Jing Ge created FLINK-25416:
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Summary: Build unified Parquet BulkFormat for both Table API and
DataStream API
Key: FLINK-25416
URL: https://issues.apache.org/jira/browse/FLINK-25416
Project: Flink
Issue Type: New Feature
Reporter: Jing Ge
*Background information*
Current AvroParquet implementation AvroParquetRecordFormat uses the high level
API ParquetReader that does not provide offset information, which turns out the
restoreReader logic has big room to improve.
Beyond AvroParquetRecordFormat there is another format implementation
ParquetVectorizedInputFormat w.r.t. the parquet which is coupled tightly with
the Table API.
It would be better to provide an unified Parquet BulkFormat with one
implementation that can support both Table API and DataStream API.
*Some thoughts*
Use the low level API {{ParquetFileReader}} with {{BulkFormat}} directly like
'ParquetVectorizedInputFormat' did instead of with {{StreamFormat}} for the
following reasons:
* the read logic is built in the internal low level class
{{InternalParquetRecordReader}} with package private visibility in
parquet-hadoop lib which uses another low level class {{ParquetFileReader}}
internally. This makes the implementation of StreamFormat very complicated. I
think the design idea of StreamFormat is to simplify the implementation. They
do not seem to work together.
* {{{}ParquetFileReader{}}}reads data in batch mode, i.e. {{{}PageReadStore
pages = reader.readNextFilteredRowGroup();{}}}. If we build these logic into
StreamFormat({{{}AvroParquetRecordFormat{}}} in this case),
{{AvroParquetRecordFormat}} has to take over the role
{{InternalParquetRecordReader}} does, including but not limited to
## read {{PageReadStore}} in batch mode.
## manage {{{}PageReadStore{}}}, i.e. read next page when all records in the
current page have been consumed and cache it.
## manage the read index within the current {{PageReadStore}} because
StreamFormat has its own setting for read size, etc.
All of these make {{AvroParquetRecordFormat}} become the {{BulkFormat}} instead
of {{StreamFormat}}
* {{StreamFormat}} can only be used via {{{}StreamFormatAdapter{}}}, which
means everything we will do with the low level APIs for parquet-hadoop lib
should have no conflict with the built-in logic provided by
{{{}StreamFormatAdapter{}}}.
Now we could see if we build these logics into a {{StreamFormat}}
implementation, i.e. {{{}AvroParquetRecordFormat{}}}, all convenient built-in
logic provided by the {{StreamFormatAdapter}} turns into obstacles. There is
also a violation of single responsibility principle, i.e.
{{AvroParquetRecordFormat }}will take some responsibility of
{{{}BulkFormat{}}}. These might be the reasons why
'ParquetVectorizedInputFormat' implemented {{BulkFormat}} instead of
{{{}StreamFormat{}}}.
In order to build a unified parquet implementation for both Table API and
DataStream API, it makes more sense to consider building these code into a
{{BulkFormat}} implementation class. Since the output data types are different,
{{RowData}} vs. {{{}Avro{}}}, extra converter logic should be introduced into
the architecture design. Depending on how complicated the issue will be and how
big the impact it will have on the current code base, a new FLIP might be
required.
Following code piece were suggested by Arvid Heise for the next optimized
AvroParquetReader:
{code:java}
// Injected
GenericData model = GenericData.get();
org.apache.hadoop.conf.Configuration conf = new
org.apache.hadoop.conf.Configuration();
// Low level reader - fetch metadata
ParquetFileReader reader = null;
MessageType fileSchema = reader.getFileMetaData().getSchema();
Map<String, String> metaData =
reader.getFileMetaData().getKeyValueMetaData();
// init Avro specific things
AvroReadSupport<T> readSupport = new AvroReadSupport<>(model);
ReadSupport.ReadContext readContext =
readSupport.init(
new InitContext(
conf,
metaData.entrySet().stream()
.collect(Collectors.toMap(e ->
e.getKey(), e -> Collections.singleton(e.getValue()))),
fileSchema));
RecordMaterializer<T> recordMaterializer =
readSupport.prepareForRead(conf, metaData, fileSchema, readContext);
MessageType requestedSchema = readContext.getRequestedSchema();
// prepare record reader
ColumnIOFactory columnIOFactory = new
ColumnIOFactory(reader.getFileMetaData().getCreatedBy());
MessageColumnIO columnIO =
columnIOFactory.getColumnIO(requestedSchema, fileSchema, true);
// for recovery
while (...) {
reader.skipNextRowGroup();
}
// for reading
PageReadStore pages;
for (int block = 0; (pages = reader.readNextRowGroup()) != null;
block++) {
RecordReader<T> recordReader = columnIO.getRecordReader(pages,
recordMaterializer);
for (int i = 0; i < pages.getRowCount(); i++) {
T record = recordReader.read();
emit record;
}
} {code}
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