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support custom partitioner for nebula when generate sst files #49
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26 changes: 26 additions & 0 deletions
26
exchange-common/src/main/scala/com/vesoft/exchange/common/utils/NebulaPartitioner.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,26 @@ | ||
| /* Copyright (c) 2021 vesoft inc. All rights reserved. | ||
| * | ||
| * This source code is licensed under Apache 2.0 License. | ||
| */ | ||
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| package com.vesoft.exchange.common.utils | ||
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| import java.nio.{ByteBuffer, ByteOrder} | ||
| import org.apache.spark.Partitioner | ||
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| class NebulaPartitioner(partitions: Int) extends Partitioner { | ||
| require(partitions >= 0, s"Number of partitions ($partitions) cannot be negative.") | ||
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| override def numPartitions: Int = partitions | ||
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| override def getPartition(key: Any): Int = { | ||
| var part = ByteBuffer | ||
| .wrap(key.asInstanceOf[Array[Byte]], 0, 4) | ||
| .order(ByteOrder.nativeOrder) | ||
| .getInt >> 8 | ||
| if (part <= 0) { | ||
| part = part + partitions | ||
| } | ||
| part - 1 | ||
| } | ||
| } |
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50 changes: 50 additions & 0 deletions
50
exchange-common/src/test/scala/com/vesoft/exchange/common/writer/FileBaseWriterSuite.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,50 @@ | ||
| /* Copyright (c) 2021 vesoft inc. All rights reserved. | ||
| * | ||
| * This source code is licensed under Apache 2.0 License. | ||
| */ | ||
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| package com.vesoft.exchange.common.writer | ||
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| import com.vesoft.exchange.common.config.{FileBaseSinkConfigEntry, SinkCategory} | ||
| import org.apache.spark.sql.{Dataset, Encoders, Row, SparkSession} | ||
| import org.junit.Test | ||
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| class FileBaseWriterSuite { | ||
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| @Test | ||
| def writeSstFilesSuite(): Unit = { | ||
| val spark = SparkSession.builder().master("local").getOrCreate() | ||
| import spark.implicits._ | ||
| // generate byte[] key using encoder's getVertexKey, space:"test", tag: "person" | ||
| val key1 = "01a40200310000000000000000000000000000000000000002000000" // id: "1" | ||
| val key2 = "01170000320000000000000000000000000000000000000002000000" // id: "2" | ||
| val key3 = "01fe0000330000000000000000000000000000000000000002000000" // id: "3" | ||
| val key4 = "01a90300340000000000000000000000000000000000000002000000" // id: "4" | ||
| val key5 = "01220200350000000000000000000000000000000000000002000000" // id: "5" | ||
| val value = "abc" | ||
| // construct test dataset | ||
| val data: Dataset[(Array[Byte], Array[Byte])] = spark.sparkContext | ||
| .parallelize( | ||
| List(key1.getBytes(), key2.getBytes(), key3.getBytes(), key4.getBytes(), key5.getBytes())) | ||
| .map(line => (line, value.getBytes())) | ||
| .toDF("key", "value") | ||
| .map { row => | ||
| (row.getAs[Array[Byte]](0), row.getAs[Array[Byte]](1)) | ||
| }(Encoders.tuple(Encoders.BINARY, Encoders.BINARY)) | ||
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| val generateSstFile = new GenerateSstFile | ||
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| val fileBaseConfig = | ||
| FileBaseSinkConfigEntry(SinkCategory.SST, "/tmp", "/tmp/remote", None) | ||
| val batchFailure = spark.sparkContext.longAccumulator(s"batchFailure.test}") | ||
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| data | ||
| .toDF("key", "value") | ||
| .sortWithinPartitions("key") | ||
| .foreachPartition { iterator: Iterator[Row] => | ||
| generateSstFile.writeSstFiles(iterator, fileBaseConfig, 10, null, batchFailure) | ||
| } | ||
| assert(batchFailure.value == 0) | ||
| } | ||
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| } |
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why don't use
repartitiondirectly?There was a problem hiding this comment.
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Dataframe doesn't have customed
repartitionfunction, it's RDD's function.