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Posted to issues@iceberg.apache.org by GitBox <gi...@apache.org> on 2021/04/07 09:57:47 UTC
[GitHub] [iceberg] szehon-ho commented on a change in pull request #2362: Spark: Pass Table object to executors
szehon-ho commented on a change in pull request #2362:
URL: https://github.com/apache/iceberg/pull/2362#discussion_r608506448
##########
File path: spark/src/main/java/org/apache/iceberg/spark/source/RowDataRewriter.java
##########
@@ -49,34 +46,21 @@
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
-import static org.apache.iceberg.TableProperties.DEFAULT_NAME_MAPPING;
-
public class RowDataRewriter implements Serializable {
private static final Logger LOG = LoggerFactory.getLogger(RowDataRewriter.class);
- private final Schema schema;
+ private final Broadcast<Table> tableBroadcast;
private final PartitionSpec spec;
- private final Map<String, String> properties;
private final FileFormat format;
- private final Broadcast<FileIO> io;
- private final Broadcast<EncryptionManager> encryptionManager;
- private final LocationProvider locations;
- private final String nameMapping;
private final boolean caseSensitive;
- public RowDataRewriter(Table table, PartitionSpec spec, boolean caseSensitive,
- Broadcast<FileIO> io, Broadcast<EncryptionManager> encryptionManager) {
- this.schema = table.schema();
+ public RowDataRewriter(Broadcast<Table> tableBroadcast, PartitionSpec spec, boolean caseSensitive) {
+ this.tableBroadcast = tableBroadcast;
this.spec = spec;
- this.locations = table.locationProvider();
- this.properties = table.properties();
- this.io = io;
- this.encryptionManager = encryptionManager;
-
this.caseSensitive = caseSensitive;
- this.nameMapping = table.properties().get(DEFAULT_NAME_MAPPING);
+ Table table = tableBroadcast.getValue();
Review comment:
We should use value(), right?
##########
File path: spark/src/main/java/org/apache/iceberg/spark/source/RowDataRewriter.java
##########
@@ -95,27 +79,29 @@ public RowDataRewriter(Table table, PartitionSpec spec, boolean caseSensitive,
int partitionId = context.partitionId();
long taskId = context.taskAttemptId();
- RowDataReader dataReader = new RowDataReader(
- task, schema, schema, nameMapping, io.value(), encryptionManager.value(), caseSensitive);
+ Table table = tableBroadcast.getValue();
Review comment:
Same comment
##########
File path: spark3/src/main/java/org/apache/iceberg/spark/source/SparkWrite.java
##########
@@ -513,17 +512,22 @@ protected WriterFactory(PartitionSpec spec, FileFormat format, LocationProvider
@Override
public DataWriter<InternalRow> createWriter(int partitionId, long taskId, long epochId) {
- OutputFileFactory fileFactory = new OutputFileFactory(
- spec, format, locations, io.value(), encryptionManager.value(), partitionId, taskId);
- SparkAppenderFactory appenderFactory = new SparkAppenderFactory(properties, writeSchema, dsSchema, spec);
+ Table table = tableBroadcast.getValue();
+
+ OutputFileFactory fileFactory = new OutputFileFactory(table, format, partitionId, taskId);
+ SparkAppenderFactory appenderFactory = new SparkAppenderFactory(table, writeSchema, dsSchema);
+
+ PartitionSpec spec = table.spec();
+ FileIO io = table.io();
+
if (spec.isUnpartitioned()) {
- return new Unpartitioned3Writer(spec, format, appenderFactory, fileFactory, io.value(), targetFileSize);
Review comment:
I guess in future we can simplify these writer constructors too with table?
##########
File path: spark2/src/main/java/org/apache/iceberg/spark/source/Reader.java
##########
@@ -443,52 +448,37 @@ public String toString() {
private static class ReadTask<T> implements Serializable, InputPartition<T> {
private final CombinedScanTask task;
- private final String tableSchemaString;
+ private final Broadcast<Table> tableBroadcast;
private final String expectedSchemaString;
- private final String nameMappingString;
- private final Broadcast<FileIO> io;
- private final Broadcast<EncryptionManager> encryptionManager;
private final boolean caseSensitive;
private final boolean localityPreferred;
private final ReaderFactory<T> readerFactory;
- private transient Schema tableSchema = null;
private transient Schema expectedSchema = null;
private transient String[] preferredLocations = null;
- private ReadTask(CombinedScanTask task, String tableSchemaString, String expectedSchemaString,
- String nameMappingString, Broadcast<FileIO> io, Broadcast<EncryptionManager> encryptionManager,
+ private ReadTask(CombinedScanTask task, Broadcast<Table> tableBroadcast, String expectedSchemaString,
boolean caseSensitive, boolean localityPreferred, ReaderFactory<T> readerFactory) {
this.task = task;
- this.tableSchemaString = tableSchemaString;
+ this.tableBroadcast = tableBroadcast;
this.expectedSchemaString = expectedSchemaString;
- this.io = io;
- this.encryptionManager = encryptionManager;
this.caseSensitive = caseSensitive;
this.localityPreferred = localityPreferred;
this.preferredLocations = getPreferredLocations();
this.readerFactory = readerFactory;
- this.nameMappingString = nameMappingString;
}
@Override
public InputPartitionReader<T> createPartitionReader() {
- return readerFactory.create(task, lazyTableSchema(), lazyExpectedSchema(), nameMappingString, io.value(),
- encryptionManager.value(), caseSensitive);
+ Table table = tableBroadcast.getValue();
Review comment:
Same
##########
File path: spark2/src/main/java/org/apache/iceberg/spark/source/Writer.java
##########
@@ -123,6 +123,13 @@
this.partitionedFanoutEnabled = options.getBoolean(SparkWriteOptions.FANOUT_ENABLED, tablePartitionedFanoutEnabled);
}
+ protected JavaSparkContext lazySparkContext() {
Review comment:
Out of curiosity, why do we need a lazy one?
And also what the java one provide?
##########
File path: spark2/src/main/java/org/apache/iceberg/spark/source/Writer.java
##########
@@ -274,17 +271,21 @@ public String toString() {
@Override
public DataWriter<InternalRow> createDataWriter(int partitionId, long taskId, long epochId) {
- OutputFileFactory fileFactory = new OutputFileFactory(
- spec, format, locations, io.value(), encryptionManager.value(), partitionId, taskId);
- SparkAppenderFactory appenderFactory = new SparkAppenderFactory(properties, writeSchema, dsSchema, spec);
+ Table table = tableBroadcast.getValue();
Review comment:
Same (value()?)
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