dataflow bigquery partition [duplicate] - google-bigquery

Question

I wanted to take advantage of the new BigQuery functionality of time partitioned tables, but am unsure this is currently possible in the 1.6 version of the Dataflow SDK.
Looking at the BigQuery JSON API, to create a day partitioned table one needs to pass in a
"timePartitioning": { "type": "DAY" }
option, but the com.google.cloud.dataflow.sdk.io.BigQueryIO interface only allows specifying a TableReference.
I thought that maybe I could pre-create the table, and sneak in a partition decorator via a BigQueryIO.Write.toTableReference lambda..? Is anyone else having success with creating/writing partitioned tables via Dataflow?
This seems like a similar issue to setting the table expiration time which isn't currently available either.

Answer 1

As Pavan says, it is definitely possible to write to partition tables with Dataflow. Are you using the DataflowPipelineRunner operating in streaming mode or batch mode?
The solution you proposed should work. Specifically, if you pre-create a table with date partitioning set up, then you can use a BigQueryIO.Write.toTableReference lambda to write to a date partition. For example:
/**
* 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 2

The approach I took (works in the streaming mode, too):
Define a custom window for the incoming record
Convert the window into the table/partition name
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)
);
Setting the window based on the incoming data, the End Instant can be ignored, as the start value is used for setting the partition:
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;
}
Setting the table partition dynamically:
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;
}}
Is there a better way of achieving the same outcome?

Answer 3

I believe it should be possible to use the partition decorator when you are not using streaming. We are actively working on supporting partition decorators through streaming. Please let us know if you are seeing any errors today with non-streaming mode.

Answer 4

Apache Beam version 2.0 supports sharding BigQuery output tables out of the box.

Answer 5

I have written data into bigquery partitioned tables through dataflow. These writings are dynamic as-in if the data in that partition already exists then I can either append to it or overwrite it.
I have written the code in Python. It is a batch mode write operation into bigquery.
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'
It works fine.

Answer 6

If you pass the table name in table_name_YYYYMMDD format, then BigQuery will treat it as a sharded table, which can simulate partition table features.
Refer the documentation: https://cloud.google.com/bigquery/docs/partitioned-tables