"]
------
arrays in column(location):
["beijing0" "shanghai0" "chengdu0" "beijing1" "shanghai1" "chengdu1" "beijing2" "shanghai2" "chengdu2"]
------
arrays in column(year):
[1992 1993 1994 1993 1994 1995 1994 1995 1996]
------

3.3 Record Batch -> Parquet(压缩)

Recod同样支持压缩写入Parquet,其原理与前面table压缩存储是一致的,都是通过设置WriterProperties来实现的:

// flat_record_to_parquet_compressed.go

func main() {
    ... ...
    f, err := os.Create("flat_record_compressed.parquet")
    if err != nil {
        panic(err)
    }
    defer f.Close()

    props := parquet.NewWriterProperties(parquet.WithCompression(compress.Codecs.Zstd),
        parquet.WithCompressionFor("year", compress.Codecs.Brotli))
    writer, err := pqarrow.NewFileWriter(schema, f, props,
        pqarrow.DefaultWriterProps())
    if err != nil {
        panic(err)
    }
    defer writer.Close()

    for _, rec := range records {
        if err := writer.Write(rec); err != nil {
            panic(err)
        }
        rec.Release()
    }
}

不过这次针对arrow.string类型和arrow.int16类型的压缩效果非常“差”:

$parquet_reader flat_record_compressed.parquet
File name: flat_record_compressed.parquet
Version: v2.6
Created By: parquet-go version 13.0.0-SNAPSHOT
Num Rows: 9
Number of RowGroups: 3
Number of Real Columns: 3
Number of Columns: 3
Number of Selected Columns: 3
Column 0: archer (BYTE_ARRAY/UTF8)
Column 1: location (BYTE_ARRAY/UTF8)
Column 2: year (INT32/INT_16)
--- Row Group: 0  ---
--- Total Bytes: 315  ---
--- Rows: 3  ---
Column 0
 Values: 3, Min: [97 109 121 48], Max: [116 111 110 121 48], Null Values: 0, Distinct Values: 0
 Compression: ZSTD, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 79, Compressed Size: 105
Column 1
 Values: 3, Min: [98 101 105 106 105 110 103 48], Max: [115 104 97 110 103 104 97 105 48], Null Values: 0, Distinct Values: 0
 Compression: ZSTD, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 99, Compressed Size: 125
Column 2
 Values: 3, Min: 1992, Max: 1994, Null Values: 0, Distinct Values: 0
 Compression: BROTLI, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 77, Compressed Size: 85
--- Values ---
archer            |location          |year              |
tony0             |beijing0          |1992              |
amy0              |shanghai0         |1993              |
jim0              |chengdu0          |1994              |

--- Row Group: 1  ---
--- Total Bytes: 315  ---
--- Rows: 3  ---
Column 0
 Values: 3, Min: [97 109 121 49], Max: [116 111 110 121 49], Null Values: 0, Distinct Values: 0
 Compression: ZSTD, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 79, Compressed Size: 105
Column 1
 Values: 3, Min: [98 101 105 106 105 110 103 49], Max: [115 104 97 110 103 104 97 105 49], Null Values: 0, Distinct Values: 0
 Compression: ZSTD, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 99, Compressed Size: 125
Column 2
 Values: 3, Min: 1993, Max: 1995, Null Values: 0, Distinct Values: 0
 Compression: BROTLI, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 77, Compressed Size: 85
--- Values ---
archer            |location          |year              |
tony1             |beijing1          |1993              |
amy1              |shanghai1         |1994              |
jim1              |chengdu1          |1995              |

--- Row Group: 2  ---
--- Total Bytes: 315  ---
--- Rows: 3  ---
Column 0
 Values: 3, Min: [97 109 121 50], Max: [116 111 110 121 50], Null Values: 0, Distinct Values: 0
 Compression: ZSTD, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 79, Compressed Size: 105
Column 1
 Values: 3, Min: [98 101 105 106 105 110 103 50], Max: [115 104 97 110 103 104 97 105 50], Null Values: 0, Distinct Values: 0
 Compression: ZSTD, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 99, Compressed Size: 125
Column 2
 Values: 3, Min: 1994, Max: 1996, Null Values: 0, Distinct Values: 0
 Compression: BROTLI, Encodings: RLE_DICTIONARY PLAIN RLE
 Uncompressed Size: 77, Compressed Size: 85
--- Values ---
archer            |location          |year              |
tony2             |beijing2          |1994              |
amy2              |shanghai2         |1995              |
jim2              |chengdu2          |1996              |

越压缩,parquet文件的size越大。当然这个问题不是我们这篇文章的重点,只是提醒大家选择适当的压缩算法十分重要

3.4 Record Batch <- Parquet(压缩)

和读取table转换后的压缩parquet文件一样,读取record转换后的压缩parquet一样无需特殊设置,使用flat_record_from_parquet.go即可(需要改一下读取的文件名),这里就不赘述了。

4. 小结

本文旨在介绍使用Go进行Arrow和Parquet文件相互转换的基本方法,我们以table和record两种高级数据结构为例,分别介绍了读写parquet文件以及压缩parquet文件的方法。

当然本文中的例子都是“平坦(flat)”的简单例子,parquet文件还支持更复杂的嵌套数据,我们会在后续的深入讲解parquet格式的文章中提及。

本文示例代码可以在这里下载。

5. 参考资料

  • Parquet File Format – https://parquet.apache.org/docs/file-format/
  • 《Dremel: Interactive Analysis of Web-Scale Datasets》 – https://storage.googleapis.com/pub-tools-public-publication-data/pdf/36632.pdf
  • Announcing Parquet 1.0: Columnar Storage for Hadoop – https://blog.twitter.com/engineering/en_us/a/2013/announcing-parquet-10-columnar-storage-for-hadoop
  • Dremel made simple with Parquet – https://blog.twitter.com/engineering/en_us/a/2013/dremel-made-simple-with-parquet
  • parquet项目首页 – http://parquet.apache.org/
  • Apache Parquet介绍 by influxdata – https://www.influxdata.com/glossary/apache-parquet/
  • Intro to InfluxDB IOx – https://www.influxdata.com/blog/intro-influxdb-iox/
  • Apache Arrow介绍 by influxdb – https://www.influxdata.com/glossary/apache-arrow/
  • 开源时序数据库解析 – InfluxDB IOx – https://zhuanlan.zhihu.com/p/534035337
  • Arrow and Parquet Part 1: Primitive Types and Nullability – https://arrow.apache.org/blog/2022/10/05/arrow-parquet-encoding-part-1/
  • Arrow and Parquet Part 2: Nested and Hierarchical Data using Structs and Lists – https://arrow.apache.org/blog/2022/10/08/arrow-parquet-encoding-part-2/
  • Arrow and Parquet Part 3: Arbitrary Nesting with Lists of Structs and Structs of Lists – https://arrow.apache.org/blog/2022/10/17/arrow-parquet-encoding-part-3/
  • Cost Efficiency @ Scale in Big Data File Format – https://www.uber.com/blog/cost-efficiency-big-data/

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