通过 Google Cloud Dataflow 创建/写入 Parititoned BigQuery 表

Question

我想利用时间分区表的新 BigQuery 功能，但不确定当前是否可以在 1.6 版的 Dataflow SDK 中实现。

查看BigQuery JSON API，创建天分区表需要传入一个

"timePartitioning": { "type": "DAY" }

选项，但 com.google.cloud.dataflow.sdk.io.BigQueryIO 接口只允许指定 TableReference。

我想也许我可以预先创建表，然后通过 BigQueryIO.Write.toTableReference lambda 潜入分区装饰器......？是否还有其他人通过 Dataflow 创建/写入分区表成功？

这似乎与设置当前不可用的 table expiration time 类似。

Answer 1

我相信当您不使用流式传输时应该可以使用分区装饰器。我们正在积极致力于通过流媒体支持分区装饰器。如果您今天在非流媒体模式下发现任何错误，请告诉我们。

Answer 2

正如 Pavan 所说，使用 Dataflow 写入分区表绝对是可能的。您使用的是在流模式还是批处理模式下运行的DataflowPipelineRunner？

您提出的解决方案应该可行。具体来说，如果您预先创建了一个设置了日期分区的表，那么您可以使用BigQueryIO.Write.toTableReference lambda 来写入日期分区。例如：

/**
 * A Joda-time formatter that prints a date in format like {@code "20160101"}.
 * Threadsafe.
 */
private static final DateTimeFormatter FORMATTER =
    DateTimeFormat.forPattern("yyyyMMdd").withZone(DateTimeZone.UTC);

// This code generates a valid BigQuery partition name:
Instant instant = Instant.now(); // any Joda instant in a reasonable time range
String baseTableName = "project:dataset.table"; // a valid BigQuery table name
String partitionName =
    String.format("%s$%s", baseTableName, FORMATTER.print(instant));

Answer 3

我采用的方法（也适用于流模式）：

• 为传入记录定义自定义窗口

• 将窗口转换为表/分区名称

  p.apply(PubsubIO.Read
              .subscription(subscription)
              .withCoder(TableRowJsonCoder.of())
          )
          .apply(Window.into(new TablePartitionWindowFn()) )
          .apply(BigQueryIO.Write
                         .to(new DayPartitionFunc(dataset, table))
                         .withSchema(schema)
                         .withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND)
          );
  

根据传入的数据设置窗口，可以忽略End Instant，因为开始值用于设置分区：

public class TablePartitionWindowFn extends NonMergingWindowFn<Object, IntervalWindow> {

private IntervalWindow assignWindow(AssignContext context) {
    TableRow source = (TableRow) context.element();
    String dttm_str = (String) source.get("DTTM");

    DateTimeFormatter formatter = DateTimeFormat.forPattern("yyyy-MM-dd").withZoneUTC();

    Instant start_point = Instant.parse(dttm_str,formatter);
    Instant end_point = start_point.withDurationAdded(1000, 1);

    return new IntervalWindow(start_point, end_point);
};

@Override
public Coder<IntervalWindow> windowCoder() {
    return IntervalWindow.getCoder();
}

@Override
public Collection<IntervalWindow> assignWindows(AssignContext c) throws Exception {
    return Arrays.asList(assignWindow(c));
}

@Override
public boolean isCompatible(WindowFn<?, ?> other) {
    return false;
}

@Override
public IntervalWindow getSideInputWindow(BoundedWindow window) {
    if (window instanceof GlobalWindow) {
        throw new IllegalArgumentException(
                "Attempted to get side input window for GlobalWindow from non-global WindowFn");
    }
    return null;
}

动态设置表分区：

public class DayPartitionFunc implements SerializableFunction<BoundedWindow, String> {

String destination = "";

public DayPartitionFunc(String dataset, String table) {
    this.destination = dataset + "." + table+ "$";
}

@Override
public String apply(BoundedWindow boundedWindow) {
    // The cast below is safe because CalendarWindows.days(1) produces IntervalWindows.
    String dayString = DateTimeFormat.forPattern("yyyyMMdd")
                                     .withZone(DateTimeZone.UTC)
                                     .print(((IntervalWindow) boundedWindow).start());
    return destination + dayString;
}}

有没有更好的方法来达到同样的结果？

Answer 4

Apache Beam 2.0 版支持分片 BigQuery 输出表out of the box。

Answer 5

如果您以table_name_YYYYMMDD 格式传递表名，那么BigQuery 会将其视为分片表，可以模拟分区表功能。 参考文档：https://cloud.google.com/bigquery/docs/partitioned-tables

Answer 6

我已通过数据流将数据写入 bigquery 分区表。如果该分区中的数据已经存在，那么这些写入是动态的，那么我可以附加到它或覆盖它。

我已经用 Python 编写了代码。这是对 bigquery 的批处理模式写入操作。

client = bigquery.Client(project=projectName)
dataset_ref = client.dataset(datasetName)
table_ref = dataset_ref.table(bqTableName)       
job_config = bigquery.LoadJobConfig()
job_config.skip_leading_rows = skipLeadingRows
job_config.source_format = bigquery.SourceFormat.CSV
if tableExists(client, table_ref):            
    job_config.autodetect = autoDetect
    previous_rows = client.get_table(table_ref).num_rows
    #assert previous_rows > 0
    if allowJaggedRows is True:
        job_config.allowJaggedRows = True
    if allowFieldAddition is True:
        job_config._properties['load']['schemaUpdateOptions'] = ['ALLOW_FIELD_ADDITION']
    if isPartitioned is True:
        job_config._properties['load']['timePartitioning'] = {"type": "DAY"}
    if schemaList is not None:
        job_config.schema = schemaList            
    job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE
else:            
    job_config.autodetect = autoDetect
    job_config._properties['createDisposition'] = 'CREATE_IF_NEEDED'
    job_config.schema = schemaList
    if isPartitioned is True:             
        job_config._properties['load']['timePartitioning'] = {"type": "DAY"}
    if schemaList is not None:
        table = bigquery.Table(table_ref, schema=schemaList)            
load_job = client.load_table_from_uri(gcsFileName, table_ref, job_config=job_config)        
assert load_job.job_type == 'load'
load_job.result()       
assert load_job.state == 'DONE'

效果很好。