开心一刻
昨天发了一条朋友圈：酒吧有什么好去的，上个月在酒吧当服务员兼职，一位大姐看上了我，说一个月给我 10 万，要我陪她去上海，我没同意
朋友评论道：你没同意，为什么在上海？
我回复到：上个月没同意
嘴真硬前情回顾
关于 DataX，官网有很详细的介绍，鄙人不才，也写过几篇文章
异构数据源同步之数据同步 → datax 改造，有点意思 异构数据源同步之数据同步 → datax 再改造，开始触及源码 异构数据源同步之数据同步 → DataX 使用细节 异构数据源数据同步 → 从源码分析 DataX 敏感信息的加解密
不了解的小伙伴可以按需去查看，所以了，DataX 就不做过多介绍了；官方提供了非常多的插件，囊括了绝大部分的数据源，基本可以满足我们日常需要，但数据源种类太多，DataX 插件不可能包含全部，比如 kafka，DataX 官方是没有提供读写插件的，大家知道为什么吗？你们如果对数据同步了解的比较多的话，一看到 kafka，第一反应往往想到的是 实时同步，而 DataX 针对的是 离线同步，所以 DataX 官方没提供 kafka 插件是不是也就能理解了？因为不合适嘛！
但如果客户非要离线同步也支持 kafka
人家要嘛你能怎么办？直接怼过去：实现不了？
实现不了所以没得选，那就只能给 DataX 开发一套 kafka 插件了；基于 DataX插件开发宝典，插件开发起来还是非常简单的
kafkawriter
编程接口
自定义 Kafkawriter 继承 DataX 的 Writer，实现 job、task 对应的接口即可
/**
* @author 青石路
*/
public class KafkaWriter extends Writer {
public static class Job extends Writer.Job {
private Configuration conf = null;
@Override
public List<Configuration> split(int mandatoryNumber) {
List<Configuration> configurations = new ArrayList<Configuration>(mandatoryNumber);
for (int i = 0; i < mandatoryNumber; i ) {
configurations.add(this.conf.clone());
}
return configurations;
}
private void validateParameter() {
this.conf.getNecessaryValue(Key.BOOTSTRAP_SERVERS, KafkaWriterErrorCode.REQUIRED_VALUE);
this.conf.getNecessaryValue(Key.TOPIC, KafkaWriterErrorCode.REQUIRED_VALUE);
}
@Override
public void init() {
this.conf = super.getPluginJobConf();
this.validateParameter();
}
@Override
public void destroy() {
}
}
public static class Task extends Writer.Task {
private static final Logger logger = LoggerFactory.getLogger(Task.class);
private static final String NEWLINE_FLAG = System.getProperty("line.separator", "n");
private Producer<String, String> producer;
private Configuration conf;
private Properties props;
private String fieldDelimiter;
private List<String> columns;
private String writeType;
@Override
public void init() {
this.conf = super.getPluginJobConf();
fieldDelimiter = conf.getUnnecessaryValue(Key.FIELD_DELIMITER, "t", null);
columns = conf.getList(Key.COLUMN, String.class);
writeType = conf.getUnnecessaryValue(Key.WRITE_TYPE, WriteType.TEXT.name(), null);
if (CollUtil.isEmpty(columns)) {
throw DataXException.asDataXException(KafkaWriterErrorCode.REQUIRED_VALUE,
String.format("您提供配置文件有误，[%s]是必填参数，不允许为空或者留白 .", Key.COLUMN));
}
props = new Properties();
props.put(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG, conf.getString(Key.BOOTSTRAP_SERVERS));
//这意味着leader需要等待所有备份都成功写入日志，这种策略会保证只要有一个备份存活就不会丢失数据。这是最强的保证。
props.put(ProducerConfig.ACKS_CONFIG, conf.getUnnecessaryValue(Key.ACK, "0", null));
props.put(CommonClientConfigs.RETRIES_CONFIG, conf.getUnnecessaryValue(Key.RETRIES, "0", null));
props.put(ProducerConfig.BATCH_SIZE_CONFIG, conf.getUnnecessaryValue(Key.BATCH_SIZE, "16384", null));
props.put(ProducerConfig.LINGER_MS_CONFIG, 1);
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, conf.getUnnecessaryValue(Key.KEY_SERIALIZER, "org.apache.kafka.common.serialization.StringSerializer", null));
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, conf.getUnnecessaryValue(Key.VALUE_SERIALIZER, "org.apache.kafka.common.serialization.StringSerializer", null));
Configuration saslConf = conf.getConfiguration(Key.SASL);
if (ObjUtil.isNotNull(saslConf)) {
logger.info("配置启用了SASL认证");
props.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG, saslConf.getNecessaryValue(Key.SASL_SECURITY_PROTOCOL, KafkaWriterErrorCode.REQUIRED_VALUE));
props.put(SaslConfigs.SASL_MECHANISM, saslConf.getNecessaryValue(Key.SASL_MECHANISM, KafkaWriterErrorCode.REQUIRED_VALUE));
String userName = saslConf.getNecessaryValue(Key.SASL_USERNAME, KafkaWriterErrorCode.REQUIRED_VALUE);
String password = saslConf.getNecessaryValue(Key.SASL_PASSWORD, KafkaWriterErrorCode.REQUIRED_VALUE);
props.put(SaslConfigs.SASL_JAAS_CONFIG, String.format("org.apache.kafka.common.security.plain.PlainLoginModule required username="%s" password="%s";", userName, password));
}
producer = new KafkaProducer<String, String>(props);
}
@Override
public void prepare() {
if (Boolean.parseBoolean(conf.getUnnecessaryValue(Key.NO_TOPIC_CREATE, "false", null))) {
ListTopicsResult topicsResult = AdminClient.create(props).listTopics();
String topic = conf.getNecessaryValue(Key.TOPIC, KafkaWriterErrorCode.REQUIRED_VALUE);
try {
if (!topicsResult.names().get().contains(topic)) {
new NewTopic(
topic,
Integer.parseInt(conf.getUnnecessaryValue(Key.TOPIC_NUM_PARTITION, "1", null)),
Short.parseShort(conf.getUnnecessaryValue(Key.TOPIC_REPLICATION_FACTOR, "1", null))
);
List<NewTopic> newTopics = new ArrayList<NewTopic>();
AdminClient.create(props).createTopics(newTopics);
}
} catch (Exception e) {
throw new DataXException(KafkaWriterErrorCode.CREATE_TOPIC, KafkaWriterErrorCode.REQUIRED_VALUE.getDescription());
}
}
}
@Override
public void startWrite(RecordReceiver lineReceiver) {
logger.info("start to writer kafka");
Record record = null;
while ((record = lineReceiver.getFromReader()) != null) {//说明还在读取数据,或者读取的数据没处理完
//获取一行数据，按照指定分隔符 拼成字符串 发送出去
if (writeType.equalsIgnoreCase(WriteType.TEXT.name())) {
producer.send(new ProducerRecord<String, String>(this.conf.getString(Key.TOPIC),
recordToString(record),
recordToString(record))
);
} else if (writeType.equalsIgnoreCase(WriteType.JSON.name())) {
producer.send(new ProducerRecord<String, String>(this.conf.getString(Key.TOPIC),
recordToString(record),
recordToKafkaJson(record))
);
}
producer.flush();
}
}
@Override
public void destroy() {
logger.info("producer close");
if (producer != null) {
producer.close();
}
}
/**
* 数据格式化
*
* @param record
* @return
*/
private String recordToString(Record record) {
int recordLength = record.getColumnNumber();
if (0 == recordLength) {
return NEWLINE_FLAG;
}
Column column;
StringBuilder sb = new StringBuilder();
for (int i = 0; i < recordLength; i ) {
column = record.getColumn(i);
sb.append(column.asString()).append(fieldDelimiter);
}
sb.setLength(sb.length() - 1);
sb.append(NEWLINE_FLAG);
return sb.toString();
}
private String recordToKafkaJson(Record record) {
int recordLength = record.getColumnNumber();
if (recordLength != columns.size()) {
throw DataXException.asDataXException(KafkaWriterErrorCode.ILLEGAL_PARAM,
String.format("您提供配置文件有误，列数不匹配[record columns=%d, writer columns=%d]", recordLength, columns.size()));
}
List<KafkaColumn> kafkaColumns = new ArrayList<>();
for (int i = 0; i < recordLength; i ) {
KafkaColumn column = new KafkaColumn(record.getColumn(i), columns.get(i));
kafkaColumns.add(column);
}
return JSONUtil.toJsonStr(kafkaColumns);
}
}
}DataX 框架按照如下的顺序执行 Job 和 Task 的接口
job_task 接口执行顺序重点看 Task 的接口实现
- init：读取配置项，然后创建 Producer 实例
- prepare：判断 Topic 是否存在，不存在则创建
- startWrite：通过 RecordReceiver 从 Channel 获取 Record，然后写入 Topic
支持两种写入格式：
text、json，细节请看下文中的kafkawriter.md - destroy：关闭 Producer 实例
实现不难，相信大家都能看懂
插件定义
在 resources 下新增 plugin.json
{
"name": "kafkawriter",
"class": "com.qsl.datax.plugin.writer.kafkawriter.KafkaWriter",
"description": "write data to kafka",
"developer": "qsl"
}强调下 class，是 KafkaWriter 的全限定类名，如果你们没有完全拷贝我的，那么要改成你们自己的
配置文件
在 resources 下新增 plugin_job_template.json
{
"name": "kafkawriter",
"parameter": {
"bootstrapServers": "",
"topic": "",
"ack": "all",
"batchSize": 1000,
"retries": 0,
"fieldDelimiter": ",",
"writeType": "json",
"column": [
"const_id",
"const_field",
"const_field_value"
],
"sasl": {
"securityProtocol": "SASL_PLAINTEXT",
"mechanism": "PLAIN",
"username": "",
"password": ""
}
}
}配置项说明：kafkawriter.md
打包发布
可以参考官方的 assembly 配置，利用 assembly 来打包
至此，kafkawriter 就算完成了
kafkareader
编程接口
自定义 Kafkareader 继承 DataX 的 Reader，实现 job、task 对应的接口即可
/**
* @author 青石路
*/
public class KafkaReader extends Reader {
public static class Job extends Reader.Job {
private Configuration originalConfig = null;
@Override
public void init() {
this.originalConfig = super.getPluginJobConf();
this.validateParameter();
}
@Override
public void destroy() {
}
@Override
public List<Configuration> split(int adviceNumber) {
List<Configuration> configurations = new ArrayList<>(adviceNumber);
for (int i=0; i<adviceNumber; i ) {
configurations.add(this.originalConfig.clone());
}
return configurations;
}
private void validateParameter() {
this.originalConfig.getNecessaryValue(Key.BOOTSTRAP_SERVERS, KafkaReaderErrorCode.REQUIRED_VALUE);
this.originalConfig.getNecessaryValue(Key.TOPIC, KafkaReaderErrorCode.REQUIRED_VALUE);
}
}
public static class Task extends Reader.Task {
private static final Logger logger = LoggerFactory.getLogger(Task.class);
private Consumer<String, String> consumer;
private String topic;
private Configuration conf;
private int maxPollRecords;
private String fieldDelimiter;
private String readType;
private List<Column.Type> columnTypes;
@Override
public void destroy() {
logger.info("consumer close");
if (Objects.nonNull(consumer)) {
consumer.close();
}
}
@Override
public void init() {
this.conf = super.getPluginJobConf();
this.topic = conf.getString(Key.TOPIC);
this.maxPollRecords = conf.getInt(Key.MAX_POLL_RECORDS, 500);
fieldDelimiter = conf.getUnnecessaryValue(Key.FIELD_DELIMITER, "t", null);
readType = conf.getUnnecessaryValue(Key.READ_TYPE, ReadType.JSON.name(), null);
if (!ReadType.JSON.name().equalsIgnoreCase(readType)
&& !ReadType.TEXT.name().equalsIgnoreCase(readType)) {
throw DataXException.asDataXException(KafkaReaderErrorCode.REQUIRED_VALUE,
String.format("您提供配置文件有误，不支持的readType[%s]", readType));
}
if (ReadType.JSON.name().equalsIgnoreCase(readType)) {
List<String> columnTypeList = conf.getList(Key.COLUMN_TYPE, String.class);
if (CollUtil.isEmpty(columnTypeList)) {
throw DataXException.asDataXException(KafkaReaderErrorCode.REQUIRED_VALUE,
String.format("您提供配置文件有误，readType是JSON时[%s]是必填参数，不允许为空或者留白 .", Key.COLUMN_TYPE));
}
convertColumnType(columnTypeList);
}
Properties props = new Properties();
props.put(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG, conf.getString(Key.BOOTSTRAP_SERVERS));
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, conf.getUnnecessaryValue(Key.KEY_DESERIALIZER, "org.apache.kafka.common.serialization.StringDeserializer", null));
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, conf.getUnnecessaryValue(Key.VALUE_DESERIALIZER, "org.apache.kafka.common.serialization.StringDeserializer", null));
props.put(ConsumerConfig.GROUP_ID_CONFIG, conf.getNecessaryValue(Key.GROUP_ID, KafkaReaderErrorCode.REQUIRED_VALUE));
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");
props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
props.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, maxPollRecords);
Configuration saslConf = conf.getConfiguration(Key.SASL);
if (ObjUtil.isNotNull(saslConf)) {
logger.info("配置启用了SASL认证");
props.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG, saslConf.getNecessaryValue(Key.SASL_SECURITY_PROTOCOL, KafkaReaderErrorCode.REQUIRED_VALUE));
props.put(SaslConfigs.SASL_MECHANISM, saslConf.getNecessaryValue(Key.SASL_MECHANISM, KafkaReaderErrorCode.REQUIRED_VALUE));
String userName = saslConf.getNecessaryValue(Key.SASL_USERNAME, KafkaReaderErrorCode.REQUIRED_VALUE);
String password = saslConf.getNecessaryValue(Key.SASL_PASSWORD, KafkaReaderErrorCode.REQUIRED_VALUE);
props.put(SaslConfigs.SASL_JAAS_CONFIG, String.format("org.apache.kafka.common.security.plain.PlainLoginModule required username="%s" password="%s";", userName, password));
}
consumer = new KafkaConsumer<>(props);
}
@Override
public void startRead(RecordSender recordSender) {
consumer.subscribe(CollUtil.newArrayList(topic));
int pollTimeoutMs = conf.getInt(Key.POLL_TIMEOUT_MS, 1000);
int retries = conf.getInt(Key.RETRIES, 5);
if (retries < 0) {
logger.info("joinGroupSuccessRetries 配置有误[{}], 重置成默认值[5]", retries);
retries = 5;
}
/**
* consumer 每次都是新创建，第一次poll时会重新加入消费者组，加入过程会进行Rebalance，而 Rebalance 会导致同一 Group 内的所有消费者都不能工作
* 所以 poll 拉取的过程中，即使topic中有数据也不一定能拉到，因为 consumer 正在加入消费者组中
* kafka-clients 没有对应的API、事件机制来知道 consumer 成功加入消费者组的确切时间
* 故增加重试
*/
ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(pollTimeoutMs));
int i = 0;
if (CollUtil.isEmpty(records)) {
for (; i < retries; i ) {
records = consumer.poll(Duration.ofMillis(pollTimeoutMs));
logger.info("第 {} 次重试，获取消息记录数[{}]", i 1, records.count());
if (!CollUtil.isEmpty(records)) {
break;
}
}
}
if (i >= retries) {
logger.info("重试 {} 次后，仍未获取到消息，请确认是否有数据、配置是否正确", retries);
return;
}
transferRecord(recordSender, records);
do {
records = consumer.poll(Duration.ofMillis(pollTimeoutMs));
transferRecord(recordSender, records);
} while (!CollUtil.isEmpty(records) && records.count() >= maxPollRecords);
}
private void transferRecord(RecordSender recordSender, ConsumerRecords<String, String> records) {
if (CollUtil.isEmpty(records)) {
return;
}
for (ConsumerRecord<String, String> record : records) {
Record sendRecord = recordSender.createRecord();
String msgValue = record.value();
if (ReadType.JSON.name().equalsIgnoreCase(readType)) {
transportJsonToRecord(sendRecord, msgValue);
} else if (ReadType.TEXT.name().equalsIgnoreCase(readType)) {
// readType = text，全当字符串类型处理
String[] columnValues = msgValue.split(fieldDelimiter);
for (String columnValue : columnValues) {
sendRecord.addColumn(new StringColumn(columnValue));
}
}
recordSender.sendToWriter(sendRecord);
}
consumer.commitAsync();
}
private void convertColumnType(List<String> columnTypeList) {
columnTypes = new ArrayList<>();
for (String columnType : columnTypeList) {
switch (columnType.toUpperCase()) {
case "STRING":
columnTypes.add(Column.Type.STRING);
break;
case "LONG":
columnTypes.add(Column.Type.LONG);
break;
case "DOUBLE":
columnTypes.add(Column.Type.DOUBLE);
case "DATE":
columnTypes.add(Column.Type.DATE);
break;
case "BOOLEAN":
columnTypes.add(Column.Type.BOOL);
break;
case "BYTES":
columnTypes.add(Column.Type.BYTES);
break;
default:
throw DataXException.asDataXException(KafkaReaderErrorCode.ILLEGAL_PARAM,
String.format("您提供的配置文件有误，datax不支持数据类型[%s]", columnType));
}
}
}
private void transportJsonToRecord(Record sendRecord, String msgValue) {
List<KafkaColumn> kafkaColumns = JSONUtil.toList(msgValue, KafkaColumn.class);
if (columnTypes.size() != kafkaColumns.size()) {
throw DataXException.asDataXException(KafkaReaderErrorCode.ILLEGAL_PARAM,
String.format("您提供的配置文件有误，readType是JSON时[%s列数=%d]与[json列数=%d]的数量不匹配", Key.COLUMN_TYPE, columnTypes.size(), kafkaColumns.size()));
}
for (int i=0; i<columnTypes.size(); i ) {
KafkaColumn kafkaColumn = kafkaColumns.get(i);
switch (columnTypes.get(i)) {
case STRING:
sendRecord.setColumn(i, new StringColumn(kafkaColumn.getColumnValue()));
break;
case LONG:
sendRecord.setColumn(i, new LongColumn(kafkaColumn.getColumnValue()));
break;
case DOUBLE:
sendRecord.setColumn(i, new DoubleColumn(kafkaColumn.getColumnValue()));
break;
case DATE:
// 暂只支持时间戳
sendRecord.setColumn(i, new DateColumn(Long.parseLong(kafkaColumn.getColumnValue())));
break;
case BOOL:
sendRecord.setColumn(i, new BoolColumn(kafkaColumn.getColumnValue()));
break;
case BYTES:
sendRecord.setColumn(i, new BytesColumn(kafkaColumn.getColumnValue().getBytes(StandardCharsets.UTF_8)));
break;
default:
throw DataXException.asDataXException(KafkaReaderErrorCode.ILLEGAL_PARAM,
String.format("您提供的配置文件有误，datax不支持数据类型[%s]", columnTypes.get(i)));
}
}
}
}
}重点看 Task 的接口实现
- init：读取配置项，然后创建 Consumer 实例
- startWrite：从 Topic 拉取数据，通过 RecordSender 写入到 Channel 中 这里有几个细节需要注意下
1. Consumer 每次都是新创建的，拉取数据的时候，如果消费者还未加入到指定的消费者组中，那么它会先加入到消费者组中，加入过程会进行 Rebalance，而 Rebalance 会导致同一消费者组内的所有消费者都不能工作，此时即使 Topic 中有可拉取的消息，也拉取不到消息，所以引入了重试机制来尽量保证那一次同步任务拉取的时候，消费者能正常拉取消息
2. 一旦 Consumer 拉取到消息，则会循环拉取消息，如果某一次的拉取数据量小于最大拉取量（maxPollRecords），说明 Topic 中的消息已经被拉取完了，那么循环终止；这与常规使用（Consumer 会一直主动拉取或被动接收）是有差别的
3. 支持两种读取格式：`text`、`json`，细节请看下文的配置文件说明
4. 为了保证写入 Channel 数据的完整，需要配置列的数据类型（DataX 的数据类型）destroy：
关闭 Consumer 实例插件定义
在 resources 下新增 plugin.json
{
"name": "kafkareader",
"class": "com.qsl.datax.plugin.reader.kafkareader.KafkaReader",
"description": "read data from kafka",
"developer": "qsl"
}class 是 KafkaReader 的全限定类名
配置文件
在 resources 下新增 plugin_job_template.json
{
"name": "kafkareader",
"parameter": {
"bootstrapServers": "",
"topic": "test-kafka",
"groupId": "test1",
"writeType": "json",
"pollTimeoutMs": 2000,
"columnType": [
"LONG",
"STRING",
"STRING"
],
"sasl": {
"securityProtocol": "SASL_PLAINTEXT",
"mechanism": "PLAIN",
"username": "",
"password": "2"
}
}
}配置项说明：kafkareader.md
打包发布
可以参考官方的 assembly 配置，利用 assembly 来打包
至此，kafkareader 也完成了
总结
- 完整代码：qsl-datax
- kafkareader 重试机制只能降低拉取不到数据的概率，并不能杜绝；另外，如果上游一直往 Topic 中发消息，kafkareader 每次拉取的数据量都等于最大拉取量，那么同步任务会一直进行而不会停止，这还是离线同步吗？
- 离线同步，不推荐走 kafka，因为用 kafka 走实时同步更香
