Status

Motivation

Apache Thrift (along with protocol-buf ) has been widely adopted as a de facto standard of high throughput network traffic protocol. Historically, Companies like Pinterest have been utilizing thrift to encode strongly typed Kafka messages. Benefits of choosing thrift format are simplified schema management in code  and lower infra storage cost. 

Flink has no built-in support when users leverage thrift to encoding events they need to process. In this proposal, we aim to add this support to Flink.

Proposed Changes Summary

Although Thrift support can be a broad topic, we focus on thrift format encoding/decoding can be natively supported in Flink APIs (DataStream and Table as well as PyFlink)

DataStream Thrift Serialization/Deserialization

ThriftSerializationSchema and ThriftDeserializationSchema could be added to encode/decode thrift messages. Users should generate thrift java stub and add thrift schema jar dependencies.

Implementation wise should be straightforward in a few lines. 

public class ThriftSerializationSchema<T extends TBase> implements SerializationSchema<T> {

 public byte[] serialize(T element) {

       byte[] message = null;

       TSerializer serializer = new TSerializer();

      try{

          message = serializer.serialize(element);

      } catch(...){

         //mark fail to serialize metric

      }finally{

       return message;

     }

   }

}

setDeserializer(new ThriftDeSerializationSchema(thrift_class))

With datastream API, emitted TBase objects could be serialized/deserialized via kryo (unfortunately)

DataStream<? extends TBase> stream = env.addSource(...);

Table API Thrift Serialization/Deserialization

Unlike DataStream API, Table API / SQL requires converting byte arrays from and to Row/RowData. Certain mapping rules are required to ensure correctness and backward compatibility of such conversion.

TBase & Row Type Mapping

In order to support FlinkSQL workload reading from the Kafka topic, we proposed following data type mapping from Thrift Type System to Flink Row type system. We are in favor of debugging and user readability as we map enum to string type.

public final class TType {

 public static final byte STOP = 0;

 public static final byte VOID = 1;

 public static final byte BOOL = 2;

 public static final byte BYTE = 3;

 public static final byte DOUBLE = 4;

 public static final byte I16 = 6;

 public static final byte I32 = 8;

 public static final byte I64 = 10;

 public static final byte STRING = 11;

 public static final byte STRUCT = 12;

 public static final byte MAP = 13;

 public static final byte SET = 14;

 public static final byte LIST = 15;

 public static final byte ENUM = 16;

 public TType() {

 }

}

[1] Thrift Type system

TBase Field to Row Field Index Matching

Handling tbase payload and converting to Row type, Flinks needs to have deterministic mapping from each field in thrift payload to specific row index. We propose using ASC sorted thrift_id indexes (marked in brown below) when we map from tbase to row and vice versa. If a field is UNSET, in order to avoid UNSET fields causing mismatches, we use NULL as a placeholder.

struct Xtruct3

{

  1:  string string_thing,

  4:  i32    changed,

  9:  i32    i32_thing,

  11: i64    i64_thing

}

[2] https://raw.githubusercontent.com/apache/thrift/master/test/ThriftTest.thrift

Example of index matching can be found below

Note, from runtime performance consideration, we propose having a type to sort field information cache.

Row Field to TBase Field index matching

We propose a reverse approach above during row-to thrift payload conversion. If row arity is smaller than tbase schema fields, we propose adding null as placeholder.

Example of user update thrift schema with additional fields, Flink SQL jobs were deployed with new schema and restore with state using old schema (without new_thing), adding placeholder would avoid job fall into restart loop or skip messages.

struct Xtruct3

{

  1:  string string_thing,

  4:  i32    changed,

  9:  i32    i32_thing,

  11: i64    i64_thing,

  12: string new_thing

}

Handling Nested Field in Row

It’s common to have thrift schemas highly nested and keep growing as more product features are added.In order to properly decode/encode thrift payload, we propose a recursive converter from nested thrift struct to Flink Row field. Based on each sub struct class schema type mapping along with field index mapping, this recursive converter could handle very sophisticated nested fields (more than 7 levels deep, 24k characters of schema string) thrift schema

case TType.STRUCT:

 return getRow((TBase) val);

public Row getRow(TBase tBase) {

   List<Map.Entry<? extends TFieldIdEnum, FieldMetaData>> entries = ….

   // allocate row by thrift field size

   Row result = new Row(entries.size());

   int i = 0;

   String fieldAnnotation = tBase.getClass().toString();

   for (Map.Entry<? extends TFieldIdEnum, FieldMetaData> entry : entries) {

       if (tBase.isSet(entry.getKey())) {

           Object val = tBase.getFieldValue(entry.getKey());

           result.setField(i, getPrimitiveValue(entry.getValue().valueMetaData, val,

             fieldAnnotation + entry.getKey().getFieldName()));

       } else {

           result.setField(i, getDefaultValue(entry.getValue().valueMetaData,

             fieldAnnotation + entry.getKey().getFieldName()));

       }

       i++;

   }

 return result;

}

Hive MetaStore dependencies conflict

Hive meta store libraries introduced a specific thrift library version that is not compatible with our internal flink version. This led to our Flink SQL jobs version conflict. 

We propose shading  libthrift and fb303 in the flink hive connector, and move those two packages to the project root level maven config; users could place their own customized libthrift jar as well as thrift schema jar into the /lib folder during release.

Thrift format table DDL script

Thrift format Table (Kafka or FileSystem) schemas were inference based on thrift schema file definition. As users update thrift schema files and release new schema, thrift format table definitions need to be updated.

Unfortunately, Flink has no built-in support for automated schema inference. We plan to offer a script to generate thrift schema fields from the thrift skeleton.

Users could modify and automate table creation and updates on their own.

flink_thrift_ddl_gen.py dummy.thrift -o dummy_ddl.sql

Public Interfaces

DataStream API

Including flink-thrift in flink-formats projects including three files to support datastream api encoding/decoding storing state with thrift format

flink-formats/flink-thrift/

ThriftDeserializationSchema

ThriftSerializationSchema

In the flink-thrift  pom.xml file, include thrift release defined in flink-parent <properties> 

flink-thrift  pom.xml

<dependency>

  <groupId>org.apache.thrift</groupId>

  <artifactId>libthrift</artifactId>

  <version>${thrift.version}</version>

 </dependency>

flink-parent pom.xml

<thrift.version>0.5.0-p6</thrift.version>

StreamExecutionEnvironment env = …

// user can opt-in to skip corrupted state 

env.addDefaultKryoSerializer(Event.class, PinterestTBaseSerializer.class);

FlinkKafkaConsumer<Event> kafkaConsumer =

new FlinkKafkaConsumer<Event>(

   topicName,

   new ThriftDeserializationSchema<>(Event.class),

   Configuration.configurationToProperties(kafkaConsumerConfig));

// user can opt-in to deserialize list of fields 

FlinkKafkaConsumer<Event> kafkaConsumer =

new FlinkKafkaConsumer<Event>(

   topicName,

   new ThriftDeserializationSchema<>(Event.class),

   Configuration.configurationToProperties(kafkaConsumerConfig));

FlinkKafkaProducer<Event> sink =

   new FlinkKafkaProducer(topicName,

       new ThriftSerializationSchema<Event>(topicName),

       Configuration.configurationToProperties(kafkaProducerConfig),

       FlinkKafkaProducer.Semantic.AT_LEAST_ONCE);

Metrics

We propose to introduce the following Gauge metrics:

1. numFailedDeserializeThriftRecord: The number of messages that failed to deserialize to thrift class payload
2. numFailedSerializeThriftRecord: The number of messages that failed to serialize to thrift binary payload

Table API

We propose adding ThriftRowFormatFactory,ThriftToRowDataConverter and RowDataToThriftConverter

flink-formats/flink-thrift/

ThriftRowFormatFactory

ThriftToRowConverter

RowToThriftConverter

*/

@Internal

public class ThriftValidator extends FormatDescriptorValidator {

  public static final String FORMAT_TYPE_VALUE = "thrift";

  public static final String FORMAT_THRIFT_CLASS = "format.thrift-struct";

}

protected final DecodingFormat<DeserializationSchema<RowData>> valueDecodingFormat;

Table DDL

We propose adding format.type thrift and thrift-struct ub table DDL

CREATE TABLE event_base_kafka_prod(

   `_time` BIGINT,

   `eventType` STRING,

   `userId` BIGINT,

   ...

   rowTime AS CAST(from_unixtime(_time/1000000000) as timestamp),

   WATERMARK FOR rowTime AS rowTime - INTERVAL '60' SECOND

) WITH (

   'connector.type' = 'kafka',

   'connector.version' = 'universal',

   'format.type' = 'thrift',

   'format.thrift-struct'='xxx.schemas.event.Event',

   ...

)

Metrics

We propose to introduce the following Gauge metrics:

1. numFailedConvertRowDataRecord: The number of messages failed to convert to specific rowdata
2. numFailedConvertThriftRecord: The number of rows failed to convert to thrift binary payload
   

Schema Compatibility 

Our proposal leverage thrift protocol handles adding new fields without changing thrift id sequence scenarios backward and forward compatibility.  One caveat is when the thrift enum adding new value not defined in the old thrift schema will break the job running with the old thrift schema.

• data encode with old thrift schema, decode with new thrift schema
• data encode with new thrift schema, decode with old thrift schema

For table api implementation, we propose changes to match data stream api full compatibility behavior. However, when the user schema introduces breaking changes, we propose showing metrics of failed messages.

Compatibility, Deprecation, and Migration Plan Test Plan

Thrift support is a less intrusive change, the user would opt-in with a set of configurations.  We plan to follow best practices by adding unit tests and integration tests.

Rejected Alternatives

If there are alternative ways of accomplishing the same thing, what were they? The purpose of this section is to motivate why the design is the way it is and not some other way.