CODEWITHSUNDEEP
google-bigquerygcloud
saifuddin778
saifuddin778

Reputation: 7298

BigQuery: Split table based on a column

Short question: I would like to split a BQ table into multiple small tables, based on the distinct values of a column. So, if column country has 10 distinct values, it should split the table into 10 individual tables, with each having respective country data. Best, if done from within a BQ query (using INSERT, MERGE, etc.).

What I am doing right now is importing data to gstorage -> local storage -> doing splits locally and then pushing into tables (which is kind of a very time consuming process).

Thanks.

Upvotes: 3

Views: 2348

Answers (2)

medvedev1088
medvedev1088

Reputation: 3755

You can use Dataflow for this. This answer gives an example of a pipeline that queries a BigQuery table, splits the rows based on a column and then outputs them to different PubSub topics (which could be different BigQuery tables instead):

Pipeline p = Pipeline.create(PipelineOptionsFactory.fromArgs(args).withValidation().create());

PCollection<TableRow> weatherData = p.apply(
        BigQueryIO.Read.named("ReadWeatherStations").from("clouddataflow-readonly:samples.weather_stations"));

final TupleTag<String> readings2010 = new TupleTag<String>() {
};
final TupleTag<String> readings2000plus = new TupleTag<String>() {
};
final TupleTag<String> readingsOld = new TupleTag<String>() {
};

PCollectionTuple collectionTuple = weatherData.apply(ParDo.named("tablerow2string")
        .withOutputTags(readings2010, TupleTagList.of(readings2000plus).and(readingsOld))
        .of(new DoFn<TableRow, String>() {
            @Override
            public void processElement(DoFn<TableRow, String>.ProcessContext c) throws Exception {

                if (c.element().getF().get(2).getV().equals("2010")) {
                    c.output(c.element().toString());
                } else if (Integer.parseInt(c.element().getF().get(2).getV().toString()) > 2000) {
                    c.sideOutput(readings2000plus, c.element().toString());
                } else {
                    c.sideOutput(readingsOld, c.element().toString());
                }

            }
        }));
collectionTuple.get(readings2010)
        .apply(PubsubIO.Write.named("WriteToPubsub1").topic("projects/fh-dataflow/topics/bq2pubsub-topic1"));
collectionTuple.get(readings2000plus)
        .apply(PubsubIO.Write.named("WriteToPubsub2").topic("projects/fh-dataflow/topics/bq2pubsub-topic2"));
collectionTuple.get(readingsOld)
        .apply(PubsubIO.Write.named("WriteToPubsub3").topic("projects/fh-dataflow/topics/bq2pubsub-topic3"));

p.run();

Upvotes: 1

Martin Weitzmann
Martin Weitzmann

Reputation: 4746

If the data has the same schema, just leave it in one table and use the clustering feature: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#creating_a_clustered_table

#standardSQL
 CREATE TABLE mydataset.myclusteredtable
 PARTITION BY dateCol
 CLUSTER BY country
 OPTIONS (
   description="a table clustered by country"
 ) AS (
   SELECT ....
 )

https://cloud.google.com/bigquery/docs/clustered-tables

The feature is in beta though.

Upvotes: 1

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