boyuanzz commented on a change in pull request #12223:
URL: https://github.com/apache/beam/pull/12223#discussion_r457712606



##########
File path: 
sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetIO.java
##########
@@ -235,12 +283,164 @@ public ReadFiles withAvroDataModel(GenericData model) {
       return toBuilder().setAvroDataModel(model).build();
     }
 
+    public ReadFiles withSplit() {
+      return toBuilder().setSplit(true).build();
+    }
+
     @Override
     public PCollection<GenericRecord> expand(PCollection<FileIO.ReadableFile> 
input) {
       checkNotNull(getSchema(), "Schema can not be null");
-      return input
-          .apply(ParDo.of(new ReadFn(getAvroDataModel())))
-          .setCoder(AvroCoder.of(getSchema()));
+      if (!getSplit()) {
+        return input
+            .apply(ParDo.of(new SplitReadFn(getAvroDataModel())))
+            .setCoder(AvroCoder.of(getSchema()));
+      } else {
+        return input
+            .apply(ParDo.of(new ReadFn(getAvroDataModel())))
+            .setCoder(AvroCoder.of(getSchema()));
+      }
+    }
+
+    @DoFn.BoundedPerElement
+    static class SplitReadFn extends DoFn<FileIO.ReadableFile, GenericRecord> {
+      private Class<? extends GenericData> modelClass;
+      private static final Logger LOG = 
LoggerFactory.getLogger(SplitReadFn.class);
+      ReadSupport<GenericRecord> readSupport;
+
+      SplitReadFn(GenericData model) {
+        this.modelClass = model != null ? model.getClass() : null;
+      }
+
+      private static <K, V> Map<K, Set<V>> toSetMultiMap(Map<K, V> map) {
+        Map<K, Set<V>> setMultiMap = new HashMap<K, Set<V>>();
+        for (Map.Entry<K, V> entry : map.entrySet()) {
+          Set<V> set = new HashSet<V>();
+          set.add(entry.getValue());
+          setMultiMap.put(entry.getKey(), Collections.unmodifiableSet(set));
+        }
+        return Collections.unmodifiableMap(setMultiMap);
+      }
+
+      private InputFile getInputFile(FileIO.ReadableFile file) throws 
IOException {
+        if (!file.getMetadata().isReadSeekEfficient()) {
+          throw new RuntimeException(
+              String.format("File has to be seekable: %s", 
file.getMetadata().resourceId()));
+        }
+        return new BeamParquetInputFile(file.openSeekable());
+      }
+
+      @ProcessElement
+      public void processElement(
+          @Element FileIO.ReadableFile file,
+          RestrictionTracker<OffsetRange, Long> tracker,
+          OutputReceiver<GenericRecord> outputReceiver)
+          throws Exception {
+        ReadSupport<GenericRecord> readSupport;
+        InputFile inputFile = getInputFile(file);
+        Configuration conf = setConf();
+        GenericData model = null;
+        if (modelClass != null) {
+          model = (GenericData) modelClass.getMethod("get").invoke(null);
+        }
+        readSupport = new AvroReadSupport<GenericRecord>(model);
+        ParquetReadOptions options = HadoopReadOptions.builder(conf).build();
+        ParquetFileReader reader = ParquetFileReader.open(inputFile, options);
+        Filter filter = checkNotNull(options.getRecordFilter(), "filter");
+        conf = ((HadoopReadOptions) options).getConf();
+        for (String property : options.getPropertyNames()) {
+          conf.set(property, options.getProperty(property));
+        }
+        FileMetaData parquetFileMetadata = 
reader.getFooter().getFileMetaData();
+        MessageType fileSchema = parquetFileMetadata.getSchema();
+        Map<String, String> fileMetadata = 
parquetFileMetadata.getKeyValueMetaData();
+
+        ReadSupport.ReadContext readContext =
+            readSupport.init(new InitContext(conf, 
toSetMultiMap(fileMetadata), fileSchema));
+        ColumnIOFactory columnIOFactory = new 
ColumnIOFactory(parquetFileMetadata.getCreatedBy());
+        MessageType requestedSchema = readContext.getRequestedSchema();
+        RecordMaterializer<GenericRecord> recordConverter =
+            readSupport.prepareForRead(conf, fileMetadata, fileSchema, 
readContext);
+        boolean strictTypeChecking = options.isEnabled(STRICT_TYPE_CHECKING, 
true);
+        boolean filterRecords = options.useRecordFilter();
+        reader.setRequestedSchema(requestedSchema);
+        MessageColumnIO columnIO =
+            columnIOFactory.getColumnIO(requestedSchema, fileSchema, 
strictTypeChecking);
+        long currentBlock = tracker.currentRestriction().getFrom();
+        for (int i = 0; i < currentBlock; i++) {
+          reader.skipNextRowGroup();
+        }
+        while (tracker.tryClaim(currentBlock)) {
+          PageReadStore pages = reader.readNextRowGroup();
+          currentBlock += 1;
+          RecordReader<GenericRecord> recordReader =
+              columnIO.getRecordReader(
+                  pages, recordConverter, filterRecords ? filter : 
FilterCompat.NOOP);
+          GenericRecord read;
+          long currentRow = 0;
+          long totalRows = pages.getRowCount();
+          while (currentRow < totalRows) {
+            outputReceiver.output(recordReader.read());
+            currentRow += 1;
+          }
+        }
+      }
+
+      private Configuration setConf() throws Exception {
+        Configuration conf = new Configuration();
+        GenericData model = null;
+        if (modelClass != null) {
+          model = (GenericData) modelClass.getMethod("get").invoke(null);
+        }
+        if (model != null
+            && (model.getClass() == GenericData.class || model.getClass() == 
SpecificData.class)) {
+          conf.setBoolean(AvroReadSupport.AVRO_COMPATIBILITY, true);
+        } else {
+          conf.setBoolean(AvroReadSupport.AVRO_COMPATIBILITY, false);
+        }
+        return conf;
+      }
+
+      @GetInitialRestriction
+      public OffsetRange getInitialRestriction(@Element FileIO.ReadableFile 
file) throws Exception {
+        InputFile inputFile = getInputFile(file);
+        Configuration conf = setConf();
+        ParquetReadOptions options = HadoopReadOptions.builder(conf).build();
+        ParquetFileReader reader = ParquetFileReader.open(inputFile, options);
+        return new OffsetRange(0, reader.getRowGroups().size());
+      }
+
+      @SplitRestriction
+      public void split(@Restriction OffsetRange restriction, 
OutputReceiver<OffsetRange> out) {
+        for (OffsetRange range : restriction.split(1, 0)) {
+          out.output(range);
+        }
+      }
+
+      @NewTracker
+      public OffsetRangeTracker newTracker(@Restriction OffsetRange 
restriction) {
+        return new OffsetRangeTracker(restriction);
+      }
+
+      @GetSize
+      public double getSize(@Element FileIO.ReadableFile file, @Restriction 
OffsetRange restriction)
+          throws Exception {
+        InputFile inputFile = getInputFile(file);
+        Configuration conf = setConf();
+        ParquetReadOptions options = HadoopReadOptions.builder(conf).build();
+        ParquetFileReader reader = ParquetFileReader.open(inputFile, options);
+        if (restriction == null) {
+          return reader.getRecordCount();
+        } else {
+          long start = restriction.getFrom();
+          long end = restriction.getTo();
+          List<BlockMetaData> blocks = reader.getRowGroups();
+          double size = 0;
+          for (long i = start; i < end; i++) {
+            size += blocks.get((int) i).getRowCount();
+          }
+          return size;

Review comment:
       If it's feasible, it would be nice to track an estimated avg row size 
like `KafkaIO`. Then the size will be avgSize * rowCount. @chamikaramj Do you 
think it's necessary to do so?




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