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 threat administration platform that helps companies optimize on-line transactions by distinguishing official prospects from fraudulent ones.

Utilizing synthetic intelligence and machine studying (AI/ML), Riskified analyzes real-time transaction information to detect and forestall fraud whereas maximizing transaction approval charges. The platform offers a chargeback assure, defending retailers from losses as a result of fraudulent transactions. Riskified’s options embody account safety, coverage abuse prevention, and chargeback administration software program, making it a complete instrument for lowering threat and enhancing buyer expertise. Companies throughout numerous 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 elementary a part of its resolution.

Companies usually can’t afford to attend for batch processing to make crucial choices. With real-time information streaming applied sciences like Apache Flink, Apache Spark, and Apache Kafka Streams, organizations can react immediately to rising traits, detect anomalies, and improve buyer experiences. These applied sciences are highly effective processing engines that carry out analytical operations at scale. Nevertheless, unlocking the complete potential of streaming information usually 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 crucial characteristic 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 by means of 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

Prospects have a variety of open supply information processing applied sciences to select from, akin to Flink, Spark, ksqlDB, and RisingWave. Every platform affords a streaming API for information processing. SQL streaming jobs supply a robust and intuitive technique to course of real-time information with minimal complexity. These pipelines use SQL, a extensively recognized and declarative language, to carry out real-time transformations, filtering, aggregations, and joins in steady information streams.

For instance the ability of streaming SQL in ecommerce fraud prevention, contemplate the idea of velocity checks, that are a crucial 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 person patterns. Frequent examples embody 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 totally different accounts.

Use case: Riskified’s velocity checks

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

On this setup, transaction information is constantly streamed by means of a Kafka subject. Every message comprises person agent info originating from the browser, together with the uncooked transaction information. Streaming SQL queries are used to mixture the variety of transactions originating from a single person identifier inside quick time home windows.

For instance, if the variety of transactions from a given person identifier exceeds a sure threshold inside a 10-second interval, this may sign fraudulent exercise. When that threshold is breached, the system can mechanically 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 may seem simple, growing and sustaining strong streaming functions introduces distinctive challenges. Conventional SQL operates on bounded datasets, that are finite collections of information 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 resolution based mostly on Confluent ksqlDB, utilizing its SQL interface and built-in Kafka integration. Engineers may 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 supplied a quick and approachable start line, 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 different applied sciences that might higher help its increasing streaming use instances. The next sections define these challenges in additional element.

Evolving the stream processing structure

In evaluating alternate options, Riskified centered on applied sciences that might handle the particular calls for of fraud detection whereas preserving the simplicity that made the unique strategy interesting. The crew encountered the next challenges in sustaining the earlier resolution:

• 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 strategy to schema evolution didn’t mechanically incorporate newly added fields. This habits required dropping streams and recreating them so as to add new fields as an alternative of simply restarting the applying to include new fields. This strategy brought on 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 blended methods within the schema registry brought on errors with Kafka shopper shoppers trying to decode messages, as a result of the consumer implementation anticipated a RecordNameStrategy based on Riskified’s normal.
• 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 on deserialization errors when trying emigrate native Kafka shopper functions 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 Choice. Reworking each area as non-obligatory within the schema definition required heavy refactoring, treating all Enum fields as Strings, and dealing with the Choice information kind for each area that requires secure dealing with. This cascading impact made the migration course of extra concerned, requiring extra 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 somewhat than remoted. This strategy usually 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 activity can impression different duties operating on the identical node.

Useful resource rivalry between duties on the identical node is widespread in a production-intensive surroundings when utilizing a cluster structure resolution. Operation groups usually 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 shopper publishes to a number of subjects. In ksqlDB, every subject (each enter and output) should be managed as a separate stream abstraction. Nevertheless, there isn’t a high-level abstraction to characterize a complete pipeline that chains these streams collectively. In consequence, engineering groups should manually assemble particular person streams right into a cohesive information move, with out built-in help for managing them as a single, full pipeline.

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

Flink in its place

Riskified started exploring alternate options for its streaming platform. The necessities have 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 essentially the most demanding manufacturing environments.

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

Flink affords a number of deployment choices, together with standalone clusters, native Kubernetes deployments utilizing operators, and Hadoop YARN clusters. For enterprises looking for a completely managed choice, cloud suppliers like AWS supply managed Flink companies that assist alleviate operational overhead, akin 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 selection supplied a number of key benefits:

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

With these options, Riskified may concentrate on what really issues—getting nearer to the enterprise aim and beginning to write functions.

Utilizing Flink’s streaming SQL engine

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

To exchange their earlier resolution, they envisioned sustaining a “construct as soon as, deploy many” software, which encapsulates the complexity of the Flink programming and permits the customers to concentrate on 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 by means of runtime properties. Though ksqlDB primarily focuses on Kafka integration, Flink’s in depth connector ecosystem permits it to increase to numerous information sources and locations in future phases.

Managed Service for Apache Flink offers a versatile technique to configure streaming functions 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 strategy consists of the next steps:

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

The next diagram illustrates this workflow.

Performing Flink SQL question compilation with no Flink runtime surroundings

Offering end-users with important management to outline their pipelines makes it crucial to confirm the SQL question outlined by the person earlier than deployment. This validation prevents failed or hanging jobs that might devour pointless sources and incur pointless prices.

A key problem was validating Flink SQL queries with out deploying the complete 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 information storage. This perception enabled utilizing Calcite instantly for question validation earlier than job deployment.

It’s essential 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, sudden errors may happen when trying 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 may 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 kind checking and area 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 fundamental area 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);
    }
    personal static CalciteSchema createSchemaWithAvro(Schema avroSchema) {
        CalciteSchema rootSchema = CalciteSchema.createRootSchema(true);
        rootSchema.add("TABLE", new SimpleAvroTable(avroSchema));
        return rootSchema;
    }
    personal static class SimpleAvroTable extends org.apache.calcite.schema.impl.AbstractTable {
        personal last Schema avroSchema;
        public SimpleAvroTable(Schema avroSchema) {
            this.avroSchema = avroSchema;
        }
        @Override
        public RelDataType getRowType(RelDataTypeFactory typeFactory) {
            RelDataTypeFactory.Builder builder = typeFactory.builder();
            for (Schema.Discipline area : avroSchema.getFields()) {
                builder.add(area.title(), convertAvroType(area.schema(), typeFactory));
            }
            return builder.construct();
        }
        personal RelDataType convertAvroType(Schema schema, RelDataTypeFactory typeFactory) {
            swap (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 strategy 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 kind mapping limitation

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

• Flink SQL sits on the highest stage, permitting customers to precise information transformations utilizing acquainted SQL syntax, which is good for analysts and groups comfy with relational ideas.
• The Desk API affords an identical strategy 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. Nevertheless, when deploying their first Flink job to a staging surroundings, 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 help. In consequence, Enum fields have been transformed to Strings, resulting in mismatches throughout serialization and errors when trying to sink processed information again to Kafka utilizing Flink’s Desk API.

Riskified developed another strategy to beat the Enum serialization limitations whereas sustaining schema necessities. They found that Flink’s DataStream API may appropriately deal with Confluent’s Avro data serialization with Enum fields, in contrast to the Desk API. They carried out a hybrid resolution combining each APIs as a result of the pipeline solely required SQL processing on the supply Kafka subject. It could possibly sink to the output with none extra processing. The Desk API is used for information processing and transformations, solely changing to the DataStream API on the last output stage.

Managed Service for Apache Flink helps Flink APIs. It could possibly swap between the Desk API and the DataStream API.
A MapFunction can convert the Row kind of the Desk API right into a DataStream of GenericRecord. The MapFunction maps Flink’s Row information kind into GenericRecord sorts by iterating over the Avro schema fields and constructing the GenericRecord from the Flink Row kind, casting the Row fields into the proper information kind 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 help extra use instances in addition to supporting self-service SQL. Every Flink job supply code within the repository is an impartial Java module. The CI pipeline carried out a sturdy construct and deployment course of consisting of the next steps:

1. Construct and compile every module.
2. Run exams.
3. Bundle the modules.
4. Add the artifact to the artifacts bucket twice: one JAR underneath -.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. Nevertheless, a duplicate of previous artifacts is stored for code rollback causes.

A CD course of follows this course of:

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

To permit secure deployment, this course of is completed progressively for each surroundings, beginning with the staging surroundings.

Self-service interface for submitting SQL jobs

Riskified believes an intuitive UI is essential for system adoption and effectivity. Nevertheless, growing a devoted UI for Flink job submission requires a practical strategy, as a result of it won’t be price investing in until there’s already an online interface for inside improvement operations.

Investing in UI improvement ought to align with the group’s current 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 elements. The consumer receives person 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 instrument 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 features:

• Schema evolution and information dealing with – Riskified can both periodically fetch up to date schemas or restart functions when schemas evolve. They’ll use current 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 computerized scaling, proving to be extra cost-efficient.
• Job administration and move visibility – Flink offers a cohesive information move abstraction by means of its job and activity mannequin. It manages your complete information move in a single job and distributes the workload evenly over a number of nodes. This unified strategy permits higher visibility into your complete information pipeline, simplifying monitoring, troubleshooting, and optimizing complicated streaming workflows.
• Constructed-in restoration mechanism – Managed Service for Apache Flink mechanically creates checkpoints and savepoints that allow stateful Flink functions to get better from failures and resume processing with out information loss. With this characteristic, streaming jobs are sturdy and might get better safely from errors.
• Complete observability – Managed Service for Apache Flink exposes CloudWatch metrics that monitor Flink software efficiency and statistics. It’s also possible to 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 normal, 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 wide selection of sink connectors affords the potential of pluggable multi-destination pipelines. This versatility is on Riskfied’s roadmap for future enhancements.

Flink’s DataStream API offers capabilities that stretch 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 affords options akin to together with remoted jobs environments, computerized 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 features resolved these points. The transition has paved the best way for future growth with multi-targets and superior fraud detection capabilities, solidifying Flink as a sturdy and scalable resolution for Riskified’s streaming wants.

If Riskified’s journey has sparked your curiosity in constructing a self-service streaming SQL platform, right here’s 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 akin to Apache Kafka and Apache Flink. He focuses on constructing scalable, real-time information pipelines that energy Riskified’s core merchandise. Gal is especially involved in 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 techniques.

Sofia Zilberman works as a Senior Streaming Options Architect at AWS, serving to prospects 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.

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