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Hyperdrive - An extensible streaming ingestion pipeline on top of Apache Spark

Build Status

master	develop

What is Hyperdrive?

Hyperdrive is a configurable and scalable ingestion platform that allows data movement and transformation from streaming sources with exactly-once fault-tolerance semantics by using Apache Spark Structured Streaming.

In Hyperdrive, each ingestion is defined by the three components reader, transformer and writer. This separation allows adapting to different streaming sources and sinks, while reusing transformations common across multiple ingestion pipelines.

Motivation

Similar to batch processing, data ingestion pipelines are needed to process streaming data sources. While solutions for data pipelines exist, exactly-once fault-tolerance in streaming processing is an intricate problem and cannot be solved with the same strategies that exist for batch processing.

This is the gap the Hyperdrive aims to fill, by leveraging the exactly-once guarantee of Spark's Structured Streaming and by providing a flexible data pipeline.

Architecture

The data ingestion pipeline of Hyperdrive consists of four components: readers, transformers, writers.

• Readers define how to connect to sources, e.g. how to connect to Kafka in a secure cluster by providing security directives, which topic and brokers to connect to.
• Transformers define transformations to be applied to the decoded DataFrame, e.g. dropping columns.
• Writers define where DataFrames should be sent after the transformations, e.g. into HDFS as Parquet files.

Built-in components

• KafkaStreamReader - reads from a Kafka topic.
• ParquetStreamReader - reads Parquet files from a source directory.
• ConfluentAvroDecodingTransformer - decodes the payload as Confluent Avro (through ABRiS), retrieving the schema from the specified Schema Registry. This transformer is capable of seamlessly handling whatever schemas the payload messages are using.
• ConfluentAvroEncodingTransformer - encodes the payload as Confluent Avro (through ABRiS), updating the schema to the specified Schema Registry. This transformer is capable of seamlessly handling whatever schema the dataframe is using.
• ColumnSelectorStreamTransformer - selects all columns from the decoded DataFrame.
• AddDateVersionTransformerStreamWriter - adds columns for ingestion date and an auto-incremented version number, to be used for partitioning.
• ParquetStreamWriter - writes the DataFrame as Parquet, in append mode.
• KafkaStreamWriter - writes to a Kafka topic.
• DeltaCDCToSnapshotWriter - writes the DataFrame in Delta format. It expects CDC events and performs merge logic and creates the latest snapshot table.

Custom components

Custom components can be implemented using the Component Archetype following the API defined in the package za.co.absa.hyperdrive.ingestor.api

• A custom component has to be a class which extends either of the abstract classes StreamReader, StreamTransformer or StreamWriter
• The class needs to have a companion object which implements the corresponding trait StreamReaderFactory, StreamTransformerFactory or StreamWriterFactory
• The implemented components have to be packaged to a jar file, which can then be added to the classpath of the driver. To use a component, it has to be configured as described under Usage

After that, the new component will be able to be seamlessly invoked from the driver.

Usage

Hyperdrive has to be executed with Spark. Due to Spark-Kafka integration issues, it will only work with Spark 2.3 and higher.

How to run

git clone [email protected]:AbsaOSS/hyperdrive.git
mvn clean package

Given a configuration file has already been created, hyperdrive can be executed as follows:

spark-submit --class za.co.absa.hyperdrive.driver.drivers.PropertiesIngestionDriver driver/target/driver*.jar config.properties

Alternatively, configuration properties can also be passed as command-line arguments

spark-submit --class za.co.absa.hyperdrive.driver.drivers.CommandLineIngestionDriver driver/target/driver*.jar \
  component.ingestor=spark \
  component.reader=za.co.absa.hyperdrive.ingestor.implementation.reader.kafka.KafkaStreamReader \
  # more properties ...

Configuration

The configuration file may be created from the template located at driver/src/resources/Ingestion.properties.template.

CommandLineIngestionDriverDockerTest may be consulted for a working pipeline configuration.

General settings

Pipeline settings

Property Name	Required	Description
component.ingestor	Yes	Defines the ingestion pipeline. Only spark is currently supported.
component.reader	Yes	Fully qualified name of reader component, e.g.za.co.absa.hyperdrive.ingestor.implementation.reader.kafka.KafkaStreamReader
component.transformer.id.{order}	No	An arbitrary but unique string, referenced in this documentation as {transformer-id}
component.transformer.class.{transformer-id}	No	Fully qualified name of transformer component, e.g. za.co.absa.hyperdrive.ingestor.implementation.transformer.column.selection.ColumnSelectorStreamTransformer
component.writer	Yes	Fully qualified name of writer component, e.g. za.co.absa.hyperdrive.ingestor.implementation.writer.parquet.ParquetStreamWriter

Multiple transformers can be configured in the pipeline, including multiple instances of the same transformer. For each transformer instance, component.transformer.id.{order} and component.transformer.class.{transformer-id} have to specified, where {order} and {transformer-id} need to be unique. In the above table, {order} must be an integer and may be negative. {transformer-id} is only used within the configuration to identify which configuration options belong to a certain transformer instance.

Spark settings

Property Name	Required	Description
ingestor.spark.termination.method	No	Either processAllAvailable (stop query when no more messages are incoming) or awaitTermination (stop query on signal, e.g. Ctrl-C). Default: awaitTermination. See also Combination of trigger and termination method
ingestor.spark.await.termination.timeout	No	Timeout in milliseconds. Stops query when timeout is reached. This option is only valid with termination method awaitTermination

Settings for built-in components

KafkaStreamReader

Property Name	Required	Description
reader.kafka.topic	Yes	The name of the kafka topic to ingest data from. Equivalent to Spark property subscribe
reader.kafka.brokers	Yes	List of kafka broker URLs . Equivalent to Spark property kafka.bootstrap.servers

Any additional properties for kafka can be added with the prefix reader.option.. E.g. the property kafka.security.protocol can be added as reader.option.kafka.security.protocol

See e.g. the Structured Streaming + Kafka Integration Guide for optional kafka properties.

ParquetStreamReader

The parquet stream reader infers the schema from parquet files that already exist in the source directory. If no file exists, the reader will fail.

Property Name	Required	Description
reader.parquet.source.directory	Yes	Source path for the parquet files. Equivalent to Spark property path for the DataStreamReader

Any additional properties can be added with the prefix reader.parquet.options.. See Spark Structured Streaming Documentation

ConfluentAvroDecodingTransformer

The ConfluentAvroDecodingTransformer is built on ABRiS. More details about the configuration properties can be found there. Caution: The ConfluentAvroDecodingTransformer requires the property reader.kafka.topic to be set.

Property Name	Required	Description
transformer.{transformer-id}.schema.registry.url	Yes	URL of Schema Registry, e.g. http://localhost:8081. Equivalent to ABRiS property SchemaManager.PARAM_SCHEMA_REGISTRY_URL
transformer.{transformer-id}.value.schema.id	Yes	The schema id. Use latest or explicitly provide a number. Equivalent to ABRiS property SchemaManager.PARAM_VALUE_SCHEMA_ID
transformer.{transformer-id}.value.schema.naming.strategy	Yes	Subject name strategy of Schema Registry. Possible values are topic.name, record.name or topic.record.name. Equivalent to ABRiS property SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY
transformer.{transformer-id}.value.schema.record.name	Yes for naming strategies record.name and topic.record.name	Name of the record. Equivalent to ABRiS property SchemaManager.PARAM_SCHEMA_NAME_FOR_RECORD_STRATEGY
transformer.{transformer-id}.value.schema.record.namespace	Yes for naming strategies record.name and topic.record.name	Namespace of the record. Equivalent to ABRiS property SchemaManager.PARAM_SCHEMA_NAMESPACE_FOR_RECORD_STRATEGY
transformer.{transformer-id}.consume.keys	No	If set to true, keys will be consumed and added as columns to the dataframe. Key columns will be prefixed with key__
transformer.{transformer-id}.key.schema.id	Yes if consume.keys is true	The schema id for the key.
transformer.{transformer-id}.key.schema.naming.strategy	Yes if consume.keys is true	Subject name strategy for key
transformer.{transformer-id}.key.schema.record.name	Yes for key naming strategies record.name and topic.record.name	Name of the record.
transformer.{transformer-id}.key.schema.record.namespace	Yes for key naming strategies record.name and topic.record.name	Namespace of the record.
transformer.{transformer-id}.keep.columns	No	Comma-separated list of columns to keep (e.g. offset, partition)
transformer.{transformer-id}.disable.nullability.preservation	No	Set to true to ignore fix #137 and to keep the same behaviour as for versions prior to and including v3.2.2. Default value: false
transformer.{transformer-id}.schema.registry.basic.auth.user.info.file	No	A path to a text file, that contains one line in the form <username>:<password>. It will be passed as basic.auth.user.info to the schema registry config
transformer.{transformer-id}.use.advanced.schema.conversion	No	Set to true to convert the avro schema using AdvancedAvroToSparkConverter, which puts default value and underlying avro type to struct field metadata. Default false

For detailed information on the subject name strategy, please take a look at the Schema Registry Documentation.

Any additional properties for the schema registry config can be added with the prefix transformer.{transformer-id}.schema.registry.options.

Note: use.advanced.schema.conversion only works with a patched version of Spark, due to bug SPARK-34805. For the latest version of Spark, the patch is available in apache/spark#35270. For other versions of Spark, the changes need to be cherry-picked and built locally.

ConfluentAvroEncodingTransformer

The ConfluentAvroEncodingTransformer is built on ABRiS. More details about the configuration properties can be found there. Caution: The ConfluentAvroEncodingTransformer requires the property writer.kafka.topic to be set.

Property Name	Required	Description
transformer.{transformer-id}.schema.registry.url	Yes	URL of Schema Registry, e.g. http://localhost:8081. Equivalent to ABRiS property SchemaManager.PARAM_SCHEMA_REGISTRY_URL
transformer.{transformer-id}.value.schema.naming.strategy	Yes	Subject name strategy of Schema Registry. Possible values are topic.name, record.name or topic.record.name. Equivalent to ABRiS property SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY
transformer.{transformer-id}.value.schema.record.name	Yes for naming strategies record.name and topic.record.name	Name of the record. Equivalent to ABRiS property SchemaManager.PARAM_SCHEMA_NAME_FOR_RECORD_STRATEGY
transformer.{transformer-id}.value.schema.record.namespace	Yes for naming strategies record.name and topic.record.name	Namespace of the record. Equivalent to ABRiS property SchemaManager.PARAM_SCHEMA_NAMESPACE_FOR_RECORD_STRATEGY
transformer.{transformer-id}.value.optional.fields	No	Comma-separated list of nullable value columns that should get default value null in the avro schema. Nested columns' names should be concatenated with the dot (.)
transformer.{transformer-id}.produce.keys	No	If set to true, keys will be produced according to the properties key.column.prefix and key.column.names of the Hyperdrive Context
transformer.{transformer-id}.key.schema.naming.strategy	Yes if produce.keys is true	Subject name strategy for key
transformer.{transformer-id}.key.schema.record.name	Yes for key naming strategies record.name and topic.record.name	Name of the record.
transformer.{transformer-id}.key.schema.record.namespace	Yes for key naming strategies record.name and topic.record.name	Namespace of the record.
transformer.{transformer-id}.key.optional.fields	No	Comma-separated list of nullable key columns that should get default value null in the avro schema. Nested columns' names should be concatenated with the dot (.)
transformer.{transformer-id}.schema.registry.basic.auth.user.info.file	No	A path to a text file, that contains one line in the form <username>:<password>. It will be passed as basic.auth.user.info to the schema registry config
transformer.{transformer-id}.use.advanced.schema.conversion	No	Set to true to convert the avro schema using AdvancedSparkToAvroConverter, which reads default value and underlying avro type from struct field metadata. Default false

Any additional properties for the schema registry config can be added with the prefix transformer.{transformer-id}.schema.registry.options.

ColumnSelectorStreamTransformer

Property Name	Required	Description
transformer.{transformer-id}.columns.to.select	Yes	Comma-separated list of columns to select. * can be used to select all columns. Only existing columns using column names may be selected (i.e. expressions cannot be constructed)

AddDateVersionTransformer

The AddDateVersionTransformer adds the columns hyperdrive_date and hyperdrive_version. hyperdrive_date is the ingestion date (or a user-defined date), while hyperdrive_version is a number automatically incremented with every ingestion, starting at 1. For the auto-increment to work, hyperdrive_date and hyperdrive_version need to be defined as partition columns. Caution: This transformer requires a writer which defines writer.parquet.destination.directory.

Property Name	Required	Description
transformer.{transformer-id}.report.date	No	User-defined date for hyperdrive_date in format yyyy-MM-dd. Default date is the date of the ingestion

ColumnRenamingStreamTransformer

ColumnRenamingStreamTransformer allows renaming of columns specified in the configuration.

To add the transformer to the pipeline use this class name:

component.transformer.class.{transformer-id} = za.co.absa.hyperdrive.ingestor.implementation.transformer.column.renaming.ColumnRenamingStreamTransformer

Property Name	Required	Description
transformer.{transformer-id}.columns.rename.from	Yes	A comma-separated list of columns to rename. For example, column1, column2.
transformer.{transformer-id}.columns.rename.to	Yes	A comma-separated list of new column names. For example, column1_new, column2_new.

ColumnCopyStreamTransformer

ColumnCopyStreamTransformer allows copying of columns specified in the configuration. Dots in column names are interpreted as nested structs, unless they are surrounded by backticks (same as Spark convention)

Note that usage of the star-operator * within column names is not supported and may lead to unexpected behaviour.

To add the transformer to the pipeline use this class name:

component.transformer.class.{transformer-id} = za.co.absa.hyperdrive.ingestor.implementation.transformer.column.copy.ColumnCopyStreamTransformer

Property Name	Required	Description
transformer.{transformer-id}.columns.copy.from	Yes	A comma-separated list of columns to copy from. For example, column1.fieldA, column2.fieldA.
transformer.{transformer-id}.columns.copy.to	Yes	A comma-separated list of new column names. For example, newColumn.col1_fieldA, newColumn.col2_fieldA.

Example

Given a dataframe with the following schema

 |-- column1
 |    |-- fieldA
 |    |-- fieldB
 |-- column2
 |    |-- fieldA
 |-- column3

Then, the following column parameters

• transformer.{transformer-id}.columns.copy.from=column1.fieldA, column2.fieldA
• transformer.{transformer-id}.columns.copy.to=newColumn.col1_fieldA, newColumn.col2_fieldA

will produce the following schema

 |-- column1
 |    |-- fieldA
 |    |-- fieldB
 |-- column2
 |    |-- fieldA
 |-- column3
 |-- newColumn
 |    |-- col1_fieldA
 |    |-- col2_fieldA

DeduplicateKafkaSinkTransformer

DeduplicateKafkaSinkTransformer deduplicates records in a query from a Kafka source to a Kafka destination in a rerun after a failure. Records are identified across source and destination topic by a user-defined id, which may be a composite id and may include consumer record properties such as offset, partition, but also fields from the key or value schema. Deduplication is needed because the Kafka-destination provides only a at-least-once guarantee. Deduplication works by getting the ids from the last partial run in the destination topic and excluding them in the query.

Note that there must be only one source and one destination topic, and there must be only one writer writing to the destination topic, and no records must have been written to the destination topic after the partial run. Otherwise, records may still be duplicated.

To use this transformer, KafkaStreamReader, ConfluentAvroDecodingTransformer, ConfluentAvroEncodingTransformer and KafkaStreamWriter must be configured as well.

Note that usage of the star-operator * within column names is not supported and may lead to unexpected behaviour.

To add the transformer to the pipeline use this class name:

component.transformer.class.{transformer-id} = za.co.absa.hyperdrive.ingestor.implementation.transformer.deduplicate.kafka.DeduplicateKafkaSinkTransformer

Property Name	Required	Description
transformer.{transformer-id}.source.id.columns	Yes	A comma-separated list of consumer record properties that define the composite id. For example, offset, partition or key.some_user_id.
transformer.{transformer-id}.destination.id.columns	Yes	A comma-separated list of consumer record properties that define the composite id. For example, value.src_offset, value.src_partition or key.some_user_id.
transformer.{transformer-id}.kafka.consumer.timeout	No	Kafka consumer timeout in seconds. The default value is 120s.

The following fields can be selected on the consumer record

• topic
• offset
• partition
• timestamp
• timestampType
• serializedKeySize
• serializedValueSize
• key
• value

In case of key and value, the fields of their schemas can be specified by adding a dot, e.g. key.some_nested_record.some_id or likewise value.some_nested_record.some_id

See Pipeline settings for details about {transformer-id}.

ParquetStreamWriter

Property Name	Required	Description
writer.parquet.destination.directory	Yes	Destination path of the sink. Equivalent to Spark property path for the DataStreamWriter
writer.parquet.partition.columns	No	Comma-separated list of columns to partition by.
writer.parquet.metadata.check	No	Set this option to true if the consistency of the metadata log should be checked prior to the query. For very large tables, the check may be very expensive
writer.common.trigger.type	No	See Combination writer properties
writer.common.trigger.processing.time	No	See Combination writer properties

Any additional properties for the DataStreamWriter can be added with the prefix writer.parquet.options, e.g. writer.parquet.options.key=value

KafkaStreamWriter

Property Name	Required	Description
writer.kafka.topic	Yes	The name of the kafka topic to ingest data from. Equivalent to Spark property topic
writer.kafka.brokers	Yes	List of kafka broker URLs . Equivalent to Spark property kafka.bootstrap.servers
writer.common.trigger.type	No	See Combination writer properties
writer.common.trigger.processing.time	No	See Combination writer properties

MongoDbStreamWriter

Property Name	Required	Description
writer.mongodb.uri	Yes	Output MongoDB URI, e.g. mongodb://host:port/database.collection.
writer.mongodb.database	No	Database name (if not specified as the part of URI).
writer.mongodb.collection	No	Collection name (if not specified as the part of URI).
writer.common.trigger.type	No	See Combination writer properties
writer.common.trigger.processing.time	No	See Combination writer properties

Any additional properties for the DataStreamWriter can be added with the prefix writer.mongodb.options, e.g. writer.mongodb.options.key=value

Common MongoDB additional options

Property Name	Default	Description
writer.mongodb.option.spark.mongodb.output.ordered	true	When set to false inserts are done in parallel, increasing performance, but the order of documents is not preserved.
writer.mongodb.option.spark.mongodb.output.forceInsert	false	Forces saves to use inserts, even if a Dataset contains _id.
More on these options: https://docs.mongodb.com/spark-connector/current/configuration

DeltaCDCToSnapshotWriter

Property Name	Required	Description
writer.deltacdctosnapshot.destination.directory	Yes	Destination path of the sink. Equivalent to Spark property path for the DataStreamWriter
writer.deltacdctosnapshot.partition.columns	No	Comma-separated list of columns to partition by.
writer.deltacdctosnapshot.key.column	Yes	A column with unique entity identifier.
writer.deltacdctosnapshot.operation.column	Yes	A column containing value marking a record with an operation.
writer.deltacdctosnapshot.operation.deleted.values	Yes	Values marking a record for deletion in the operation column.
writer.deltacdctosnapshot.precombineColumns	Yes	When two records have the same key value, we will pick the one with the largest value for precombine columns. Evaluated in provided order.
writer.deltacdctosnapshot.precombineColumns.customOrder	No	Precombine column's custom order in ascending order.
writer.common.trigger.type	No	See Combination writer properties
writer.common.trigger.processing.time	No	See Combination writer properties

Any additional properties for the DataStreamWriter can be added with the prefix writer.deltacdctosnapshot.options, e.g. writer.deltacdctosnapshot.options.key=value

Example

• component.writer=za.co.absa.hyperdrive.compatibility.impl.writer.delta.snapshot.DeltaCDCToSnapshotWriter
• writer.deltacdctosnapshot.destination.directory=/tmp/destination
• writer.deltacdctosnapshot.key.column=key
• writer.deltacdctosnapshot.operation.column=ENTTYP
• writer.deltacdctosnapshot.operation.deleted.values=DL,FD
• writer.deltacdctosnapshot.precombineColumns=TIMSTAMP, ENTTYP
• writer.deltacdctosnapshot.precombineColumns.customOrder.ENTTYP=PT,FI,RR,UB,UP,DL,FD

Common writer properties

Property Name	Required	Description
writer.common.checkpoint.location	Yes	Used for Spark property checkpointLocation. The checkpoint location has to be unique among different workflows.
writer.common.trigger.type	No	Either Once for one-time execution or ProcessingTime for micro-batch executions for micro-batch execution. Default: Once.
writer.common.trigger.processing.time	No	Interval in ms for micro-batch execution (using ProcessingTime). Default: 0ms, i.e. execution as fast as possible.

Behavior of Triggers

Trigger (writer.common.trigger.type)	Termination method (ingestor.spark.termination.method)	Runtime	Details
Once	AwaitTermination or ProcessAllAvailable	Limited	Consumes all data that is available at the beginning of the micro-batch. The query processes exactly one micro-batch and stops then, even if more data would be available at the end of the micro-batch.
Once	AwaitTermination with timeout	Limited	Same as above, but terminates at the timeout. If the timeout is reached before the micro-batch is processed, it won't be completed and no data will be committed.
ProcessingTime	ProcessAllAvailable	Only long-running if topic continuously produces messages, otherwise limited	Consumes all available data in micro-batches and only stops when no new data arrives, i.e. when the available offsets are the same as in the previous micro-batch. Thus, it completely depends on the topic, if and when the query terminates.
ProcessingTime	AwaitTermination with timeout	Limited	Consumes data in micro-batches and only stops when the timeout is reached or the query is killed.
ProcessingTime	AwaitTermination	Long-running	Consumes data in micro-batches and only stops when the query is killed.

• Note 1: The first micro-batch of the query will contain all available messages to consume and can therefore be quite large, even if the trigger ProcessingTime is configured, and regardless of what micro-batch interval is configured. To limit the size of a micro-batch, the property reader.option.maxOffsetsPerTrigger should be used. See also http://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html
• Note 2: It's possible to define a timeout for trigger Once. If the timeout is reached before the micro-batch is processed, it won't be completed and no data will be committed. Such a behavior seems quite unpredictable and therefore we don't recommend it.

See the Spark Documentation for more information about triggers.

Hyperdrive Context

HyperdriveContext is an object intended to be used by the components to share data. It is a key-value store, where the key is a string and the value can be of any type. The following context variables are currently used by the default implementation.

Name	Type	Description
key.column.prefix	String	If ConfluentAvroDecodingTransformer is configured to consume keys, it prefixes the key columns with key__ such that they can be distinguished in the dataframe. If key__ happens to be a prefix of a value column, a random alphanumeric string is used instead.
key.column.names	Seq[String]	If ConfluentAvroDecodingTransformer is configured to consume keys, it contains the original column names (without prefix) in the key schema.

Other

Hyperdrive uses Apache Commons Configuration 2. This allows properties to be referenced, e.g. like so

transformer.[avro.decoder].schema.registry.url=http://localhost:8081
writer.kafka.schema.registry.url=${transformer.[avro.decoder].schema.registry.url}

Workflow Manager

Hyperdrive ingestions may be triggered using the Workflow Manager, which is developed in a separate repository: https://github.com/AbsaOSS/hyperdrive-trigger

A key feature of the Workflow Manager are triggers, which define when an ingestion should be executed and how it should be requested. The workflow manager supports cron-based triggers as well as triggers that listen to a notification topic.

How to build

• Scala 2.12, Spark 2.4 (default)

mvn clean install

• Scala 2.12, Spark 3.0

mvn clean install -Pscala-2.12,spark-3

• Scala 2.11, Spark 2.4

mvn scala-cross-build:change-version -Pscala-2.11,spark-2
mvn clean install -Pscala-2.11,spark-2
mvn scala-cross-build:restore-version

E2E tests with Docker

E2E tests require a running Docker instance on the executing machine and are not executed by default. To execute them, build using the profile all-tests

mvn clean test -Pall-tests