Unlock self-serve streaming SQL with Amazon Managed Service for Apache Flink

This put up is co-written with Gal Krispel from Riskified.

Riskified is an ecommerce fraud prevention and danger administration platform that helps companies optimize on-line transactions by distinguishing reputable clients from fraudulent ones.

Utilizing synthetic intelligence and machine studying (AI/ML), Riskified analyzes real-time transaction information to detect and stop fraud whereas maximizing transaction approval charges. The platform offers a chargeback assure, defending retailers from losses on account of fraudulent transactions. Riskified’s options embrace account safety, coverage abuse prevention, and chargeback administration software program, making it a complete device for lowering danger and enhancing buyer expertise. Companies throughout varied industries, together with retail, journey, and digital items, use Riskified to extend income whereas minimizing fraud-related losses. Riskified’s core enterprise of real-time fraud prevention makes low-latency streaming applied sciences a basic a part of its answer.

Companies typically can’t afford to attend for batch processing to make essential choices. With real-time information streaming applied sciences like Apache Flink, Apache Spark, and Apache Kafka Streams, organizations can react immediately to rising developments, detect anomalies, and improve buyer experiences. These applied sciences are highly effective processing engines that carry out analytical operations at scale. Nonetheless, unlocking the total potential of streaming information typically requires complicated engineering efforts, limiting accessibility for analysts and enterprise customers.

Streaming pipelines are in excessive demand from Riskified’s Engineering division. Subsequently, a user-friendly interface for creating streaming pipelines is a essential function to extend analytical precision for detecting fraudulent transactions.

On this put up, we current Riskified’s journey towards enabling self-service streaming SQL pipelines. We stroll via the motivations behind the shift from Confluent ksqlDB to Apache Flink, the structure Riskified constructed utilizing Amazon Managed Service for Apache Flink, the technical challenges they confronted, and the options that helped them make streaming accessible, scalable, and production-ready.

Utilizing SQL to create streaming pipelines

Clients have a spread of open supply information processing applied sciences to select from, comparable to Flink, Spark, ksqlDB, and RisingWave. Every platform provides a streaming API for information processing. SQL streaming jobs supply a strong and intuitive option to course of real-time information with minimal complexity. These pipelines use SQL, a broadly recognized and declarative language, to carry out real-time transformations, filtering, aggregations, and joins in steady information streams.

For example the ability of streaming SQL in ecommerce fraud prevention, think about the idea of velocity checks, that are a essential fraud detection sample. Velocity checks are a sort of safety measure used to detect uncommon or fast exercise by monitoring the frequency and quantity of particular actions inside a given timeframe. These checks assist establish potential fraud or abuse by analyzing repeated behaviors that deviate from regular consumer patterns. Widespread examples embrace detecting a number of transactions from the identical IP handle in a short while span, monitoring bursts of account creation makes an attempt, or monitoring the repeated use of a single cost technique throughout completely different accounts.

Use case: Riskified’s velocity checks

Riskified carried out a real-time velocity verify utilizing streaming SQL to observe buying habits based mostly on consumer identifier.

On this setup, transaction information is repeatedly streamed via a Kafka subject. Every message incorporates consumer agent info originating from the browser, together with the uncooked transaction information. Streaming SQL queries are used to combination the variety of transactions originating from a single consumer identifier inside brief time home windows.

For instance, if the variety of transactions from a given consumer identifier exceeds a sure threshold inside a 10-second interval, this would possibly sign fraudulent exercise. When that threshold is breached, the system can routinely flag or block the transactions earlier than they’re accomplished. The next determine and accompanying code present a simplified instance of the streaming SQL question used to detect this habits.

SELECT userIdentifier,TUMBLE_START(createdAt, INTERVAL '10' SECONDS) 
  AS windowStart,TUMBLE_END(createdAt, INTERVAL '10' SECONDS) 
  AS windowEnd, COUNT(*) AS paymentAttempts
FROM transactions
  WINDOW TUMBLING (SIZE 10 SECONDS)
GROUP BY userIdentifier;

Though defining SQL queries over static datasets would possibly seem easy, growing and sustaining sturdy streaming purposes introduces distinctive challenges. Conventional SQL operates on bounded datasets, that are finite collections of knowledge saved in tables. In distinction, streaming SQL is designed to course of steady, unbounded information streams resembling the SQL syntax.

To handle these challenges at scale and make streaming job creation accessible to engineering groups, Riskified carried out a self-serve answer based mostly on Confluent ksqlDB, utilizing its SQL interface and built-in Kafka integration. Engineers might outline and deploy streaming pipelines utilizing SQL, chaining ksqlDB streams from supply to sink. The system supported each stateless and stateful processing instantly on Kafka subjects, with Avro schemas used to outline the construction of streaming information.

Though ksqlDB offered a quick and approachable place to begin, it will definitely revealed a number of limitations. These included challenges with schema evolution, difficulties in managing compute sources, and the absence of an abstraction for managing pipelines as a cohesive unit. In consequence, Riskified started exploring various applied sciences that would higher assist its increasing streaming use instances. The next sections define these challenges in additional element.

Evolving the stream processing structure

In evaluating options, Riskified centered on applied sciences that would handle the precise calls for of fraud detection whereas preserving the simplicity that made the unique method interesting. The crew encountered the next challenges in sustaining the earlier answer:

Schemas are managed in Confluent Schema Registry, and the message format is Avro with FULL compatibility mode enforced. Schemas are continuously evolving based on enterprise necessities. They’re model managed utilizing Git with a strict steady integration and steady supply (CI/CD) pipeline. As schemas grew extra complicated, ksqlDB’s method to schema evolution didn’t routinely incorporate newly added fields. This habits required dropping streams and recreating them so as to add new fields as a substitute of simply restarting the applying to include new fields. This method brought about inconsistencies with offset administration because of the stream’s tear-down.
ksqlDB enforces a TopicNameStrategy schema registration technique, which offers 1:1 schema-to-topic coupling. This implies the precise schema definition must be registered a number of occasions, one time for every subject it’s used for. Riskified’s schema registry deployment makes use of RecordNameStrategy for schema registration. It’s an environment friendly schema registry technique that enables for sharing schemas throughout a number of subjects, storing fewer schemas, and lowering registry administration overhead. Having combined methods within the schema registry brought about errors with Kafka client shoppers making an attempt to decode messages, as a result of the consumer implementation anticipated a RecordNameStrategy based on Riskified’s commonplace.
ksqlDB internally registers schema definitions in particular methods the place fields are interpreted as nullable, and Avro Enum sorts are transformed to Strings. This habits brought about deserialization errors when making an attempt emigrate native Kafka client purposes to make use of the ksqlDB output subject. Riskified’s code base makes use of the Scala programming language, the place non-obligatory fields within the schema are interpreted as Possibility. Reworking each subject as non-obligatory within the schema definition required heavy refactoring, treating all Enum fields as Strings, and dealing with the Possibility information sort for each subject that requires protected dealing with. This cascading impact made the migration course of extra concerned, requiring further time and sources to realize a easy transition.

Managing useful resource rivalry in ksqlDB streaming workloads

ksqlDB queries are compiled right into a Kafka Streams topology. The question definition defines the topology’s habits.

Streaming question sources are shared slightly than remoted. This method sometimes results in the overallocation of cluster sources. Its duties are distributed throughout nodes in a ksqlDB cluster. This structure means processing duties with no useful resource isolation, and a selected process can impression different duties working on the identical node.

Useful resource rivalry between duties on the identical node is frequent in a production-intensive atmosphere when utilizing a cluster structure answer. Operation groups typically fine-tune cluster configurations to keep up acceptable efficiency, continuously mitigating points by over-provisioning cluster nodes.

Challenges with ksqlDB pipelines

A ksqlDB pipeline is a series of particular person streams and lacks flow-level abstraction. Think about a posh pipeline the place a client publishes to a number of subjects. In ksqlDB, every subject (each enter and output) have to be managed as a separate stream abstraction. Nonetheless, there is no such thing as a high-level abstraction to symbolize a complete pipeline that chains these streams collectively. In consequence, engineering groups should manually assemble particular person streams right into a cohesive information circulation, with out built-in assist for managing them as a single, full pipeline.

This architectural method significantly impacts operational duties. Troubleshooting requires analyzing every stream individually, making it troublesome to observe and preserve pipelines that comprise dozens of interconnected streams. When points happen, the well being of every stream must be checked individually, with no logical information circulation part to assist perceive the relationships between streams or their function within the total pipeline. The absence of a unified view of the information circulation considerably elevated operational complexity.

Flink instead

Riskified started exploring options for its streaming platform. The necessities had been clear: a robust processing expertise that mixes a wealthy low-level API and a streaming SQL engine, backed by a robust open supply neighborhood, confirmed to carry out in probably the most demanding manufacturing environments.

In contrast to the earlier answer, which supported solely Kafka-to-Kafka integration, Flink provides an array of connectors for varied databases and Streaming platforms. It was shortly acknowledged that Flink had the potential to deal with complicated streaming use instances.

Flink provides a number of deployment choices, together with standalone clusters, native Kubernetes deployments utilizing operators, and Hadoop YARN clusters. For enterprises in search of a totally managed choice, cloud suppliers like AWS supply managed Flink providers that assist alleviate operational overhead, comparable to Managed Service for Apache Flink.

Advantages of utilizing Managed Service for Apache Flink

Riskified determined to implement an answer utilizing Managed Service for Apache Flink. This alternative supplied a number of key benefits:

It provides a fast and dependable option to run Flink purposes and reduces the operational overhead of independently managing the infrastructure.
Managed Service for Apache Flink offers true job isolation by working every streaming software in its devoted cluster. This implies you may handle sources individually for every job and scale back the danger of heavy streaming jobs inflicting useful resource hunger for different working jobs.
It provides built-in monitoring utilizing Amazon CloudWatch metrics, software state backup with managed snapshots, and automated scaling.
AWS provides complete documentation and sensible examples to assist speed up the implementation course of.

With these options, Riskified might give attention to what really issues—getting nearer to the enterprise purpose and beginning to write purposes.

Utilizing Flink’s streaming SQL engine

Builders can use Flink to construct complicated and scalable streaming purposes, however Riskified noticed it as greater than only a device for specialists. They wished to democratize the ability of Flink right into a device for all the group, to unravel complicated enterprise challenges involving real-time analytics necessities with no need a devoted information skilled.

To exchange their earlier answer, they envisioned sustaining a “construct as soon as, deploy many” software, which encapsulates the complexity of the Flink programming and permits the customers to give attention to the SQL processing logic.

Kafka was maintained because the enter and output expertise for the preliminary migration use case, which has similarities to the ksqlDB setup. They designed a single, versatile Flink software the place end-users can modify the enter subjects, SQL processing logic, and output locations via runtime properties. Though ksqlDB primarily focuses on Kafka integration, Flink’s in depth connector ecosystem permits it to develop to numerous information sources and locations in future phases.

Managed Service for Apache Flink offers a versatile option to configure streaming purposes with out modifying their code. By utilizing runtime parameters, you may change the applying’s habits with out modifying its supply code.

Utilizing Managed Service for Apache Flink for this method contains the next steps:

Apply parameters for the enter/output Kafka subject, a SQL question, and the enter/output schema ID (assuming you’re utilizing Confluent Schema Registry).
Use AvroSchemaConverter to transform an Avro schema right into a Flink desk.
Apply the SQL processing logic and save the output as a view.
Sink the view outcomes into Kafka.

The next diagram illustrates this workflow.

Performing Flink SQL question compilation with no Flink runtime atmosphere

Offering end-users with vital management to outline their pipelines makes it essential to confirm the SQL question outlined by the consumer earlier than deployment. This validation prevents failed or hanging jobs that would eat pointless sources and incur pointless prices.

A key problem was validating Flink SQL queries with out deploying the total Flink runtime. After investigating Flink’s SQL implementation, Riskified found its dependency on Apache Calcite – a dynamic information administration framework that handles SQL parsing, optimization, and question planning independently of knowledge storage. This perception enabled utilizing Calcite instantly for question validation earlier than job deployment.

You will need to understand how the information is structured to validate a Flink SQL question on a streaming supply like a Kafka subject. In any other case, surprising errors would possibly happen when making an attempt to question the streaming supply. Though an anticipated schema is used with relational databases, it’s not enforced for streaming sources.

Schemas assure a deterministic construction for the information saved in a Kafka subject when utilizing a schema registry. A schema might be materialized right into a Calcite desk that defines how information is structured within the Kafka subject. It permits inferring desk constructions instantly from schemas (on this case, Avro format was used), enabling thorough field-level validation, together with sort checking and subject existence, all earlier than job deployment. This desk can later be used to validate the SQL question.

The next code is an instance of supporting primary subject sorts validation utilizing Calcite’s AbstractTable:

public class FlinkValidator {
    public static void validateSQL(String sqlQuery, Schema avroSchema) throws Exception {
        SqlParser.Config sqlConfig = SqlParser.config()
                .withCaseSensitive(true);
        SqlParser sqlParser = SqlParser.create(sqlQuery, sqlConfig);
        SqlNode parsedQuery = sqlParser.parseQuery();
        RelDataTypeFactory typeFactory = new SqlTypeFactoryImpl(RelDataTypeFactory.DEFAULT);
        CalciteSchema rootSchema = createSchemaWithAvro(avroSchema);
        SqlValidator validator = SqlValidatorUtil.newValidator(
                Frameworks.newConfigBuilder().construct().getOperatorTable(),
                rootSchema.createCatalogReader(Collections.emptyList(), typeFactory),
                typeFactory,
                SqlValidator.Config.DEFAULT
        );
        validator.validate(parsedQuery);
    }
    non-public static CalciteSchema createSchemaWithAvro(Schema avroSchema) {
        CalciteSchema rootSchema = CalciteSchema.createRootSchema(true);
        rootSchema.add("TABLE", new SimpleAvroTable(avroSchema));
        return rootSchema;
    }
    non-public static class SimpleAvroTable extends org.apache.calcite.schema.impl.AbstractTable {
        non-public closing Schema avroSchema;
        public SimpleAvroTable(Schema avroSchema) {
            this.avroSchema = avroSchema;
        }
        @Override
        public RelDataType getRowType(RelDataTypeFactory typeFactory) {
            RelDataTypeFactory.Builder builder = typeFactory.builder();
            for (Schema.Area subject : avroSchema.getFields()) {
                builder.add(subject.title(), convertAvroType(subject.schema(), typeFactory));
            }
            return builder.construct();
        }
        non-public RelDataType convertAvroType(Schema schema, RelDataTypeFactory typeFactory) {
            change (schema.getType()) {
                case STRING:
                    return typeFactory.createSqlType(SqlTypeName.VARCHAR);
                case INT:
                    return typeFactory.createSqlType(SqlTypeName.INTEGER);
                default:
                    return typeFactory.createSqlType(SqlTypeName.ANY);
            }
        }
    }
}

You’ll be able to combine this validation method as an intermediate step earlier than creating the applying. You’ll be able to create a streaming job programmatically with the AWS SDK, AWS Command Line Interface (AWS CLI), or Terraform. The validation happens earlier than submitting the streaming job.

Flink SQL and Confluent Avro information sort mapping limitation

Flink offers a number of APIs designed for various ranges of abstraction and consumer experience:

Flink SQL sits on the highest stage, permitting customers to specific information transformations utilizing acquainted SQL syntax, which is good for analysts and groups comfy with relational ideas.
The Desk API provides an identical method however is embedded in Java or Python, enabling type-safe and extra programmatic expressions.
For extra management, the DataStream API exposes low-level constructs to handle occasion time, stateful operations, and complicated occasion processing.
On the most granular stage, the ProcessFunction API offers full entry to Flink’s runtime options. It’s appropriate for superior use instances that demand detailed management over state and processing habits.

Riskified initially used the Desk API to outline streaming transformations. Nonetheless, when deploying their first Flink job to a staging atmosphere, they encountered serialization errors associated to the avro-confluent library and Desk API. Riskified’s schemas rely closely on Avro Enum sorts, which the avro-confluent integration doesn’t totally assist. In consequence, Enum fields had been transformed to Strings, resulting in mismatches throughout serialization and errors when making an attempt to sink processed information again to Kafka utilizing Flink’s Desk API.

Riskified developed an alternate method to beat the Enum serialization limitations whereas sustaining schema necessities. They found that Flink’s DataStream API might accurately deal with Confluent’s Avro information serialization with Enum fields, not like the Desk API. They carried out a hybrid answer combining each APIs as a result of the pipeline solely required SQL processing on the supply Kafka subject. It may well sink to the output with none further processing. The Desk API is used for information processing and transformations, solely changing to the DataStream API on the closing output stage.

Managed Service for Apache Flink helps Flink APIs. It may well change between the Desk API and the DataStream API.
A MapFunction can convert the Row sort of the Desk API right into a DataStream of GenericRecord. The MapFunction maps Flink’s Row information sort into GenericRecord sorts by iterating over the Avro schema fields and constructing the GenericRecord from the Flink Row sort, casting the Row fields into the proper information sort based on the Avro schema. This conversion is required to beat the avro-confluent library limitation with Flink SQL.

The next diagram and illustrates this workflow.

The next code is an instance question:

// SQL Question for filtering
Desk queryResults = tableEnv.sqlQuery(
       "SELECT * FROM InputTable");
// 1. Convert question outcomes from Desk API to a DataStream and use DataStream API to sink question outcomes to Kafka subject
DataStream rowStream = tableEnv.toDataStream(queryResults);
// Fetch the schema string from the schema registry
String schemaString = fetchSchemaString(schemaRegistryURL, schemaSubjectName);
// 2. Convert Row to GenericRecord with specific TypeInformation, utilizing customized AvroMapper
TypeInformation typeInfo = new GenericRecordAvroTypeInfo(avroSchema);
DataStream genericRecordStream = rowStream
       .map(new AvroMapper(schemaString))
       .returns(typeInfo); // Explicitly set TypeInformation
// 3. Outline Kafka sink utilizing ConfluentRegistryAvroSerializationSchema
KafkaSink kafkaSink = KafkaSink.builder()
       .setBootstrapServers(bootstrapServers)
       .setRecordSerializer(
               KafkaRecordSerializationSchema.builder()
                       .setTopic(sinkTopic)
                       .setValueSerializationSchema(
                               ConfluentRegistryAvroSerializationSchema.forGeneric(
                                       schemaSubjectName,
                                       avroSchema,
                                       schemaRegistryURL
                               )
                       )
                       .construct()
       )
       .construct();
// Sink to Kafka
genericRecordStream.sinkTo(kafkaSink);

CI/CD With Managed Service for Apache Flink

With Managed Service for Apache Flink, you may run a job by choosing an Amazon Easy Storage Service (Amazon S3) key containing the applying JAR. Riskified’s Flink code base was structured as a multi-module repository to assist further use instances moreover supporting self-service SQL. Every Flink job supply code within the repository is an impartial Java module. The CI pipeline carried out a strong construct and deployment course of consisting of the next steps:

Construct and compile every module.
Run assessments.
Package deal the modules.
Add the artifact to the artifacts bucket twice: one JAR beneath -.jar and the second as -latest.jar, resembling a Docker registry like Amazon Elastic Container Registry (Amazon ECR). Managed Service for Apache Flink jobs makes use of the newest tag artifact on this case. Nonetheless, a replica of previous artifacts is saved for code rollback causes.

A CD course of follows this course of:

When merged, it lists all jobs for every module utilizing the AWS CLI for Managed Service for Apache Flink.
The appliance JAR location is up to date for every software, which triggers a deployment.
When the applying is in a working state with no errors, the next software can be continued.

To permit protected deployment, this course of is finished regularly for each atmosphere, beginning with the staging atmosphere.

Self-service interface for submitting SQL jobs

Riskified believes an intuitive UI is essential for system adoption and effectivity. Nonetheless, growing a devoted UI for Flink job submission requires a practical method, as a result of it may not be value investing in until there’s already an internet interface for inside growth operations.

Investing in UI growth ought to align with the group’s present instruments and workflows. Riskified had an inside internet portal for comparable operations, which made the addition of Flink job submission capabilities a pure extension of the self-service infrastructure.

An AWS SDK was put in on the net server to permit interplay with AWS parts. The consumer receives consumer enter from the UI and interprets it into runtime properties to regulate the habits of the Flink software. The net server then makes use of the CreateApplication API motion to submit the job to Managed Service for Apache Flink.

Though an intuitive UI considerably enhances system adoption, it’s not the one path to accessibility. Alternatively, a well-designed CLI device or REST API endpoint can present the identical self-service capabilities.

The next diagram illustrates this workflow.

Manufacturing expertise: Flink’s implementation upsides

The transition to Flink and Managed Service for Apache Flink proved environment friendly in quite a few elements:

Schema evolution and information dealing with – Riskified can both periodically fetch up to date schemas or restart purposes when schemas evolve. They’ll use present schemas with out self-registration.
Useful resource isolation and administration – Managed Service for Apache Flink runs every Flink job as an remoted cluster, lowering useful resource rivalry between jobs.
Useful resource allocation and cost-efficiency – Managed Service for Apache Flink permits minimal useful resource allocation with automated scaling, proving to be extra cost-efficient.
Job administration and circulation visibility – Flink offers a cohesive information circulation abstraction via its job and process mannequin. It manages all the information circulation in a single job and distributes the workload evenly over a number of nodes. This unified method permits higher visibility into all the information pipeline, simplifying monitoring, troubleshooting, and optimizing complicated streaming workflows.
Constructed-in restoration mechanism – Managed Service for Apache Flink routinely creates checkpoints and savepoints that allow stateful Flink purposes to get better from failures and resume processing with out information loss. With this function, streaming jobs are sturdy and may get better safely from errors.
Complete observability – Managed Service for Apache Flink exposes CloudWatch metrics that monitor Flink software efficiency and statistics. You may also create alarms based mostly on these metrics. Riskfied determined to make use of the Cloudwatch Prometheus Exporter to export these metrics to Prometheus and construct PrometheusRules to align Flink’s monitoring to the Riskified commonplace, which makes use of Prometheus and Grafana for monitoring and alerting.

Subsequent steps

Though the preliminary focus was Kafka-to-Kafka streaming queries, Flink’s big selection of sink connectors provides the potential for pluggable multi-destination pipelines. This versatility is on Riskfied’s roadmap for future enhancements.

Flink’s DataStream API offers capabilities that reach far past self-serving streaming SQL capabilities, opening new avenues for extra refined fraud detection use instances. Riskified is exploring methods to make use of DataStream APIs to boost ecommerce fraud prevention methods.

Conclusions

On this put up, we shared how Riskified efficiently transitioned from ksqlDB to Managed Service for Apache Flink for its self-serve streaming SQL engine. This transfer addressed key challenges like schema evolution, useful resource isolation, and pipeline administration. Managed Service for Apache Flink provides options comparable to together with remoted jobs environments, automated scaling, and built-in monitoring, which proved extra environment friendly and cost-effective. Though Flink SQL limitations with Kafka required workarounds, utilizing Flink’s DataStream API and user-defined capabilities resolved these points. The transition has paved the way in which for future enlargement with multi-targets and superior fraud detection capabilities, solidifying Flink as a strong and scalable answer for Riskified’s streaming wants.

If Riskified’s journey has sparked your curiosity in constructing a self-service streaming SQL platform, right here’s find out how to get began:

Study extra about Managed Service for Apache Flink:
Get hands-on expertise:

In regards to the authors

Gal Krispel is a Knowledge Platform Engineer at Riskified, specializing in streaming applied sciences comparable to Apache Kafka and Apache Flink. He focuses on constructing scalable, real-time information pipelines that energy Riskified’s core merchandise. Gal is especially concerned with making complicated information architectures accessible and environment friendly throughout the group. His work spans real-time analytics, event-driven design, and the seamless integration of stream processing into large-scale manufacturing programs.

Sofia Zilberman works as a Senior Streaming Options Architect at AWS, serving to clients design and optimize real-time information pipelines utilizing open-source applied sciences like Apache Flink, Kafka, and Apache Iceberg. With expertise in each streaming and batch information processing, she focuses on making information workflows environment friendly, observable, and high-performing.

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Lorenzo Nicora works as Senior Streaming Answer Architect at AWS, serving to clients throughout EMEA. He has been constructing cloud-centered, data-intensive programs for over 25 years, working throughout industries each via consultancies and product firms. He has used open-source applied sciences extensively and contributed to a number of tasks, together with Apache Flink, and is the maintainer of the Flink Prometheus connector.