我正在尝试使用DataFlowRunner执行以下操作:
我对Apache Beam来说还很陌生,所以我不是100%地确定我想做的事是否可行。
我的问题是当我尝试连接两行时,使用CoGroupByKey转换后,尽管窗口策略相同(固定窗口30秒,窗口触发结束并触发了丢弃),但似乎数据从未同时到达窗格)。
我的代码的一些相关块:
/* Getting the data and windowing */
PCollection<PubsubMessage> pubsub = p.apply("ReadPubSub sub",PubsubIO.readMessages().fromSubscription(SUB_ALIM_REC));
String query = /* The query */
PCollection<TableRow> bqData = p.apply("Reading BQ",BigQueryIO.readTableRows().fromQuery(query).usingStandardSql())
.apply(Window.<TableRow>into(FixedWindows.of(Duration.standardSeconds(30)))
.triggering(AfterWatermark.pastEndOfWindow())
.withAllowedLateness(Duration.standardSeconds(0)).accumulatingFiredPanes());
PCollection<TableRow> tableRow = pubsub.apply(Window.<PubsubMessage>into(FixedWindows.of(Duration.standardSeconds(120)))
.triggering(AfterWatermark.pastEndOfWindow())
.withAllowedLateness(Duration.standardSeconds(0)).accumulatingFiredPanes())
.apply("JSON to TableRow",ParDo.of(new ToTableRow()));
/* Join code */
PCollection<TableRow> finalResultCollection =
kvpCollection.apply("Join TableRows", ParDo.of(
new DoFn<KV<Long, CoGbkResult>, TableRow>() {
private static final long serialVersionUID = 6627878974147676533L;
@ProcessElement
public void processElement(ProcessContext c) {
KV<Long, CoGbkResult> e = c.element();
Long idPaquete = e.getKey();
Iterable<TableRow> it1 = e.getValue().getAll(packTag);
Iterable<TableRow> it2 = e.getValue().getAll(alimTag);
for(TableRow t1 : itPaq) {
for (TableRow t2 : itAlimRec) {
TableRow joinedRow = new TableRow();
/* set the required fields from each collection */
c.output(joinedRow);
}
}
}
}));
在过去的两天内,我也一直收到此错误:
java.io.IOException: Failed to start reading from source: org.apache.beam.runners.core.construction.UnboundedReadFromBoundedSource$BoundedToUnboundedSourceAdapter@2808d228
com.google.cloud.dataflow.worker.WorkerCustomSources$UnboundedReaderIterator.start(WorkerCustomSources.java:783)
com.google.cloud.dataflow.worker.util.common.worker.ReadOperation$SynchronizedReaderIterator.start(ReadOperation.java:360)
com.google.cloud.dataflow.worker.util.common.worker.ReadOperation.runReadLoop(ReadOperation.java:193)
com.google.cloud.dataflow.worker.util.common.worker.ReadOperation.start(ReadOperation.java:158)
com.google.cloud.dataflow.worker.util.common.worker.MapTaskExecutor.execute(MapTaskExecutor.java:75)
com.google.cloud.dataflow.worker.StreamingDataflowWorker.process(StreamingDataflowWorker.java:1227)
com.google.cloud.dataflow.worker.StreamingDataflowWorker.access$1000(StreamingDataflowWorker.java:135)
com.google.cloud.dataflow.worker.StreamingDataflowWorker$6.run(StreamingDataflowWorker.java:966)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.UnsupportedOperationException: BigQuery source must be split before being read
org.apache.beam.sdk.io.gcp.bigquery.BigQuerySourceBase.createReader(BigQuerySourceBase.java:153)
org.apache.beam.runners.core.construction.UnboundedReadFromBoundedSource$BoundedToUnboundedSourceAdapter$ResidualSource.advance(UnboundedReadFromBoundedSource.java:463)
org.apache.beam.runners.core.construction.UnboundedReadFromBoundedSource$BoundedToUnboundedSourceAdapter$ResidualSource.access$300(UnboundedReadFromBoundedSource.java:442)
org.apache.beam.runners.core.construction.UnboundedReadFromBoundedSource$BoundedToUnboundedSourceAdapter$Reader.advance(UnboundedReadFromBoundedSource.java:293)
org.apache.beam.runners.core.construction.UnboundedReadFromBoundedSource$BoundedToUnboundedSourceAdapter$Reader.start(UnboundedReadFromBoundedSource.java:286)
com.google.cloud.dataflow.worker.WorkerCustomSources$UnboundedReaderIterator.start(WorkerCustomSources.java:778)
com.google.cloud.dataflow.worker.util.common.worker.ReadOperation$SynchronizedReaderIterator.start(ReadOperation.java:360)
com.google.cloud.dataflow.worker.util.common.worker.ReadOperation.runReadLoop(ReadOperation.java:193)
com.google.cloud.dataflow.worker.util.common.worker.ReadOperation.start(ReadOperation.java:158)
com.google.cloud.dataflow.worker.util.common.worker.MapTaskExecutor.execute(MapTaskExecutor.java:75)
com.google.cloud.dataflow.worker.StreamingDataflowWorker.process(StreamingDataflowWorker.java:1227)
com.google.cloud.dataflow.worker.StreamingDataflowWorker.access$1000(StreamingDataflowWorker.java:135)
com.google.cloud.dataflow.worker.StreamingDataflowWorker$6.run(StreamingDataflowWorker.java:966)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
java.lang.Thread.run(Thread.java:745)
非常感谢您的指导,以了解我正在尝试做的事情是否可行,或者是否有解决此问题的替代方法。
答案 0 :(得分:0)
我试图做同样的事情。根据{{3}}的了解,目前尚不可能。我试着在this question之后使用PeriodicImpulse自己进行操作(尽管我不希望有侧面输入)。我写了类似的以下代码,得到了ValueError: BigQuery source is not currently available for use in streaming pipelines.
。
segments = p | 'triggering segments fetch' >> PeriodicImpulse() \
| "loading segments" >> beam.io.Read(beam.io.BigQuerySource(
use_standard_sql=True,
query=f'''
SELECT
id,
segment
FROM `some_table`''')) \
| "windowing info" >> beam.WindowInto(window.FixedWindows(5))
info = p | "reading info" >> beam.io.ReadFromPubSub(
topic='my_test_topic') \
| "parsing info" >> beam.Map(message_to_json) \
| "mapping info" >> beam.Map(lambda x: (x['id'], x['username'])) \
| "windowing info" >> beam.WindowInto(window.FixedWindows(5))
results = ({'segments': segments, 'info': info} | beam.CoGroupByKey()) | "printing" >> beam.Map(print_out)
我认为目前最好的解决方案是使用数据存储之类的外部存储。我在另一条生产线中使用了这种方法,效果很好。您可以找到说明this example。