platinumhamburg commented on code in PR #2161:
URL: https://github.com/apache/fluss/pull/2161#discussion_r2650268505
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fluss-server/src/main/java/org/apache/fluss/server/kv/KvTablet.java:
##########
@@ -468,8 +474,9 @@ private long applyInsert(
PaddingRow latestSchemaRow,
long logOffset)
throws Exception {
- walBuilder.append(ChangeType.INSERT,
latestSchemaRow.replaceRow(currentValue.row));
- kvPreWriteBuffer.put(key, currentValue.encodeValue(), logOffset);
+ BinaryValue newValue = autoIncProcessor.processAutoInc(currentValue);
Review Comment:
I've been wondering about the relationship between auto-increment column
handling and the Merge Engine. I think they should probably be integrated in
the design to avoid redundant encoding of row data, which would otherwise lead
to significant performance overhead.
##########
fluss-common/src/main/java/org/apache/fluss/config/ConfigOptions.java:
##########
@@ -1424,6 +1424,13 @@ public class ConfigOptions {
+ "The `first_row` merge engine will keep
the first row of the same primary key. "
+ "The `versioned` merge engine will keep
the row with the largest version of the same primary key.");
+ public static final ConfigOption<Long> TABLE_AUTO_INC_BATCH_SIZE =
+ key("table.auto-inc.batch-size")
+ .longType()
+ .defaultValue(100000L)
+ .withDescription(
+ "The batch size of auto-increment IDs fetched from
the distributed counter each time. This value determines the length of the ID
segment cached locally. Default: 100000.");
Review Comment:
New configuration options should be documented, including recommended values
and a description of the behavioral impact associated with different settings.
##########
fluss-client/src/test/java/org/apache/fluss/client/table/FlussTableITCase.java:
##########
@@ -456,6 +457,47 @@ void testInvalidPrefixLookup() throws Exception {
+ "because the lookup columns [b, a] must
contain all bucket keys [a, b] in order.");
}
+ @Test
+ void testPutAutoIncColumnAndLookup() throws Exception {
Review Comment:
To achieve more precise test assertions, I recommend explicitly creating a
single-bucket table, writing multiple batches of data—some with duplicate
primary keys and others with distinct primary keys—and then verifying that the
auto-increment column behaves correctly.
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