数据质量工作流程

数据质量运行流程分为2个部分：在web端进行数据质量检测的流程定义，通过dolphinscheduer进行调度，提交到spark计算引擎；spark端负责解析数据质量模型的参数，通过读取数据、执行转换、输出三个步骤，完成数据质量检测任务，工作流程如下图所示。
在这里插入图片描述

在web端进行定义

数据质量定义如下图所示，这里只定义了一个节点。
在这里插入图片描述以一个空值检测的输入参数为例，这个json文件会以字符串形式提交给spark端

{
    "name": "$t(null_check)",
    "env": {
        "type": "batch",
        "config": null
    },
    "readers": [
        {
            "type": "JDBC",
            "config": {
                "database": "ops",
                "password": "***",
                "driver": "com.mysql.cj.jdbc.Driver",
                "user": "root",
                "output_table": "ops_ms_alarm",
                "table": "ms_alarm",
                "url": "jdbc:mysql://192.168.3.211:3306/ops?allowLoadLocalInfile=false&autoDeserialize=false&allowLocalInfile=false&allowUrlInLocalInfile=false"
            }
        }
    ],
    "transformers": [
        {
            "type": "sql",
            "config": {
                "index": 1,
                "output_table": "total_count",
                "sql": "SELECT COUNT(*) AS total FROM ops_ms_alarm"
            }
        },
        {
            "type": "sql",
            "config": {
                "index": 2,
                "output_table": "null_items",
                "sql": "SELECT * FROM ops_ms_alarm WHERE (alarm_time is null or alarm_time = '') "
            }
        },
        {
            "type": "sql",
            "config": {
                "index": 3,
                "output_table": "null_count",
                "sql": "SELECT COUNT(*) AS nulls FROM null_items"
            }
        }
    ],
    "writers": [
        {
            "type": "JDBC",
            "config": {
                "database": "dolphinscheduler3",
                "password": "***",
                "driver": "com.mysql.cj.jdbc.Driver",
                "user": "root",
                "table": "t_ds_dq_execute_result",
                "url": "jdbc:mysql://192.168.3.212:3306/dolphinscheduler3?characterEncoding=utf-8&allowLoadLocalInfile=false&autoDeserialize=false&allowLocalInfile=false&allowUrlInLocalInfile=false",
                "sql": "select 0 as rule_type,'$t(null_check)' as rule_name,0 as process_definition_id,25 as process_instance_id,26 as task_instance_id,null_count.nulls AS statistics_value,total_count.total AS comparison_value,7 AS comparison_type,3 as check_type,0.95 as threshold,3 as operator,1 as failure_strategy,'hdfs://xmaster:9000/user/hadoop/data_quality_error_data/0_25_211-ops-ms_alarm-空值检测' as error_output_path,'2022-11-16 03:40:32' as create_time,'2022-11-16 03:40:32' as update_time from null_count full join total_count"
            }
        },
        {
            "type": "JDBC",
            "config": {
                "database": "dolphinscheduler3",
                "password": "***",
                "driver": "com.mysql.cj.jdbc.Driver",
                "user": "root",
                "table": "t_ds_dq_task_statistics_value",
                "url": "jdbc:mysql://192.168.3.212:3306/dolphinscheduler3?characterEncoding=utf-8&allowLoadLocalInfile=false&autoDeserialize=false&allowLocalInfile=false&allowUrlInLocalInfile=false",
                "sql": "select 0 as process_definition_id,26 as task_instance_id,1 as rule_id,'ZKTZKDBTRFDKXKQUDNZJVKNX8OIAEVLQ91VT2EXZD3U=' as unique_code,'null_count.nulls'AS statistics_name,null_count.nulls AS statistics_value,'2022-11-16 03:40:32' as data_time,'2022-11-16 03:40:32' as create_time,'2022-11-16 03:40:32' as update_time from null_count"
            }
        },
        {
            "type": "hdfs_file",
            "config": {
                "path": "hdfs://xmaster:9000/user/hadoop/data_quality_error_data/0_25_211-ops-ms_alarm-空值检测",
                "input_table": "null_items"
            }
        }
    ]
}

spark端源码分析

DataQualityApplication程序入口

DataQualityApplication#main

public static void main(String[] args) throws Exception {
    //...
//从命令行获取参数
    String dataQualityParameter = args[0];
//   将json参数转为DataQualityConfiguration对象
    DataQualityConfiguration dataQualityConfiguration = JsonUtils.fromJson(dataQualityParameter,DataQualityConfiguration.class);
    //...
//构建 SparkRuntimeEnvironment的参数Config对象
    EnvConfig envConfig = dataQualityConfiguration.getEnvConfig();
    Config config = new Config(envConfig.getConfig());
    config.put("type",envConfig.getType());
    if (Strings.isNullOrEmpty(config.getString(SPARK_APP_NAME))) {
        config.put(SPARK_APP_NAME,dataQualityConfiguration.getName());
    }

    SparkRuntimeEnvironment sparkRuntimeEnvironment = new SparkRuntimeEnvironment(config);
//委托给 DataQualityContext执行
    DataQualityContext dataQualityContext = new DataQualityContext(sparkRuntimeEnvironment,dataQualityConfiguration);
    dataQualityContext.execute();
}

数据质量配置类

public class DataQualityConfiguration implements IConfig {
    @JsonProperty("name")
    private String name; // 名称
    @JsonProperty("env")
    private EnvConfig envConfig; // 环境配置
    @JsonProperty("readers")
    private List<ReaderConfig> readerConfigs; // reader配置
    @JsonProperty("transformers")
    private List<TransformerConfig> transformerConfigs;  // transformer配置
    @JsonProperty("writers")
    private List<WriterConfig> writerConfigs; // writer配置
//...
}

DataQualityContext#execute

从dataQualityConfiguration类中获取 readers、transformers、writers，委托给SparkBatchExecution

public void execute() throws DataQualityException {
// 将List<ReaderConfig>转为List<BatchReader>
    List<BatchReader> readers = ReaderFactory
            .getInstance()
            .getReaders(this.sparkRuntimeEnvironment,dataQualityConfiguration.getReaderConfigs());
// 将List<TransformerConfig>转为List<BatchTransformer>
    List<BatchTransformer> transformers = TransformerFactory
            .getInstance()
            .getTransformer(this.sparkRuntimeEnvironment,dataQualityConfiguration.getTransformerConfigs());
// 将List<WriterConfig>转为List<BatchWriter>
    List<BatchWriter> writers = WriterFactory
            .getInstance()
            .getWriters(this.sparkRuntimeEnvironment,dataQualityConfiguration.getWriterConfigs());
// spark 运行环境
    if (sparkRuntimeEnvironment.isBatch()) {
// 批模式
        sparkRuntimeEnvironment.getBatchExecution().execute(readers,transformers,writers);
    } else {
// 流模式， 暂不支持
        throw new DataQualityException("stream mode is not supported now");
    }
}

ReaderFactory 类采用了单例和工厂方法的设计模式，目前支持JDBC和HIVE 的数据源的读取，对应Reader类HiveReader、JdbcReader
WriterFactory 类采用了单例和工厂方法的设计模式，目前支持JDBC、HDFS、LOCAL_FILE 的数据源的输出，对应Writer类 HdfsFileWriter LocalFileWriter JdbcWriter
TransformerFactory 类采用了单例和工厂方法的设计模式，目前仅支持TransformerType.SQL的转换器类型

结合json 可以看出一个空值检测的reader、tranformer、 writer情况
1个reader ：读取源表数据

3个tranformer： total_count 行总数、null_items 空值项（行数据）、null_count （空值数），计算sql 如下
– SELECT COUNT() AS total FROM ops_ms_alarm
– SELECT * FROM ops_ms_alarm WHERE (alarm_time is null or alarm_time = ‘’)
– SELECT COUNT() AS nulls FROM null_items

3个writer：
第一个是jdbc writer，将比较值、统计值输出t_ds_dq_execute_result 数据质量执行结果表，

SELECT
   //...
    null_count.nulls AS statistics_value,
    total_count.total AS comparison_value,
   //...
    'hdfs://xmaster:9000/user/hadoop/data_quality_error_data/0_25_211-ops-ms_alarm-空值检测' AS error_output_path,
   //...
FROM
    null_count
    FULL JOIN total_count

第二个是jdbc writer，将statistics_value写入到表 t_ds_dq_task_statistics_value

SELECT
    //...
    //...
    'null_count.nulls' AS statistics_name,
    null_count.nulls AS statistics_value,
    //...
FROM
    null_count

第3个是hdfs writer，将空值项写入到hdfs 文件目录

{
    "type": "hdfs_file",
    "config": {
        "path": "hdfs://xmaster:9000/user/hadoop/data_quality_error_data/0_25_211-ops-ms_alarm-空值检测",
        "input_table": "null_items"
    }
}

目前 DolphinScheduler占不支持实时数据的质量检测。

SparkBatchExecution#execute

public class SparkBatchExecution implements Execution<BatchReader, BatchTransformer, BatchWriter> {
    private final SparkRuntimeEnvironment environment;
    public SparkBatchExecution(SparkRuntimeEnvironment environment) throws ConfigRuntimeException {
        this.environment = environment;
    }
    
    @Override
    public void execute(List<BatchReader> readers, List<BatchTransformer> transformers, List<BatchWriter> writers) {
// 为每一个reader注册输入临时表
        readers.forEach(reader -> registerInputTempView(reader, environment));

        if (!readers.isEmpty()) {
// 取readers列表的第一个reader读取数据集合， reader的实现类有HiveReader、JdbcReader
            Dataset<Row> ds = readers.get(0).read(environment);
            for (BatchTransformer tf:transformers) {
// 执行转换
                ds = executeTransformer(environment, tf, ds);
// 将转换后结果写到临时表
                registerTransformTempView(tf, ds);
            }

            for (BatchWriter sink: writers) {
// 执行将转换结果由writer输出， writer的实现类有JdbcWriter、LocalFileWriter、HdfsFileWriter
                executeWriter(environment, sink, ds);
            }
        }
// 结束
        environment.sparkSession().stop();
    }
}

SparkBatchExecution#registerInputTempView

//注册输入临时表, 临时表表名为OUTPUT_TABLE的名字
    private void registerInputTempView(BatchReader reader, SparkRuntimeEnvironment environment) {
        Config conf = reader.getConfig();
        if (Boolean.TRUE.equals(conf.has(OUTPUT_TABLE))) {// ops_ms_alarm
            String tableName = conf.getString(OUTPUT_TABLE);        
            registerTempView(tableName, reader.read(environment));
        } else {
            throw new ConfigRuntimeException(
                "[" + reader.getClass().getName() + "] must be registered as dataset, please set \"output_table\" config");
        }
    }

调用 Dataset.createOrReplaceTempView方法

private void registerTempView(String tableName, Dataset<Row> ds) {
    if (ds != null) {
        ds.createOrReplaceTempView(tableName);
    } else {
        throw new ConfigRuntimeException("dataset is null, can not createOrReplaceTempView");
    }
}

执行转换executeTransformer

private Dataset<Row> executeTransformer(SparkRuntimeEnvironment environment, BatchTransformer transformer, Dataset<Row> dataset) {
    Config config = transformer.getConfig();
    Dataset<Row> inputDataset;
    Dataset<Row> outputDataset = null;
    if (Boolean.TRUE.equals(config.has(INPUT_TABLE))) {
// 从INPUT_TABLE获取表名
        String[] tableNames = config.getString(INPUT_TABLE).split(",");

// outputDataset合并了inputDataset数据集合
        for (String sourceTableName: tableNames) {
            inputDataset = environment.sparkSession().read().table(sourceTableName);
            if (outputDataset == null) {
                outputDataset = inputDataset;
            } else {
                outputDataset = outputDataset.union(inputDataset);
            }
        }
    } else {
//  配置文件无INPUT_TABLE
        outputDataset = dataset;
    }
// 如果配置文件中配置了TMP_TABLE， 将outputDataset 注册到TempView
    if (Boolean.TRUE.equals(config.has(TMP_TABLE))) {
        if (outputDataset == null) {
            outputDataset = dataset;
        }
        String tableName = config.getString(TMP_TABLE);
        registerTempView(tableName, outputDataset);
    }
//  转换器进行转换
    return transformer.transform(outputDataset, environment);
}

SqlTransformer#transform 最终是使用spark-sql进行处理，所以核心还是这个sql语句，sql 需要在web端生成好，参加前面的json文件。

public class SqlTransformer implements BatchTransformer {
    private final Config config;
    public SqlTransformer(Config config) {
        this.config = config;
    }
//...
    @Override
    public Dataset<Row> transform(Dataset<Row> data, SparkRuntimeEnvironment env) {
        return env.sparkSession().sql(config.getString(SQL));
    }
}

将数据输出到指定的位置executeWriter

private void executeWriter(SparkRuntimeEnvironment environment, BatchWriter writer, Dataset<Row> ds) {
    Config config = writer.getConfig();
    Dataset<Row> inputDataSet = ds;
    if (Boolean.TRUE.equals(config.has(INPUT_TABLE))) {
        String sourceTableName = config.getString(INPUT_TABLE);
        inputDataSet = environment.sparkSession().read().table(sourceTableName);
    }

    writer.write(inputDataSet, environment);
}