Bucketed Append

You can define the bucket and bucket-key to get a bucketed append table.

Example to create bucketed append table:

Flink

CREATE TABLE my_table (
    product_id BIGINT,
    price DOUBLE,
    sales BIGINT
) WITH (
    'bucket' = '8',
    'bucket-key' = 'product_id'
);

Streaming

An ordinary Append table has no strict ordering guarantees for its streaming writes and reads, but there are some cases where you need to define a key similar to Kafka’s.

Every record in the same bucket is ordered strictly, streaming read will transfer the record to down-stream exactly in the order of writing. To use this mode, you do not need to config special configurations, all the data will go into one bucket as a queue.

Compaction in Bucket

By default, the sink node will automatically perform compaction to control the number of files. The following options control the strategy of compaction:

Streaming Read Order

For streaming reads, records are produced in the following order:

• For any two records from two different partitions
  • If scan.plan-sort-partition is set to true, the record with a smaller partition value will be produced first.
  • Otherwise, the record with an earlier partition creation time will be produced first.
• For any two records from the same partition and the same bucket, the first written record will be produced first.
• For any two records from the same partition but two different buckets, different buckets are processed by different tasks, there is no order guarantee between them.

Watermark Definition

You can define watermark for reading Paimon tables:

CREATE TABLE t (
    `user` BIGINT,
    product STRING,
    order_time TIMESTAMP(3),
    WATERMARK FOR order_time AS order_time - INTERVAL '5' SECOND
) WITH (...);
-- launch a bounded streaming job to read paimon_table
SELECT window_start, window_end, COUNT(`user`) FROM TABLE(
 TUMBLE(TABLE t, DESCRIPTOR(order_time), INTERVAL '10' MINUTES)) GROUP BY window_start, window_end;

You can also enable Flink Watermark alignment, which will make sure no sources/splits/shards/partitions increase their watermarks too far ahead of the rest:

Bounded Stream

Streaming Source can also be bounded, you can specify ‘scan.bounded.watermark’ to define the end condition for bounded streaming mode, stream reading will end until a larger watermark snapshot is encountered.

Watermark in snapshot is generated by writer, for example, you can specify a kafka source and declare the definition of watermark. When using this kafka source to write to Paimon table, the snapshots of Paimon table will generate the corresponding watermark, so that you can use the feature of bounded watermark when streaming reads of this Paimon table.

CREATE TABLE kafka_table (
    `user` BIGINT,
    product STRING,
    order_time TIMESTAMP(3),
    WATERMARK FOR order_time AS order_time - INTERVAL '5' SECOND
) WITH ('connector' = 'kafka'...);
-- launch a streaming insert job
INSERT INTO paimon_table SELECT * FROM kakfa_table;
-- launch a bounded streaming job to read paimon_table
SELECT * FROM paimon_table /*+ OPTIONS('scan.bounded.watermark'='...') */;

Batch

Bucketed table can be used to avoid shuffle if necessary in batch query, for example, you can use the following Spark SQL to read a Paimon table:

SET spark.sql.sources.v2.bucketing.enabled = true;
CREATE TABLE FACT_TABLE (order_id INT, f1 STRING) TBLPROPERTIES ('bucket'='10', 'bucket-key' = 'order_id');
CREATE TABLE DIM_TABLE (order_id INT, f2 STRING) TBLPROPERTIES ('bucket'='10', 'primary-key' = 'order_id');
SELECT * FROM FACT_TABLE JOIN DIM_TABLE on t1.order_id = t4.order_id;

The spark.sql.sources.v2.bucketing.enabled config is used to enable bucketing for V2 data sources. When turned on, Spark will recognize the specific distribution reported by a V2 data source through SupportsReportPartitioning, and will try to avoid shuffle if necessary.

The costly join shuffle will be avoided if two tables have same bucketing strategy and same number of buckets.