pvary commented on code in PR #10859:
URL: https://github.com/apache/iceberg/pull/10859#discussion_r1705275468
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docs/docs/flink-writes.md:
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@@ -262,6 +262,91 @@ INSERT INTO tableName /*+ OPTIONS('upsert-enabled'='true')
*/
Check out all the options here:
[write-options](flink-configuration.md#write-options)
+## Distribution mode
+
+Flink streaming writer supports both `HASH` and `RANGE` distribution mode.
+You can enable it via `FlinkSink#Builder#distributionMode(DistributionMode )`
+or via [write-options](flink-configuration.md#write-options).
+
+### Hash distribution
+
+HASH distribution shuffle data by partition key (partitioned table) or
+equality fields (non-partitioned table). It simply leverages Flink's
+`DataStream#keyBy` to distribute the data.
+
+HASH distribution has a few limitations.
+<ul>
+<li>It doesn't handle skewed data well. E.g. some partitions have a lot more
data than others.
+<li>It can result in unbalanced traffic distribution if cardinality of the
partition key or
+equality fields is low as demonstrated by [PR
4228](https://github.com/apache/iceberg/pull/4228).
+<li>Writer parallelism is limited to the cardinality of the hash key.
+if the cardinality is 10, only at most 10 writer tasks would get the traffic.
+Having higher writer parallelism (even if traffic volume requires) won't help.
+</ul>
+
+### Range distribution (experimental)
+
+RANGE distribution shuffle data by partition key or sort order via a custom
range partitioner.
+Range distribution collects traffic statistics to guide the range partitioner
to
+evenly distribute traffic to writer tasks.
+
+RANGE distribution can be applied an Iceberg table that either is partitioned
or
+has SortOrder defined. For a partitioned table without SortOrder, partition
columns
+are used as sort columns. If SortOrder is defined for the table, it is used by
+the range partitioner.
+
+Range distribution can handle skewed data. E.g.
+<ul>
+<li>Table is partitioned by event time. Typically, recent hours have more data,
+while the long-tail hours have less and less data.
+<li>Table is partitioned by country code, where some countries (like US) have
+a lot more traffic and smaller countries have a lot less data
+<li>Table is partitioned by event type, where some types have a lot more data
than others.
+</ul>
+
+Range distribution can also cluster data on non-partition columns.
+E.g., table is partitioned hourly on ingestion time. Queries often include
+predicate on a non-partition column like `device_id` or `country_code`.
+Range partition would improve the query performance by clustering on the
non-partition column.
+
+Range distribution only shuffle the data via range partitioner. Rows are *not*
sorted within
+a data file, which Flink streaming writer doesn't support yet.
+
+Statistics are collected by every shuffle operator subtask and aggregated by
the coordinator
+for every checkpoint cycle. Aggregated statistics are broadcast to all
subtasks and
+applied to the range partitioner in the next checkpoint. So it may take up to
two checkpoint
+cycles to detect traffic distribution change and apply the new statistics to
range partitioner.
Review Comment:
I remember that once we discussed the possibility to handle changes around
partition boundaries. Like when we have a time series data and the partitioning
is hourly. When the previous hour ends, and the new hour starts, then the naive
approach will take some time to recover and distribute the new data correctly.
Did we do any optimisations there?
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