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https://issues.apache.org/jira/browse/SPARK-25390?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17178052#comment-17178052
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Rafael edited comment on SPARK-25390 at 8/15/20, 6:26 PM:
----------------------------------------------------------
Hey guys [~cloud_fan] [~b...@cloudera.com]
I'm trying to migrate the package where I was using *import
org.apache.spark.sql.sources.v2._ into Spark3.0.0*
and haven't found a good guide so may I ask my questions here.
Here my migration plan can you highlight what interfaces should I use now
1. Cannot find what should I use instead of ReadSupport, ReadSupport,
DataSourceReader,
if instead of ReadSupport we have to use now Scan then what happened to method
createReader?
{code:java}
class DefaultSource extends ReadSupport {
override def createReader(options: DataSourceOptions): DataSourceReader = new
GeneratingReader()
}
{code}
2.
Here instead of
{code:java}
import org.apache.spark.sql.sources.v2.reader.partitioning.{Distribution,
Partitioning}
import org.apache.spark.sql.sources.v2.reader.{InputPartition,
SupportsReportPartitioning}
{code}
I should use
{code:java}
import org.apache.spark.sql.connector.read.partitioning.{Distribution,
Partitioning}
import org.apache.spark.sql.connector.read.{InputPartition,
SupportsReportPartitioning}
{code}
right?
{code:java}
class GeneratingReader() extends DataSourceReader {
override def readSchema(): StructType = {...}
override def planInputPartitions(): util.List[InputPartition[InternalRow]] = {
val partitions = new util.ArrayList[InputPartition[InternalRow]]()
...
partitions.add(new GeneratingInputPartition(...))
}
override def outputPartitioning(): Partitioning = {...}
}
{code}
3.
Haven't found what should I use instead of
{code:java}
import org.apache.spark.sql.sources.v2.reader.InputPartition
import org.apache.spark.sql.sources.v2.reader.InputPartitionReader{code}
if a new interface is from package read then it has totally different new
contract.
{code:java}
import org.apache.spark.sql.connector.read.InputPartition{code}
{code:java}
class GeneratingInputPartition() extends InputPartition[InternalRow] {
override def createPartitionReader(): InputPartitionReader[InternalRow] = new
GeneratingInputPartitionReader(...)
}
class GeneratingInputPartitionReader() extends
InputPartitionReader[InternalRow] {
override def next(): Boolean = ...
override def get(): InternalRow = ...
override def close(): Unit = ...
}
{code}
And usage will remain the same?
{code:java}
val df = sparkSession.read
.format("sources.v2.generating")
.option(OPT_PARTITIONS, calculateNumberOfPartitions(numberOfRows))
.option(OPT_DESCRIPTOR, descriptorJson)
.option(OPT_SOURCE_TYPE,
getSqlName(jobExecutionPlanContext.jobDescriptor.jobHeader.sourceConnection))
.load()
{code}
was (Author: kyrdan):
Hey guys [~cloud_fan] [~b...@cloudera.com]
I'm trying to migrate the package where I was using *import
org.apache.spark.sql.sources.v2._ into Spark3.0.0*
and haven't found a good guide so may I ask my questions here.
Here my migration plan can you highlight what interfaces should I use now
1. Cannot find what should I use instead of ReadSupport, ReadSupport,
DataSourceReader,
if instead of ReadSupport we have to use now Scan then what happened to method
createReader?
{code:java}
class DefaultSource extends ReadSupport {
override def createReader(options: DataSourceOptions): DataSourceReader = new
GeneratingReader()
}
{code}
2.
Here instead of
{code:java}
import org.apache.spark.sql.sources.v2.reader.partitioning.{Distribution,
Partitioning}
import org.apache.spark.sql.sources.v2.reader.{InputPartition,
SupportsReportPartitioning}
{code}
I should use
{code:java}
import org.apache.spark.sql.connector.read.partitioning.{Distribution,
Partitioning}
import org.apache.spark.sql.connector.read.{InputPartition,
SupportsReportPartitioning}
{code}
right?
{code:java}
class GeneratingReader() extends DataSourceReader {
override def readSchema(): StructType = {...}
override def planInputPartitions(): util.List[InputPartition[InternalRow]] = {
val partitions = new util.ArrayList[InputPartition[InternalRow]]()
...
partitions.add(new GeneratingInputPartition(...))
}
override def outputPartitioning(): Partitioning = {...}
}
{code}
3.
Haven't found what should I use instead of
{code:java}
import org.apache.spark.sql.sources.v2.reader.InputPartition
import org.apache.spark.sql.sources.v2.reader.InputPartitionReader{code}
if a new interface is from package read then it has totally different new
contract.
{code:java}
import org.apache.spark.sql.connector.read.InputPartition{code}
{code:java}
class GeneratingInputPartition() extends InputPartition[InternalRow] {
override def createPartitionReader(): InputPartitionReader[InternalRow] = new
GeneratingInputPartitionReader(...)
}
class GeneratingInputPartitionReader() extends
InputPartitionReader[InternalRow] {
override def next(): Boolean = ...
override def get(): InternalRow = ...
override def close(): Unit = ...
}
{code}
> Data source V2 API refactoring
> ------------------------------
>
> Key: SPARK-25390
> URL: https://issues.apache.org/jira/browse/SPARK-25390
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Wenchen Fan
> Assignee: Wenchen Fan
> Priority: Major
> Fix For: 3.0.0
>
>
> Currently it's not very clear how we should abstract data source v2 API. The
> abstraction should be unified between batch and streaming, or similar but
> have a well-defined difference between batch and streaming. And the
> abstraction should also include catalog/table.
> An example of the abstraction:
> {code}
> batch: catalog -> table -> scan
> streaming: catalog -> table -> stream -> scan
> {code}
> We should refactor the data source v2 API according to the abstraction
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