How to integrate Flink connector with version layers

Create table source for version layer

The main entry point of the Flink Connector API is OlpStreamConnectorHelper.

import com.here.platform.data.client.flink.scaladsl.OlpStreamConnectorHelper

import com.here.platform.data.client.flink.scaladsl.OlpStreamConnectorHelper;

An instance of OlpStreamConnectorHelper used to create flink.table.api.Schema and build SQL statement. The following code snippet shows how to create an instance of OlpStreamConnectorHelper, build flink.table.api.Schema and create table with given schema and options:

// define the properties
val properties = Map(
  "olp.layer.query" -> "mt_version==LATEST"
)

// create the Table Source
val sourceHelper: OlpStreamConnectorHelper =
  OlpStreamConnectorHelper(HRN(inputCatalogHrn), "sample-versioned-layer", properties)

// register the Table Source
val tEnv = StreamTableEnvironment.create(env)

tEnv.executeSql(
  s"CREATE TABLE InputTable ${sourceHelper.prebuiltSchema(tEnv).build()} " +
    s"WITH ${sourceHelper.options}")

OlpStreamConnectorHelper sourceHelper =
    OlpStreamConnectorHelper.create(
        HRN.fromString(inputCatalogHrn), inputLayerId, sourceProperties);
Schema sourceSchema = sourceHelper.prebuiltSchema(tEnv).build();
tEnv.executeSql(
    String.format("CREATE TABLE InputTable %s WITH %s", sourceSchema, sourceHelper.options()));

The source factory supports the following properties for Version layers:

olp.layer.query: a string written in the RSQL query language to query the version layer. If it is not defined, the value "mt_version==LATEST" will be used by default, and it would mean that all the partitions present in the latest version will be read.
olp.connector.download-parallelism: the maximum number of blobs that are being read in parallel in one flink task. The number of tasks corresponds to the set parallelism. As a result, the number of blobs that your pipeline can read in parallel equals the parallelism level times the value of this property. The default value is 10.
olp.connector.download-timeout: the overall timeout in milliseconds that is applied when reading a blob from the Blob API. The default value is 300000 milliseconds.

Before creating a table schema, the helper fetches the catalog configuration using the passed HRN. Then it checks the data format and schema if it exists for the passed layerId. As the last step, Flink Connector automatically translates the layer schema into a flink.table.api.Schema.

Data formats

The Flink Connector supports the following data formats for Version layer payload:

Raw. The decoding and encoding logic is not applied and you get your data payload as an array of bytes. Your Table schema appears as follows:

  root
    |-- data: Array[Byte]
    |-- mt_partition: String
    |-- mt_version: Long
    |-- mt_timestamp: Long
    |-- mt_checksum: String
    |-- mt_crc: String
    |-- mt_dataSize: Long
    |-- mt_compressedDataSize: Long

The column with the payload data is called data. The metadata columns follow the data column and have the mt_ prefix. In the list of metadata columns, only mt_partition is a required column. The rest are optional and you can use null as a value for them.

This format is used if your layer content type is configured as application/octet-stream.

Protobuf. Flink uses the attached Protobuf schema (that you specify in your layer configuration) to derive a Flink Table schema.

  root
    |-- protobuf_field_1: String
    |-- protobuf_field_2: String
    |-- probobuf_field_3.nested_column: Long
    |-- ...
    |-- mt_partition: String
    |-- mt_version: Long
    |-- mt_timestamp: Long
    |-- mt_checksum: String
    |-- mt_crc: String
    |-- mt_dataSize: Long
    |-- mt_compressedDataSize: Long

The Flink Connector puts the top level protobuf fields as the top level Row columns, then the metadata columns follow.

This format is used if your layer content type is configured as application/x-protobuf and you have a specified schema. If the schema is not specified, an error will be thrown.

Note

Self-referencing protobuf fields are not supported because there is no way to represent them in the Flink TypeInformation-based schema.

Avro. Flink uses the passed Avro schema (that you specify in the factory Map) to derive a Flink Table schema.

  root
    |-- avro_field_1: String
    |-- avro_field_2: String
    |-- ...
    |-- mt_partition: String
    |-- mt_version: Long
    |-- mt_timestamp: Long
    |-- mt_checksum: String
    |-- mt_crc: String
    |-- mt_dataSize: Long
    |-- mt_compressedDataSize: Long

The Flink Connector puts the top level Avro fields as the top level Row columns, then the metadata columns follow.

This format is used if your layer content type is configured as application/x-avro-binary and you have a specified schema. If the schema is not specified, an error will be thrown.

WARNING: New version of connector is not support metadata columns for avro data type.

Parquet. Flink uses the passed Avro schema (that you specify in the factory Map) to derive a Flink Table schema.

  root
    |-- parquet_field_1: String
    |-- parquet_field_2: String
    |-- ...
    |-- mt_partition: String
    |-- mt_version: Long
    |-- mt_timestamp: Long
    |-- mt_checksum: String
    |-- mt_crc: String
    |-- mt_dataSize: Long
    |-- mt_compressedDataSize: Long

The Flink Connector puts the top level parquet fields as the top level Row columns, then the metadata columns follow.

This format is used if your layer content type is configured as application/x-parquet and you have a specified schema. If the schema is not specified, an error will be thrown.

WARNING: New version of connector is not support metadata columns for parquet data type.

The Apache parquet-avro module expects the hadoop client to be available in the class path.

The hadoop client is not provided by the streaming environment at the moment. As a result, if you want to use the parquet-format you have to include the hadoop client dependency in your fat jar:

<dependencies>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>2.7.3</version>
        <scope>compile</scope>
        <exclusions>
            <exclusion>
                <groupId>org.apache.htrace</groupId>
                <artifactId>htrace-core</artifactId>
            </exclusion>
        </exclusions>
    </dependency>
</dependencies>

libraryDependencies ++=
Seq("org.apache.hadoop" % "hadoop-client" % "2.7.3" exclude ("org.apache.htrace", "htrace-core"))

Other formats

If your layer uses a format other than the described formats, an error will be thrown.

Table Source have the same schema for the same layer.

You can always print your Table schema using the standard Flink API:

// imagine that we have already registered InputTable
tEnv.from("InputTable").printSchema()

// imagine that we have already registered InputTable
tEnv.from("InputTable").printSchema();

Read raw data

Using SQL:

/// [create-table-source]
// define the properties
val properties = Map(
  "olp.layer.query" -> "mt_version==LATEST"
)

// create the Table Source
val sourceHelper: OlpStreamConnectorHelper =
  OlpStreamConnectorHelper(HRN(inputCatalogHrn), "sample-versioned-layer", properties)

// register the Table Source
val tEnv = StreamTableEnvironment.create(env)

tEnv.executeSql(
  s"CREATE TABLE InputTable ${sourceHelper.prebuiltSchema(tEnv).build()} " +
    s"WITH ${sourceHelper.options}")

/// [create-table-source]

// register the Table Source
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

/// [create-table-source-java]
OlpStreamConnectorHelper sourceHelper =
    OlpStreamConnectorHelper.create(
        HRN.fromString(inputCatalogHrn), inputLayerId, sourceProperties);
Schema sourceSchema = sourceHelper.prebuiltSchema(tEnv).build();
tEnv.executeSql(
    String.format("CREATE TABLE InputTable %s WITH %s", sourceSchema, sourceHelper.options()));
/// [create-table-source-java]

Table result =
    tEnv.sqlQuery(
        "SELECT tileId, mt_timestamp, mt_checksum, mt_dataSize, mt_crc FROM InputTable");

Read protobuf data

Using SQL:

val sourceProperties = Map[String, String]("olp.layer.query" -> "mt_version==LATEST",
                                           "olp.connector.metadata-columns" -> "true")

val sourceHelper: OlpStreamConnectorHelper =
  OlpStreamConnectorHelper(HRN(inputCatalogHrn), "sample-versioned-layer", sourceProperties)

val tEnv = StreamTableEnvironment.create(env)

tEnv.executeSql(
  s"CREATE TABLE InputTable ${sourceHelper.prebuiltSchema(tEnv).build()} " +
    s"WITH ${sourceHelper.options}")

tEnv.from("InputTable").printSchema()

val result: Table = tEnv.sqlQuery("""
SELECT
    tileId,
    mt_timestamp,
    mt_checksum,
    mt_dataSize,
    mt_crc
FROM InputTable
  """)

tEnv
  .toDataStream(result)
  .print()

StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

OlpStreamConnectorHelper sourceHelper =
    OlpStreamConnectorHelper.create(
        HRN.fromString(inputCatalogHrn), inputLayerId, sourceProperties);
Schema sourceSchema = sourceHelper.prebuiltSchema(tEnv).build();
tEnv.executeSql(
    String.format("CREATE TABLE InputTable %s WITH %s", sourceSchema, sourceHelper.options()));

Table result =
    tEnv.sqlQuery(
        "SELECT tileId, messages, mt_timestamp, mt_checksum, mt_crc, mt_dataSize, mt_compressedDataSize FROM InputTable");

DataStream<Row> stream = tEnv.toDataStream(result);
stream.print();

Read Avro data

Using SQL:

    val tEnv = StreamTableEnvironment.create(env)

    val layerSchema =
      """
{
  "type" : "record",
  "name" : "Event",
  "namespace" : "my.example",
  "fields" : [
    {"name" : "city", "type" : "string"},
    {"name" : "event_timestamp", "type" : "long"},
    {"name" : "latitude", "type" : "double"},
    {"name" : "longitude", "type" : "double"}
  ]
}
    """
    val sourceProperties = Map(
      "olp.catalog.layer-schema" -> layerSchema,
      "olp.layer.query" -> "mt_version==LATEST"
    )

    val sourceHelper: OlpStreamConnectorHelper =
      OlpStreamConnectorHelper(HRN(inputCatalogHrn), "version-layer-avro-output", sourceProperties)

    tEnv.executeSql(
      s"CREATE TABLE InputTable ${sourceHelper.prebuiltSchema(tEnv).build()} " +
        s"WITH ${sourceHelper.options}")

    val result: Table = tEnv.sqlQuery("""
    SELECT
        city,
        event_timestamp,
        latitude,
        longitude
    FROM InputTable
      """)

    tEnv
      .toDataStream(result)
      .print()

StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

OlpStreamConnectorHelper sourceHelper =
    OlpStreamConnectorHelper.create(
        HRN.fromString(inputCatalogHrn), inputLayerId, sourceProperties);
Schema sourceSchema = sourceHelper.prebuiltSchema(tEnv).build();
tEnv.executeSql(
    String.format("CREATE TABLE InputTable %s WITH %s", sourceSchema, sourceHelper.options()));

Table result = tEnv.sqlQuery("SELECT 'Berlin', refs, messages FROM InputTable");

DataStream<Row> stream = tEnv.toDataStream(result);
stream.print();

Read Parquet data

Using SQL:

    val tEnv = StreamTableEnvironment.create(env)

    val inputLayerSchema = """
{
  "type" : "record",
  "name" : "Event",
  "namespace" : "my.example",
  "fields" : [
    {"name" : "event_timestamp", "type" : "long"},
    {"name" : "latitude", "type" : "double"},
    {"name" : "longitude", "type" : "double"}
  ]
}
    """

    val sourceProperties = Map(
      "olp.catalog.layer-schema" -> inputLayerSchema,
      "olp.layer.query" -> "mt_version==LATEST"
    )

    val sourceHelper: OlpStreamConnectorHelper =
      OlpStreamConnectorHelper(HRN(inputCatalogHrn),
                               "version-layer-parquet-input",
                               sourceProperties)

    tEnv.executeSql(
      s"CREATE TABLE InputTable ${sourceHelper.prebuiltSchema(tEnv).build()} " +
        s"WITH ${sourceHelper.options}")

    val result: Table = tEnv.sqlQuery("""
    SELECT
        city,
        event_timestamp,
        latitude,
        longitude
    FROM InputTable
      """)

    tEnv
      .toDataStream(result)
      .print()

StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

OlpStreamConnectorHelper sourceHelper =
    OlpStreamConnectorHelper.create(
        HRN.fromString(inputCatalogHrn), inputLayerId, sourceProperties);
Schema sourceSchema = sourceHelper.prebuiltSchema(tEnv).build();
tEnv.executeSql(
    String.format("CREATE TABLE InputTable %s WITH %s", sourceSchema, sourceHelper.options()));

Table result = tEnv.sqlQuery("SELECT 'Berlin', refs, messages FROM InputTable");

DataStream<Row> stream = tEnv.toDataStream(result);
stream.print();

Updated 22 days ago