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Posted to issues@flink.apache.org by "Jing Ge (Jira)" <ji...@apache.org> on 2022/03/02 10:29:00 UTC
[jira] [Comment Edited] (FLINK-25416) Build unified Parquet BulkFormat for both Table API and DataStream API
[ https://issues.apache.org/jira/browse/FLINK-25416?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17500033#comment-17500033 ]
Jing Ge edited comment on FLINK-25416 at 3/2/22, 10:28 AM:
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Could be used as the background information for the design of the unified parquet bulkformat. FLINK-26349 FLINK-26301
was (Author: jingge):
Could be used as the background information for the design of the unified parquet bulkformat.
> 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
> Components: Formats (JSON, Avro, Parquet, ORC, SequenceFile)
> Reporter: Jing Ge
> Assignee: Jing Ge
> Priority: Major
>
> *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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