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support custom partitioner for nebula when generate sst files #49
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Nicole00
commented
Jan 5, 2022
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- whether to use custom partitioner is configurable.
- use custom partitioner make sure the keys in different sst files does not overlap.
- When ingest sst files generated with custom partitioner, all most sst files lies on L6 (space is empty before ingest).
close #46 |
Codecov Report
@@ Coverage Diff @@
## master #49 +/- ##
============================================
+ Coverage 50.19% 54.60% +4.40%
- Complexity 74 76 +2
============================================
Files 16 17 +1
Lines 1291 1315 +24
Branches 246 249 +3
============================================
+ Hits 648 718 +70
+ Misses 525 472 -53
- Partials 118 125 +7
Continue to review full report at Codecov.
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Well done
data: Dataset[(Array[Byte], Array[Byte])], | ||
partitionNum: Int): Dataset[(Array[Byte], Array[Byte])] = { | ||
import spark.implicits._ | ||
data.rdd |
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why don't use repartition
directly?
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why don't use
repartition
directly?
Dataframe doesn't have customed repartition
function, it's RDD's function.