106 基于消息队列来做 mysql 大数据表数据的遍历处理

前言

最近有这样的一个需求, 我们存在一张很大的 mysql 数据表, 数据量大概是在六百万左右

然后需要获取所有的记录, 将数据传输到 es 中

然后当时我就写了一个脚本来读取这张大表, 然后分页获取数据, 然后按页进行数据处理转换到 es

但是存在的问题是, 前面还效率还可以, 但是约到后面, 大概是到三百多页, 的时候从 mysql 读取数据已经快不行了

十分耗时, 这里就是记录这个问题的另外的处理方式

我这里的处理是基于消息中间件, 从 mysql 通过 datax/spoon 传输数据到 kafka 很快

然后 java 程序从 kafka 中消费队列的数据也很快, 最终六百万的数据读取 + 处理合计差不多是一个多小时完成, 其中处理有一部分地方业务上面比较耗时

待处理的数据表

待处理的数据表如下, 里面合计 600w 的数据

CREATE TABLE `student_all` (`id` int NOT NULL AUTO_INCREMENT,`field0` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field1` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field2` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field3` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field4` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field5` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field6` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field7` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field8` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field9` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field10` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field11` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field12` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field13` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field14` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field15` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field16` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field17` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field18` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field19` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field20` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field21` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field22` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field23` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field24` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field25` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field26` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field27` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field28` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`field29` varchar(128) COLLATE utf8mb4_general_ci NOT NULL,`CREATED_AT` bigint NOT NULL,`UPDATED_AT` bigint NOT NULL,PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=4379001 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_general_ci

基于 mysql 的数据分页处理

基于 mysql 的处理程序如下, 就是一个简单的 mysql 分页

然后将需要提取的数据封装, 然后批量提交给 es

总的情况来说是前面的一部分页是可以很快的响应数据, 但是越到后面, mysql 服务器越慢

/*** Test05PostQy2Es** @author Jerry.X.He* @version 1.0* @date 2022/11/21 16:00*/
public class Test05PostEsFromMysql {private static String mysqlUrl = "jdbc:mysql://127.0.0.1:3306/test?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&autoReconnectForPools=true";private static String mysqlUsername = "postgres";private static String mysqlPassword = "postgres";private static JdbcTemplate mysqlJdbcTemplate = JdbcTemplateUtils.getJdbcTemplate(mysqlUrl, mysqlUsername, mysqlPassword);private static RestHighLevelClient esClient = getEsClient();private static IndicesClient indicesClient = esClient.indices();// Test05PostQy2Espublic static void main(String[] args) throws Exception {String esIndexName = "student_all_20221211";bulkEsData(esIndexName);}private static void bulkEsData(String esIndexName) throws Exception {String queryDbTableName = "student_all";List<String> fieldList = Arrays.asList("id", "field0", "field1", "field2", "field3", "field4", "field5", "field6", "field7", "field8", "field9", "field10", "field11", "field12", "field13", "field14", "field15", "field16", "field17", "field18", "field19", "field20", "field21", "field22", "field23", "field24", "field25", "field26", "field27", "field28", "field29", "CREATED_AT", "UPDATED_AT");String idKey = "id";String whereCond = "";
//        String orderBy = "order by id asc";String orderBy = "";AtomicInteger counter = new AtomicInteger(0);int pageSize = 1000;int startPage = 0;pageDo(queryDbTableName, whereCond, orderBy, pageSize, startPage, (pageNo, list) -> {BulkRequest bulkRequest = new BulkRequest();for (Map<String, Object> entity : list) {IndexRequest indexRequest = new IndexRequest(esIndexName);Map<String, Object> sourceMap = new LinkedHashMap<>();List<String> allFieldsListed = new ArrayList<>();for (String fieldName : fieldList) {String fieldValue = String.valueOf(entity.get(fieldName));sourceMap.put(fieldName, fieldValue);allFieldsListed.add(Objects.toString(fieldValue, ""));}String id = String.valueOf(entity.get(idKey));indexRequest.id(id);sourceMap.put("_allFields", StringUtils.join(allFieldsListed, "$$"));indexRequest.source(sourceMap);bulkRequest.add(indexRequest);}try {BulkResponse bulkResponse = esClient.bulk(bulkRequest, RequestOptions.DEFAULT);counter.addAndGet(list.size());} catch (Exception e) {e.printStackTrace();}System.out.println(" page : " + pageNo + ", flushed " + counter.get() + " records ");});}private static void pageDo(String tableName, String whereCond, String orderBy, int pageSize, int startPage,BiConsumer<Integer, List<Map<String, Object>>> func) {if (StringUtils.isNotBlank(whereCond) && (!whereCond.trim().toLowerCase().startsWith("where"))) {whereCond = " where " + whereCond;}if (StringUtils.isNotBlank(orderBy) && (!orderBy.trim().toLowerCase().startsWith("order"))) {orderBy = " order by " + orderBy;}String queryCountSql = String.format(" select count(*) from %s %s %s", tableName, whereCond, orderBy);Integer totalCount = mysqlJdbcTemplate.queryForObject(queryCountSql, Integer.class);Integer totalPage = (totalCount == null || totalCount == 0) ? 0 : (totalCount - 1) / pageSize + 1;for (int i = startPage; i < totalPage; i++) {int offset = i * pageSize;String queryPageSql = String.format(" select * from %s %s %s limit %s,%s ", tableName, whereCond, orderBy, offset, pageSize);List<Map<String, Object>> list = mysqlJdbcTemplate.queryForList(queryPageSql);func.accept(i, list);}}}

基于中间件 kafka 的处理

首先通过 spoon/datax 将数据从 mysql 转换到 kafka

然后再由脚本从 kafka 消费数据, 处理传输到 es 中

入了一次消息队列之后, 然后程序再来消费, 就会快很多了, 消息队列本身功能比较单纯比较适合于做做顺序遍历就会有优势一些

这里以 spoon 将数据从 mysql 转换到 kafka

我这里本地环境内存等什么的都不足, 因此是一分钟入库三万条, 但是实际生产环境会很快

在生产环境五百多w 的数据, 基于 datax 传输 mysql 到 kafka, 差不多是五六分钟就可以了

基于 kafka 将数据传输到 es

如下程序仅仅是将 kafka 中的数据原样照搬过去了, 但是实际的场景中会做一些额外的业务处理, 这里仅仅是为了演示

/*** Test05PostQy2Es** @author Jerry.X.He* @version 1.0* @date 2022/11/21 16:00*/
public class Test05PostEsFromKafka {private static RestHighLevelClient esClient = getEsClient();private static IndicesClient indicesClient = esClient.indices();private static String esIndexName = "student_all_20221211";private static String groupId = "group-01";// Test05PostQy2Espublic static void main(String[] args) throws Exception {bulkKafka2EsData(esIndexName, groupId);}private static void bulkKafka2EsData(String esIndexName, String groupId) throws Exception {List<Pair<String, String>> hjk2StdFieldMap = hjk2StdFieldMap();Properties properties = kafkaProperties(groupId);String idKey = "ID";KafkaConsumer<String, String> kafkaConsumer = new KafkaConsumer<>(properties);kafkaConsumer.subscribe(Arrays.asList("STUDENT_ALL_20221211"));AtomicInteger counter = new AtomicInteger(0);long start = System.currentTimeMillis();while (true) {ConsumerRecords<String, String> records = kafkaConsumer.poll(100);if (records.isEmpty()) {Thread.sleep(10 * 1000);long spent = System.currentTimeMillis() - start;System.out.println(" spent : " + (spent / 1000) + " s ");continue;}BulkRequest bulkRequest = new BulkRequest();boolean isEmpty = true;for (ConsumerRecord<String, String> record : records) {IndexRequest indexRequest = new IndexRequest(esIndexName);String value = record.value();JSONObject entity = JSON.parseObject(value);// 获取 idString id = StringUtils.defaultIfBlank(entity.getString(idKey), "");if (isFilterByQy(id)) {continue;}Map<String, Object> sourceMap = new LinkedHashMap<>();List<String> allFieldsListed = new ArrayList<>();for (Pair<String, String> entry : hjk2StdFieldMap) {String hjkKey = entry.getKey(), stdKey = entry.getValue();String fieldValue = StringUtils.defaultIfBlank(entity.getString(hjkKey), "");sourceMap.put(stdKey, fieldValue);allFieldsListed.add(Objects.toString(fieldValue, ""));}indexRequest.id(id);sourceMap.put("_allFields", StringUtils.join(allFieldsListed, "$$"));isEmpty = false;indexRequest.source(sourceMap);bulkRequest.add(indexRequest);}if (isEmpty) {continue;}try {BulkResponse bulkResponse = esClient.bulk(bulkRequest, RequestOptions.DEFAULT);counter.addAndGet(bulkRequest.requests().size());} catch (Exception e) {e.printStackTrace();}System.out.println(" flushed " + counter.get() + " records ");}}private static List<Pair<String, String>> hjk2StdFieldMap() {List<Pair<String, String>> hjk2StdFieldMap = new ArrayList<>();hjk2StdFieldMap.add(new ImmutablePair<>("id", "id"));hjk2StdFieldMap.add(new ImmutablePair<>("CREATED_AT", "CREATED_AT"));hjk2StdFieldMap.add(new ImmutablePair<>("UPDATED_AT", "UPDATED_AT"));for (int i = 0; i < Test05CreateMysqlBigTable.maxFieldIdx; i++) {String fieldName = String.format("field%s", i);hjk2StdFieldMap.add(new ImmutablePair<>(fieldName, fieldName));}return hjk2StdFieldMap;}private static Properties kafkaProperties(String groupId) {Properties properties = new Properties();properties.put("bootstrap.servers", "192.168.0.190:9092");properties.put("group.id", groupId);properties.put("enable.auto.commit", "true");properties.put("auto.commit.interval.ms", "1000");properties.put("auto.offset.reset", "earliest");properties.put("session.timeout.ms", "30000");properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");return properties;}private static boolean isFilterByQy(String qy) {if (StringUtils.isBlank(qy)) {return true;}return false;}}

spoon 安装 kakfa 插件

来自 Kettle安装Kafka Consumer和Kafka Producer插件

    1.从github上下载kettle的kafka插件，地址如下Kafka Consumer地址：https://github.com/RuckusWirelessIL/pentaho-kafka-consumer/releases/tag/v1.7Kafka Producer地址：https://github.com/RuckusWirelessIL/pentaho-kafka-producer/releases/tag/v1.92.进入 kettle 安装目录：在plugin目录下创建steps目录3.把下载的插件解压后放到 steps 目录下5.重启 spoon.bat 即可

完